Academia.edu no longer supports Internet Explorer.

To browse Academia.edu and the wider internet faster and more securely, please take a few seconds to  upgrade your browser .

Enter the email address you signed up with and we'll email you a reset link.

  • We're Hiring!
  • Help Center

paper cover thumbnail

Literature Review on the Solar Energy Potential for Botswana

Profile image of European Scientific Journal ESJ

Renewable energy sources are easily accessible and clean to the environment. Solar energy is one of the best forms of renewable energy, particularly for a country such as Botswana. Paradoxically, Botswana finds herself importing electricity and experiencing power cuts, yet she has abundant sunshine almost all-year round that could be converted to electric power. This paper seeks to investigate through literature search, the potential for Botswana to convert her copious solar radiation into solar energy. A comparison of different forms of renewable energy are made. Particular attention is drawn to the case of Australia which has experience in use of solar energy and has a similar climate to that of Botswana. Findings from literature search reveal that Botswana stands a great chance of using solar power to improve the livelihood of its people and businesses. Recommendations to policy makers and the private sector are that there is urgent need to put in place policies, regulations and frameworks to support solar generation and reduce reliance on electricity importation and coal production which is not sustainable. The authors propose a conceptual model to help policy makers in implementing solar energy projects in Botswana.

Related Papers

Emmanuel A Essah

Botswana has a basic need to explore its energy concept, this being its energy sources, generation and percentage of the population with access to electricity. At present, Botswana generates electricity from coal, which supplies about 29% (on average) of the country's demand. The other 71% is imported mainly from South Africa (Eskom). Consequently, the dependence of Botswana on imports posses threats to the security of its energy supply. As a result, there is the need to understand the bases for a possible generation expansion that would substantiate existing documentation. In view of this need, this study investigates the existing energy sources as well as energy consumption and production levels in Botswana. The study would be further developed by making projections of the energy demand up until the year 2020. The key techniques that were used include; literature review, questionnaire survey and an empirical study. The results presented indicated that, current dependable opera...

literature review on solar energy pdf

Engineering, Technology & Applied Science Research

Hüseyin Gökçekuş

The main aim of the present study is to investigate the solar energy potential and evaluate the economic viability of a 5kW grid-connected rooftop photovoltaic (PV) system as an electricity generation source in three selected regions (Gaborone, Maun, and Tshabong) in Botswana for the first time. In this study, NASA POWER data were used for evaluating the solar potential in the selected regions. The results showed that the selected locations are suitable for the installation of various scales of PV systems due to the high global horizontal solar radiation. RETScreen Expert software was used to assess the techno-economic feasibility of the proposed systems. The performance of the proposed systems with various PV technologies (mono-crystalline silicon and poly-crystalline silicon) is analyzed. Furthermore, economic and financial indicators such as net present value, annual life cycle savings, payback, benefit-cost ratio, and cost of energy production were calculated. The results indica...

Koen van Kuijk

Samuel Asumadu-Sarkodie

The Eight Hundred and Thirty-Second ACT of the Parliament of the Republic of Ghana entitled: Renewable Energy Act, 2011 has assented to provide for the development, management, utilization, sustainability and adequate supply of renewable energy for generation of heat and power for related matters by the year 2020. With the current load shedding and inadequate energy supply in Ghana, it was essential to examine other options that can be added to the grid installed capacity to ease the burden on Ghanaians. The study examined the solar energy resources in Ghana and how this huge potential can be utilized to grow and modernize the Ghanaian economy in order to decrease the high prevalence of poverty. Access to energy has been proven to directly affect human development, therefore, an option of the diversified local energy mix for electricity production will be the best choice to improve an energy economy that would guarantee a safe and consistent supply of high-quality energy services for all Ghanaian. Evidence from the study shows that Ghana has a good potential for both concentrating and non-concentrating solar technologies. It is estimated that 50–100 MW solar energy potential are still untapped in Ghana which requires an investment of US $100–150 million. Developing and harnessing this renewable energy potential will boost the productivity of businesses, industries, and the transport sector while making a significant contribution to the export earnings of the country.

Mammo Muchie

Energy Research and Social Science

Ankit Kumar , Raihana Ferdous , Britta Turner

Lanterns, homes systems, hot water systems and micro-grids based on small-scale solar have become prominent ways to address the energy access challenge. As momentum grows for this form of energy transition this paper draws together research on small-scale solar in six different countries – Bangladesh, Brazil, India, Mozambique, Sri Lanka and South Africa – to argue for a need to understand how, when, and for whom solar provides energy access. It argues that an assemblage perspective can provide vital insights into the diversity and dynamism of energy access. The paper demonstrates that the diverse ways in which solar provides energy access is a function of the flexibility/fixity of the socio-technical assemblage and the de/centralisation of agency. The central thesis of this paper is that energy access is fluid and ever changing and we need fluid, easily maintainable, locally modifiable 'assemblages' for providing such access. Using this perspective, we find three common features of solar energy access across our case studies. First, there are significant gaps between what solar projects are designed to achieve and what they deliver, which are highly contingent on the flexibility of their structure and the decentralisation of agency within them. Second, access needs to endure continuously. Third, to foster enduring access, projects should embed logics of improvisation. This paper is based on six separate qualitative research projects conducted during 2010-2016. It draws data from 482 interviews, 91 home tours and 12 group discussions.

Boaventura Cuamba

Saudi Journal of Engineering and Technology

Dennis E Amadi

Energy Conversion and Management

Tunde Oladiran

RELATED PAPERS

Jean Francois Koly Onivogui

International Journal of Electrical and Computer Engineering (IJECE)

Muhammad Yudianto

Molecular and Cellular Biochemistry

chinmoy sarkar

Endah Triastuti 990900500

Armando Contreras Hernández

"It's not like they're dropping from heaven" - Leadership succession in New Zealand's faith-based integrated schools

Francine Bennett

IFIP Advances in Information and Communication Technology

Huricha Bao

Journal of Physical Oceanography

Robert Jensen

Journal of Infrastructure, Policy and Development

Jason Chavez

Natalia Rius

Egyptian Journal of Aquatic Biology and Fisheries

Prof. La Sara Ph.D

Revista chilena de neuro-psiquiatría

Mario Quiroz

mahdi farttoosi

Respiration

Edouard Battegay

Gabriele de Luccas

Schweizerische Ärztezeitung

Roger Darioli

IEEE Transactions on Intelligent Transportation Systems

LUIS JAVIER GARCIA VILLALBA

RELATED TOPICS

  •   We're Hiring!
  •   Help Center
  • Find new research papers in:
  • Health Sciences
  • Earth Sciences
  • Cognitive Science
  • Mathematics
  • Computer Science
  • Academia ©2024

Renewable Energy and Environmental Sustainability

Message placeholder

literature review on solar energy pdf

1 Introduction

2 review methodology, 3 results and discussion, 4 conclusions, acknowledgements.

  • List of tables
  • List of figures

Review Article

A literature review on Building Integrated Solar Energy Systems (BI-SES) for façades − photovoltaic, thermal and hybrid systems

Karol Bot 1 * , Laura Aelenei 1 , Maria da Glória Gomes 2 and Carlos Santos Silva 3

1 Laboratório Nacional de Energia e Geologia (LNEG), 1649–038 Lisbon, Portugal 2 CERIS, Department of Civil Engineering, Architecture and Georesources (DECivil), Instituto Superior Técnico, Universidade de Lisboa, 1049–001 Lisbon, Portugal 3 IN+, Center for Innovation, Technology and Policy Research /LARSyS, Department of Mechanical Engineering (DEM), Instituto Superior Técnico, Universidade de Lisboa, 1049–001 Lisbon, Portugal

* e-mail: [email protected]

Received: 9 August 2021 Received in final form: 11 November 2021 Accepted: 11 November 2021

The building façade has a crucial role in acting as the interface between the environment and the indoor ambient, and from an engineering and architecture perspective, in the last years, there has been a growing focus on the strategic development of building façades. In this sense, this work aims to present a literature review for the Building Integrated Solar Energy Systems (BI-SES) for façades, subdivided into three categories: thermal, photovoltaic and hybrid (both thermal and photovoltaic). The methodology used corresponds to a systematic review method. A sample of 75 works was reviewed (16 works on thermal BI-SES, 37 works on photovoltaic BI-SES, 22 works on hybrid BI-SES). This article summarises the works and later classifies them according to the type of study (numerical or experimental), simulation tool, parametric analysis and performance when applied.

© K. Bot et al., Published by EDP Sciences, 2022

Licence Creative Commons

In order to overcome the substantial challenges faced by building sector in European Commission, being responsible for approximately 40% of the energy consumption and 36% of the greenhouse gas emissions, the scientific community together with policy makers are continuously working on delivering and adopting innovative solutions, advanced practices and regulations, respectively. In recent years, building regulations have gradually introduced new requirements to ensure a phased decarbonization of building sector and an increasing of its energy performance. In 2010, the Energy Performance of Buildings Directive (EPBD) recast [ 1 ] introduced requirements with the objective of attaining environmental and energy efficiency goals adopting the nearly Zero Energy Buildings (nZEB) Performance for new and existing buildings. A special attention was given to the public building sectors in terms of energy efficiency measures, drivers and barriers [ 2 ] and their optimal calculation [ 3 ].

For a building to be considered nZEB, it must reduce its energy consumption and generate energy from renewable sources, which can compensate for the majority of the building's consumption assuring at the same time thermal comfort. Taking into account the specific requirements and specifications for nZEB performance, a special attention has been paid to the integration of renewable systems in the buildings footprint or nearby. At building level these renewable technologies are mostly integrated in the building envelope (walls and roofs).

Usually, the building façade has a crucial role in performing as the interface between the environment and the indoor ambient. With the integration of renewable energy (especially solar), the buildinǵs facade has a significant impact on the occupant's comfort, building energy demands, and the aesthetics of the building. Commonly, designing a building façade takes into consideration several factors, as the climatic conditions and surrounding structures, indoor and spatial characteristics, needs of the building occupants regarding comfort and costs, among others. From an engineering and architecture perspective, in the last years, there has been a growing focus on the strategic development of building façades, it is, to contribute to meet the requirements of the high-performance regulations while being sustainable and aesthetically pleasant. This strategic development brings new experiments, innovative systems, and technology to be integrated into the formal functions of the envelope [ 4 ].

The façade elements may improve the energy flexibility of the building by the adequation of the constructive elements thermal performance to climate and building usage profile, also by being adaptive or automated to adapt to the different boundary conditions. Given this context and the flexibility that façade elements can offer in the design process, innovative façade elements based on solar energy systems can significantly reduce the building energy demand [ 5 ].

Entire buildings are broad, multi-scale, multi-material, with exceptionally unique analysis approach frameworks with vast influences. When addressing the design, applications and control of Building Integrated Photovoltaic System (BIPV) and its relationship with the building itself, it becomes very complex to create functional systems that are adaptable and generally relevant to the improvement of energy performance; once there must be a trade-off between factors as life-cycle assessment and real improvement it brings to the energy demand reduction [ 6 ].

The present article provides a concise review of a sample of studies concerning Building Integrated Solar Energy Systems integrated into façades published in the last five years. This article presents the main scope of the works, a comparison of the outcomes through a table classification, and a discussion about trends in the field.

The present study presents a systematic review concerning innovative systems on façade BI-SES. The source of information used to acquire the data is the Clarivate Analytics Web-of-Science. Figure 1 presents in detail the survey method and rationale used for the systematic review. In summary, the eligibility criteria and study selection are based on the published material within the search terms, period, the relevance, keywords and abstract pertinent to the objectives, and consideration through the screening of appropriate content throughout the text. Thus, Figure 1 also presents the results regarding the number of publications filtered through the adopted survey methodology. The data items, summary measures and report characteristics are based on the study details (reference), study characteristics (study type and technology type), extension of analysis, among others.

In the survey step previous to the detailed consideration of the title and abstract pertinent to the objective of the study (resulting in 115 articles), the results obtained by the source of information were segmented concerning the year of publication ( Fig. 2 ), journal of publication ( Fig. 3 ) and country of submission ( Fig. 4 ).

The remaining 75 articles were later segmented in the three mentioned categories: thermal, photovoltaic and hybrid BI-SES. The results are then presented in terms of summary of the manuscripts, and classification concerning the detailed system type, study type (experimental and/or numerical), simulation tool or technique, parameters under study in case of existence of a parametric analysis, and performance of the system considering its thermal, electrical and total efficieny.

The obtained results were segmented into three categories: integrated solar thermal systems, integrated photovoltaic systems and integrated hybrid systems (both thermal and photovoltaic). The thermal system converts the solar radiation into thermal energy, the photovoltaic converts it into electricity and the hybrid converts both in electricity and thermal energy. The results presented here are described concerning their core information and are further classified in a table to compare the different studies.

3.1 Integrated solar thermal systems

A sample of 16 scientific articles was considered representative innovative solar thermal systems pertinent, among the 75 articles reviewed. A summary of the most pertinent is presented here, followed by a table summarising the studies.

In Prieto et al. [ 7 ], there is a detailed review concerning the possibilities of using solar cooling integrated façades by exploring their feasibility concerning orientation, efficiency, and climate in which it is used. It is concluded that warm-dry climates and east/west orientations are the best situations for solar cooling façade applications, reaching a theoretical solar fraction of 100% [ 7 ]. In Maurer et al. [ 8 ], a review is done on the most important contributions of recent years of building-integrated solar thermal systems, in terms of systems being designed, results being achieved in terms of thermal characterization, and simple models to evaluate the systems − being this publication an interesting compilation of studies to have an overall view of the current technology status for building integration. Valladares-Rendon et al. [ 9 ] developed a review of shading thermal solutions to decrease direct solar gains and improve energy savings, balanced with visual comfort. This publication emphasizes the importance of employing the solar thermal elements with more than one purpose in a single element, reinforcing that solar façade elements shall not have a static goal.

Velasco et al. [ 10 ], a Venetian blind double-skin façade with the integrated solar thermal collector is analysed through CFD software. The authors emphasize that the system would promote energy efficiency through avoiding direct solar gains while being aesthetically pleasant. In Sun et al. [ 11 ], the authors present a façade system with parallel transparent plastic slats sandwiched between glass panes to form a parallel slat transparent insulation material to reduce coupled convective and radiative heat transfer inside the air cavity of the panes of a double glazed window. It contributes to increasing the thermal resistance without constraining the daylight access to the point of visual comfort reduction. In Li et al. [ 12 ], the work focuses on the innovation of building-integrated solar thermal shading systems to reduce the energy demand and improve the daylight levels through modelling and simulations.

In O’Hegarty et al. [ 13 ], the authors review and analyse solar thermal façades in terms of the type, technology used, and the materials that constitute it. Daily efficiency models are presented based on a combination of analysis methods, comprising a good data resource for comparison among technologies. Lamnatou et al. [ 14 ] give a critical review of building-integrated solar thermal systems' simulation methods and usage. Not only thermal but other types of BI solar configurations such as photovoltaic and hybrid systems are covered.

In Buonomano et al. [ 15 ], the design and the thermodynamic analysis of a new prototype of a flat-plate water-based solar thermal collector are developed, to integrate the system in building façades. The innovation is based on inexpensive materials and simplified design, aiming to reduce production and installation costs to improve market penetration. The applications are the production of hot water for domestic uses and space thermal comfort. This study contrasts with academia's tendency to develop expensive prototypes, as it aims to reach buildings in a faster manner by implications and technology transfer. As in the previously mentioned work, in Agathokleous et al. [ 16 ], it is also possible to find a flat-plate based thermal collector integrated into building façade envelopes but based on using air as the fluid. The authors also focused on the use of cost-effective materials and simple design solutions. They developed an energy dynamic simulation model and economic performance analyses and concluded that the system payback would be close to six years.

In Garnier et al. [ 17 ], a novel incorporated solar collector with storage for water heaters was created, followed by a praiseworthy CFD investigation. The proposed project is composed of a heating component to give household independence through the high-temperature water system and considers the coordination of the system and the rooftop configuration, enabling the system unit to be inserted inside an auxiliary protected material board framework. In Resch-Fauster et al. [ 18 ], the proposition focuses on an integrated solar thermal collector and latent heat storage modules. The overheating protection supplied by this system has high efficiency of the optimized configuration, calculated in function of other thermophysical characteristics. This study also reinforces the modularity that the BI-SES systems have been adopting in recent years. In Ibanez-Puy et al. [ 19 ], a ventilated active thermoelectric envelope component is studied. It focuses on a modular active ventilated façade prototype with a thermoelectric system to be installed in the building envelope and provide a high comfort level. The system integrates a passive design strategy through the ventilation and an active strategy through an active thermoelectric solution. This study is an example of coupling passive and active techniques to improve the overall system performance.

In Guarino et al. [ 20 ], the authors study the performance of a building-integrated thermal storage system, intending to improve the energy performances of the system in a cold climate. Navarro et al. [ 21 ] presented a novel phase change material (PCM) system inside the structural horizontal building component. The structural element was a composed concrete with micro-encapsulated PCM located into 14 channels, coupled to a solar air collector to melt and induce the phase change. The technique presented by these authors may be considered more intrusive once coupled with hard materials of civil construction (concrete), which per si deliver a reliable structural performance throughout the whole building lifetime. In Hengstberger et al. [ 22 ], a solution is presented by using PCM embedded into the absorber insulation which buffers the heat during the day and releases it at night. A parametric analysis is developed using a dynamic simulation tool to find the best melting temperature of a thin layer of PCM at different positions.

In Shen et al. [ 23 ], the authors introduce an innovative compact solar thin film with an interiorly extruded pin-fin flow channel convenient for building integration. A simulation model was used, and a prototype of the solar thin film was fabricated to test the system under different controlled conditions. The methodology presented by this work is pertinent once it discusses the process of designing and testing. In He et al. [ 24 ], an innovative tile-shaped dual-function solar collector is analysed for water heating. The study is developed using CFD software and aims not only to provide optimal designs but also to meet pleasing aesthetics. In Giovanardi et al. [ 25 ], a modular unglazed solar thermal façade system was developed to aid the installation of active solar façades, with a particular focus on the renovation of existing buildings. In He et al. [ 26 ], the authors investigate the loop-heat-pipe water heating performance of an innovative heat pump assisted solar, using theoretical and experimental methods.

Table 1 presents the complete list and classification of the solar thermal systems reviewed in this work, considering the system type, existence/non-existence of experimental and numerical analysis, existence/non-existence of parametrical analysis and details, reached efficiency of the system under study. The terminology (N.S.) stands for “not stated”, meaning that the article did not mention the feature.

The results obtained show that there is no specific trend concerning the systems under study in the most recent publications concerning the solar thermal systems. However, the focus can be given to the integration between passive and active techniques and the modularity and multiple purposes of the same element. The technologies vary from innovative system design to innovative methods of operation or material combination. Also, the thermal efficiency ( η t ) of the systems is, in most cases, not directly assessed. Most of the studies using dynamic simulation evaluated the impact of the systems in thermal behaviour by calculating nominal energy needs for heating and cooling of the thermal zone, based on determined setpoints. Others use computational fluid dynamics analysis to have a detailed profile of the thermal behaviour of the systems given the specified boundary conditions and evaluate the systems in terms of temperatures (primarily based on the outlet-inlet differences). Parametric analysis is not always done in the reviewed studies. Still, in the studies that develop this component, the geometry, inlet velocity and inlet temperature ( T inl ) are the most used variables of variation.

Summary of the studies − solar thermal systems.

3.2 Integrated photovoltaic systems

A sample of 37 scientific articles presented innovative solar photovoltaic systems (working only with the photovoltaic effect), among the 75 articles reviewed. A summary of the most pertinent is presented here, followed by a table summarising the studies. In Shukla et al. [ 28 ], an extensive review is given concerning the design of Building Integrated Photovoltaic (BIPV) systems. It focuses on developing the technology, classification of cells and products, and industry/research opportunities. Another study, developed by Tripathy et al. [ 29 ], presents a review of the state-of-the-art PV products for building different components of envelopes, their properties and their accordance with international standards.

In Aguacil et al. [ 30 ], the work aims to provide a methodology to contribute to the decision-making process concerning the use of BIPV in the urban renewal process. It considers the surface types and trade-offs between self-consumption and self-sufficiency. It is a straightforward approach that aims to facilitate the analysis of suitability concerning different factors. Chen et al. [ 31 ] explored the impact of archetypes and confounding factors in optimising the design. They focus specifically on high-rise buildings with BIPV façades, using data-driven models incorporating qualitative and quantitative analysis. It intends to facilitate the analysis by defining typical types of façades in which the buildings In Biyik et al. [ 32 ], the authors reviewed the BIPV and BIPVT possible uses in terms of types, supply, generation power, performance characterization, and approaches of analysis. They identify two crucial research areas concerning this subject: (i) increase in system efficiency utilizing ventilation while reducing the modules temperature; (ii) use of thin-film applicable for integration in buildings. This study is an excellent source to assess the comparison between BIPVT and BIPVT. In Shukla et al. [ 33 ], the study also presents a comprehensive review of the BIPV commercial solutions and their characteristics and a comparison of international testing and operation standards and instructions. The authors focus on BIPV solutions for different façade elements.

In Agathokleous and Kalogirou [ 34 ], the authors study a naturally ventilated BIPV system, and the assessment is based on experimental thermal analysis. This study is particularly attractive, and further results obtained by the authors are presented in Table 2 . In Agathokleous et al. [ 16 ], the authors continued the previous work by introducing a simulation-based thermal analysis of the same system. In Wang et al. [ 45 ], a ventilated PV double-skin façade and a PV insulating glass unit are studied through comparative experiments to evaluate the systems' solar heat gain and U-value. In Cipriano et al. [ 54 ], the focus was on a PV ventilated component and a data-driven approach to iteratively identify the unknown parameters, determine their impact in the simulation outputs and ultimately, assess the deviations of the computational outcomes against the measured data. In Peng et al. [ 56 ], the authors used EnergyPlus and developed a whole-year energy performance evaluation and saving potential of a ventilated photovoltaic double-skin façade in a cool-summer Mediterranean climate zone. The work developed a sensitivity analysis over the numerical model, considering different air gap width and operation models of the ventilation. In Pantic et al. [ 57 ], they present a theory-based and experimental investigation of electricity generation potential concerning different orientations of the modules in the façade elements.

In Asfour [ 37 ], the study focuses on the association of the PV modules in shading devices, and the investigation is oriented to hot climates. They also develop a parametric simulation to evaluate the potential of different designs. In Luo et al. [ 60 ], PV-blind embedded double skin façade is studied by coupling thermal-electrical-optical models. The aim was to evaluate and optimize the system by using ray-tracing, radiosity and net radiation methods, and other usual thermal models for buildings. In Tablada et al. [ 40 ], the authors also study the use of PV coupled to shading devices for farming plants growing application − focusing on windows and balconies. In [ 35 ], the study derived a new metric for assessing the daylight quality by comparing different coverage ratios of the PV cell and window-wall ratios. They also compared different orientations and estimated the net electricity use of the building. Karthick et al. [ 42 ], they investigated semi-transparent building integrated photovoltaic modules on façades, focusing on different coverage ratios. In Zhang et al. [ 43 ], the authors also explore the potential savings generated by the use of PV associated with shading elements, developing a parametric analysis concerning tilt angles and orientation of the system.

In Connelly et al. [ 48 ], the idea of semi-transparent BIPV with concentrator is additionally investigated. They propose a “smart window” framework comprising a thermotropic layer with integrated PV modules. The authors propose a system that naturally reacts to climatic conditions and analyse the power generation, natural light availability and heat transfer from the system to the building structure through parametric analysis of different solar energy ratios incident on the PV. In Wang et al. [ 49 ], they evaluated the energy performance of an a-Si semi-transparent PV insulating glass unit via numerical simulation and experimental tests. Considering the measured optical and electrical features of the PV, an integrated model was made to simulate the system's energy performance under analysis. In Favoino et al. [ 50 ], they propose a novel simulation framework for the performance evaluation of a responsive structure based on envelope advances in the switchable photochromatic coating. The analysis is done by incorporating building energy simulation and lighting simulation and varying parameters as the climate in which it is inserted.

In Wu et al. [ 52 ], a novel static concentrating PV system, reasonable for use in windows or coating exteriors, has been proposed. The proposed concentrating PV system is lightweight, with minimal economic effort and ready to produce power. Moreover, this system consequently reacts to atmospheric conditions by changing the parity of power created by the PV with the measure of sunlight-based light and heat allowed through it into the structure. It also offers the possibility to control the energy utilization in the building. Liu et al. [ 58 ], improved the structure of a commonplace semitransparent PV module and investigated the utilization of three sorts of high-reflectivity heat protection movies to frame the BIPV. Hence, the creators broke down the impact of the system structures on the optical, heat, and control time execution of the semitransparent PV module and how much the execution improved.

Qiu et al. [ 36 ] investigate mergers of vacuum glazing and BIPV integration and analyse its capacity to reduce the energy needs of the buildings. Huang et al. [ 38 ] also present a detailed investigation of a similar novel system's thermal and power efficiencies, a combined design improvement of photovoltaic envelope solutions. In Sun et al. [ 48 ], they combine optical, electrical and energy models to assess the integration of semi-transparent photovoltaic in commercial buildings. The publication assesses the effect of window design on the energy needs of the building. In Tak et al. [ 44 ], the authors structured a semi-transparent sun-powered cell window, in which the transparency can be changed by modifying its temperature and dissolvable vapour pressure. Further details may be seen in the reference. A modelling test with the proposed system was led to look at the impacts on energy utilization, power generation, and inhabitant comfort. The outcomes demonstrate that the proposed window has a significant potential to generate electrical energy.

In Sornek et al. [ 41 ], a Fresnel lens is used to increase the efficiency of BIPV systems. The analysis of the system is made both employing dynamic simulations and experimental campaigns. They improved the general productivity of the building integrated photovoltaic systems by the use of a Fresnel lens. During the tests, the efficiency of the photovoltaic module increased by about 7% (reaching an η e of 22%). In Bunthof et al. [ 47 ], they build up the examination dependent on three Concentrator Photovoltaic (CPV) systems arrangements that consider the development of semi-straightforward structure veneer components. The systems likewise are a Fresnel focal point based concentrator and a novel level planar optic concentrator. In Correia et al. [ 51 ], Luminescent Solar Concentrators are displayed as financially savvy parts effectively incorporated in PV that can improve and advance the integration between PV components and building structures, with considerable potential outcomes for energy generation in façades, while improving urban aesthetics. In Sabry [ 53 ], a range of prismatic total interior reflection low concentration PV façades with different head angles has been evaluated, dependent on the location and characteristics of the surrounding areas of the building. Every veneer design is mimicked by ray-tracing procedure. Its presentation is examined against sensible direct sun-based radiation information in two clear sky days representing the summer and winter of the area under study. Ray-tracing recreations uncovered that most of the chosen arrangements could gather the vast majority of the direct solar radiation in summer.

Kang et al. [ 59 ] developed a light-catching system connected to BI-SES based on the PV use, which naturally promotes light exposure during the entire year. The structure is streamlined for the precise scope of the occurrence light by breaking the underlying symmetry. The authors show the viability of the designed light-catching structure for different occurrence point ranges employing exhaustive reproduction studies and trial results utilizing organic photovoltaic elements. In Hofer et al. [ 55 ], they present a modelling framework, coupling parametric 3D with high-resolution electrical modelling of the shading devices composed by thin-film PV modules, to reenact electric energy of geometrically complex PV applications. The proposed modelling framework can foresee with high spatial-transient resolution the shading positioning and adapt it over each PV module, being critical to improving the electricity generation through the adequate positioning of the modules and contributing to the control of direct solar gains in the building.

In Palacios-Jaimes et al. [ 46 ], a plan to transform a university building into NZEB is presented. It demonstrates that the BIPV system may provide the power needs and lessen the structure's energy use in a financially savvy way. The investigation emphatically centres around the life cycle assessment, surveying the net emissions of CO 2 and the harms caused in a near setting with traditional power sources. In Yang [ 61 ], they identify the technical barriers and risks related to the utilization of BIPV in different building life-cycle stages, together with the proposal of potential arrangements. When a straightforward answer could not be proposed, suggestions for future innovative work are made. The proposed approach incorporates assessment of past productions and gathering of criticism from the business experts.

Table 2 presents the complete list and classification of the solar photovoltaic systems reviewed in this work, considering the system type, existence/non-existence of experimental and numerical analysis, existence/non-existence of parametrical analysis and details, reached efficiency of the system under study.

The results of this sub-section show a considerable amount of studies being made concerning BI-SES based on photovoltaic technology. Based on this review, three main design trends were identified: (i) improvement of standard BIPV configurations through smart ventilation; (ii) use of photovoltaic technology integrated into building façades as shading devices; and (iii) use of concentrators in the PV systems integrated into building façades and rooftop. As in the previous category, many studies do not approach the systems in direct terms of efficiency (in this case, η e ). They are approached in terms of nominal energy needs, energy balances (demand and on-site supply), and system temperatures. Also, a parametric analysis is done mainly by varying parameters as orientation, cell coverage ratio, air gap width, ventilation rates, and geometries.

Summary of the studies − solar photovoltaic systems.

3.3 (Building) integrated hybrid systems

Compared with solar thermal collectors and photovoltaic systems, the integrated hybrid systems employ both technologies in the same system, generating both thermal energy and electricity. A sample of 22 scientific articles was considered as presenting coupled innovative solar photovoltaic and thermal systems, among the 75 are reviewed. A summary of the most pertinent is presented here, followed by a table summarising the studies.

In Lee et al. [ 62 ], an extensive review is presented on PV/T systems, being of particular interest to works concerning the design of innovative energy façade elements due to the novelty of the strategies presented. The study reviews the structure guidelines and working instruments of the PV/T façade systems, execution, control procedures and building applications. They highlight the use of electrochromic coating as the most used smart coating for thermal applications in PV systems and also stress that concerning PV shading, the external shading is the most utilized due to its low initial costs. The authors also state that algae growth façades and folding façades (complex geometry) shading systems are rising solutions, with high initial investment costs and requiring professional installers. They are, indeed, a promising arrangement because of their multi-purpose capabilities. Dynamic shading systems were found to spare 12% to 50% of the structure cooling power utilization. In Lai and Hokoi [ 63 ], a survey of a significant number of shading systems on the main façades facing south or north (depending on the hemisphere, referred to as sun-oriented façades) is presented, considering studies that have been published after 2010, segmenting the study in opaque and translucid elements.

In a most recent study by Lai and Hokoi [ 64 ], the state of the art sun-oriented control systems for façades are introduced, with a comparative assessment of sun-powered control systems and guidelines for improving new ones. It incorporates multifunctional frameworks and modelling with BIPV and thermal energy generation. In complement, in Debbarma et al. [ 65 ], the authors survey the BIPV and BIPVT advancements and energy, and the exergy examination of BIPV and BIPVT systems are likewise discussed. This work reviews the ongoing betterment of innovation around the world. In Agathokleous and Kalogirou [ 66 ], the work presents state of the art on thermal analysis of double skin façades with BIPV in terms of the published studies on these systems. In Zhang et al. [ 67 ], an in-depth review of the recently emerging active building-integrated solar thermal/PV technologies is also provided. The authors elaborate on the concept, parameters of classification and assessment, among other topics.

In Nagy et al. [ 68 ], they propose a modular adaptive solar façade to couple the element with the very dynamic environment surrounding the building boundaries. The energy behaviour and aesthetic expression of the façade can be managed to employ high Spatio-temporal resolution responses. The design process and operational plan are described, along with simulation results of the thermal behaviour and power production/consumption. In Peng et al. [ 69 ], the authors elaborate on the energy performance of a ventilated photovoltaic façade under varied ventilation modes and controlling modes for different climacteric conditions, aiming to improve the energy conversion efficiency.

In Chialastri and Isaacson [ 70 ], a prototype of a BIPVT was constructed based on thermal and electrical energy, aiming to achieve visual comfort and shading control through the system application. In this article, the prototype was evaluated under various conditions to characterize its performance. Dehra [ 71 ] presents a study on energy evaluation of a photovoltaic wall using either natural convection incited or fan-helped ventilation system. The vertical photovoltaic sun-oriented wall was introduced on the façade of a pre-assembled outside test room. The prototype was developed with two economically accessible photovoltaic modules, an air cavity and an insulated back layer.

In Smyth et al. [ 72 ], the authors propose a modular hybrid photovoltaic/solar thermal façade technology that uses an Integrated collector storage solar technology. In light of a patented solar thermal diode concept and shaped into a flat modular profile incorporating PV cells/module, the proposed system aims to heat the indoor environment, provide hot water, and generate electricity. In Luo et al. [ 60 ], the authors proposed a building-integrated photovoltaic, thermoelectric wall solution. It is examined by a numerical model comprising a PV framework and thermoelectric brilliant wall element. The thermal and electrical components of the system under cooling prevailing atmospheres was numerically researched utilizing an iterative system model. The presentation of the system is optimized by a comparative investigation with a traditional solid wall.

In Barman et al. [ 73 ], the study investigates the outcomes of a solar transparent photovoltaic window, focusing on angles of incidence, thermal gains using direct solar gains and energy generation. In Ahmed-Dahmane et al. [ 74 ], the proposed BIPVT system prototype comprises air collectors connected to an air handling unit to manage the airflow. The solution works based on two applications, namely for heating and cooling needs.

In Gaur and Tiwari [ 75 ], a BIPVT system is analysed. There is a focus on improving the articulation between electrical and thermal efficiencies and heat transfer through the structure. These thermal and electrical efficiencies articulations are crucial for various climatic conditions and diverse façade BI-SES designs. The system modules have been intensely studied for their energy, exergy and operational attributes with and without associated air pipe. Buonomano et al. [ 76 ], a BIPVT system has been analysed for residential applications, assessing active and passive operational applications. In Oh et al. [ 77 ], they built up an incorporated model for evaluating the techno-financial execution of the BIPVT on façades, emphasising energy demand and supply. In [ 78 , 79 ], the authors develop an experimental study of a Building-Integrated Photovoltaic system combined with a water storage tank prototype. The authors achieve a thermal efficiency of nearly 8% during the winter and 40% during the summer. In [ 80 ], a CFD study is presented for the prototype with an interior module of insulation instead of the water tank. This new modular prototype constituted a next step study of previous prototypes proposed by the research group, as may be consulted in [ 81 , 82 ]. Also to note is the work presented in [ 83 ], in which they assess a BIPVT-PCM prototype via genetic algorithm optimization. Having as case study the same living lab in which these prototypes were tested, in [ 84 ] it is possible to find a numerical study of a full scale BIPVT system. In [ 85 ], the experimental results for this BIPVT system are presented.

Table 3 presents the complete list and classification of the hybrid solar systems reviewed in this work, considering the system type, existence/non-existence of experimental and numerical analysis, existence/non-existence of parametrical analysis and details, reached efficiency of the system under study.

The hybrid systems presented by the sample of publications reviewed in the scope of this work are, mainly, façade elements of BIPVT walls, in which the principal analysis is made through numerical simulation via a finite element of CFD analysis. Also, as in the previous sub-sections, many of the studies do not present the results in terms of system efficiency, and parametric analysis is developed in nearly half of them. The parameters under examination in the parametric analysis are ventilation nodes and velocity, geometry (duct width, for example) and glazing type.

Summary of the studies − hybrid systems.

This article intended to present a literature review to contribute to increasing knowledge and systematization of different building-integrated solar energy systems. The façades of the buildings offer huge potential to increase the sustainability of the built sector. Its association with building-integrated solar energy systems demonstrates that they can not only increase the comfort of the building and reduce the energy consumption but also respond to the necessities of the grid, especially concerning adaptive systems. A sample of 71 studies was reviewed in this study, and the results were segmented into three categories: thermal systems, photovoltaic systems, and hybrid systems integrated into the façades. When applicable, the studies were further classified regarding the type of study, the tool used, parametric analysis parameters, and performance.

Concerning the solar thermal systems, the results show that there is not a specific trend concerning the systems under study in the most recent publications. However, the focus can be given to the integration between passive and active techniques and the modularity and multiple purposes of the same element. The technologies vary from innovative system design to innovative methods of operation or material combination. The results concerning the photovoltaic systems presented three main design trends were identified based on this review: i) improvement of standard BIPV configurations through smart ventilation; ii) use of photovoltaic technology integrated into building façades as shading devices, and iii) use of concentrators in the PV systems integrated into building façades and rooftop. The hybrid systems presented by the sample of publications reviewed in the scope of this work are, mainly, façade elements of BIPVT walls, in which the principal analysis is made through numerical simulation via a finite element of CFD analysis.

NZEB_LAB—Research Infrastructure on Integration of Solar Energy Systems in Buildings” (Refª. LISBOA-01-0145-FEDER-022075)” is financed by national funds FCT/MCTES (PIDDAC) and European FEDER from Regional Operation Program of Lisbon.

  • European Parliament ‘Directive 2010/31/EU of the European Parliament and of the Council of 19 May 2010 on the energy performance of buildings (recast)’ Official Journal of the European Union 2010; 2010 [Google Scholar]
  • L. Aelenei, H. Petran et al., New challenge of the public buildings: nZEB findings from IEE RePublic_ZEB Project, Energy Proc. 78 , 2016–2021 (2015) [CrossRef] [Google Scholar]
  • L. Aelenei, S. Paduos et al., Implementing cost-optimal methodology in existing public buildings, Energy Proc. 78 , 2022–2027 (2015) [CrossRef] [Google Scholar]
  • M. Formentini, S. Lenci, An innovative building envelope (kinetic façade) with shape memory alloys used as actuators and sensors, Autom. Constr. 85 , 220–231 (2018) [CrossRef] [Google Scholar]
  • F. Pomponi et al., Energy performance of Double-Skin Façades in temperate climates: a systematic review and meta-analysis, Renew. Sustain. Energy Rev. 54 , 1525–1536 (2016) [CrossRef] [Google Scholar]
  • J. Borggaard et al., ‘Control, estimation and optimization of energy efficient buildings, Proc. Am. Control Conf. 837–841 (2009) [Google Scholar]
  • A. Prieto et al., Feasibility study of self-sufficient solar cooling facade applications in different warm regions, Energies 11 (2018) [Google Scholar]
  • C. Maurer, C. Cappel, T.E. Kuhn, Progress in building-integrated solar thermal systems, Solar Energy 154 , 158–186 (2017) [CrossRef] [Google Scholar]
  • L.G. Valladares-Rendon, G. Schmid, S.-L. Lo, Review on energy savings by solar control techniques and optimal building orientation for the strategic placement of facade shading systems, Energy Build. 140 , 458–479 (2019) [Google Scholar]
  • A. Velasco et al., Assessment of the use of venetian blinds as solar thermal collectors in double skin facades in mediterranean climates, Energies 10 (2017) [Google Scholar]
  • Y. Sun et al., Thermal evaluation of a double glazing facade system with integrated Parallel Slat Transparent Insulation Material (PS-TIM), Build. Environ. 105 , 69–81 (2016) [CrossRef] [Google Scholar]
  • Y. Sun et al., Integrated semi-transparent cadmium telluride photovoltaic glazing into windows: Energy and daylight performance for different architecture designs, Appl. Energy 231 , 972–984 (2018) [CrossRef] [Google Scholar]
  • R. O'Hegarty, O. Kinnane, S.J. McCormack, Review and analysis of solar thermal facades, Solar Energy 135 , 408–422 (2016) [CrossRef] [Google Scholar]
  • C. Lamnatou et al., Modelling and simulation of Building-Integrated solar thermal systems: behaviour of the system, Renew. Sustain. Energy Rev. 45 , 36–51 (2015) [CrossRef] [Google Scholar]
  • A. Buonomano et al., Building-façade integrated solar thermal collectors: energy-economic performance and indoor comfort simulation model of a water based prototype for heating, cooling, and DHW production, Renew. Energy (2018) [Google Scholar]
  • R. Agathokleous et al. , Building facade integrated solar thermal collectors for air heating: experimentation, modelling and applications, Appl. Energy 239 , 658–679 (2019) [CrossRef] [Google Scholar]
  • C. Garnier, T. Muneer, J. Currie, Numerical and empirical evaluation of a novel building integrated collector storage solar water heater, Renew. Energy 126 , 281–295 (2018) [CrossRef] [Google Scholar]
  • K. Resch-Fauster et al., Overheating protection of solar thermal facades with latent heat storages based on paraffin-polymer compounds, Energy Build. 169 , 254–259 (2018) [CrossRef] [Google Scholar]
  • M. Ibanez-Puy et al., Ventilated Active Thermoelectric Envelope (VATE): analysis of its energy performance when integrated in a building, Energy Build. 158 , 1586–1592 (2018) [CrossRef] [Google Scholar]
  • F. Guarino et al., PCM thermal storage design in buildings: Experimental studies and applications to solaria in cold climates, Appl. Energy 185 , 95–106 (2017) [CrossRef] [Google Scholar]
  • L. Navarro et al., Experimental study of an active slab with PCM coupled to a solar air collector for heating purposes, Energy Build. 128 , 12–21 (2016) [CrossRef] [Google Scholar]
  • F. Hengstberger et al., High temperature phase change materials for the overheating protection of facade integrated solar thermal collectors, Energy Build. 124 , 1–6 (2016) [CrossRef] [Google Scholar]
  • J. Shen et al., Characteristic study of a novel compact Solar Thermal Facade (STF) with internally extruded pin-fin flow channel for building integration, Appl. Energy 168 , 48–64 (2016) [CrossRef] [Google Scholar]
  • W. He et al., CFD and comparative study on the dual-function solar collectors with and without tile-shaped covers in water heating mode, Renew. Energy 86 , 1205–1214 (2016) [CrossRef] [Google Scholar]
  • A. Giovanardi et al. , Integrated solar thermal facade system for building retrofit, Solar Energy 122 , 1100–1116 (2015) [CrossRef] [Google Scholar]
  • W. He et al., Operational performance of a novel heat pump assisted solar facade loop-heat-pipe water heating system, Appl. Energy 146 , 371–382 (2015) [CrossRef] [Google Scholar]
  • L. Li, M. Qu, S. Peng, Performance evaluation of building integrated solar thermal shading system: building energy consumption and daylight provision, Energy Build. 113 , 189–201 (2016) [CrossRef] [Google Scholar]
  • A.K. Shukla, K. Sudhakar, P. Baredar, A comprehensive review on design of building integrated photovoltaic system, Energy Build. 128 , 99–110 (2016) [CrossRef] [Google Scholar]
  • M. Tripathy, P.K. Sadhu, S.K. Panda, A critical review on building integrated photovoltaic products and their applications, Renew. Sustain. Energy Rev. 61 , 451–465 (2016) [CrossRef] [Google Scholar]
  • S. Aguacil, S. Lufkin, E. Rey, Active surfaces selection method for building-integrated photovoltaics (BIPV) in renovation projects based on self-consumption and self-sufficiency, Energy Build. 193 , 15–28 (2019) [CrossRef] [Google Scholar]
  • X. Chen, H. Yang, J. Peng, Energy optimization of high-rise commercial buildings integrated with photovoltaic facades in urban context, Energy 172 , 1–17 (2019) [CrossRef] [Google Scholar]
  • E. Biyik et al. , A key review of building integrated photovoltaic (BIPV) systems, Eng. Sci. Technol. 20 , 833–858 (2017) [Google Scholar]
  • A.K. Shukla, K. Sudhakar, P. Baredar, Recent advancement in BIPV product technologies: a review, Energy Build. 140 , 188–195 (2017) [CrossRef] [Google Scholar]
  • R.A. Agathokleous, S.A. Kalogirou, Part II: thermal analysis of naturally ventilated BIPV system: modeling and simulation, Solar Energy 169 , 682–691 (2018) [CrossRef] [Google Scholar]
  • Y. Cheng et al. , An optimal and comparison study on daylight and overall energy performance of double-glazed photovoltaics windows in cold region of China, Energy 170 , 356–366 (2019) [CrossRef] [Google Scholar]
  • C. Qiu, H. Yang, W. Zhang, Investigation on the energy performance of a novel semi-transparent BIPV system integrated with vacuum glazing, Build. Simul. 12 , 29–39 (2019) [CrossRef] [Google Scholar]
  • O.S. Asfour, Solar and shading potential of different configurations of building integrated photovoltaics used as shading devices considering hot climatic conditions, Sustainability 10 (2018) doi: 10.3390/su10124373 [CrossRef] [Google Scholar]
  • J. Huang et al., Numerical investigation of a novel vacuum photovoltaic curtain wall and integrated optimization of photovoltaic envelope systems, Appl. Energy 229 , 1048–1060 (2018) [CrossRef] [Google Scholar]
  • Y. Luo, L. Zhang, Z. Liu, X. Su et al., Coupled thermal-electrical-optical analysis of a photovoltaic-blind integrated glazing facade, Appl. Energy 228 , 1870–1886 (2018) [CrossRef] [Google Scholar]
  • A. Tablada et al., design optimization of productive facades: integrating photovoltaic and farming systems at the tropical technologies laboratory, Sustainability 10 (2018) [Google Scholar]
  • K. Sornek, M. Filipowicz, J. Jasek, The use of fresnel lenses to improve the efficiency of photovoltaic modules for building-integrated concentrating photovoltaic systems', J. Sustain. Dev. Energy Water Environ. Syst. 6 , 415–426 (2018) [CrossRef] [Google Scholar]
  • A. Karthick et al. , Performance study of building integrated photovoltaic modules, Adv. Build. Energy Res. 12 , 178–194 (2018) [CrossRef] [Google Scholar]
  • W. Zhang, L. Lu, J. Peng, Evaluation of potential benefits of solar photovoltaic shadings in Hong Kong, Energy 137 , 1152–1158 (2017) [CrossRef] [Google Scholar]
  • S. Tak et al. , ‘Effect of the changeable organic semi-transparent solar cell window on building energy efficiency and user comfort, Sustainability 9 (2017) [Google Scholar]
  • M. Wang et al. , Comparison of energy performance between PV double skin facades and PV insulating glass units, Appl. Energy 194 , 148–160 (2017) [CrossRef] [Google Scholar]
  • G.Y. Palacios-Jaimes et al. , Transformation of a University Lecture Hall in Valladolid (Spain) into a NZEB: LCA of a BIPV system integrated in its facade, Int. J. Photoenergy (2017) doi: 10.1155/2017/2478761 [Google Scholar]
  • L.A.A. Bunthof et al. , Impact of shading on a flat CPV system for facade integration, Solar Energy 140 , 162–170 (2016) [CrossRef] [Google Scholar]
  • K. Connelly et al. , Design and development of a reflective membrane for a novel Building Integrated Concentrating Photovoltaic (BICPV) ‘Smart Window’ system', Appl. Energy 182 , 331–339 (2016) [CrossRef] [Google Scholar]
  • M. Wang et al. , Assessment of energy performance of semi-transparent PV insulating glass units using a validated simulation model, Energy 112 , 538–548 (2016) [CrossRef] [Google Scholar]
  • F. Favoino et al. , Optimal control and performance of photovoltachromic switchable glazing for building integration in temperate climates, Appl. Energy 178 , 943–961 (2016) [CrossRef] [Google Scholar]
  • S.F.H. Correia et al., Scale up the collection area of luminescent solar concentrators towards metre-length flexible waveguiding photovoltaics, Progr. Photovolt. 24 , 1178–1193 (2016) [CrossRef] [Google Scholar]
  • Y. Wu et al. , Smart solar concentrators for building integrated photovoltaic facades, Sol. Energy 133 , 111–118 (2016) [CrossRef] [Google Scholar]
  • M. Sabry, Prismatic TIR (total internal reflection) low-concentration PV (photovoltaics)-integrated facade for low latitudes, Energy 107 , 473–481 (2016) [CrossRef] [Google Scholar]
  • J. Cipriano et al. , Development of a dynamic model for natural ventilated photovoltaic components and of a data driven approach to validate and identify the model parameters, Solar Energy 129 , 310–331 (2016) [CrossRef] [Google Scholar]
  • J. Hofer et al. , Parametric analysis and systems design of dynamic photovoltaic shading modules, Energy Sci. Eng. 4 , 134–152 (2016) [CrossRef] [Google Scholar]
  • J. Peng et al., Numerical investigation of the energy saving potential of a semi-transparent photovoltaic double-skin facade in a cool-summer Mediterranean climate, Appl. Energy 165 , 345–356 (2016) [CrossRef] [Google Scholar]
  • L.S. Pantic et al., Electrical energy generation with differently oriented photovoltaic modules as facade elements, Thermal Sci. 20 , 1377–1386 (2016) [CrossRef] [Google Scholar]
  • H.-M. Liu et al., Improving the performance of a semitransparent bipv by using high-reflectivity heat insulation film, Int. J. Photoenergy (2016) doi: 10.1155/2016/4174216 [Google Scholar]
  • J. Kang, C. Cho, J.-Y. Lee, Design of asymmetrically textured structure for efficient light trapping in building-integrated photovoltaics, Org. Electr. 26 , 61–65 (2015) [CrossRef] [Google Scholar]
  • Y. Luo, L. Zhang, Z. Liu, J. Wu et al., Numerical evaluation on energy saving potential of a solar photovoltaic thermoelectric radiant wall system in cooling dominant climates, Energy 142 , 384–399 (2018) [CrossRef] [Google Scholar]
  • R.J. Yang, Overcoming technical barriers and risks in the application of building integrated photovoltaics (BIPV): hardware and software strategies, Autom. Construct. 51 , 92–102 (2015) [CrossRef] [Google Scholar]
  • J. Lee et al., Renewable energy potential by the application of a building integrated photovoltaic and wind turbine system in global urban areas, Energies 10 (2017) [Google Scholar]
  • C.-M. Lai, S. Hokoi, Solar facades: a review, Build. Environ. 91 , 152–165 (2015) [CrossRef] [Google Scholar]
  • C. Lai, S. Hokoi, Experimental and numerical studies on the thermal performance of ventilated BIPV curtain walls, Indoor Built Environ. 26 , 1243–1256 (2017) [CrossRef] [Google Scholar]
  • M. Debbarma, K. Sudhakar, P. Baredar, Thermal modeling, exergy analysis, performance of BIPV and BIPVT: a review, Renew. Sustain. Energy Rev. 73 , 1276–1288 (2015) [Google Scholar]
  • R.A. Agathokleous, S.A. Kalogirou, Double skin facades (DSF) and building integrated photovoltaics (BIPV): a review of configurations and heat transfer characteristics, Renew. Energy 89 , 743–756 (2016) [CrossRef] [Google Scholar]
  • X. Zhang et al., Active Solar Thermal Facades (ASTFs): From concept, application to research questions, Renew. Sustain. Energy Rev. 50 , 32–63 (2015) [CrossRef] [Google Scholar]
  • Z. Nagy et al., The adaptive solar facade: from concept to prototypes, Front. Architectur. Res. 5 , 143–156 (2016) [CrossRef] [Google Scholar]
  • J. Peng et al., Comparative study of the thermal and power performances of a semi-transparent photovoltaic facade under different ventilation modes, Appl. Energy 138 , 572–583 (2015) [CrossRef] [Google Scholar]
  • A. Chialastri, M. Isaacson, Performance and optimization of a BIPV/T solar air collector for building fenestration applications, Energy Build. 150 , 200–210 (2017) [CrossRef] [Google Scholar]
  • H. Dehra, An investigation on energy performance assessment of a photovoltaic solar wall under buoyancy-induced and fan-assisted ventilation system, Appl. Energy 191 , 55–74 (2017) [CrossRef] [Google Scholar]
  • M. Smyth et al., Experimental performance characterisation of a hybrid photovoltaic/solar thermal facade module compared to a flat integrated collector storage solar water heater module, Renew. Energy 137 , 137–143 (2019) [CrossRef] [Google Scholar]
  • S. Barman et al., Assessment of the efficiency of window integrated CdTe based semi-transparent photovoltaic module', Sustain. Cities Soc. 37 , 250–262 (2018) [CrossRef] [Google Scholar]
  • M. Ahmed-Dahmane, A. Malek, T. Zitoun, Design and analysis of a BIPV/T system with two applications controlled by an air handling unit', Energy Convers. Manag. 175 , 49–66 (2018) [CrossRef] [Google Scholar]
  • A. Gaur, G.N. Tiwari, Analytical expressions for temperature dependent electrical efficiencies of thin film BIOPVT systems, Appl. Energy 146 , 442–452 (2015) [CrossRef] [Google Scholar]
  • A. Buonomano et al. , BIPVT systems for residential applications: an energy and economic analysis for European climates, Appl. Energy 184 , 1411–1431 (2016) [CrossRef] [Google Scholar]
  • J. Oh et al. , An integrated model for estimating the techno-economic performance of the distributed solar generation system on building façades: focused on energy demand and supply, Appl. Energy 228 , 1071–1090 (2018) [CrossRef] [Google Scholar]
  • M.A.C. Sousa, L. Aelenei, H. Gonçalves, Comportamento térmico de um protótipo BIPV combinado com armazenamentode água: análise experimental, in CIES2020-XVII Congresso Ibérico e XIII Congresso Ibero-americano de Energia Solar . LNEG-Laboratório Nacional de Energia e Geologia (2020) 1167–1174 [Google Scholar]
  • J.M. Lourenço et al., Thermal behavior of a BIPV combined with water storage: an experimental analysis, Energies 14 , 2545 (2021) [CrossRef] [Google Scholar]
  • K. Bot, L. Aelenei, H. Gonçalves, Design de um protótipo BIPVT e análise por meio de computação dinâmica de fluídos, in CIES2020-XVII Congresso Ibérico e XIII Congresso Ibero-americano de Energia Solar . LNEG-Laboratório Nacional de Energia e Geologia (2020) pp. 1185–1192 [Google Scholar]
  • L. Aelenei, R. Pereira, A. Ferreira et al., Building Integrated Photovoltaic System with integral thermal storage: a case study, Energy Proc. 58 , 172–178 (2014) [CrossRef] [Google Scholar]
  • L. Aelenei, R. Pereira, H. Gonçalves et al., Thermal performance of a hybrid BIPV-PCM: modeling, design and experimental investigation, Energy Proc. 48 , 474–483 (2014) [CrossRef] [Google Scholar]
  • R. Pereira, L. Aelenei, Optimization assessment of the energy performance of a BIPV/T-PCM system using genetic algorithms, Renew. Energy (2018) [Google Scholar]
  • K. Bot et al. , Performance assessment of a building integrated photovoltaic thermal system in mediterranean climate—a numerical simulation approach’, Energies 13 (2020) [Google Scholar]
  • K. Bot et al. , Performance assessment of a building-integrated photovoltaic thermal system in a mediterranean climate—an experimental analysis approach, Energies 14 , 2191 (2021) [CrossRef] [Google Scholar]

Cite this article as : Karol Bot, Laura Aelenei, Maria da Glória Gomes, Carlos Santos Silva, A literature review on Building Integrated Solar Energy Systems (BI-SES) for façades − photovoltaic, thermal and hybrid systems, Renew. Energy Environ. Sustain. 7 , 7 (2022)

All Figures

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.

Intelligent deep learning techniques for energy consumption forecasting in smart buildings: a review

  • Open access
  • Published: 05 February 2024
  • Volume 57 , article number  35 , ( 2024 )

Cite this article

You have full access to this open access article

  • R. Mathumitha 1 ,
  • P. Rathika 1 &
  • K. Manimala 2  

297 Accesses

1 Altmetric

Explore all metrics

Urbanization increases electricity demand due to population growth and economic activity. To meet consumer’s demands at all times, it is necessary to predict the future building energy consumption. Power Engineers could exploit the enormous amount of energy-related data from smart meters to plan power sector expansion. Researchers have made many experiments to address the supply and demand imbalance by accurately predicting the energy consumption. This paper presents a comprehensive literature review of forecasting methodologies used by researchers for energy consumption in smart buildings to meet future energy requirements. Different forecasting methods are being explored in both residential and non-residential buildings. The literature is further analyzed based on the dataset, types of load, prediction accuracy, and the evaluation metrics used. This work also focuses on the main challenges in energy forecasting due to load fluctuation, variability in weather, occupant behavior, and grid planning. The identified research gaps and the suitable methodology for prediction addressing the current issues are presented with reference to the available literature. The multivariate analysis in the suggested hybrid model ensures the learning of repeating patterns and features in the data to enhance the prediction accuracy.

Similar content being viewed by others

literature review on solar energy pdf

Deep Learning Techniques for Load Forecasting

literature review on solar energy pdf

Predictive Analysis of Energy Consumption for Energy Management in Smart Homes

literature review on solar energy pdf

Forecasting peak energy demand for smart buildings

Mona A. Alduailij, Ioan Petri, … Abdulrahman S. Aldawood

Avoid common mistakes on your manuscript.

1 Introduction

Due to the ongoing advancement of urbanization, there has been an increase in energy consumption during the past few decades (Bhosale and Gadekar 2014 ). 40% of the world's electrical energy is consumed by the building sector. The generation of electricity is closely related to the emission of carbon dioxide (CO 2 ) due to the predominant use of fossil fuels in many electricity generation processes. Fossil fuel-based power plants, such as those fueled by coal, oil, and natural gas, release significant amounts of CO 2 into the atmosphere when these fuels are burned to produce electricity. Electricity generation produces CO2 emissions that lead to the greenhouse effect, which traps heat in the atmosphere and causes a climatic change on a worldwide scale. The more electricity generated from fossil fuel sources, the higher the associated CO 2 emissions. Burning fossil fuels for power generation accounts for over 40% of energy-related CO2 emissions. Therefore, predicting building energy consumption has gained the utmost importance in tackling the swift rise of CO2 emissions. Furthermore, this capability empowers power engineers to resolve the supply and demand gap. Also, this helps to render proficient and impactful choices while designing, constructing, and operating building structures (Dong et al. 2018 ). This can be achieved by improving the overall performance of energy prediction in buildings (Zhang et al. 2020a ). A minimal improvement in forecast accuracy can also result in savings of millions of rupees (Zor et al. 2020 ). Hence, accurate load forecasts are also driven by a significant incentive related to economic motivation (Xu et al. 2019 ). Numerous methodologies and computational techniques have been developed to increase forecast accuracy (Agyeman et al. 2020 ).

The energy forecasting horizon is usually classified by time horizon into three categories: long-term (more than a year), mid-term (from a month to a year), and short-term (from one day to a month) forecasting (Kiprijanovska et al. 2020 ; Liu et al. 2020 ). Forecasting methods can be classified as supervised learning (neural network models such as support vector machines and classifiers (Das et al. 2020 ), extreme learning machines (ELM; Fu 2018 ), random forests (Wang and Srinivasan 2017 ), and DL algorithms) and unsupervised learning (a variety of clustering algorithms such as k-means clustering (Singh and Dwivedi 2018 ), fuzzy clustering (Cheng and Li 2008 ), and other improved clustering methods). The choice of forecasting method depends on the forecasting horizon, which refers to the period for which predictions are made. Short-term forecasting often uses methods like autoregressive integrated moving average (ARIMA; Farzana et al. 2014 ), exponential smoothing (Liu et al. 2016 ), or recurrent neural networks (RNNs). Medium-term forecasting may employ seasonal ARIMA, state space models, or machine learning algorithms. Long-term forecasting (years or more) relies on trend extrapolation, econometric models, or causal models. However, the selection should also consider data availability, quality, underlying assumptions, and specific problem characteristics. Combinations and overlaps of methods can occur based on the forecasting task. The literature has extensive research on predicting energy usage for both commercial and residential buildings. Recently, several academics have concentrated on enhancing the effectiveness and accuracy of electricity consumption forecasts using deep learning architectures (Bandic and Kevric 2018 ; Munkhdalai and Munkhdalai 2019 ). Most of the research community uses deep learning-based techniques because of their excellent results on unsupervised problems. DL is a complex computational model designed with multiple hidden layers that use features as input to represent data with different abstractions. DL algorithms are used for various learning tasks, especially unsupervised learning. Load forecasting is one application that has benefited from DL algorithms in many kinds of literature. Recently, RNNs and convolution neural networks (CNNs) are two powerful architectures proposed in the literature for the analysis of time series data. For instance, a study presented in Mocanu et al. 2016 proposed a deep CNN network for day-ahead load forecasting and compared the results with an (ELM), ARIMA, CNN, and RNN. Several studies also used RNN models for electricity load prediction, whereas in paper (Ali et al. 2016 ) used RNN, gated recurrent unit (GRU), and long-short term memory (LSTM) models for electricity load prediction in Turkey and extensively decreased the error. The electricity consumption data is time-series data, which comprises spatial and temporal information. The CNN models perform well for spatial information extraction but are insufficient for temporal information, whereas the RNN models are insufficient for spatial information and can learn temporal information. Therefore, to develop an optimal model for electricity load prediction, hybrid models are introduced in the recent literature.

For instance, another study (Kolehmainen et al. 2015 ) developed a hybrid model combining CNN with LSTM for short-term load prediction and compared their results with GRU, attention LSTM, LSTM, and bidirectional LSTM. In the paper (Manembu et al. 2018 ), they also developed a hybrid model with a combination of CNN and multi-layer bidirectional LSTM and compared their results with bidirectional LSTM, LSTM, and CNN-LSTM. Similarly, another study presented in Choksi et al. ( 2020 ) integrated a CNN with an LSTM auto-encoder and compared the final results with LSTM, LSTM autoencoder, and CNN-LSTM. Moreover, studies (Jain et al. 2019 ; Singh and Yassine 2019 ) presented the performance of a CNN-GRU based model for electricity forecasting. A study (Pirbazari and Sharma 2021 ) describes solar power forecasting in addition to load forecasting which uses the CNN-LSTM hybrid model with the help of climatic scenarios, and during experimental analysis, they have classified the error based on sunny days, and cloudy days. In recent years, hybrid CNN with CNN-ESN (echo state network) and LSTM-AE (autoencoder) have the potential to enhance the overall efficiency of the existing forecasting models. In a paper (Khan et al. 2020 ), they proposed a hybrid CNN with the LSTM-AE model. A CNN model extracts features from the input data, which are fed to an LSTM encoder to produce an encoded sequence. The encoded sequence is decoded by another subsequent LSTM decoder and forwarded to the final dense layer for energy prediction. In the paper (Liu et al. 2022 ), the author proposed the temporal convolutional network model (TCN) architecture and proved with better accuracy than LSTM. Most of the forecasting methods are designed to forecast a single or small group of time series. However, in the paper (Rick and Berton 2020 ), the author focused on short-term forecasting over many time series of unequal lengths. The author (Rick and Berton 2020 ) proposed a deep learning approach based on LSTM, CNN, and auto-encoder for training only one model for the many time series. Compared to TCN, they achieved a smaller error rate. The author suggests a hybrid combination technique, called CESN, based on a deep learning model combining Convolutional Neural Networks and Echo State Networks, to produce a high-quality prediction of power consumption (Ghimire et al. 2023 ). Peak handling is another difficult area for researchers in load predictions. The occurrences of the peaks are irregular, and their time of occurrence cannot be determined apriori because the customer’s consumption behavior is uncertain. This poses a challenge in modeling peaks as there is only a minimal dependency of present consumption on its past data. Thus the peaks have to be modeled precisely. The available state-of-the-art techniques (Gao et al. 2012 ) are not able to make accurate predictions at peak load conditions as machine learning (ML) algorithms poorly predict peak for hourly mean load prediction. In the paper (Sulaiman et al. 2022 ), the author proposed a novel hybrid method based on EMD (extreme mode decomposition) with ELM to handle peak residential loads. In the paper (Imani and Ghassemian 2019 ), wavelet decomposition is applied to remove redundant values. In addition, a collaborative representation is introduced including information on the neighboring points (previous and future time instances) of the considered load point. Hybrid models exploited well in other fields like the finance sector to predict stock market trends are not explored that much in power consumption forecasting. The finance sector benefits from combining LSTM models with Generative Adversarial Networks (GANs; Zhang, et al. 2015 ; Polamuri et al. 2022 ) to predict stock market trends. Such hybrid models can be applied to capture the stochastic nature of power consumption data and to generate synthetic datasets for augmentation. The performance of the hybrid model is very promising, reaching the highest level of accuracy. However, the best prediction of the electricity load needs further improvement by choosing an appropriate method.

The main contributions of this paper are:

Literature review of the previous research works for energy consumption forecasting in smart buildings, exploring their contribution and inference

Detailed framework for power forecasting

Analysis of the various methodologies used in the forecasting of energy consumption in buildings from various perspectives, their findings, and limitations

A research gap in the existing literature is identified and suggestions are given for the new researchers working in this area

The remainder of the paper is organized in the following way. Section  2 discusses the common framework for power forecasting, In Sect.  3 , different DL algorithm is discussed based on energy consumption forecasting Then the paper is followed by Sects.  4 and 5 . Section  4 briefly discusses about application of DL using forecasting horizon and Sect.  5 discusses evaluation metrics. Section  6 describes a detailed discussion of energy forecasting methodologies. Finally, the paper concludes with the identification of the research gap in Sect.  7 and concludes in Sect.  8 .

2 Inference from literature

The literature survey has provided valuable insights into the field of energy consumption forecasting in smart buildings. The diverse body of research underscores several key trends and perspectives that significantly contribute to this critical domain. Power forecasting needs to analyze large amounts of historical power data, especially for high-resolution forecasting. Different ML techniques have been applied for predicting future electricity consumption for a more ideal solution, which lacks in handling large datasets. This gap is filled by DL based techniques because they can handle and learn from massive datasets more efficiently than traditional ML models First and foremost, data-driven approaches have emerged as a fundamental pillar in energy consumption forecasting. Leveraging advanced machine learning, deep learning, and data analytics techniques, harness historical data, weather information, occupancy patterns, and other relevant variables to create accurate forecasts, offering a promising avenue for improving energy management in smart buildings.

Previous research in energy consumption forecasting for smart buildings has revealed several notable trends and unique perspectives. Consumers are now becoming prosumers, who not only consume electricity but also generate it, often through renewable energy sources like solar panels, wind turbines, or small-scale hydroelectric systems. Prosumers are essentially consumers who become self-sufficient energy producers, and they may feed surplus energy back into the grid. While the literature primarily focuses on load forecasting, recent trends in renewable energy forecasting should be considered. The integration of renewable energy sources, such as solar and wind power, into the energy grid requires accurate forecasting to ensure grid stability and efficient energy management. Accurate forecasting is crucial for optimizing energy storage systems, which play a key role in grid integration of renewables and in managing demand spikes, such as during peak loads. Peak load prediction remains a challenge, and most machine learning algorithms struggle to provide accurate forecasts for peak consumption. Improving peak load prediction is crucial for efficient grid management. The literature suggests the potential of hybrid methods, such as combining EMD with ELM, wavelet decomposition, and collaborative representation, to enhance the accuracy of residential load forecasting.

3 Basic forecasting framework

Predicting power consumption is critical for smart grids to manage and conserve energy, avoid waste, and use it efficiently. Due to the influence of numerous unpredictable situations or the noisy disordering of smart meter data, it is difficult to anticipate power usage accurately, and the methods employed can sometimes yield inaccurate results. Moreover, various techniques based on conventional networks have been developed, but they cannot predict energy demand efficiently (Cai et al. 2019 ). Conventional networks have problems related to short-term memory and learning from scratch. These problems are easily solved using LSTM, a special type of RNN that has attracted a lot of attention in the field of deep learning. LSTM networks have a unique architecture that includes memory cells and gates. The memory cells allow the network to store and retain information over longer sequences, enabling the model to capture and learn from long-term dependencies in the data. The common schematic diagram used for forecasting power is shown in Fig.  1 .

figure 1

Basic block diagram for forecasting power

3.1 Smart meter data

The first step is to collect smart meter data. Smart meters collect a huge range of data related to power consumption and weather data. It typically requires separate weather monitoring equipment or accessing weather data from weather stations (Makonin 2019 ). Weather data includes various parameters such as temperature, humidity, wind speed, and wind speed precipitation. It records data with a timestamp, allowing for real-time monitoring and historical analysis. Hence, the smart meter data becomes time series data (Le and Vo 2020 ). The specific data collected can vary based on the utility's requirements and the capabilities of the smart meter. It's important to note that smart meters provide valuable insights for consumers and utilities. Privacy and security issues must be considered to protect sensitive information and ensure responsible data usage.

3.2 Smart meter configuration

Smart meters are advanced digital devices used to measure and record electricity usage in residential, commercial, and industrial environments. Unlike traditional meters, smart meters provide two-way communication between utilities and consumers. They can send data back to the utility company in real time, which enables better monitoring, management, and optimization of energy usage. Designing a smart meter for building energy consumption forecasting involves several key considerations to ensure accurate data collection, communication, and compatibility with forecasting algorithms. The outline of the smart meter design is depicted in Fig.  2 . Here's a conceptual outline of how a smart meter can be designed to support energy consumption forecasting (https: www.electronics-notes.com/ articles/eco-green-engineering/smart-energy-meters/smart-meter-electronic-circuit-design.php):

figure 2

Design of Smart meter for building energy consumption forecasting

3.2.1 Data collection

High-Frequency Data: The smart meter should be capable of collecting energy consumption data at regular intervals (e.g., every 15 min or hourly) to capture detailed consumption patterns.

Timestamps: Each data point should be associated with a timestamp, enabling chronological analysis and forecasting.

Granular Data: The meter should record various consumption parameters such as active power, reactive power, voltage, and current.

3.2.2 Communication

Two-Way Communication: Implement bidirectional communication capabilities to allow data transmission to utility companies and receipt of signals or commands.

Data Transmission: Use wired or wireless communication protocols (e.g., cellular, Wi-Fi, Zigbee) to transmit collected data to central servers or cloud platforms.

3.2.3 Data storage and logging

Local Storage: Incorporate local storage to buffer and log data in case of communication disruptions, ensuring no data loss.

Data Logging: Store consumption data along with timestamps in a structured format for easy retrieval and analysis.

3.2.4 Data quality and accuracy

Calibration: Calibrate the smart meter to ensure accurate energy readings and minimize measurement errors.

Quality Control: Implement mechanisms to monitor and ensure data accuracy over time. Regular maintenance and calibration checks are essential.

3.2.5 Security and privacy

Data Encryption: Encrypt collected data to ensure its security during transmission and storage.

Anonymization: Protect consumer privacy by anonymizing data before analysis, and removing personally identifiable information.

3.2.6 Compatibility

Open Standards: Design the smart meter to adhere to open communication protocols and standards to ensure interoperability with utility systems.

Integration: Ensure compatibility with utility company systems and energy management platforms to facilitate data integration and usage.

3.2.7 Remote management and updates

Remote Configuration: Allow for remote configuration and parameter adjustments to adapt to changing requirements.

Firmware Updates: Design the smart meter to receive firmware updates remotely to improve functionality and security (Munoz and Ruelas 2022 ).

3.2.8 Data analysis and forecasting support

Data Preprocessing: Include capabilities for data preprocessing, such as filtering out noise and handling missing data.

Integration with Forecasting Algorithms: Enable integration with energy consumption forecasting algorithms, whether based on time series analysis, deep learning, or statistical methods.

3.2.9 User interfaces

Display and User Interface: Provide an intuitive display that shows real-time consumption data to users for energy awareness and management. For integration with the forecasting algorithm, the is retrieved in Excel or notepad form.

Interfaces for Utilities: Implement interfaces that utilities can use to retrieve historical and real-time consumption data (Jaiswal and Thakre 2022 ).

3.2.10 Scalability and future-proofing

Scalability: Design the smart meter to handle large volumes of data as the number of smart meters in deployment increases.

Future Upgrades: Plan for potential enhancements in communication technologies and data analytics methods to ensure the meter's long-term relevance.

The design of a smart meter for energy consumption forecasting requires collaboration between meter manufacturers, utility companies, and data analytics experts. It should strike a balance between accurate data collection, data security, interoperability, and usability to provide reliable and valuable insights for energy consumption forecasting.

3.3 Review of smart meter dataset

Smart meters usually collect data every second, minute, or hour. The smart meters record detailed information about electricity consumption, providing valuable insights into usage patterns and trends. Researchers and analysts often utilize publicly available smart meter datasets to develop and test energy consumption forecasting models, anomaly detection algorithms, and other applications related to smart building and energy management. Table 1 provides the various datasets, their availability as public or private, along with the website link, data characteristics, and a number of papers referring to the dataset of smart buildings. Table 2 provides a sample smart meter dataset. Researchers carried out their research using different datasets. Here are a few limitations and challenges associated with smart meter datasets.

3.3.1 Limitation in dataset selection

The availability of certain datasets may change over time. A dataset that was accessible during the initial stages of a project may become unavailable, which could disrupt ongoing research or analysis.

Metadata, which provides context and additional information about the data, may be lacking in some datasets. This absence can make it challenging to interpret the data accurately.

Working with large datasets can pose limitations in terms of storage, computational resources, and processing time, particularly for researchers with limited infrastructure.

3.3.2 Challenges in datasets selection

Selecting effective power consumption datasets can be challenging, as it involves various considerations and potential pitfalls. Here are some challenges that might be encountered during the selection of datasets.

Data availability

Availability of quality power consumption data can be a significant challenge. Not all regions or utilities provide access to detailed consumption data in realtime setups, especially for research or non-commercial purposes. There is a need to create a benchmark dataset for power prediction research. Exploring open data initiatives and repositories that may host publicly available power consumption data. The research also suggests that in the absence of a real-time setup, there is a need to develop simulation software or tools to generate.

Data preprocessing

Due to several number of reasons, including faults in meter sensors, variable weather conditions, and abnormal customer consumption patterns, the raw power consumption data often requires preprocessing to clean, normalize, and transform it for analysis. This can be time-consuming and may introduce errors if not done carefully.

Data granularity

The level of granularity in the data can be a challenge. Some datasets might provide data at a very high temporal resolution (e.g., every minute), while others might only offer daily or monthly averages. The level of granularity that needs to be depends on the specific research or analysis goals. Therefore, the selection of datasets must match the level of granularity needed for the specific analysis.

Data heterogeneity

The different utilities and regions may use different data formats and collection methods. The data needs to deal with various sources, and integrating heterogeneous data can be complex. So, the data collection requires standardization in a data format.

3.4 Data pre-processing

Next, the data is split into training and testing sets. Smart meters collect data that may contain missing, redundant, and outlier values. To address these issues, preprocessing techniques are applied to both the training and testing sets. Before training the model, the data needs to be refined. Therefore, before training, the input raw dataset is refined by adding missing values and removing outliers.

Missing values in smart meter data can occur due to communication failures, technical malfunctions, meter installation/replacement, data processing errors, or intentional anonymization/aggregation (Dahunsi and Olawumi 2021 ). These factors can lead to gaps in the data, where certain time intervals or measurements are not captured or recorded. This may lead to incorrect data collection.

Redundant values refer to duplicate or repetitive entries in the smart meter data. They can arise from issues like data transmission errors, system glitches, or multiple data sources providing overlapping or identical information (Ali et al. 2016 ). Redundant values can skew the analysis and inflate the importance of certain data points if not properly identified and handled.

Outlier values in smart meter data are extreme or unusual measurements that deviate significantly from the expected or normal range. Outliers can occur due to meter malfunctions, data transmission errors, incorrect sensor readings, or anomalies in the underlying processes being measured (Wang et al. 2018 ). Outliers can negatively impact the accuracy and reliability of data analysis and forecasting models if not detected and appropriately treated.

Similarly, power consumption patterns vary widely and networks are sensitive to them. Hence, the data normalization techniques are applied to keep the dataset within the normal range. (Pascanu et al. 2013 ; Khan et al. 2020a ; Rick and Berton 2020 ).

3.5 Training deep learning model and error metrics

Then, the normalized data is fed to the deep learning model to create a final model. Then, the final model is used for testing to provide forecasted power as output at every instant of time.

For analyzing and verifying the effectiveness of the model With different kinds of experiments, its performance is evaluated and analyzed using error metrics and cross-validation (Ghimire et al. 2023 ). The quality of the prediction model was evaluated using the error metrics used.

This is the common framework used by the researchers. But still, the researchers can incorporate some advanced techniques to make the framework give more accurate results. The framework excels in integrating a wide array of data sources, including real-time data, and making effective use of this information for forecasting. This results in more accurate and dynamic predictions.

There exist several strategies to deal with multistep forecasting problems (Gao et al. 2012 ): the recursive strategy, which performs one-step predictions and feeds the result as the last input for the next prediction; the direct strategy, which builds one model for each time step; and the multi-output approach, which outputs the complete forecasting horizon vector using just one model. The SISO strategy belongs to the time series problem. As suggested in recent forecasting studies (Sulaiman et al. 2022 ; Imani and Ghassemian 2019 ), the single input and single output (SISO) strategy is not performed in most of the studies. The recursive approach is particularly useful for capturing short-term patterns and trends in the data. By using the most recent prediction as input, it can quickly adjust to changes in the time series, making it responsive to short-term fluctuations.

The Key Component and Innovation in the framework is integrating hybrid deep learning models. TCNs are employed to capture long-range dependencies in time series data. This innovative architecture provides an alternative to traditional RNNs and LSTMs, making it suitable for complex temporal patterns in power consumption.

4 Application of deep learning for forecasting energy consumption

Deep learning methods are promising for time series forecasting, such as automatically learning temporal dependencies and automatically handling temporal structures such as trends and seasonality (Bandic and Kevric 2018 ). In the paper (Pirbazari and Sharma 2021 ), they have used several common algorithms in time series forecasting, e.g., support vector regression (SVR), ARIMA, LSTM, etc. for training. RNNs can be extended to deep recurrent neural networks (DRNNs) in various ways (Cai et al. 2019 ). According to the RNN framework, we can deepen the hidden function, the transition function, and the hidden output function to create a DRNN (Cai et al. 2019 ). There are different variants of DRNN, but the focus is on the widely known LSTM network and GRU network used in the context of electricity predictions. Because the LSTM and GRU techniques have long term dependencies. A multilayer perceptron neural network (MLP) is a unique form of feedforward network called a universal approximation. Due to its simplicity, it is one of the most widely used neural network frameworks. (Pascanu, et al. 2013 ).

In the paper (Dehalwar et al. 2017 ), LSTM neural networks are used to perform prediction operations. They proved that stationarization of the wavelet transform could improve the LSTM prediction results. Finally, the prediction results are synthesized using the inverse stationary wavelet transform. In a paper (Lara-benitez 2020 ), the researchers suggested a TCN model with (DenseNet) densely connected convolution networks. In a paper (Mocanu et al. 2016 ), they present a hybrid intelligent technique that combines a CNN with a multi-layer bidirectional long-short term memory (M-BDLSTM) method. In paper (Rahman et al. 2018 ), for very short term forecasting, proposed a hybrid electricity demand forecasting model that combines LSTM and CNN. The input sequence consists of multiple pairs and the key value is the power demand value. Context values contain contextual information such as temperature, humidity, and time of year. In a paper (Spiliotis et al. 2020 ), for a small industry profile, they performed a 30-min energy consumption forecast. Instead of the conventional MSE, the pinball loss was used as a guideline for adjusting the LSTM neural network's parameters. Pinball loss is indeed a term used in the context of training and tuning parameters for LSTM models, particularly in time series forecasting. Pinball loss, also known as quantile loss or quantile regression loss, is a loss function used to train models to estimate conditional quantiles of a target variable. In the case of time series forecasting with LSTM models, it is often used to estimate and optimize quantiles of the predicted distribution for different forecasting horizons.

Table 3 depicts the summary of previous work related to deep learning. In the table, research findings and their corresponding accuracy were mentioned. The limitations of each method were tabulated. From the table, inferred that the evaluation metrics play a vital role in prediction accuracy. And also input data contributes more to DL performance and forecasting accuracy, especially weather variables.

5 Various applications of forecasting using forecasting horizon

Electricity load forecasting plays an important role in the planning and operation of energy systems. It is the process of predicting future power consumption using historical load profiles and weather information. Electricity prediction is categorized into three groups. These include short-term forecasts, typically ranging from days to weeks, medium-term forecasts, typically ranging from weeks to a year, and long-term forecasts, typically ranging from a year or more (Sajjad et al. 2020 ). The decision-making process, such as the scheduling of generation amount, maintenance and investment plan, and so on, is supported by the expected amount of energy demand (Le et al. 2020 ). Based on different applications of forecasting in the electrical field, it is classified into two categories.

5.1 Based on HVAC

The energy consumption of a building is impacted by several factors, including the building's structure, HVAC system, occupant behavior, and lighting (Liu et al. 2020 ). Predicting building energy usage can be categorized into five main types: heating energy, cooling energy, combined heating and cooling energy, total building energy consumption, and other factors (Mocanu et al. 2016 ). Anticipating power consumption has become essential for enhancing power management and fostering collaboration between a building's energy consumption and the electrical grid (Xu et al. 2019 ). Accurate predictions play a crucial role in enabling efficient power management, such as helping suppliers generate the right amount of electricity to meet demand. Energy/cost investment funds and general control execution are legitimately impacted by how accurately energy use is estimated (Cai et al. 2019 ).

5.2 Based on power grid planning

It is crucial to categorize and forecast the energy consumption of residential buildings using historical data based on the impact of dynamic real-time changes on both the supply and demand sides to provide adequate decision-making for planning power transmission configuration patterns that maintain regional characteristics. Short and very short term forecasting techniques for energy consumption are useful for domestic energy demand management, electricity price market design, energy efficiency, and maintenance planning for large and complex smart grids (Abuella and Chowdhury 2017 ; Khan et al. 2019 ; Tokgoz and Unal 2018 ). Forecasting aims to provide as accurate predictions as possible, utilizing the wealth of available data (Kim and Cho 2019b ; Ullah et al. 2019 ). In recent years, numerous approaches have been suggested to harness this information for making predictions (Afrasiabi et al. 2020 ). These forecasting techniques effectively anticipate energy consumption by establishing connections between energy and its consuming systems, thereby minimizing inefficiencies arising from over or undersupply and undersupply (Mocanu et al. 2016 ).

Figure  3 shows the forecasting horizon in the works of literature. It depicts the percentage of literature that contributes to Short term, Medium term, and Long term forecasting. Most of the literature concentrates more on short-term forecasting. Short-term energy prediction is preferred over medium and long-term prediction due to its higher accuracy, immediate applicability for real-time decision making, optimal resource allocation, contribution to grid stability, and reliance on up-to-date data. The more predictable patterns and behaviors of energy consumption in the short term, along with the availability of timely data, make short-term energy prediction more reliable and actionable for efficient energy management and operational adjustments in response to changing demand and supply dynamics.

figure 3

Related works by forecasting horizon

6 Evaluation metrics

To validate the predictive models, a variety of accuracy criteria are employed. The most commonly used are mean squared error (MSE), root mean squared error (RMSE), coefficient of variation of root mean square error (CV-RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and determination coefficient (R2). The evaluation metrics used to validate forecasting models are shown in Fig.  3 . Each of these metrics has a specific function. MSE is a commonly used metric in the context of forecasting and predictive modeling to evaluate the accuracy of a forecasting model's predictions. It measures the average squared difference between the predicted values and the actual (observed) values. It is a way to quantify how well the forecasting model's predictions match the actual outcomes. RMSE is widely utilized to examine the accuracy of various anticipating criteria because it is generally offered as an expectation quality estimation (Kim et al. 2019 ). CV-RMSE can standardize the expected error and provide a useful unitless metric. MAE relies on absolute error and can indicate the normal distance between expected and actual values. Large errors are eliminated because the measurement mathematically enhances the error (Wang et al. 2019 ). MAPE exhibits rate accuracy and mitigates the impact of absolute error caused by a single exception (Kim et al. 2019 ). R2 evaluates how well the model fits the real information and provides a measure of model consistency (Amasyali and El-Gohary 2018 ).

The five evaluation metrics are listed in Table  4 . Let \(n\) be the number of test data, \({x}_{pred}\) be the value predicted by the proposed algorithm, and \({x}_{act}\) be the actual value in the quantitative, \({\overline{x} }_{act}\) be the average of the actual value.

7 Discussion

Based on the review of previous research work, a detailed summary of building energy consumption forecasting methods is presented in Table  5 . The summary includes the contribution of each paper and inference from that work was discussed briefly. And also, explains the lists of buildings used, the network used, the input parameters used, and the types of forecasting used.

All prediction methods discussed in the literature have weaknesses, which depend on the approach chosen. A table comparing different forecasting methods is provided to help you easily identify which forecasting model type to use. Due to the relevance of information for planning, a lot of effort is required when it comes to power forecasting. As stated in the literature, numerous strategies have been used. Based on the survey, it is obvious that DL has been proven to outperform other prediction approaches in terms of accuracy. It has been discovered that linear models, which were previously relegated because of their inability to solve nonlinear problems, are nevertheless applicable in the context of energy projections. RNNs are effective in solving nonlinear issues with great prediction accuracy. Despite the power of RNN’s as discussed in the literature, it has a significant drawback, called the problem of vanishing gradient For this reason, few researchers have applied traditional RNNs. DRNN improved the boundary vanishing gradient by introducing memory (Kaur and Ahuja 2017 ). Therefore, LSTM with GRU is currently used in the context of energy prediction because it can model complex functions with high accuracy.

Accurate customer-level energy forecasting has a direct impact on overall system efficiency. However, it is difficult to predict building energy consumption, especially in the medium and long term changes in climatic conditions, thermal system performance, and patterns in occupancy. Therefore, current state-of-the-art technology cannot contain building-level uncertainty due to many influencing factors. These can include changes in weather, occupant behavior, changes in building structure and operations, missing data in datasets, computation time impacting forecast accuracy, and more. As reported in the literature, few researchers have used occupancy profiles in predictive models (Nepal 2020 ; Zhao and Magoules 2020 ; Ma et al. 2019 ), and only one study (Das et al. 2020 ) considered building design.

From the statistical report of this review paper, about 72% of the research reviewed focused on developing methods to predict building energy use in non-residential buildings, 18% on residential buildings, and just 10% on both. It is depicted in Fig.  4 .

figure 4

Comparison chart between building type

Regarding the forecast horizon, about 68% of the studies focused on short-term forecasts, 27% on long-term forecasts, and 5% on medium-term forecasts. A comparison of building types and prediction horizon is shown in Fig.  5 . This explains the increasing use of demand-side management strategies such as shifting of load, which allows the loads to shift from peak hours to off-peak hours and knowing the energy consumption of building for 24 h. It should be noted that the short-term horizon has expanded in recent studies since it has proven to be quite useful in predicting the supply of energy resources in buildings.

figure 5

Comparison chart between type of building and Forecast horizon

About 54% of the research focused on the use of calendar and historical data in prediction methods, 41% focused on the use of weather, calendar data, and historical and 3% on historical, occupancy rates and focusing on the use of calendar data. Only 2% of the studies used calendar, weather, occupancy, and historical data. These phenomena may arise from the reliance on diverse sensors to capture building energy consumption, while data concerning occupancy often remains unaccounted for due to the intricate data acquisition process, which can vary based on the building's characteristics. In Fig.  6 . comparison chart between the type of building and input parameters was given. On the other hand, the availability of this data will further improve the accuracy of the predictions.

figure 6

Comparison chart between type of building and input parameters. His indicates Historical, cal indicates calendar, wea represents weather, and occ indicates Occupancy

In summary, the analysis of methodologies for energy consumption forecasting has revealed the prominence of deep learning, the importance of feature selection, and the influence of weather and occupancy patterns. There is a need for real-time data integration, the adoption of ensemble and hybrid models, the emphasis on explainability and behavioral insights, and the consideration of energy efficiency measures. These findings and observations collectively contribute to the advancement of accurate and actionable energy consumption forecasts for smart buildings. The novel deep learning architectures, including CNN-LSTM, CNN-LSTM autoencoders, and TCN, have been proposed to address the complexities of energy consumption time series data and to achieve better forecasting results. While machine learning algorithms have advanced the field of energy forecasting, challenges remain, particularly in peak load prediction. Ongoing research focuses on improving the accuracy of these models and addressing their limitations.

These findings and insights reflect the dynamic nature of the field of power forecasting, with researchers continually developing and refining methodologies to address the complexities of energy consumption and integrate renewable energy sources for a more sustainable energy system. These critical observations reflect the ongoing evolution of energy forecasting methodologies, with a focus on enhancing accuracy, addressing challenges, and adapting to the changing energy landscape.

8 Research gap

Energy demand forecasting is a useful tool for identifying, measuring, and managing demand flexibility. Nowadays, renewable resources play a vital role in energy generation. The review of such vast existing research work suggested the necessity to address the obstacles in the future. Further research work needs to focus on the following:

Research gap-1 A key component that reduces the prediction accuracy is the weather (temperature, humidity, etc.). Therefore, future studies should concentrate on the impact of weather variables on power forecasting and incorporate weather components by analyzing the contribution and impact of each variable on the consumption profile. (Mocanu et al. 2016 ).

Implication-1 A deeper understanding of how weather influences power consumption can lead to more energy-efficient practices. For instance, businesses and households can optimize their energy use by adjusting their consumption patterns in response to weather forecasts, potentially reducing costs and environmental impact.

Avenues -1 A detailed analysis is needed about, how weather-informed forecasts can optimize energy storage systems. To determine how renewable energy generation can be better aligned with weather patterns to match demand. A Study needs how consumers respond to weather-informed energy consumption predictions. To investigate the effectiveness of behavioral interventions based on weather forecasts in reducing energy consumption.

Research gap-2 Based on the knowledge from the works of literature, no study takes into account the functionality of the buildings in terms of space and share percentages, nor is there one that uses ML approaches and future weather scenarios to evaluate the effects of global climatic change on the energy performance of urban buildings. This will be a worthwhile area for research.

Implication-2 Research in this area can contribute to more sustainable urban development by optimizing the energy performance of buildings, which is crucial for reducing carbon emissions and mitigating the effects of climate change in urban areas.

Avenues-2 To develop and refine energy simulation models that incorporate detailed building functionality data, real-world weather scenarios, and ML techniques for accurate predictions of energy consumption. The creation of databases that consolidate building functionality data, historical weather data, and building energy usage data.

Research gap-3 Future lines of research should encourage considering various time scales, different environmental conditions, and various horizons like hours, months, and years. To enable the efficient use of electrical energy across various industries and smart grids, these horizons can be used (Mocanu et al. 2016 ).

Implication-3 This research approach promotes multi-temporal energy planning that spans various time scales. It enables utilities, industries, and smart grid operators to develop comprehensive strategies for optimizing energy use, production, and distribution. For long-term forecasts, the model needs a huge number of data to make efficient predictions. Therefore, research in this area can drive the development of data collection technologies and data analytics tools that are tailored to various time scales and environmental conditions. Addressing different time horizons allows for more accurate energy demand forecasting. Short-term forecasts (hours) help manage grid stability, while long-term forecasts (months and years) inform infrastructure investments.

Avenues-3 Invest in advanced data collection and data analytics tools to process and analyze diverse data sources, enabling precise multi-temporal forecasting and decision-making.

Research gap4- Challenges such as compensation for forecasting errors, problems with dynamic model selection, the creation of adaptive predictive models, and data integrity must be addressed by current techniques. (Singh and Dwivedi 2018 ). Improving energy management requires achieving high accuracy in the use of energy forecasts. In any instance, this necessitates the selection of appropriate estimating models that are prepared to capture each of the predicted array's features, which is a task fraught with uncertainty (Wen et al. 2020 ).

Implication 4 Improving the accuracy of energy forecasts can enhance grid stability and resilience. Grid operators can better anticipate demand fluctuations, reducing the risk of blackouts and ensuring the reliability of energy supply, even in the face of unexpected events. Energy markets rely on accurate energy forecasts. Addressing these challenges can lead to more efficient market operations, better matching supply and demand, and potentially reducing price volatility.

Avenues-4 Developing advanced forecasting models that integrate dynamic model selection and adaptive predictive capabilities to improve forecasting accuracy and robustness in the face of changing conditions.

Research gap-5 Most research has been done on predicting power usage in a single building, but there hasn't been much done to aggregate the power usage across a wider area using a large number of data samples to assure the model's accuracy.

Implication-5 Accurate and aggregated energy data can facilitate the design of targeted energy efficiency programs that address the specific needs of a region or community, resulting in reduced energy waste. By aggregating data, it becomes easier to manage the integration of renewable energy sources into the grid. This research can lead to better strategies for balancing energy supply and demand.

Avenues-5 Develop real-time monitoring and control systems that provide actionable insights and allow for dynamic adjustments in energy use and distribution based on aggregated data. Most of the real-time datasets are not publicly available on the website. So there is a need for a benchmark dataset for this domain.

The identified research gaps in the area of energy consumption forecasting have significant implications for improving the accuracy and sustainability of energy management. Focusing on the impact of weather variables, integrating building functionality with DL, addressing long-term forecasting challenges, and considering various time scales can lead to more precise predictions,, better energy planning, and enhanced grid efficiency. The identified gaps impact the field by reducing the accuracy and efficiency of energy consumption forecasts in smart buildings. Inaccurate forecasts can result in suboptimal energy management, increased costs, and a failure to meet peak demand. Research in this area can support smart city initiatives by providing accurate and comprehensive data on energy consumption patterns across various sectors, enabling better urban planning and resource allocation.

9 Conclusion

The 40% rise in the amount of electricity used in residential and commercial is because of the recent increase in urbanization. Accurately predicting electricity demand has become crucial. Forecasting can be used to meet the supply and demand gap for electrical energy. Since it helps decision-makers and planners in government, this forecast is important on a global scale. To increase the accuracy of energy consumption predictions, new automated paradigms are required. This paper reviews the recent advanced forecasting methods. While analyzing other methods, DL gives better results. DNN has recently been effectively used in this context. Furthermore, the study provides taxonomies for these methodologies based on various forecasting horizons and data sources utilized to predict future energy usage. As a result, this assessment can help researchers identify research gaps that require addressing in the future and come up with novel approaches to enhance power forecasting in commercial and residential buildings. This review paper presented the significance of smart meter data for energy forecasting. Researchers can take the initiative to extract useful information from the smart meter for the benefit of society. The review of prior works will provide useful guidance to future researchers. Based on the continuation of this review paper, a novel hybrid deep learning approach will be proposed for improving forecasting accuracy in residential buildings from short to long term horizon.

Abuella M, Chowdhury B (2017) Random forest ensemble of support vector regression models for solar power forecasting. In: 2017 IEEE power & energy society innovative smart grid technologies conference (ISGT). IEEE Xplore. https://doi.org/10.1109/ISGT.2017.8086027 .

Afrasiabi M et al (2020) Deep-based conditional probability density function forecasting of residential loads. IEEE Trans Smart Grid 11:3646–3657

Article   Google Scholar  

Agyeman KA, Kim G, Jo H, Park S, Han S (2020) An ensemble stochastic forecasting framework for variable distributed demand loads. Energies. https://doi.org/10.3390/en13102658

Akter R et al (2021) Analysis and prediction of hourly energy consumption based on long short-term memory neural network. In: 2021 ınternational conference on ınformation networking (ICOIN). IEEE Xplore. https://doi.org/10.1109/ICOIN50884.2021.9333968

Alhussein M et al (2020) Hybrid CNN-LSTM model for short-term individual household load forecasting. IEEE Access 8:180544–180557. https://doi.org/10.1109/ACCESS.2020.3028281

Ali U, Buccella C, Cecati C (2016) Households electricity consumption analysis with data mining techniques. In: IECON Proceedings of the Industrial Electronic Conference, pp. 3966–3971

Amasyali K, El-Gohary NM (2018) A review of data-driven building energy consumption prediction studies. Renew Sustain Energy Rev 81:1192–1205. https://doi.org/10.1016/j.rser.2017.04.095

Antonopoulos I et al (2021) Data-driven modelling of energy demand response behaviour based on a large-scale residential trial. Energy and AI 4:100071. https://doi.org/10.1016/j.egyai.2021.100071

El Astal M-T et al. (2020) Office appliances ıdentification and monitoring using deep leaning based energy disaggregation for smart buildings. In: IECON 2020 the 46th annual conference of the IEEE ındustrial electronics society. https://doi.org/10.1109/IECON43393.2020.9255127

Bandic L, Kevric J (2018) Near zero-energy home prediction of appliances energy consumption using the reduced set of features and random decision tree algorithms. Proc. Int. Symp. Innov. Interdiscipl. Appl. Adv. Technol. Cham. 59:164–171

Google Scholar  

Behera DK et al (2022) XGBoost regression model-based electricity tariff plan recommendation in smart grid environment. Int J Innovative Comput Appl 13:79–87. https://doi.org/10.1504/ijica.2022.123223

Bhosale H, Gadekar D (2014) A review paper on big data and Hadoop. Int J Sci Res 4:1–7

Cai H, Shen S et al (2019) Predicting the energy consumption of residential buildings for regional electricity supply-side and demand-side management. IEEE Access 7:30386–30397

Chen R et al (2022) MultiCycleNet: multiple cycles self-boosted neural network for short-term electric household load forecasting. Sustain Cities Soc 76:103484. https://doi.org/10.1016/j.scs.2021.103484

Cheng J, Li Q (2008) Reliability analysis of structures using artificial neural network based genetic algorithms. Comput Methods Appl Mech Eng 197:3742–3750

Article   ADS   Google Scholar  

Choksi KA, Jain S, Pindoriya NM (2020) Feature based clustering technique for investigation of domestic load profiles and probabilistic variation assessment: smart meter dataset. Sustain Energy Grids Netw 22:1–11

Cui L et al (2023) Intelligent power grid energy supply forecasting and economic operation management using the snake optimizer algorithm with Bigur-attention model. Front. Energy Res. 11:1273947. https://doi.org/10.3389/fenrg.2023.1273947

Dahunsi FM, Olawumi AE et al (2021) A systematic review of data pre-processing methods and unsupervised mining methods used in profiling smart meter data. AIMS Electron Electr Eng 5:284–314. https://doi.org/10.3934/electreng.2021015

Das A et al (2020) Occupant-centric miscellaneous electric loads prediction in buildings using state-of-the-art deep learning methods. Elsevier Appl Energy 269:115–135. https://doi.org/10.1016/j.apenergy.2020.115135

Dehalwar V et al. (2017) Electricity load forecasting for urban area using weather forecast. In: 2016 IEEE ınternational conference on power and renewable energy (ICPRE). IEEE Xplore. https://doi.org/10.1109/ICPRE.2016.7871231

Dong C, Du L, Ji F, Song Z, Zheng Y et al. (2018) Forecasting smart meter energy usage using distributed systems and machine learning. In: IEEE 20th international conference on high performance computing and communications. https://doi.org/10.1109/HPCC/SmartCity/DSS.2018.00216

Ellabban O, Alassi A (2019) Integrated economic adoption model for residential grid-connected photovoltaic systems: an Australian case study. Energy Rep 5:310–326. https://doi.org/10.1016/j.egyr.2019.02.004

Egrioglu E et al (2015) Recurrent multiplicative neuron model artificial neural network for non-linear time series forecasting. Neural Proces Lett 41:249–258. https://doi.org/10.1007/s11063-014-9342-0

Farzana S et al (2014) Multi-model prediction and simulation of residential building energy in urban areas of Chongqing, South West China. Energy Build 81:161–169

Fu G (2018) Deep belief network based ensemble approach for cooling load forecasting of air-conditioning system. Elsevier Energy 148:269–282. https://doi.org/10.1016/j.energy.2018.01.180

Gao PX et al (2012) On hourly home peak load prediction. 2012 IEEE Third İnt Conf Smart Grid Commun. https://doi.org/10.1109/SmartGridComm.2012.6485977

Ghimire S et al (2023) A novel approach based on integration of convolutional neural networks and echo state network for daily electricity demand prediction. Energy. https://doi.org/10.1016/j.energy.2023.127430

Gupta R, Al-Ali AR, Zualkernana IA, Das SK (2020) Big data energy management, analytics and visualization for residential areas. IEEE Access 8:156153–156164

Harb H et al (2016) Development and validation of grey-box models for forecasting the thermal response of occupied buildings. Energy Build 117:199–207. https://doi.org/10.1016/j.enbuild.2016.02.021

Himeur Y et al (2020a) A novel approach for detecting anomalous energy consumption based on micro-moments and deep neural networks. Cogn Comput 12:1387–1401

Himeur Y et al (2020b) Effective non-intrusive load monitoring of buildings based on a novel multi-descriptor fusion with dimensionality reduction. Appl Energy 279:115872. https://doi.org/10.1016/j.apenergy.2020.115872

https://www.electronics-notes.com/articles/eco-green-engineering/smart-energy-meters/smart-meter-electronic-circuit-design.php

https://data.gov.au/dataset/ds-dga-4e21dea3-9b87-4610-94c7-15a8a77907ef/details

https://www.nature.com/articles/sdata201848

https://www.ceicdata.com/en/korea/electricity-generation-and-consumption/electricity-consumption-seoul#:~:text=Korea%20Electricity%3A%20Consumption%3A%20Seoul%20data%20is%20updated%20monthly%2C%20averaging,kWh%20mn%20in%20May%201998

https://www.kaggle.com/datasets/jehanbhathena/smart-meter-data-mathura-and-bareilly

https://www.ausgrid.com.au/Industry/Our-Research/Data-to-share/Solar-home-electricity-data

Imani M, Ghassemian H (2019) Residential load forecasting using wavelet and collaborative representation transforms. Appl Energy 253:113505. https://doi.org/10.1016/j.apenergy.2019.113505

Islam SN et al. (2020) Load profile segmentation using residential energy consumption data. In: 2020 ınternational conference on smart grids and energy systems (SGES). IEEE Xplore. https://doi.org/10.1109/SGES51519.2020.00112

Jain S, Choksi KA, Pindoriya NM (2019) Rule-based classification of energy theft and anomalies in consumers load demand profile. IET Smart Grid 2:612–624

Jaiswal DM, Thakre MP (2022) Modeling & designing of smart energy meter for smart grid applications. Glob Transitions Proc 3:311–316. https://doi.org/10.1016/j.gltp.2022.03.017

Kaur H, Ahuja S (2017) Time series analysis and prediction of electricity consumption of health care institution using ARIMA model. Proceedings of sixth international conference on soft computing for problem solving. Springer

Kell AJM et al (2023) Optimizing a domestic battery and solar photovoltaic system with deep reinforcement learning. In: 2022 IEEE ınternational conference on big data (big data). IEEE Xplore. https://doi.org/10.1109/BigData55660.2022.10021028

Khan S et al (2019) Electricity load forecasting for each day of week using deep CNN. Advances in intelligent systems and computing. Springer, pp 1107–1119

Khan ZA et al (2020a) Towards efficient electricity forecasting in residential and commercial buildings: a novel hybrid CNN with a LSTM-AE based framework. Sensors 20:1399. https://doi.org/10.3390/s20051399

Article   ADS   PubMed   PubMed Central   Google Scholar  

Khan ZA et al (2020b) Electrical energy prediction in residential buildings for short-term horizons using hybrid deep learning strategy. Appl Sci 10:8634. https://doi.org/10.3390/app10238634

Article   CAS   Google Scholar  

Khan ZA et al (2023) Modelling electricity consumption during the COVID19 pandemic: datasets, models, results and a research agenda. Energy and Buildings 294:113204. https://doi.org/10.1016/j.enbuild.2023.113204

Article   PubMed   PubMed Central   Google Scholar  

Kim T-Y, Cho S-B (2019b) Predicting residential energy consumption using CNN-LSTM neural networks. Energy 182:72–81

Kim M, Choi W, Jeon Y, Liu L et al (2019) A hybrid neural network model for power demand forecasting. Energies 12:931. https://doi.org/10.3390/en12050931

Kiprijanovska I, Stankoski S, Ilievski I, Jovanovski S, Gams M et al (2020) Day-ahead household electrical energy consumption forecasting using deep learning. Energies. https://doi.org/10.3390/en13102672

Kolehmainen M, Mononen M, Niska H (2015) A data mining approach for producing small area statistics-based load profiles for distribution network planning. IEEE Int Conf Industrial Technol 2015:1236–1240

Lara-benitez P (2020) Temporal convolutional networks applied to energy-related series forecasting. Appl Sci. https://doi.org/10.3390/app10072322

Le T et al (2020) Multiple electric energy consumption forecasting using a cluster-based strategy for transfer learning in smart building. Sensors 20:2668. https://doi.org/10.3390/s20092668

Li K et al (2015) Building's electricity consumption prediction using optimized artificial neural networks and principal component analysis. Energy Build 108:106–113.  https://doi.org/10.1016/j.enbuild.2015.09.002

Liu T, Liu K, Fang P, Zhao J (2016) A hybrid model of AR and PNN method for building thermal load forecasting. Theory, methodology, tools and applications for modeling and simulation of complex systems, vol 643. Springer, pp 146–155

Chapter   Google Scholar  

Liu T, Tan Z, Chengliang Xu, Chen H, Li Z (2020) Study on deep reinforcement learning techniques for building energy consumption forecasting. Energy Build. https://doi.org/10.1016/j.enbuild.2019.109675

Liu M et al (2022) Short-term load forecasting based on improved TCN and DenseNet. IEEE Access 10:115945–115957

Ma Z, Zhang C, Zhang C (2019) Review of machine learning applications in power system. In: 2019 IEEE ınnovative smart grid technologies—Asia (ISGT Asia). IEEE Xplore. https://doi.org/10.1109/ISGT-Asia.2019.8881330 .

Makonin S (2019) HUE: the hourly usage of energy dataset for buildings in British Columbia. Harvard Dataverse 23:103774. https://doi.org/10.7910/DVN/N3HGRN

Manembu P, Engineering I, Nielsen PS, et al. (2018) Multi-grained household load profile analysis using smart meter data : the case of Indonesia. In: 2018 2nd Borneo international conference on applied mathematics and engineering (BICAME), pp. 213–217.

Mawson VJ et al (2020) Deep learning techniques for energy forecasting and condition monitoring in the manufacturing sector. Energy Build 217: 109966.  https://doi.org/10.1016/j.enbuild.2020.109966

Mocanu E, Nguyen PH, Gibescu M, Kling WL (2016) Deep learning for estimating building energy consumption. Sustain Energy Grids Net 6:91–99

Moldovan D, Slowik A (2021) Energy consumption prediction of appliances using machine learning and multi-objective binary grey wolf optimization for feature selection. Appl Soft Comput 111:107745. https://doi.org/10.1016/j.asoc.2021.107745

Mujeeb S et al (2021) Electricity theft detection with automatic labeling and enhanced RUSBoost classification using differential evolution and jaya algorithm. IEEE Access 9:128521–128539. https://doi.org/10.1109/ACCESS.2021.3102643

Munkhdalai L, Munkhdalai T et al (2019) An endto-end adaptive input selection with dynamic weights for forecasting multivariate time series. IEEE Access 7:99099–99114

Munoz O, Ruelas A et al (2022) Design and development of an IoT smart meter with load control for home energy management systems. Smart IoT Syst Renew Energy Resour 22:7536. https://doi.org/10.3390/s22197536

Naz A et al (2019) Short-term electric load and price forecasting using enhanced extreme learning machine optimization in smart grids. Energies 12:866. https://doi.org/10.3390/en12050866

Nepal B et al (2020) Electricity load forecasting using clustering and ARIMA model for energy management in buildings. Jpn Archit Rev 3:62–76. https://doi.org/10.1002/2475-8876.12135

Osama S et al (2019) An efficient algorithm for extracting appliance-time association using smart meter data. Heliyon 5:e02226. https://doi.org/10.1016/j.heliyon.2019.e02226

Paraskevas L et al (2021) Domestic smart metering infrastructure and a method for home appliances identification using low-rate power consumption data. IET Smart Cities 3:91–106

Pascanu R et al. (2013) How to construct deep recurrent neural networks. arXiv preprint.

Pirbazari AM, Sharma E et al (2021) An ensemble approach for multi-step ahead energy forecasting of household communities. IEEE Access 9:36218–36240

Polamuri SR et al (2022) Multi-model generative adversarial network hybrid prediction algorithm (MMGAN-HPA) for stock market prices prediction. J King Saud Univ—Comput Inf Sci 34:7433–7444

Prakash A, Xu S, Rajagopal R, Noh HR (2018) Building energy load forecasting using physically-based Kernel models. Energies 11:862. https://doi.org/10.3390/en11040862

Rahman A, Srikumar V, Smith AD (2008) Predicting electricity consumption for commercial and residential buildings using deep recurrent neural networks. Appl Energy 212:372–385

Rashid H et al. (2021) Evaluation of non-intrusive load monitoring algorithms for appliance-level anomaly detection. In: ICASSP 2019—2019 IEEE ınternational conference on acoustics, speech and signal processing. IEEE Xplore. https://doi.org/10.1109/ICASSP.2019.8683792

Renaux DPB et al (2020) A dataset for non-ıntrusive load monitoring: design and implementation: energy data analytics for smart meter data. Energies. https://doi.org/10.3390/en13205371

Rick R, Berton L (2020) Energy forecasting model based on CNN-LSTM-AE for many time series with unequal lengths. Eng Appl Artif Intell. https://doi.org/10.1016/j.engappai.2022.104998

Sajjad M et al (2020) A novel CNN-GRU-based hybrid approach for short-term residential load forecasting. IEEE Access 8:143759–143768

Singh P, Dwivedi P (2018) Integration of new evolutionary approach with artificial neural network for solving short term load forecast problem. Appl Energy 217:537–549

Singh S, Yassine A (2018) Big data mining of energy time series for behavioral analytics and energy consumption forecasting. Energies 11:458. https://doi.org/10.3390/en11020452

Singh S, Yassine A (2019) Mining energy consumption behavior patterns for households in smart grid. IEEE Trans Emerg Topic Comput 7:404–419

Somu N et al (2020) A hybrid model for building energy consumption forecasting using long short term memory networks. Appl Energy 261:114131. https://doi.org/10.1016/j.apenergy.2019.114131

Spiliotis E et al (2020) Cross-temporal aggregation: Improving the forecast accuracy of hierarchical electricity consumption. Appl Energy 261:114339. https://doi.org/10.1016/j.apenergy.2019.114339

Sulaiman SM, Aruna Jeyanthy P, Devaraj D, Shihabudheen KV (2022) A novel hybrid short-term electricity forecasting technique for residential loads using empirical mode decomposition and extreme learning machines. Comput Electr Eng 98:107663. https://doi.org/10.1016/j.compeleceng.2021.107663

Sun G et al (2020) Short-term building load forecast based on a data-mining feature selection and LSTM-RNN method. IEEJ Trans Electr Electron Eng. https://doi.org/10.1002/tee.23144

Sunny MR et al. (2021). Residential energy management: a machine learning perspective. In: 2020 IEEE green technologies conference (GreenTech). IEEE Xplore. https://doi.org/10.1109/GreenTech46478.2020.9289737

Tekler ZD et al (2020) ROBOD, room-level occupancy and building operation dataset. Build Simul 15:2127–2137

Todic T et al (2023) An active learning framework for the low-frequency non-intrusive load monitoring problem. Appl Energy 341:121078. https://doi.org/10.1016/j.apenergy.2023.121078

Tokgoz A, Unal G (2018) A RNN based time series approach for forecasting turkish electricity load. In: Proceedings of the 26th signal processing and communications applications conference (SIU). https://doi.org/10.1109/SIU.2018.8404313 .

Ullah FUM et al (2019) Short-term prediction of residential power energy consumption via CNN and multi-layer bi-directional LSTM networks. IEEE Access 8:123369–123380

Wahab A et al (2021) A novel technique for short-term load forecasting using sequential models and feature engineering. IEEE Access 9:96221–96232. https://doi.org/10.1109/ACCESS.2021.3093481

Wang Z, Srinivasan RS (2017) A review of artificial intelligence based building energy use prediction: contrasting the capabilities of single and ensemble prediction models. Renew Sustain Energy Rev 75:796–808

Wang H et al (2016) Deep belief network based deterministic and probabilistic wind speed forecasting approach. Appl Energy 182:80–93

Wang Y, Chen Q, Hong T et al (2018) Review of smart meter data analytics: applications, methodologies, and challenges. IEEE Trans Smart Grid 10:3125–3148

Wang Y et al (2019) Probabilistic individual load forecasting using pinball loss guided LSTM. Appl Energy 235:10–20

Wei Y et al (2019) Prediction of occupancy level and energy consumption in office building using blind system identification and neural networks. Appl Energy 240:276–294

Wen L, Zhou K, Yang S (2020) Load demand forecasting of residential buildings using a deep learning model. Electr Power Syst Res 179:106073. https://doi.org/10.1016/j.epsr.2019.106073

Xiang L et al (2020) Prediction model of household appliance energy consumption based on machine learning. J Phys: Conf Ser. https://doi.org/10.1088/1742-6596/1453/1/012064

Xu L, Wang S, Tang R (2019) Probabilistic load forecasting for buildings considering weather forecasting uncertainty and uncertain peak load. Appl Energy 237:180–195. https://doi.org/10.1016/j.apenergy.2019.01.022

Yan K et al (2019) A hybrid LSTM neural network for energy consumption forecasting of individual households. IEEE Access 7:157633–157642

Zhang K et al (2015) Stock market prediction based on generative adversarial network. Procedia Comput Sci 147:400–406

Zhang W, Dong X, Li H, Xu J, Wang D (2020a) Unsupervised detection of abnormal electricity consumption behavior based on feature engineering. Artif Intell Technol Electr Power Syst 8:55483–55500. https://doi.org/10.1109/ACCESS.2020.2980079

Zhang G, Tian C, Li C, Zhang JJ, Zuo W (2020b) Accurate forecasting of building energy consumption via a novel ensembled deep learning method considering the cyclic feature. Energy 201:117531. https://doi.org/10.1016/j.energy.2020.117531

Zhao H-X, Magoules F (2020) A review on the prediction of building energy consumption. Renew Sustain Energy Rev 16:3586–4359

Zor K, Çelik Ö, Timur O, Teke A (2020) Short-term building electrical energy consumption forecasting by employing gene expression programming and GMDH networks. Energies. https://doi.org/10.3390/en13051102

Download references

Author information

Authors and affiliations.

Department of Electronics and Communication Engineering, PSN College of Engineering and Technology, Tirunelveli, India

R. Mathumitha & P. Rathika

Department of Information Science and Technology, College of Engineering, Guindy, Anna University, Chennai, India

K. Manimala

You can also search for this author in PubMed   Google Scholar

Contributions

RM prepared the Tables and figures; PR prepared the content and KM designed the research gap.

Corresponding author

Correspondence to R. Mathumitha .

Ethics declarations

Conflict of interest.

The authors declare no competing interests.

Additional information

Publisher's note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ .

Reprints and permissions

About this article

Mathumitha, R., Rathika, P. & Manimala, K. Intelligent deep learning techniques for energy consumption forecasting in smart buildings: a review. Artif Intell Rev 57 , 35 (2024). https://doi.org/10.1007/s10462-023-10660-8

Download citation

Accepted : 19 December 2023

Published : 05 February 2024

DOI : https://doi.org/10.1007/s10462-023-10660-8

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

  • Energy consumption
  • Deep learning
  • Smart meter
  • Energy forecasting
  • Smart building
  • Find a journal
  • Publish with us
  • Track your research

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • View all journals
  • My Account Login
  • Explore content
  • About the journal
  • Publish with us
  • Sign up for alerts
  • Open access
  • Published: 08 February 2024

Life cycle environmental impact assessment of natural gas distributed energy system

  • Yakun Wang 1 ,
  • Ting Ni 1 , 2 ,
  • Bing He 3 &
  • Jiuping Xu 2  

Scientific Reports volume  14 , Article number:  3292 ( 2024 ) Cite this article

221 Accesses

Metrics details

  • Environmental sciences
  • Environmental social sciences

Natural gas distributed energy is recognized as a pivotal means to enhance energy efficiency and mitigate carbon dioxide emissions through localized energy cascading. Positioned as a key option for advancing the Sustainable Development Goals, this system optimizes energy utilization near end-users. While maximizing energy efficiency, it is imperative to address potential environmental challenges. A thorough, comprehensive environmental assessment, facilitated by the life cycle assessment method, proves instrumental in meeting this standard. Employing this method enables an intuitive grasp of the environmental strengths and weaknesses inherent in natural gas distributed energy within the power structure. This insight serves as a foundation for informed project decision-making, fostering the growth of the industry. We selected six environmental impact assessment categories based on the CML 2001 method, and conducted the life cycle analysis across four stages. China's inaugural natural gas distributed energy demonstration project was chosen as a model case, and an environmental impact assessment inventory was established, utilizing survey data and literature for comprehensive data collection and analysis. Results from case testing yield environmental impact assessment outcomes, with a specific sensitivity analysis for stages with notable environmental impact factors. The study underscores that the operation phase has the highest environmental impact, comprising 78.37% of the total combined environmental impact, followed by the fuel production phase. Comparative analyses with coal-fired and conventional natural gas power generation, based on dimensionless literature data, reveal that abiotic resources depletion potential is the primary contributor to the environmental impact of 1 kWh of electricity product, constituting 52.76% of the total impact value, followed by global warming potential. Concrete strategies have been outlined for decision-making in both the operational and planning phases of natural gas distributed energy projects. The strengthening of policies is pinpointed towards grid connection and scale expansion.

Introduction

Natural gas stands as a prominent contemporary clean energy source, demonstrating cost-effectiveness and a state of relative maturity. Its utilization holds the potential to significantly diminish the environmental repercussions stemming from coal mining and production, contributing to the mitigation of climate change and fostering sustainable development. In recent years, the extensive utilization of fossil energy by humanity has led to significant environmental issues. The awareness of climate change and associated environment problems has been gradually increasing within the public and governments globally, resulting in the commitment of most countries to cut their emissions to a certain level 1 . Against this backdrop, there is a widespread acknowledgment that addressing environmental challenges necessitates a substantial augmentation in renewable energy generation 2 . Renewable energy has emerged as a key engine for expanding electricity production in China, with clean energy substitution playing an increasing role 3 . Although sustainable power production technologies such as solar and wind are rapidly developing, their implementation is challenging due to their intermittent nature 4 . Natural gas stands out as a clean and low-carbon fossil energy source, with its carbon content per unit of calorific value amounting to only 58% of coal and 74% of oil 5 . Moreover, the carbon emission reduction benefits of natural gas power generation are strikingly evident 6 . The global reserves of natural gas are exceedingly abundant, with shale gas emerging as a recently developed form of natural gas 7 , contributing significantly to the augmentation of natural gas supply. Simultaneously, its role is pivotal in enhancing the energy landscape and addressing environmental concerns 8 . Notably, in recent years, both the United States and the United Kingdom have transitioned from coal to natural gas and renewable energy sources, impacting carbon dioxide emissions reduction and substantially diminishing other air pollutants 9 . China stands as the world's foremost energy consumer and is anticipated to emerge as a significant demander of natural gas. Given the pivotal role of natural gas in China's decarbonization policy, it is projected that a substantial share of its future gas demand will be met through imports 10 .

Natural gas distributed energy systems have attracted significant attention for their low-carbon, flexible, and safe use of energy cascading close to the customer. Distributed natural gas energy is acknowledged for its superior energy efficiency and enhanced environmental performance compared to conventional coal-fired power generation, attributed to operational fuel distinctions 11 , 12 . Traditional natural gas-fired power generation and natural gas-fired distributed energy have relatively large differences. Although both utilize natural gas as the exclusive fuel, distributed power generation with natural gas attains gradient energy utilization, thereby enhancing energy efficiency and manifesting positive environmental impacts 13 . And natural gas distributed energy uses multiple small combustion engines for energy supply, which is more flexible and safer. Many distributed energy stations in foreign countries now rely on natural gas as the primary driving energy source. The energy utilization efficiency is higher than that of combined-cycle power plants, which can reach about 80% or more 14 . Natural gas has huge development potential, so vigorously developing distributed natural gas projects is still the mainstream of China's future energy structure adjustment. China’s shale gas industry has made great progress in recent years, and shale gas has the potential for sustainable development in terms of technology, economy and environment 15 . In comparison to alternative forms of distributed energy systems, those utilizing natural gas as a fuel exhibit several advantages. Firstly, their environmental performance surpasses others, as natural gas combustion does not produce dust 16 , thereby minimizing environmental impact. Secondly, their application is more versatile, contrasting with solar and wind energy, which are subject to geographical and climatic constraints 17 . Finally, shale gas, an unconventional natural gas resource, is developing rapidly in several countries around the world. It has also become an important strategic energy option for China. The development of the shale gas industry is conducive to China's ability to cope with its growing energy demand and reduce its dependence on imported fossil fuels 18 .

The Combined Cooling, Heating, and Power (CCHP) system, commonly referred to as tri-generation, is experiencing rapid global development due to its notable advantages, including high energy efficiency, low emissions, and enhanced reliability 19 . In comparison to conventional natural gas applications, the CCHP system has the capacity to significantly reduce greenhouse gas emissions, particularly carbon dioxide, thereby mitigating the risks associated with climate change and its environmental impact 20 . CCHP system overcomes the disadvantage of supplying a single form of energy and meets the energy needs of users. Electricity on the CCHP system is generated on-site, closer to the user's needs, thereby minimizing losses incurred during the transmission and distribution process 21 . Fundamentally based on the concept of energy cascading, the CCHP system addresses heat loads through the effective use of recovered heat. This design enables the system to achieve energy efficiencies exceeding 70% 22 , and in certain configurations, reaching as high as 90%, surpassing conventional stand-alone energy supply systems 23 . Remarkably, the CCHP system stands out as a smaller, more flexible, and decentralized energy supply system, providing enhanced reliability and stability throughout the entire process 24 .

In the initial phases of development, distributed energy systems primarily relied on natural gas-based combined heat and power system 25 . Subsequently, there was a widespread development of natural gas-fueled combined cooling, heating, and power (CCHP) systems, and the integration of distributed energy systems with renewable energy sources gradually emerged 26 . CCHP is the main form of utilization of natural gas distributed energy systems 27 . According to the definition of natural gas distributed energy by National Energy Administration in China, the distributed energy system (DES) mentioned in this paper refers to an energy system that utilizes natural gas as a fuel and employs cogeneration of cooling, heating and electricity. Technological advances are transforming the quality of life for billions of people, yet as the world's population grows and wealth, the environmental burdens of realizing these amenities are enormous. In this situation, it is particularly important to be able to assess and mitigate the environmental burdens involved in the systems.

The objective of this paper is to evaluate and analyze the comprehensive life cycle environmental impacts of the DES system in a real project using GaBi software. This aims to demonstrate the pivotal role of the DES system in attaining sustainable development goals. The rest of the research in this paper is as follows: Sect. 2 provides a review of the relevant literature. Section 3 introduces material and methods, including case background, research methodology and data sources. Section 4 provides a life cycle assessment of the case. Section 5 presents the results and performs a sensitivity analysis. Section 6 compares the evaluation results of the case of this paper with those of coal-fired and natural gas-fired power generation, and provides relevant recommendations for China's actual situation. Section 7 summaries some conclusions.

Literature review

Des from techno-economic perspective.

Currently, in natural gas distributed energy systems, gas turbines and internal combustion engines are frequently employed in CCHP systems due to their high efficiency and compact size. Li 28 compared the changing law of energy efficiency of natural gas distributed energy driven by gas internal combustion engine and gas turbine respectively, and based on it, gave the scope of application of the two in the actual popularization and application. Xiao et al. 29 converted a gasoline engine into a gas engine with a CCHP system to form a small natural gas CCHP system. The study explored the waste heat and emission characteristics of the system, revealing that as the load increases, the amount of waste heat recovery grows, albeit with a decreasing proportion of total energy, maintaining an overall unit energy utilization above 80%. Advancements in technology have spurred interest in hybrid systems combining natural gas with renewable energy. Wang et al. 30 proposed a configuration of a solar-assisted natural gas-fired CCHP system, and simulated the thermodynamic performance and the complementary characteristics of the coupling in different cases. It was found that the primary energy efficiency of the system could reach 71% under the designed operating conditions. Wang et al. 31 presented the design and energy and economic multi-performance analysis of a solar-assisted CCHP distributed generation system. The article shows that under the design conditions, the energy efficiency of the cooling operation mode can reach 83.6% and that of the heating operation mode reaches 66.0%. Compared with conventional distributed energy sources without solar energy, the system consumes about 41% less natural gas per unit of energy. Yan et al. 32 developed a thermodynamic model to simulate the performance of a natural gas CCHP system using an innovative approach combining a phosphoric acid fuel cell with solar technology. Results indicated that the integration of solar energy reduced natural gas consumption and enhanced overall efficiency by approximately 15%. In addition, Fang et al. 33 devised a novel CCHP system integrating a three-stage organic Rankine cycle and a double organic flash cycle with liquefied natural gas as a heat sink. The article provided a thermodynamic analysis of the proposed CCHP system based on stipulated assumptions. In the multi-objective optimization outcomes, the system demonstrated an optimal efficiency of 80.49%, substantiating its superior performance.

Various scholars have conducted extensive studies on the economics of energy utilization in CCHP systems. Arsalis et al. 34 have carried out thermodynamic analysis, fire-use analysis, and cost analysis of small-scale LNG-fueled CCHP plants to demonstrate the feasibility of this system as a substitute solution for distributed generation applications. Tookanlou et al. 35 employed a particle swarm optimization algorithm to ascertain optimal hourly electricity and natural gas tariffs for CCHP systems, considering perspectives from both energy consumers and utilities. The study compared these tariffs with actual energy prices, confirming the interdependence of electricity and natural gas prices. Notably, operating the CCHP system in parallel with the distribution grid resulted in an 18% increase in the present value of revenues for distribution utilities. Yuan et al. 36 developed an optimization model to enhance previous methods for optimizing the operation of a CCHP system integrating electricity and natural gas. The article compared the proposed method with others, validating its effectiveness, and designed four scenarios to assess the economics of electricity and natural gas. Hua et al. 37 modeled a CCHP system for the coupled utilization of natural gas and geothermal energy, using a Beijing hotel as a case study. The proposed exergo-environmental cost method was applied to allocate the cost of multiple products, optimize the high-temperature flue gas allocation ratio, and evaluate energy consumption. The study concluded that the unit fire-environment cost is minimized when the flue gas allocation ratio is 0.63. Zhang et al. 38 conducted a comparative economic analysis experiment using the National Natural Gas Distributed Energy Demonstration Project to assess the profitability of the CCHP project at different tariffs with fixed natural gas, heat, and cooling prices. They established a critical value model for calculating the operating profitability of CCHP projects, facilitating the calculation of the break-even tariff to optimize the operating strategy of CCHP units and maximize project revenue.

With the global emphasis on sustainable development, scholars are progressively incorporating environmental considerations into their research on energy systems. Chen et al. 39 proposed a new multi-objective optimization model and tested it in the case of an integrated electrical and natural gas network for a CCHP plant. The case study demonstrates the model's effectiveness in enhancing the profitability and environmental performance of the system. Di Marcoberardino et al. 40 investigated the environmental potential and economics of an innovative micro-DES based on a membrane reactor and a PEM fuel cell. The environmental analysis was accomplished using a life cycle assessment, while the economics were evaluated in terms of their maximum system cost that is cost-effective over their lifetime.

CCHP from environmental perspective based on LCA

Life Cycle Assessment (LCA) endeavors to quantify the potential environmental impacts of a product, process, or service throughout its life cycle, encompassing direct and indirect emissions, as well as resource utilization from raw material acquisition through production, use, end-of-life treatment, recycling, and final disposal 41 . Over the past three decades, the methodology has evolved into a central tool for environmental management and decision support. Gradually, it has expanded to the level of sustainability analysis, introducing environmental, economic, and social evaluation indicators to enhance completeness and reliability. In the face of numerous sustainability challenges worldwide, a comprehensive assessment of relevant environmental issues can aid in addressing potential trade-offs for sustainability 42 . Our article focuses on studying the environmental impacts throughout the full life cycle process of the case, and the chosen method aligns with our current research problem. After years of development, more than 20 life cycle assessment (LCA) software have been developed worldwide, among which GaBi software is one of the most widely used LCA software at present. Developed by Thinkstep in Germany, its database integrates background databases from relevant research organizations and industries across various countries, comprising a total of more than 4000 available inventory data 43 .

As the application of Life Cycle Assessment is becoming more widespread in various industries, its Life Cycle Impact Assessment (LCIA) methodology is also evolving, as shown in Table 1 . LCIA methods are classified into midpoint and endpoint methods based on differences in evaluation purposes. The endpoint method emphasizes ecological risks and human health end-effects more than the midpoint method. However, due to its complexity and high data requirements, the uncertainty of the results is slightly higher than that of the midpoint method, and its practical application is still challenging. In comparison to other midpoint methods, the CML2001 method reduces assumptions and model complexity, resulting in less uncertainty 44 . CML 2001 is a methodology published by the Center for Institute of Environmental Sciences at Leiden University in 2001. It has undergone development over the years, gaining widespread acceptance and making it suitable for comparing the results of this paper with other studies related to LCA.

There is a consensus that CCHP systems are more efficient than conventional energy generation and can reduce energy losses. Scholars are gradually focusing on whether CCHP has sustainable energy and how to improve the environmental friendliness of the system. Initially, researchers delved into the environmental aspects of Building Combined Heat and Power (BCHP) systems. Jing et al. 48 developed a LCA model for a solar BCHP system. They applied this model to a BCHP system in an office building located in Beijing, China, and conducted a comparative analysis of the full life cycle energy and environmental performance under different operating strategies. Their findings indicate that, in terms of comprehensive performance, BCHP with the power load-following strategy yields superior benefits. Wang et al. 49 proposed an optimization methodology for biomass gasification-based BCHP system combined with a life cycle inventory (LCI). Applying this method, a biomass BCHP case in Harbin, China was optimized to analyze the performance of multiple metrics such as cost, energy, and emission, and to evaluate its comprehensive performance. This study serves to illustrate that the integration of the optimization method with LCI is a robust and effective approach in the design of biomass BCHP systems.

Currently, some researchers have studied and compared the environmental performance of CCHP systems in different operating modes based on the LCA method. Yan et al. 50 developed a parametric life cycle assessment framework using the TRACI methodology and simulated the energy generation and supply of a distributed CCHP system integrating office renewable energy and energy storage systems. The simulation results demonstrate that the system and the proposed technology have a lower environmental impact compared to conventional power generation. Regarding cost, the life cycle cost of the system exceeds that of conventional energy generation, with small and large offices proving to be more economical than medium-sized offices. Montazerinejad et al. 19 introduced a novel solar CCHP system and applied LCA based on the Eco-indicator 99 methodology, complemented by exergo-environmental analysis to assess and comprehend the system's performance. Wang et al. 51 proposed a Robust multi-objective optimization method integrated with LCA to minimize the environmental impact of a hybrid solar assisted natural gas CCHP system. Based on a case study to validate the optimization method, the environmental impact potential of the system was evaluated and the effectiveness of the optimization algorithm was demonstrated. Liu et al. 52 conducted a life cycle assessment of DES to quantify their environmental impacts in comparison with conventional energy systems provided by natural gas and electricity. The GaBi software was employed for the LCA, Environmental impacts were evaluated using the CML methodology and the Eco-indicator 99 methodology, respectively, and sensitivity analyses were performed. The findings indicate that DES exhibit better life cycle performance during the use phase compared to conventional energy systems. The sensitivity analysis further showed that the environmental damage caused by DES can be reduced by optimizing natural gas and electricity consumption. Herrando et al. 53 conducted a lifecycle assessment (LCA) of the solar combined cooling, heating and power (S-CCHP) system based on the ReCiPe method. The LCA results were then compared with conventional PV systems and grid-based systems. In addition, a sensitivity analysis was performed to analyze the impact of multiple metrics on the LCA results. The results of the study demonstrate that the S-CCHP system is more environmentally friendly and can reduce environmental impacts.

From the reviewed literature, it can be inferred that although there has been a gradual increase in the number of studies on DES, few studies have conducted detailed life cycle environmental assessment studies on DES systems. In addition, most of the hybrid systems in the cases in the literature are based on simulations and assumptions, while the case in this paper uses a real project of natural gas distributed energy CCHP. This paper offers a more realistic case reference, and provides valuable insights for product environmental impact studies.

Life cycle modeling and testing of the DES

Given the increasing attention to reducing environmental pollution, it is crucial to assess the potential environmental impact of DES. It is necessary to carry out a full life cycle evaluation of DES to obtain their impact on the environment. Based on the guidelines of ISO14040 54 and ISO14044 55 , LCA has the following four steps (Fig.  1 ). The determination of goals and scope mainly clarifies functional units and system boundaries, etc., which is the starting step of the entire life cycle assessment. Inventory analysis is the process of quantifying and inventorying all inputs and outputs involved in the entire life cycle of a product, process or activity. Life cycle impact assessment is the core link in LCA, which requires the inventory data to be calculated and quantified into different environmental impact types for evaluation. The interpretation is an analysis and summary of the three stages.

figure 1

Flow chart of environmental impact assessment.

System description and data preparation

The case study in this research pertains to the China Resources Snow Breweries natural gas distributed energy project in Sichuan province of China, which was recognized by the National Development and Reform Commission as the inaugural national natural gas distributed energy demonstration project. In the previous studies on this project conducted by other scholars 56 , life cycle analysis was adopted to compare the energy consumption and greenhouse gas (GHG) emissions of a natural gas-fired distributed generation project in China with five other scenarios. Their findings indicated that renewable natural gas possesses the potential to enhance energy efficiency and reduce GHG emissions. Their primary focus lay in assessing the project's overall performance of the project in terms of energy savings, GHG reductions, and economic efficiency. Different from existing research conducted in the project decision stage, this paper complements the environmental impact assessment of natural gas distributed energy based on the field research in the operation phase.

The case examined in this paper is in an administrative district of Chengdu, China. It was developed to provide energy services to an industrial park in its vicinity. The case study employs a gas-steam cycle unit with 6 MW installed capacity. The project system configuration comprises a SolarT60 gas turbine, one supplementary-fired waste heat boiler, two 20 t/h gas boilers, a steam accumulator, a hot-water lithium bromide machine, and a 300 m 3 heat storage water tank, and is not equipped with a steam turbine. The operation configuration principle of this case is shown in Fig.  2 . According to National Energy Administration 57 , the annual steam supply of the case reaches 94,900 t (0.6–0.8 MPa/160–180 °C), and the annual cooling supply is 4900 GJ. The annual power generation is 31,154,200 kWh, the natural gas usage is 14,033,000 m 3 /a. Under annual average load conditions, natural gas consumption is 19,490,300 m 3 /h, and the low calorific value of the natural gas used is 33.93 MJ/m 3 , and density of 0.7145 kg/m 3 .

figure 2

System configuration and operation schematic of the case.

The data for the fuel production and fuel transport phases were mainly obtained from GaBi software. The fuel production phase calculates the energy consumption of the domestic natural gas production phase based on the 2021 China Energy Statistics Yearbook 58 . The fuel transport phase encompasses various aspects, including the volume of natural gas consumed, combustion emissions, gas leakage, electricity consumption, and pipeline distances traveled. Given the absence of specific data on consumables for the construction phase of this study, energy and material data for this phase were derived from comparable projects. During the operation phase of the project, we conducted field research at the project site in September 2020. This yielded measured data spanning the years 2016 to 2020 and annual data for each year of these years. The annual data for the whole operation period in this phase were computed as the average over the five-year duration of the study.

Objective and scope definition

The system boundary of the DES system in this study encompasses four phases: fuel production, fuel transport, project construction, and project operation as shown in Fig.  3 . The decommissioning phase impact is not considered at this stage. At present, life cycle assessments of power systems typically employ unit power generation as the functional unit. Therefore, this paper selects the natural gas distributed energy output of 1 kWh of electricity as the functional unit.

figure 3

System boundary of the DES system.

Environmental impact assessment

Selection of six impact categories.

From Section 2.2 we know that CML2001 approach is widely used by scholars due to minimized assumptions and model intricacies with decreased uncertainty. There are 11 categories. However, according to the different evaluation systems and the differences in the local environment, the number of selected impact categories vary from four 59 to ten 60 . Through our environmental investigation of the case project site, we have found that the region tends to experience persistent pollution processes every late winter with poor meteorological dispersion conditions and continuous cumulative impacts of pollutant concentrations. In addition, the DES system studied in this paper is fueled by natural gas, and the combustion process emits pollutants such as CO 2 and NO X . Our analysis and assessment of the categories with high environmental impacts can provide an overall picture of the environmental impacts of the project. Therefore, this paper selected six impact categories of Global Warming Potential (GWP), Eutrophication Potential (EP), Acidification Potential (AP), Photochemical Ozone Creation Potential (POCP), Abiotic Resources Depletion Potential (ADP), and Human Toxicity (HTP) as indicators, considering their significance in the study.

Inventory analysis in four phases

Fuel production phase.

The inventory analysis for this stage considers the primary and secondary energy consumption consumed to produce natural gas, as well as the emissions generated during the process. Emission coefficients of different energy sources with different utilization modes were referred to Xu 61 . The results of direct emissions from the fuel production phase are shown in the Table 2 .

Fuel transport phase

This phase of the inventory analysis collects elements such as the amount of natural gas consumed as well as its combustion emissions, leaked natural gas (predominantly methane), electricity consumed, and the distance traveled by pipeline. The findings of Ou et al. 62 showed that pipeline transport of 1 kg of natural gas consumes 7.66E−04 MJ of natural gas and 6.67E−06 MJ of electricity per kilometer. The types of emission pollutants considered for the natural gas burned as fuel at this stage and the emission factors refer to the research data of Song 63 . Given the inherent likelihood of methane leakage during transport, the methane leakage is set at 0.3% of the total transported volume 64 . The results of direct emissions during the pipeline transportation phase are shown in the following Table 3 .

Project construction phase

The inventory analysis of the construction phase of the DES focuses on the energy consumed, raw materials, production of raw materials, and transport of indirect energy consumption and emissions brought about by the system during the construction process. As specific consumable data for the construction phase were not available, this paper draws upon research data by Zheng 65 to determine energy consumption and materials. The mode of transport of consumables in the construction phase consists of 80% road and 20% rail. The results of direct emissions during the construction phase are shown in the Table 4 .

Project operation phase

In this phase, the primary consideration is energy consumption, mainly the amount of natural gas consumed for system operation, while the focus regarding emissions is atmospheric emissions resulting from natural gas combustion. It is important to note that, due to the products of the energy station not being solely electricity, but also including both cooling and heating products. Therefore, we convert the energy of heating and cooling into electricity, and further convert it into the quantity of natural gas consumed. Applying the first law of thermodynamics and converting the total energy from cooling and heating into a unified electricity unit, the total energy amounts to 10.451 million kWh. Throughout the year in case project, the consumption of natural gas totaled 14.033 million m 3 , and the production of one unit of electricity during the operating phase necessitates the consumption of 0.134 m 3 of natural gas. Inventory emissions during the operational phase are shown in the Table 5 .

Characterization results

Characterization consists of assigning the environmental disturbances emitted during the life cycle of the object of study to the corresponding impact categories and transforming them into indicators that can represent potential impacts on the environment. Common impact factors for several environmental impact categories and their characterization factor values are shown in Table 6 .

Our study calculated and obtained the results of characterization of the six impact types over the life cycle of the study case according to Eq. ( 1 ), and the results were displayed in Table 7 .

where \({EIP}_{j}\) is the contribution of the product system to the \({j}\) th environmental impact type, that is the value of the characterizing score, eq/kWh, \({EF}_{j}(i)\) is the characterizing factor for the \({j}\) th environmental impact of the \({i}\) th emitting material, eq/kg, and \(M(i)\) is the number of emissions of the \({i}\) th material, kg/kWh.

Normalization results

After characterization, a standardized methodology processes the results of the various environmental impacts to establish a benchmark for comparisons and to determine the contribution of each impact type. In this paper, we use the global environmental impact benchmark values from the CML2001 impact assessment methodology as the normalization factor, with the year 2000 chosen as the base year, in standard human equivalents. Equation ( 2 ) for standardization is given below and the standardization results for the study cases were shown in Table 8 .

where \({NEIP}_{j}\) is the standardization value of the \({j}\) th environmental impact in the product system; \({EIP}_{j}\) is the results of characterization of environmental impact type j; \({NF}_{j}\) is the normalization factor.

Weighting results

By assigning different weights to each environmental impact category, the quantitative results of the integrated environmental impacts over the life cycle of the case can then be obtained through weighted calculations. These values reflect the degree of impact of a certain environmental impact on the entire ecological environment, calculated by Eq. ( 3 ). The results were shown in Table 9 .

where \({TEIP}_{j}\) is the weighted result value for category j environmental impacts; \({NEIP}_{j}\) is the normalized result value for category \({\text{j}}\) environmental impact types; and \({WF}_{j}\) is the weighted value for category j environmental impact types.

The environmental impacts have been estimated following the CML 2001 impact assessment method. We analyze the results of this paper in this section, based on which a sensitivity analysis is performed. Then we compare the assessment results of this paper with those of coal-fired and natural gas-fired power generation to accurately assess the environmental advantages of natural gas distributed energy. Meanwhile, countermeasure suggestions suitable for China's current situation are put forward for the obstacles encountered in China's development, and we hope to promote the realization of the industry's sustainable development.

Modeling result analysis

Environmental impacts of the des.

The largest contribution to the GWP, AP and EP impact potential of the case is from the operational phase of the project, followed by the fuel production phase. This is mainly due to the large amount of natural gas consumed and the large amount of CO 2 and NO X emitted during the operation phase of the project. The case's contribution to the ADP impact potential comes mainly from the use of natural gas, coal, and oil. The operational phase of the project contributes the most to the ADP impact potential, most notably because it consumes a large amount of natural gas and has the largest natural gas characterization factor value in the ADP. This is followed by the fuel production phase. The primary contribution to the POCP impact potential from the case arises from emissions of substances like N 2 O and SO 2 . The most substantial contribution is attributed to the fuel production phase, followed by the operational phase of the project. The largest contribution to the HTP impact potential is during the operational phase of the project, where emissions of polycyclic aromatic hydrocarbons from natural gas combustion are a significant contributor to the HTP impact potential. The fuel transportation phase and the project construction phase contribute less to the impact potential of each impact category.

After characterization, the units of different impact types are not the same, and normalization and weighting can provide an inter-comparable benchmark for judging the level of environmental impact hazard over the life cycle. In addition, subsequent comparisons of cases from different literatures can be made based on this. By analyzing the standardized results, we have been concluded that ADP has the largest environmental impact during the whole life cycle of the product of the study case outputting 1 kWh of electricity, followed by GWP, and EP has the least impact. Reducing natural gas leakage and improving the efficiency of energy use in actual production and operation can reduce the environmental impact values of ADP and GWP.

Tables 9 and 10 reveals that the phase with the greatest environmental impact is the operational phase of the project, which accounted for 78.37% of the total value of the combined environmental impact, followed by the fuel production phase, which accounted for 15.55%. The type with the highest environmental impact throughout the life cycle process is ADP, followed by GWP. The primary contributors to the most severe environmental impacts during the operational phase of the project are the substantial emissions from combustion and the consumption of natural gas. Focusing on the integrated environmental impacts over the life cycle of DES starts with the operational phase of the project.

Sensitivity of impact factors

Sensitivity analysis in life cycle evaluation aims to assess the impact of variations in data and parameters on the results and conclusions. The results of sensitivity analyses are indicative of the life cycle assessment 's reliability and precision 66 . In this paper, sensitivity analysis is used to study the extent to which parameter changes affect the results to obtain a basis for improving environmental impacts. The selection of parameters considers those parameters that have a greater impact in the staged results obtained in the case for the category of environmental impact under study.

In the previous section, the two phases of the case with the greatest environmental impacts were identified through the results of the environmental impact assessment as the operational phase of the project and the fuel production phase. Through the analysis, it is found that when the values of the three factors of system power generation efficiency in the operation stage, natural gas consumption, and energy consumption level in the fuel production stage change, the results of the list of cases will follow the changes, which will cause changes in the magnitude of the impact on the environment. Therefore, this paper will select these three factors for sensitivity analysis, numbered C1, C2, C3, each subjected to a ± 10% value change to assess their combined environmental impact (Table 10 ).

In summary, the high sensitivity of power generation efficiency and natural gas consumption is stems from the operation phase 's substantial contribution of 78.37% to the total environmental impacts across the study case's entire life cycle. Enhancing power generation efficiency leads to greater energy utilization efficiency and increased electricity output, while reducing natural gas consumption decreases direct emissions during the operation phase in the same situation. Therefore, future research and development efforts should prioritize improving power generation efficiency and enhancing overall energy utilization efficiency to effectively reduce the environmental impacts of natural gas distributed energy throughout its life cycle. Although the sensitivity factor of energy consumption in the fuel production stage is relatively smaller than the first two, reducing the production energy consumption and improving the extraction level of the upstream oil and gas industry is also one of the effective measures to reduce the life cycle environmental impacts of the study case.

Comparative analysis

Coal-fired power generation.

In 2022, coal power constituted 43.8% of the installed capacity, yet it contributed 58.4% to the country's total power generation 67 . Coal-fired power generation (CPG) systems pose significant environmental challenges. To highlight the environmental advantages of DES compared to coal, this paper conducts a comprehensive analysis of literature on the life cycle assessment of CPG. Given the extensive literature available, a focused approach was adopted, screening for studies using the CML2001 impact evaluation method with a functional unit of 1 kWh of electricity. The literature considered spans from 2011 up to date to facilitate a direct horizontal comparison of results and is presented in Table 11 .

The study cases in this paper compared with CPG results, and it was evident that the minimum values for environmental potentials in the other five impact types exceeded those in this paper, except for POCP. For the GWP, EP, AP, POCP, ADP, and HTP impact types, the mean impact potentials of CPG are 4.6, 84.3, 46.2, 3.4, 3.4, and 4.6 times higher than those in this paper, respectively. It can be seen from the table that the contribution of CPG to EP and AP is very prominent, and the most direct reason for this result is mainly due to the difference in fuel, which produces large amounts of carbon oxides, nitrogen oxides, sulfur compounds, and suspended particulate matter when coal is burned. Natural gas is a clean energy source, the gases emitted after combustion are mainly methane, ethane, propane, isopentane and carbon dioxide, with very little soot emission. Although natural gas also contains a small amount of sulfur, only a trace amount of sulfur dioxide will be produced after combustion, far less than the emissions of coal power. The projects in the case study also use low nitrogen combustion technology, which can further reduce nitrogen oxide emissions. In addition, the different efficiency of energy utilization can also have an impact, DES follows the stepwise utilization of energy, and the comprehensive energy utilization can reach 70% to 90% 79 .

Conventional natural gas power generation

The key differences of conventional and distributed natural gas-fired power generation lie in scale, proximity to end-users, and the integration of combined heat and power technologies in distributed natural gas systems. A comparative assessment of the environmental performance of gas-electricity and the results of this paper can help determine the superior form of utilization. Like the inclusion and exclusion criteria used before, we filter literature that uses the CML2001 impact evaluation method with a functional unit of 1 kWh of electricity. We consider literature published from 2011 to present, and display the evaluation results from various references meeting these criteria in Table 12 .

One of the reasons for the large differences in the characterization results between the different literatures for the same impact type is that there are differences in the efficiency and unit conditions of the different gas and electricity projects. The second is that natural gas comes from different sources in different countries, which is related to the resource endowment of the location of the gas and electricity projects. If the demand for natural gas imports is high, it is usually transported by marine transportation vessels, and there are differences in the results of the life cycle assessment between transportation vessel transportation and pipeline transportation.

The difference between the resultant values of this paper's case and those of conventional natural gas power generation (CNGPG) is less pronounced than with CPG. CNGPG exhibits larger overall characterization results, particularly in terms of GWP, EP, AP, POCP, ADP, and HTP impact types. The reason for this difference is that the research case in this paper is compared with traditional natural gas power generation. Although both use natural gas as their sole fuel source, CNGPG lacks secondary energy utilization. The production of natural gas distributed energy results in a smaller lifecycle output of materials energy consumption per unit of electricity. Moreover, the distribution of energy in natural gas distributed energy is closer to end-users, reducing energy losses in distribution and obviating the need for large-scale transmission facilities, which helps reduce the investment and the life cycle of energy consumption and emissions.

Comprehensive comparison

Figure  4 . illustrates the combined environmental impacts of CPG and CNGPG for 1 kWh of electricity output, achieved through a normalization and weighting process. In CPG systems, the largest environmental impact is attributed to GWP, followed by ADP. In contrast, for CNGPG systems and the DES system discussed in this paper, the primary environmental impact is ADP, followed by GWP. The environmental impact of the thermal power industry is mainly manifested with energy consumption and the emissions of various greenhouse gases. The combined environmental impact value of the case in this paper is 18.63% of coal power generation; 46.98% of natural gas power generation. This suggests that CPG has the most significant environmental impact, followed by CNGPG. The DES system in this paper exhibits notable advantages. Therefore, under the national carbon peaking and carbon neutrality goals, natural gas distributed energy has a very good development prospect and environmental advantages when transforming the energy structure from the results of the environmental impact assessment.

figure 4

Combined environmental impact value of the three power generation approaches.

Although DES has the advantages of low carbon, stability, and flexibility, its development is restricted by factors such as resource endowment and economic cost and cannot completely replace the existing power generation system. Global natural gas reserves are declining, and in the absence of breakthroughs in shale gas, natural gas distributed power generation needs to be used as one of various forms of power generation to protect people's livelihood. With the advancement of science and technology, the application of biomass, geothermal, solar and other renewable energy sources integrated with natural gas to generate electricity will be beneficial to the sustainable development of the natural gas industry.

Recommendation

Strategies for the des projects.

The sensitivity analyses conducted in this case identified power generation efficiency and natural gas consumption during the operation phase as the most sensitive factors. And the LCA-weighted results also indicated that the operation phase had the highest environmental impact value in the whole life cycle process of the study case. Therefore, we offer the following recommendations for projects that are in operation.

Use clean energy and advanced technology . We recommend the use of clean energy sources and raw materials, the adoption of advanced technology and equipment, and the enhancement of management practices 88 . This approach aims to achieve comprehensive resource utilization, pollution reduction at the source, improved resource efficiency, and a reduction of hazards to both human health and the environment.

Collaborate with gas enterprises . Jointly operate the project with gas enterprises to ensure the effective supply of gas 89 . The normal operation of gas units requires the effective cooperation of gas enterprises, which will jointly construct and operate the project, improve the gas infrastructure, and ensure that the units have enough gas sources.

Implement energy monitoring and control . Developing energy demand and consumption monitoring is crucial. Real-time monitoring and data analysis can help optimize unit operations 90 . We recommend the creation of an intelligent control application platform for energy stations to enable intelligent production regulation based on demand and production supply dynamics.

Enhance operation and maintenance . To maintain reliable operation, it is vital to strengthen the operation and maintenance of core equipment, such as gas turbines. At the same time, the economic, social, and safety aspects associated with gas facilities should be strictly regulated 91 . Regulate the use and management of infrastructure and improve the system.

For projects in the planning stage, we put forward three points for the reference of the relevant enterprises:

Local government cooperation . Actively collaborate with local governments to create an integrated energy system that aligns with local conditions. Leveraging local natural resources, such as solar energy, geothermal energy, wind energy, and hydropower, can lead to a comprehensive and intelligent energy system 92 . This can enhance energy efficiency and the security of supply.

Optimize system configuration . Conduct adequate research and validation from the project planning stage to determine the most suitable system configuration and operation mode 93 . Maximize comprehensive energy utilization efficiency and power generation efficiency.

Research and development . Relevant enterprises should prioritize research and development in core technologies and instruments. This includes manufacturing and technological upgrades of essential instruments like gas internal combustion engines and gas turbines. Additionally, strengthening research in areas such as system integration and optimized operation is essential.

Policy implications

From the experience of foreign countries' development, government-issued policy support plays a crucial role in facilitating the rapid growth of natural gas distributed energy. In 2011, four ministries and commissions jointly issued the "Guidance on the Development of Natural Gas Distributed Energy" guidance document, one after another in the energy strategy plan are proposed to vigorously develop natural gas distributed energy, "Twelfth Five-Year" plan, "Thirteenth Five-Year" plan, "Fourteenth Five-Year" plan of natural gas distributed energy as a need to promote the focus of the field. Since 2013, the National Energy Administration, the National Development and Reform Commission, the State Council and other departments have issued a series of supportive policy documents to promote the development of natural gas distributed energy, and some of the key related policies are shown in the Fig.  5 .

figure 5

Specific contents on distributed natural gas in the related policy documents from 2013 to the present in China. Contents in yellow boxes are related to the consumption structure of the energy market and the energy price. Contents in red boxes are related to the construction acceleration of grid connection. Contents in green boxes are related to the state's efforts to strengthen the industrial development of natural gas distributed energy projects. NDRC is short for National Development and Reform Commission in China.

A detailed interpretation of the figure reveals that China's policy direction for promoting natural gas distributed energy development primarily focuses on several aspects. These include increasing the proportion of natural gas consumption by adjusting the consumption structure of the energy market. Reduce the operating costs of natural gas distributed energy, thereby enhancing the competitiveness of the industry. Focusing on the preparation of natural gas distributed energy to be connected to the grid. Analyzing the policy direction alone showcases the country's determination and commitment to developing natural gas distributed energy. However, the existing policy system has deficiencies, hindering the standardized development of natural gas distributed energy. We have provided relevant recommendations for the future policy direction, aiming to contribute to the improvement of the situation.

Enhance policy implementation rules . Strengthening the implementation rules on policy implementation in conjunction with fiscal, taxation, finance and pricing. Taking the Implementing Rules for Natural Gas Distributed Energy Demonstration Projects as an example, the document proposes to have certain investment incentives or subsidized interest rates for natural gas distributed energy projects, but it is not clear how the investment incentives and subsidized interest rates are to be implemented.

Harmonized of electricity subsidies . China's existing electricity pricing policy for natural gas distributed energy generation lacks a unified subsidy standard for Internet access. The government should proactively implement measures, including financial subsidies and gas price concessions, to resolve price conflicts in natural gas power generation 94 .

Inclusion of technical standard specifications . Enhance the policy content by incorporating more relevant technical standards and norms. The grid connection of natural gas distributed energy power generation is a problem 95 . The Electricity Law emphasizes that enterprises with power generation qualifications must not only meet the criteria for connecting to the electricity grid but also obtain the consent of power grid enterprises. The miniaturization, multi-purpose, and fragmented characteristics of natural gas distributed energy pose challenges in meeting legal requirements for grid connection.

Enhance local policies and implementation . Expand the scope of policy implementation by introducing more targeted local policies. The development of natural gas distributed energy varies across different provinces and municipalities, and policies are often generalized. Currently, aside from the more developed North, Shanghai, Guangzhou, and the Yangtze River Delta region, many provinces lack clear preferential policies 96 . To genuinely promote institutional reform, it is crucial to effectively implement supporting policies in provinces, municipalities, and regions.

Due to the study's inherent scope limitations and the unavailability of precise data in certain sections, various areas still pose challenges, prompting the need for further investigation. Despite meticulous efforts to collect the latest statistical data, some information had to be substituted with data from earlier years, particularly in a less-explored subset. Consequently, additional field research is imperative to enhance the data quality in these specific domains.

Moreover, it is crucial to note that this study revolves around an operational project, and the absence of data from the decommissioning phase hinders the strict comprehensiveness of our life cycle assessment. To address this gap, future endeavors should focus on obtaining relevant data during the decommissioning phase.

This paper chose China's inaugural natural gas distributed energy demonstration project as a model case, and established an environmental impact assessment inventory across four stages. Results from case testing yield environmental impact assessment outcomes, with a specific sensitivity analysis for four stages with notable environmental impact factors. Comparative analyses with coal-fired and conventional natural gas power generation was conducted based on dimensionless literature data. The following insights serve as a foundation for informed project decision-making, fostering the growth of the industry.

When the study case outputs 1 kWh of electricity, the operation phase had the highest environmental impact in the study's life cycle, comprising 78.37% of the total combined environmental impact, followed by the fuel production phase with 15.55%, and the fuel transport phase and the project construction phase with 3.87% and 2.21%. Specifically, the operation phase contributed the largest proportion of GWP, EP, AP, ADP, and HTP impact potentials, which were 81.82%, 71.90%, 56.03%, 82.97%, and 55.19%, respectively. The POCP impact potential was mainly tied to the fuel production phase, with the construction phase contributing the least to each impact. The possible reasons were identified as the large amount of gas released by combustion and the consumption of natural gas during the operation phase for the most serious environmental.

Sensitivity analyzes highlighted power generation efficiency (0.75) and natural gas consumption (0.62) in the operation phase as critical factors. Recommendations for operational projects include utilizing clean energy and advanced technology, collaborating with gas enterprises for effective gas supply, implementing energy monitoring and control for optimized operations, and enhancing the operation and maintenance of core equipment while strictly regulating economic, social, and safety aspects associated with gas facilities. For DES projects in the planning stage, recommendations include actively collaborating with local governments to create an integrated energy system based on local resources, optimizing system configuration for maximum efficiency, and prioritizing research and development in core technologies and instruments, including gas internal combustion engines and turbines.

Based on dimensionless data from the case study and literature using the CML2001 method, we found that GWP was the primary environmental impact in CPG systems and ADP in CNGPG and the discussed DES system when illustrating the combined environmental impacts of CPG and CNGPG for 1 kWh of electricity output. The study revealed substantial benefits of DES in minimizing overall life cycle energy consumption and reducing greenhouse gas emissions. These natural gas distributed power generation projects can strike a balance between efficiency and environmental protection within the domestic context, underscoring the need for enhanced policy and economic support. At the national level, the analysis of China's policy direction for natural gas distributed energy focuses on increasing natural gas consumption, reducing operating costs, and facilitating grid connection. However, existing deficiencies in the policy system hinder standardized development. Recommendations include enhancing policy implementation rules, harmonizing electricity subsidies, incorporating technical standards, and expanding targeted local policies to promote institutional reform and effective support in provinces, municipalities, and regions.

In future research endeavors, an exploration of integrating the life cycle cost method into the fundamental LCA framework will be undertaken to calculate the comprehensive life cycle power generation cost of DES. This integration aims to establish a meaningful correlation between environmental impacts and economic costs, with the goal of constructing an all-encompassing evaluation system that addresses both environmental considerations and financial aspects. Additionally, we envision conducting case studies on distributed energy in diverse contexts and locations to enhance the comprehensiveness and applicability of the LCA approach. This strategic approach is intended to provide a more nuanced understanding of the system's dynamics and contribute to a more robust and widely applicable framework.

Data availability

The data that support the findings of this study are available from the corresponding author, [Ting Ni], upon reasonable request.

Lee, H. H., Iraqui, O. & Wang, C. The impact of future fuel consumption on regional air quality in southeast Asia. Sci. Rep. 9 , 2648. https://doi.org/10.1038/s41598-019-39131-3 (2019).

Article   ADS   CAS   PubMed   PubMed Central   Google Scholar  

Li, X., Paster, M., Stubbins, J. The dynamics of electricity grid operation with increasing renewables and the path toward maximum renewable deployment. Renewable and Sustainable Energy Reviews 47, 1007–1015. doi: https://doi.org/10.1016/j.rser.2015.03.039 . (2015)

Ali, U. et al. The impact of renewable and non-renewable energy consumption on carbon emission intensity in China: Fresh evidence from novel dynamic ARDL simulations. J. Environ. Manage. 320 , 115782. https://doi.org/10.1016/j.jenvman.2022.115782 (2022).

Article   CAS   PubMed   Google Scholar  

Lai, H. & Adams, T. A. II. Life cycle analyses of SOFC/gas turbine hybrid power plants accounting for long-term degradation effects. J. Clean. Prod. 412 , 137411. https://doi.org/10.1016/j.jclepro.2023.137411 (2023).

Article   Google Scholar  

Huo, Y. & Wang, J. China’s discourse on moving from high-carbon energy to low-carbon economy. Manag. Observ. 15 , 1. https://doi.org/10.3969/j.issn.1674-2877.2012.15.007 (2012) ( in Chinese ).

Lin, X. et al. Evolutionary trend of carbon emissions from energy consumption in Hong Kong and its influencing factors. Sci. Technol. Manag. Res. 42 , 231–238. https://doi.org/10.3969/j.issn.1000-7695.2022.21.027 (2022) ( in Chinese ).

Wang, Q. & Zhan, L. Assessing the sustainability of the shale gas industry by combining DPSIRM model and RAGA-PP techniques: An empirical analysis of Sichuan and Chongqing, China. Energy 176 , 353–364. https://doi.org/10.1016/j.energy.2019.03.158 (2019).

Wang, Q. & Li, S. Shale gas industry sustainability assessment based on WSR methodology and fuzzy matter-element extension model: The case study of China. J. Clean. Prod. 226 , 336–348. https://doi.org/10.1016/j.jclepro.2019.03.346 (2019).

Article   ADS   Google Scholar  

Ladage, S. et al. On the climate benefit of a coal-to-gas shift in Germany’s electric power sector. Sci. Rep. 11 , 11453. https://doi.org/10.1038/s41598-021-90839-7 (2021).

Global Gas Report 2023. https://www.igu.org/resources/global-gas-report-2023-edition . Accessed 16 Dec 2023.

Li, Y. & Xia, Y. DES/CCHP: The best utilization mode of natural gas for China’s low carbon economy. Energy Policy 53 , 477–483. https://doi.org/10.1016/j.enpol.2012.11.015 (2013).

Tanaka, K., Cavalett, O., Collins, W. J. & Cherubini, F. Asserting the climate benefits of the coal-to-gas shift across temporal and spatial scales. Nat. Clim. Chang. 9 , 389–396. https://doi.org/10.1038/s41558-019-0457-1 (2019).

Wang, Y., Li, B. & Cui, H. Comprehensive value analysis of natural gas distributed energy stations. Power Syst. Autom. 40 , 136–142 (2016) ( in Chinese ).

Google Scholar  

Hua, B., Yue, Y. & Lai, Y. Discussion on development strategy of natural gas (as a fuel) utilization technologies in China. Nat. Gas Ind. 24 , 1–4 (2004) ( in Chinese ).

Wang, Q. & Yang, X. Evaluating the potential for sustainable development of China's shale gas industry by combining multi-level DPSIR framework, PPFCI technique and RAGA algorithm. Sci. Total Environ . 780 , 146525. https://doi.org/10.1016/j.scitotenv.2021.146525 (2021).

Article   ADS   CAS   PubMed   Google Scholar  

National Development and Reform Commission. Natural Gas Development “13th Five-Year Plan” . https://www.ndrc.gov.cn/fggz/fzzlgh/gjjzxgh/201706/t20170607_1196794.html . Accessed 16 Dec 2023.

Wang, S., Lun, L., Xie, Y. & Fan, Z. Application of natural gas in distributed energy system. Nat. Gas Ind. 26 , 146–148 (2016) ( in Chinese ).

Wang, Q., Chen, X., Jha, A. N. & Rogers, H. Natural gas from shale formation – The evolution, evidences and challenges of shale gas revolution in United States. Renew. Sust. Energ. Rev. 30 , 1–28. https://doi.org/10.1016/j.rser.2013.08.065 (2014).

Montazerinejad, H., Ahmadi, P. & Montazerinejad, Z. Advanced exergy, exergo-economic and exrgo-environmental analyses of a solar based trigeneration energy system. Appl. Therm. Eng. 152 , 666–685. https://doi.org/10.1016/j.applthermaleng.2019.01.040 (2019).

Mago, P. J., Fumo, N. & Chamra, L. M. Methodology to perform a non-conventional evaluation of cooling, heating, and power systems. J. Power Energy 222 , 1075–1108. https://doi.org/10.1243/09576509JPE442 (2007).

Article   CAS   Google Scholar  

Fumo, N., Mago, P. J. & Chamra, L. M. Analysis of cooling, heating, and power systems based on site energy consumption. Appl. Energy 86 , 928–932. https://doi.org/10.1016/j.apenergy.2008.09.002 (2009).

Fumo, N. & Chamra, L. M. Analysis of combined cooling, heating, and power systems based on source primary energy consumption. Appl. Energy 87 , 2023–2030. https://doi.org/10.1016/j.apenergy.2009.11.014 (2010).

Gao, P. et al. Thermodynamic performance assessment of CCHP system driven by different composition gas. Appl. Energy 136 , 599–610. https://doi.org/10.1016/j.apenergy.2014.09.070 (2014).

Article   ADS   CAS   Google Scholar  

Wu, D. W. & Wang, R. Z. Combined cooling, heating and power: A review. Prog. Energy Combust. Sci. 32 , 459–495. https://doi.org/10.1016/j.pecs.2006.02.001 (2006).

Sanaye, S. & Ardali, M. R. Estimating the power and number of microturbines in small-scale combined heat and power systems. Appl. Energy 86 , 895–903. https://doi.org/10.1016/j.apenergy.2008.11.015 (2008).

Wang, J., Mao, T., Sui, J. & Jin, H. Modeling and performance analysis of CCHP (combined cooling, heating and power) system based on co-firing of natural gas and biomass gasification gas. Energy 93 , 801–815. https://doi.org/10.1016/j.energy.2015.09.091 (2015).

Shen, J., Li, F., Shi, D., Li, H. & Yu, X. Factors affecting the economics of distributed natural gas-combined cooling, heating and power systems in China: A systematic analysis based on the integrated decision making trial and evaluation laboratory-interpretative structural modeling (DEMATEL-ISM) technique. Energies 11 , 2318. https://doi.org/10.3390/en11092318 (2018).

Li, Z. Application of Two Typical Natural Gas Distributed Energy Systems (Shandong University of Architecture, 2013) ( in Chinese ).

Xiao, L. & Qin, Z. Experimental study on heat recovery characteristics of small gas internal combustion engine CCHP system. Shanghai Gas 5 , 13–16 (2014) ( in Chinese ).

Wang, J. & Ying, Y. Thermodynamic performance analysis of a combined cooling heating and power system driven by natural gas and based on solar energy. J. Eng. Therm. Energy Power 32 , 111–117 (2017).

Wang, J., Lu, Z., Li, M., Lior, N. & Li, W. Energy, exergy, exergoeconomic and environmental (4E) analysis of a distributed generation solar-assisted CCHP (combined cooling, heating and power) gas turbine system. Energy 175 , 1246–1258 (2019).

Yan, R., Wang, J., Cheng, Y., Ma, C. & Yu, T. Thermodynamic analysis of fuel cell combined cooling heating and power system integrated with solar reforming of natural gas. Sol. Energy 206 , 396–412. https://doi.org/10.1016/j.solener.2020.05.085 (2020).

Fang, Z. et al. Exergoeconomic, exergoenvironmental analysis and multi-objective optimization of a novel combined cooling, heating and power system for liquefied natural gas cold energy recovery. Energy 269 , 126752. https://doi.org/10.1016/j.energy.2023.126752 (2023).

Arsalis, A. & Alexandrou, A. Thermoeconomic modeling and exergy analysis of a decentralized liquefied natural gas-fueled combined-cooling–heating-and-power plant. J. Nat. Gas Sci. Eng. 21 , 209–220. https://doi.org/10.1016/j.jngse.2014.08.009 (2014).

Tookanlou, M. B., Ardehali, M. M. & Nazari, M. E. Combined cooling, heating, and power system optimal pricing for electricity and natural gas using particle swarm optimization based on bi-level programming approach: Case study of Canadian energy sector. J. Nat. Gas Sci. Eng. 23 , 417–430. https://doi.org/10.1016/j.jngse.2015.02.019 (2015).

Yuan, S., Wang, S. & Meng, Z. Interval optimization for integrated electrical and natural-gas systems with combined cooling, heating, and power considering demand response. Int. Trans. Electr. Energy Syst. 30 , 12447 (2020).

Hua, H., Wang, J., Chen, Y. & Wang, D. Thermal-economic optimization of natural gas combined cooling heating and power system. Therm. Power Eng. 38 , 26–32 (2023) ( in Chinese ).

Zhang, B., Chi, H. & Kang, J. Analysis of break-even electricity price for power generation of a natural gas combined cooling, heating, and power project. Build. Sci. 39 , 201–204 (2023) ( in Chinese ).

CAS   Google Scholar  

Chen, Y., Wang, Y. & Ma, J. Multi-objective optimal energy management for the integrated electrical and natural gas network with combined cooling, heat and power plants. Energies 11 , 734. https://doi.org/10.3390/en11040734 (2018).

Di Marcoberardino, G., Manzolini, G., Guignard, C. & Magaud, V. Optimization of a micro-CHP system based on polymer electrolyte membrane fuel cell and membrane reactor from economic and life cycle assessment point of view. Chem. Eng. Process. Process Intensif. 131 , 70–83. https://doi.org/10.1016/j.cep.2018.06.003 (2018).

Hellweg, S. & Milà I Canals, L. Emerging approaches, challenges and opportunities in life cycle assessment. Science 80 , 1109–1113 (2014).

Cavalett, O., Watanabe, M. D., Fleiger, K., Hoenig, V. & Cherubini, F. LCA and negative emission potential of retrofitted cement plants under oxyfuel conditions at high biogenic fuel shares. Sci. Rep. 12 , 8924. https://doi.org/10.1038/s41598-022-13064-w (2022).

Qian, W., Zhang, L. & Daqiang, C. LCA analyzing with GaBi software for pelletizing process in steel industry. in Energy for Metallurgical Industry . (2017) ( in Chinese ).

Duan, N. & Duan, S. Outline and contrast analysis of life cycle assessment methodologies. J. Anhui Agric. Sci. 36 , 3923–13925 (2008).

Bare, J. TRACI 2.0: The tool for the reduction and assessment of chemical and other environmental impacts 2.0. Clean Technol. Environ. Policy 13 , 687–696. https://doi.org/10.1007/s10098-010-0338-9 (2011).

Xie, M. et al. Research progress on the life cycle impact assessment methods and their localization in China. J. Environ. Eng. Technol. 12 , 2148–2156 (2022) ( in Chinese ).

Dong, Y. H. & Ng, S. T. Comparing the midpoint and endpoint approaches based on ReCiPe: A study of commercial buildings in Hong Kong. Int. J. Life Cycle Assess. 19 , 1409–1423. https://doi.org/10.1007/s11367-014-0743-0 (2014).

Jing, Y., Bai, H., Wang, J. & Liu, L. Life cycle assessment of a solar combined cooling heating and power system in different operation strategies. Appl. Energy 92 , 843–853. https://doi.org/10.1016/j.apenergy.2011.08.046 (2011).

Wang, J. et al. Combined methodology of optimization and life cycle inventory for a biomass gasification based BCHP system. Biomass. Bioenergy 67 , 32–45. https://doi.org/10.1016/j.biombioe.2014.03.026 (2014).

Yan, J., Broesicke, O. A., Wang, D., Li, D. & Crittenden, J. C. Parametric life cycle assessment for distributed combined cooling, heating and power integrated with solar energy and energy storage. J. Clean. Prod. 250 , 119483. https://doi.org/10.1016/j.jclepro.2019.119483 (2019).

Wang, J. et al. Robust multi-objective optimization with life cycle assessment of hybrid solar combined cooling, heating and power system. Energy Convers. Manag 232 , 113868. https://doi.org/10.1016/j.enconman.2021.113868 (2021).

Liu, C. et al. Comparison of life cycle performance of distributed energy system and conventional energy system for district heating and cooling in China. J. Cent. South Univ. 29 , 2357–2376. https://doi.org/10.1007/s11771-022-5073-y (2022).

Herrando, M., Elduque, D., Javierre, C. & Fueyo, N. Life Cycle Assessment of solar energy systems for the provision of heating, cooling and electricity in buildings: A comparative analysis. Energy Convers. Manag. 257 , 115402. https://doi.org/10.1016/j.enconman.2022.115402 (2022).

ISO 14040:2006/Amd 1:2020 Environmental management-life cycle assessment-principles and framework. http://www.iso.org/iso/home.htm (2020).

ISO 14044:2006/Amd 2:2020 Environmental management-life cycle assessment-requirements and guidelines. http://www.iso.org/iso/home.htm (2020).

Liu, H., Sheng, Z., Peng, T. & Ou, X. Life cycle energy consumption and greenhouse gas emissions analysis of natural gas-based distributed generation projects in China. Energies 10 , 1515. https://doi.org/10.3390/en10101515 (2017).

National Energy Administration. http://www.nea.gov.cn/2016-02/24/c_135126843.htm . Accessed 13 Oct 2023. ( in Chinese ).

Department of Energy Statistics National Bureau of Statistics. China Energy Statistics Yearbook 2021 (China Statistics Press, 2022).

Zhang, Y. et al. Life cycle assessment and optimization analysis of different LNG usage scenarios. Int. J. Life Cycle Assess. 23 , 1218–1227. https://doi.org/10.1007/s11367-017-1347-2 (2018).

Mannheim, V., Nehéz, K., Brbhan, S. & Bencs, P. Primary energy resources and environmental impacts of various heating systems based on life cycle assessment. Energies 16 , 6995. https://doi.org/10.3390/en16196995 (2023).

Xu, A. Life Cycle Environmental Impact Assessment of Cooling and Heating Sources for Office Buildings (Tianjin University, 2019) ( in Chinese ).

Ou, X., Yan, X. & Zhang, X. Life-cycle energy consumption and greenhouse gas emissions for electricity generation and supply in China. Appl. Energy. 88 , 289–297. https://doi.org/10.1016/j.apenergy.2010.05.010 (2011).

Song, G., Tang, L. & Jiang, W. Life cycle environmental impact assessment of a 2×200MW-class natural gas-fired cogeneration project. China Electric Power. 47 , 149–155 (2014) ( in Chinese ).

Grubert, E. & Brandt, A. R. Three considerations for modeling natural gas system methane emissions in life cycle assessment. J. Clean. Prod 222 , 760–767. https://doi.org/10.1016/j.jclepro.2019.03.096 (2019).

Zheng, X. Research on the Assessment of Environmental Benefits of Natural Gas Distributed Energy (China University of Petroleum, 2017) ( in Chinese ).

Xie, C. Environmental Impact Assessment of Permeable Paving Resources Based on Life Cycle Approach (Beijing University of Architecture, 2022) ( in Chinese ).

Chen, Z. New positioning and development of various power sources in new power systems. Energy. 03 , 27–31 (2023) ( in Chinese ).

Petrescu, L., Bonalumi, D., Valenti, G., Cormos, A.-M. & Cormos, C.-C. Life cycle assessment for supercritical pulverized coal power plants with post-combustion carbon capture and storage. J. Clean. Prod. 157 , 10–21. https://doi.org/10.1016/j.jclepro.2017.03.225 (2017).

Zhou, L. Full Life Cycle Assessment of Clean Coal-Fired Power Generation Technologies (Chongqing University, 2011) ( in Chinese ).

Nugroho, R. et al. Life cycle inventories and life cycle assessment for an electricity grid network: Case study of the Jamali grid, Indonesia. Int. J. Life Cycle Assess. 27 (8), 1081–1091. https://doi.org/10.1007/s11367-022-02082-5 (2022).

Wang, G. Full Life Cycle Evaluation of Ultra-High Parameter CO2 Coal-Fired Power Generation System (North China Electric Power University, 2019) ( in Chinese ).

Li, J., Zhang, Y., Shao, S., Sui, X. & Zhang, X. Life cycle assessment of thermal power plants with circulating fluidized bed boiler coal combustion technology. Chin. J. Environ. Eng. 8 , 2133–2140 (2014) ( in Chinese ).

Yue, P. et al. Environmental impacts of gaseous organic pollutants emitted from the life cycle of a 300 MW coal-fired power plant. Clean Coal Technol. 28 (05), 173–181 (2022) ( in Chinese ).

Zhang, X. Research on Life Cycle Evaluation of Flue Gas Ultra-Clean Emission System of Coal-Fired Power Plant (Shandong University, 2018) ( in Chinese ).

Atılgan Türkmen, B. & Azapagic, A. Life cycle environmental impacts of electricity from fossil fuels in Turkey. J. Clean. Prod. 106 , 555–564. https://doi.org/10.1016/j.jclepro.2014.07.046 (2015).

Liang, X. et al. Up-to-date life cycle assessment and comparison study of clean coal power generation technologies in China. J. Clean. Prod. 39 , 24–31. https://doi.org/10.1016/j.jclepro.2012.08.003 (2013).

Morrison, B. & Golden, J. Life cycle assessment of co-firing coal and wood pellets in the Southeastern United States. J. Clean. Prod. 150 , 188–196. https://doi.org/10.1016/j.jclepro.2017.03.026 (2017).

Siddiqui, O. & Dincer, I. A comparative life-cycle assessment of two cogeneration plants. Energy Technol. https://doi.org/10.1002/ente.201900425 (2020).

Zhou, Z. Reflections on the development of natural gas distributed energy. Energy 11 , 31–35 (2021) ( in Chinese ).

Karapekmez, A. & Dincer, I. Comparative efficiency and environmental impact assessments of a solar-assisted combined cycle with various fuels. Appl. Therm. Eng. 164 , 114409. https://doi.org/10.1016/j.applthermaleng.2019.114409 (2019).

Ozturk, M. & Dincer, I. Comparative environmental impact assessment of various fuels and solar heat for a combined cycle. Int. J. Hydrog. Energy. 44 , 5043–5053. https://doi.org/10.1016/j.ijhydene.2019.01.003 (2019).

Singh, B., Stromman, A. & Hertwich, E. Life cycle assessment of natural gas combined cycle power plant with post-combustion carbon capture, transport and storage. Int. J. Greenh. Gas Control. 5 , 457–466. https://doi.org/10.1016/j.ijggc.2010.03.006 (2011).

Bicer, Y. & Khalid, F. Life cycle environmental impact comparison of solid oxide fuel cells fueled by natural gas, hydrogen, ammonia and methanol for combined heat and power generation. Int. J. Hydrog. Energy. 45 , 3670–3685. https://doi.org/10.1016/J.IJHYDENE.2018.11.122 (2020).

Agrawal, K. K., Jain, S., Jain, A. K. & Dahiya, S. Assessment of greenhouse gas emissions from coal and natural gas thermal power plants using life cycle approach. Int. J. Environ. Sci. Technol. 11 , 1157–1164. https://doi.org/10.1007/s13762-013-0420-z (2014).

Martin-Gamboa, M., Iribarren, D. & Dufour, J. Environmental impact efficiency of natural gas combined cycle power plants: A combined life cycle assessment and dynamic data envelopment analysis approach. Sci. Total Environ. 615 , 29–37. https://doi.org/10.1016/j.scitotenv.2017.09.243 (2018).

Fadeyi, S., Arafat, H. & Abu-Zahra, M. Life cycle assessment of natural gas combined cycle integrated with CO 2 post combustion capture using chemical solvent. Int. J. Greenh. Gas Control. 19 , 441–452. https://doi.org/10.1016/j.ijggc.2013.10.008 (2013).

Binita, S. & Seema, U. Sustainability assessment of gas based power generation using a life cycle assessment approach: A case study from India. Manag. Environ. Qual. https://doi.org/10.1108/MEQ-02-2018-0034 (2018).

Wang, Y. Research on the mechanism and countermeasures of low-carbon development of natural gas power generation industry. Ecol. Econ. 01 , 114–118 (2013) ( in Chinese ).

Tian, X. Current situation and suggestions for the development of natural gas distributed energy in China. Energy Energy Conserv. 03 , 2–4 (2019) ( in Chinese ).

Zhao, Y. & Liu, X. Practice and exploration of sustainability of distributed energy station construction-taking the construction of distributed energy station in the New Hospital District of Shijiazhuang People’s Hospital as an example. Ind. Technol. Forum. 22 , 201–202 (2023).

Li, J. Reflections on the development of natural gas under the goal of “double carbon”. Nat. Gas Technol. Econ. 16 , 53–59 (2022) ( in Chinese ).

Shi, Y., Pan, J., Wang, K. & Zhao, S. Paths and strategies for the integration of natural gas and new energy in the context of “double carbon”. Oil Gas New Energy. 35 (04), 1–6 (2023) ( in Chinese ).

Zhou, Z. Reflections on the development of natural gas distributed energy. Energy. 11 , 31–35 (2021) ( in Chinese ).

Sun, Q., Li, Z., Zou, Y. & Zuo, L. Development strategy and suggestions for distributed energy of natural gas in China. Nat. Gas Technol. Econ. 14 , 1–6 (2020) ( in Chinese ).

Liu, M. Research on constraints and countermeasures for the development of distributed natural gas energy in China. Sino-Glob. Energy 19 , 3–10 (2014) ( in Chinese ).

Jiang, H., Dong, K., Wang, J. & Sun, R. Research on development strategies for distributed natural gas energy in China. Mod. Chem. Ind. 39 , 14–18 (2019) ( in Chinese ).

Download references

Acknowledgements

The authors express gratitude to three senior engineers involved in the China Resources Snow Breweries natural gas distributed energy project for providing valuable feasibility study materials. They extend thanks to the GaBi Education Team for providing the GaBi 9.2.1 Education software.

This work was supported by the Sichuan Oil and Natural Gas Development Research Center, Key Research of Social Sciences Base of Sichuan Province (Grant No. SKB17-04), Chengdu University of Technology 2023 Young and Middle-aged Backbone Teachers Development Funding Program (NO.10912-JXGG2023-06691), the Fundamental Research Funds for the Central Universities of Sichuan University (SCU2022CG13), and the National Natural Science Foundation of China (Grant No. 72171028).

Author information

Authors and affiliations.

College of Environment and Civil Engineering, Chengdu University of Technology, Chengdu, China

Yakun Wang & Ting Ni

Business School, Sichuan University, Chengdu, 610059, People’s Republic of China

Ting Ni & Jiuping Xu

China Construction Eighth Bureau Second Construction Corporation Limited, 191 Nanxinzhuang West Road, Shizhong District, Jinan, Shandong, 250024, People’s Republic of China

You can also search for this author in PubMed   Google Scholar

Contributions

Y.W. and B.H. wrote the main manuscript text and T.N. edited the original draft preparation. T.N. and J.X. conceptualized and supervised the research process. All authors reviewed the manuscript.

Corresponding authors

Correspondence to Ting Ni or Jiuping Xu .

Ethics declarations

Competing interests.

The authors declare no competing interests.

Additional information

Publisher's note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ .

Reprints and permissions

About this article

Cite this article.

Wang, Y., Ni, T., He, B. et al. Life cycle environmental impact assessment of natural gas distributed energy system. Sci Rep 14 , 3292 (2024). https://doi.org/10.1038/s41598-024-53495-1

Download citation

Received : 03 November 2023

Accepted : 01 February 2024

Published : 08 February 2024

DOI : https://doi.org/10.1038/s41598-024-53495-1

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

By submitting a comment you agree to abide by our Terms and Community Guidelines . If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Quick links

  • Explore articles by subject
  • Guide to authors
  • Editorial policies

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

literature review on solar energy pdf

IMAGES

  1. (PDF) A Review Paper on Solar Energy System

    literature review on solar energy pdf

  2. (PDF) Systematic Literature Review on Fuzzy Hybrid Methods in

    literature review on solar energy pdf

  3. (PDF) Research of Solar Panel System for Future Scope

    literature review on solar energy pdf

  4. Solar Electric Handbook: Media Bundle (Textbook / eBook)

    literature review on solar energy pdf

  5. (PDF) A Review Of Solar Energy

    literature review on solar energy pdf

  6. (PDF) Solar Tree: A Source of Energy-A Review

    literature review on solar energy pdf

VIDEO

  1. Solar Energy Essay

  2. Solar Energy Degree 2nd sem Gunshot Questions and Explanation with Tips

  3. OCR 7: Solar's problems

  4. Download The Optics of Nonimaging Concentrators: Light and Solar Energy PDF

  5. Presenting Solar Start To Finish

  6. Essay on Solar Energy in english ||Short essay or Paragraph on Solar Energy ||

COMMENTS

  1. (PDF) A Review Of Solar Energy

    This paper review the present state of solar energy capacity in the world also identifies vital approaches of improving their functionality, reliability, and affordability as well as the...

  2. Up-to-date literature review on Solar PV systems: Technology progress

    Up-to-date literature review on Solar PV systems: Technology progress, market status and R&D https://doi.org/10.1016/j.jclepro.2022.132339 Mono-crystalline photovoltaic cells Poly-crystalline photovoltaic cells Emitter wrap-through (EWT) cells Cadmium telluride (CdTe) Copper indium-gallium di-selenide (CIGS) Amorphous silicon (a-Si) solar cells

  3. PDF A Review of Solar Energy

    A Review of Solar Energy: Markets, Economics and Policies Govinda R. Timilsina a , Lado Kurdgelashvili b and Patrick A. Narbel c Key Words : solar energy; renewable energy economics and policies; climate change JEL Classification: Q42

  4. A Systematic Literature Review of the Solar Photovoltaic Value Chain

    The main objective of this paper is to systematically review the "state-of-the-art" research on the solar PV value chain (i.e., from product design to product end-of-life), including its main stages, processes, and stakeholder relationships, in order to identify areas along the value chain where circular strategies could be implemented, thereby ...

  5. Solar energy status in the world: A comprehensive review

    The global installed solar capacity over the past ten years and the contributions of the top fourteen countries are depicted in Table 1, Table 2 (IRENA, 2023). Table 1 shows a tremendous increase of approximately 22% in solar energy installed capacity between 2021 and 2022. While China, the US, and Japan are the top three installers, China's relative contribution accounts for nearly 37% of the ...

  6. PDF A Comprehensive Assessment of Solar Photovoltaic Technologies

    This literature review provides an overview of assessments of renewable energy technologies with respect to five perspectives: social, technological, economical, environmental, and political (STEEP). The focus is on photovoltaic solar energy and related technologies. Keyword searches have been performed in a number of databases

  7. Systematic literature review of photovoltaic output power forecasting

    First published: 26 January 2021 https://doi.org/10.1049/iet-rpg.2020.0351 Citations: 12 Sections PDF Tools Share Abstract Since the harmful effects of climate warming on our planet were first observed, the use of renewable energy resources has been significantly increasing.

  8. Introduction and Literature Review

    Solar Energy Chapter Introduction and Literature Review Ibrahim Moukhtar, Adel Z. El Dein, Adel A. Elbaset & Yasunori Mitani Chapter First Online: 14 November 2020 1493 Accesses 1 Citations Part of the Power Systems book series (POWSYS) Abstract

  9. PDF Advanced Methods for Incorporating Solar Energy Technologies into

    Models: Literature Review and Analysis P. Sullivan, K. Eurek, and R. Margolis . Prepared under Task No. SS13.1020 . Technical Report NREL/TP-6A20-61185 . ... Although solar energy provides only a small fraction of U.S. and global energy needs, recent technology cost reductions have driven rapid solar growth, and further solar cost ...

  10. A systematic review of solar photovoltaic energy systems design

    Solar energy is mainly utilized through thermal, and/or light ... A systematic review of solar photovoltaic energy systems design modelling, algorithms, and software: Energy Sources, Part A: Recovery, Utilization, and Environmental Effects: Vol 44 , No 3 - Get Access

  11. Solar Power Plant Site Selection: A Systematic Literature Review on

    Solar energy is found abundantly and is pollution-free. Recently solar energy plays have been playing an important role in the generation of electricity. In spite of all the advantages, as solar energy is uncertain the output characteristics of PV are non-linear and thus become complicated when the entire PV cell does not get uniform radiation ...

  12. (PDF) Literature Review on the Solar Energy Potential for Botswana

    Solar energy is one of the best forms of renewable energy, particularly for a country such as Botswana. Paradoxically, Botswana finds herself importing electricity and experiencing power cuts, yet she has abundant sunshine almost all-year round that could be converted to electric power.

  13. Literature Review on the Solar Energy Potential for Botswana

    Solar energy is one of the best forms of renewable energy, particularly for a country such as Botswana. Paradoxically, Botswana finds herself importing electricity and experiencing power cuts, yet she has abundant sunshine almost all-year round that could be converted to electric power.

  14. A literature review on Building Integrated Solar Energy Systems (BI-SES

    In this sense, this work aims to present a literature review for the Building Integrated Solar Energy Systems (BI-SES) for façades, subdivided into three categories: thermal, photovoltaic and hybrid (both thermal and photovoltaic). The methodology used corresponds to a systematic review method.

  15. Up-to-date literature review on Solar PV systems: Technology progress

    Abstract Introduction Section snippets References (178) Cited by (67) Journal of Cleaner Production Volume 362, 15 August 2022, 132339 Up-to-date literature review on Solar PV systems: Technology progress, market status and R&D Amine Allouhi a , Shafiqur Rehman b , Mahmut Sami Buker c , Zafar Said d e Add to Mendeley

  16. PDF An Updated Review of the Solar PV Installation Workforce Literature

    Sustainable Energy, LLC, for the U.S. Department of Energy (DOE) under Contract No. -AC36- DE 08GO28308. Funding provided by U.S. Department of Energy Office of Energy Efficiency and Renewable Energy Solar Energy Technologies OfficeThe views expressed . herein do not necessarily represent the views of the DOE or the U.S. Government.

  17. PDF A Systematic Literature Review of the Solar Photovoltaic Value Chain

    tematic literature review (SLR) by following the methodological framework proposed by Denyer and Tranfield [36] and Tranfield, Denyer and Smart [37]. A systematic literature review is a self-contained research project that uses existing studies to provide answers to research questions, which are usually derived from policy or practice. A ...

  18. 5HVRXUFHDQG2SWLPL]DWLRQ Charanjeet Singh Tumrate, Shambo Roy Chowdhury

    A Literature Review on Renewable Energy Resource and Optimization Vijay Prakash Sharma1, Devendra Kumar Somwanshi2, Kalpit Jain3, Raj Kumar ... solar energy. Simulation of IRES was carried out in HOMER & 3744 simulations were done to compute the unit cost of energy. The results of simulation showed the lowest cost of energy Rs 12.462

  19. PDF Literature Study on Solar Energy Resources a Geographical Analysis

    The present study reports Literature study on solar energy resources across the globe and its benefits as perceived by the consumers of non-renewable resources during the last decade 2010-2020. Key words: Population,Green Energy,National Solar Energy Mission,Air Pollution, Carbon Emission Fuels, Fire Wood, Non-Renewable Resources

  20. Environmental impacts of solar photovoltaic systems: A critical review

    The global solar energy harvesting trends (Fig. 2) ... The previous literature review reveals a well-established environmental impacts assessment of the solar PV systems is crucial. Currently, there is a gap in the literature regarding the impact of different PV system components on the environment. ... View PDF View article View in Scopus ...

  21. Full article: A review of renewable energy sources, sustainability

    3.3. Direct solar energy. The word "direct" solar energy refers to the energy base for those renewable energy source technologies that draw on the Sun's energy directly. Some renewable technologies, such as wind and ocean thermal, use solar energy after it has been absorbed on the earth and converted to the other forms.

  22. (PDF) A Review Paper on Solar Energy System

    A Review Paper on Solar Energy System Authors: Deepak Purohit Goverdhan Singh Udit Mamodiya Poornima Institute of Engineering and Technology Abstract and Figures The plant (an arrangement of...

  23. PDF Technical Potential and Meaningful Benefits of Community Solar in the

    The U.S. Department of Energy (DOE)defines community solar as any solar project or purchasing program, within a geographic area, in which the benefits flow to multiple customers, such as individuals, businesses, nonprofits, and other groups. 2. In most cases, customers benefit from energy generated by solar panels at an off-site array.

  24. Environmental Impact of Solar and Wind energy- A Review

    The global energy demand is growing substantially. Clean and secure energy supply is a must for our civilization's sustainable development. Solar and wind energy is growing fast and can contribute significantly to meet the goals set by many countries to reduce greenhouse gas emissions. A deep and wide investigation of the environmental impact of solar and wind energy is important before any ...

  25. A Systematic Literature Review on big data for solar photovoltaic

    Furthermore, literature review papers have evidenced the interest in this subject. Voyant et al [53] presented an evaluation of the papers emphasizing the important role of ANN on forecasting solar irradiance. The literature review separates papers into two groups: machine learning techniques and techniques related to an ensemble of predictors.

  26. Intelligent deep learning techniques for energy consumption ...

    Urbanization increases electricity demand due to population growth and economic activity. To meet consumer's demands at all times, it is necessary to predict the future building energy consumption. Power Engineers could exploit the enormous amount of energy-related data from smart meters to plan power sector expansion. Researchers have made many experiments to address the supply and demand ...

  27. Life cycle environmental impact assessment of natural gas distributed

    Leveraging local natural resources, such as solar energy, geothermal energy, wind energy, and hydropower, can lead to a comprehensive and intelligent energy system 92. This can enhance energy ...