1. Introduction
As global warming intensifies and resources become increasingly scarce, the world is seeking solutions to address contemporary energy shortages. The construction sector, historically a significant contributor to traditional energy consumption, is responsible for high energy use and substantial environmental pollution, contributing to 33% of global greenhouse gas emissions, including carbon dioxide. Therefore, there is a pressing need to reduce the consumption of fossil fuels and cut down on pollutant emissions. Accelerating the transformation of energy consumption structures and developing renewable and new energy sources are paramount. Solar energy, as a renewable resource, stands out for its abundance, ubiquity, and cleanliness, making it increasingly valued in energy-efficient building applications.
Solar energy, as a form of radiant energy, requires conversion into other usable forms for utilization and storage. Photovoltaic cells, leveraging the photovoltaic effect to transform solar energy into electrical energy, represent a prevalent method for this purpose. The integration of photovoltaic power generation within buildings holds immense promise. The concept of “Building-Integrated Photovoltaics” (BIPV) was first introduced by the World Energy Organization in 1986, advocating for the incorporation of solar photovoltaic systems into building structures. In 2016, the APEC organization expanded this concept, defining distributed photovoltaic systems installed on buildings or separate structures as BMPV (building mounted photovoltaic), encompassing two main forms: BAPV (building attached photovoltaic) and BIPV (building-integrated photovoltaic). BAPV systems, affixed to buildings, operate independently from the building’s structure, primarily focused on power generation without altering existing functionalities. BAPV installations are noted for their ease of installation and cost-effectiveness, contributing to their widespread adoption. On the other hand, BIPV systems are intricately incorporated into the building’s structure during construction, serving not only as power generators but also as integral building components or materials. While BIPV offers multifaceted benefits, its design and construction necessitate stringent standards. Easy-to-install and multifunctional building-integrated photovoltaics (BIPV), such as hybrid or semi-transparent BIPV, are receiving increased attention [1]. Common integration points for photovoltaic components include roofs, façades, sunshades, and skylights, with roof and façade applications being particularly prevalent [2]. Solar photovoltaic roofs, situated atop buildings to harness sunlight for electricity generation using photovoltaic technology, play a crucial role in energy conservation and emission reduction efforts. These roofs can utilize either building material-integrated photovoltaics or standalone photovoltaic installations to achieve their energy-saving objectives [3].
Since the 1970s, numerous developed countries have pioneered the integration of photovoltaic components onto building rooftops. This endeavor has led to the commercialization of building-integrated photovoltaic technology in several countries, complemented by the implementation of policies aimed at fostering the advancement of solar photovoltaic roofs. Moreover, these countries have spearheaded the development of a range of innovative technologies, culminating in the establishment of comprehensive technical systems and models. Countries such as Germany, Japan, and the United States were early leaders in developing the photovoltaic (PV) industry, with PV installed capacities steadily increasing in recent years. Although China’s PV industry started later, it has grown rapidly, with annual installed capacities doubling, driving the expansion of the global market [4], as indicated in Table 1.
The review of the literature indicates that scholars from various countries have summarized and reviewed the field of solar photovoltaic roofs within photovoltaic buildings. Sagar et al. [7] analyzed the effects of different materials of translucent solar panels installed on building roofs on the indoor room temperatures and solar cell temperatures, as well as the electricity generation rates of different materials. Maghrabie et al. [8] analyzed and compared the performance of photovoltaic systems under different parameters on building roofs and facades, finding that BIPV affects the building’s thermal load and thermal comfort, thereby impacting energy consumption. Johannes and Henrik [9] evaluated publicly available production data, configurations, and locations of rooftop photovoltaic systems in Europe, noting a lack of performance improvement in systems installed after 2008. Weerasinghe et al. [10] reviewed 45 non-residential BIPV projects in 212 Western countries to understand their actual economic value. Abdalazeem et al. [11] provided an overview of the advantages of green and photovoltaic roofs and their optimization design factors in hot climates.
This paper employs bibliometric analysis to elucidate the current status of solar photovoltaic roofs within the realm of energy conservation, discern research hotspots, and uncover developmental trends. Leveraging the widely utilized CiteSpace software for visualization and citation analysis [12], this study delves into the evolution of solar photovoltaic roofs concerning energy consumption trends over the preceding three decades. Through the identification of pivotal countries and authors, elucidation of research focal points, and delineation of developmental stages, this research endeavors to forecast future trajectories and offer theoretical and practical insights to guide subsequent studies.
2. Research Method
2.1. Data Collection
Due to the direct impact of database selection on the quality of results, choosing the right database is crucial for bibliometric analysis [13]. Available databases for conducting bibliometric analysis include Scopus, Web of Science (WoS), Medline, ScienceDirect, Google Scholar, and CNKI [14]. The Web of Science features three major citation indices (SCIE, SSCI, and A&HCI), encompassing over 12,400 authoritative and high-impact international academic journals covering disciplines such as natural sciences, engineering technology, social sciences, arts, and humanities, making it one of the most commonly used databases for bibliometric analysis. The WoS database not only covers a wide range of literature but also holds significant influence and authority; thus, it was selected for extracting publication data for this study.
Table 2 shows the data parameters used for article retrieval in this study. An advanced search was conducted in the Web of Science (WoS) core collection database. The search themes were “solar photovoltaic roof” and “energy consumption” connected with the Boolean operator “AND” during the retrieval process. The search period was set from 1993 to 2023. Under these conditions, a total of 347 literature records were retrieved. To further refine the results, the document types were limited to articles and reviews, and literature from relevant interdisciplinary fields was filtered. Ultimately, 333 documents were retrieved and exported as the main data for this study. Using CiteSpace software, a bibliometric analysis was conducted on the 333 retrieved articles. In conjunction with a detailed literature review, key publications with significant research content were identified, such as those focused on existing photovoltaic (PV) rooftop technologies and studies involving experimental performance assessments and simulations of PV rooftop systems. This approach helped to map out the research hotspots and development trends in the field.
2.2. Bibliometric Method and Visualization Tools
This study analyzes the literature in the database using bibliometric methods and CiteSpace 6.2.R4. CiteSpace employs a normalized matrix for mapping displays using the default cosine algorithm. CiteSpace facilitates the analysis of the collaboration networks among countries, research institutions, and authors, where the size of nodes indicates the volume of publications and their importance, and the color or thickness of the edges represents the strength or frequency of collaborations. The co-citation analysis feature is used to analyze the frequency and connections of cited literature. Keyword co-occurrence analysis reveals high-frequency keywords and their relationships, while intermediary centrality analysis shows changes in research hotspots. Timeline analysis and burst detection are used to display research dynamics and cutting-edge topics. Overall, using bibliometric methods and CiteSpace is suitable for analyzing the current status and development trends of energy-saving potential in solar photovoltaic roofs. The research methodology employed in this study using CiteSpace software is detailed in the workflow shown in Figure 1.
3. Results and Discussion
3.1. Publication Distribution
3.1.1. Publication Statistics
A statistical analysis of the 333 articles studied in this paper reveals the annual trends in the literature related to solar photovoltaic roofs and energy consumption, as shown in Figure 2, identifying five distinct phases of research on energy consumption of solar photovoltaic roofs. The first phase, from the early 1980s to 2004, was a preparatory phase when PV technology was just emerging and was costly. Limited by the socio-economic and technological levels of the time, research on PV roof energy consumption was minimal, with fewer than five related academic papers published annually. The second phase, from 2004 to 2011, was the initiation phase, led by Germany, where countries introduced government subsidy policies that promoted the commercialization of photovoltaics, reducing the cost of photovoltaic electricity generation and increasing the number of related publications annually. The third phase, from 2011 to 2013, was an adjustment period when European countries significantly reduced or canceled government subsidies, leading to a decrease in PV investment returns and a slowdown in the growth trend of research literature. The fourth stage, from 2013 to 2015, was a brewing period during which the PV industry underwent a process of survival of the fittest, continuous reduction in PV system costs, and a rebalance of PV investment returns, prompting more countries globally to conduct research on PV roof energy consumption. The fifth stage, from 2015 to the present, is a stable development period. Following the signing of the Paris Agreement, countries have increasingly prioritized new energy sources. Advances in PV technology have driven down the costs of PV electricity generation, entering a phase of grid parity for PV power. This has led to a steady increase in PV installations and a significant rise in the annual volume of related literature, showing an accelerating trend. Thus, it is evident that research in this field has evolved in response to the needs of the times, and the volume of research is expected to continue to grow. Energy consumption issues, especially those related to building energy, remain a significant challenge for countries; hence, research on the energy consumption of solar photovoltaic roofs still has a broad development prospect.
3.1.2. Analysis of Publication Distribution Characteristics
By running the co-citation publication analysis function in CiteSpace, we can observe the distribution of publications related to solar photovoltaic roofs from 1993 to 2023, as shown in Figure 3. The publication network comprises 673 nodes and 2906 links, with an overall density of 0.0129, indicating close connections between nodes. Journals such as Solar Energy, Applied Energy, and Renewable Energy have larger node diameters and contain more colors, signifying their long-term influence in the field of energy consumption related to solar photovoltaic roofs. Journals like Sustainable Cities and Society and Applied Thermal Engineering have smaller node diameters, but their outer rings are red, indicating a rapid increase in the number of articles published in these areas in recent years. From these results, it is evident that articles related to solar photovoltaic roofs are frequently published in journals closely associated with energy and sustainable development, aligning with the current global energy shortage and the intensive development of energy-efficient buildings.
Journals serve as crucial benchmarks for assessing academic level and influence, and the author has compiled a list of the main journals related to the energy consumption of solar photovoltaic roofs from 1993 to 2023. Common bibliometric indicators include the number of published documents (N), impact factor (IF), and high citation frequency (H Index). The impact factor refers to the average number of citations received per paper published in the journal over the past five years. These indicators are used to assess the academic level and influence of academic journals, research institutions, and scholars, as well as to understand the overall academic research landscape. This paper organizes the number of publications in the database to identify the top 12 journals, as shown in Table 3. Statistical results show that Energies has the largest share of publications, with Solar Energy and Energy and Buildings ranking second and third, respectively. Applied Energy, Sustainable Cities and Society, and Energy Conversion and Management have the highest impact factors in the field, indicating their significant influence. Applied Energy ranks fourth in the number of publications, but with an impact factor of 11.0 and an H index of 264 in the last five years, it is considered one of the most influential journals in the field of solar photovoltaic roofs.
3.2. The Most Influential Research Group
3.2.1. Country Analysis of Literature Publication
By analyzing the academic output of various countries, it is possible to determine which nations have significant influence and contributions in the field related to solar photovoltaic energy. Using CiteSpace to construct a country-region collaboration network from the 333 articles in the database revealed a network with 71 nodes and 98 links, with a network density of 0.0394, as shown in Figure 4. The larger the node of each country, the more publications that country has in the relevant field. The color of the links between nodes represents the time when two countries first collaborated, with warmer colors indicating more recent collaborations. Most countries have established collaborations between 2021 and 2023, indicating recent formations of these partnerships. Germany and Italy, as well as the USA and Spain, have established earlier and closer relationships. Countries like China, India, and Arab nations, although starting their research later, have larger node diameters and a ring color close to red, indicating that these countries have recently been actively conducting research in solar photovoltaics and have published a significant number of findings.
An analysis of the existing database reveals that 72 countries have published articles related to solar photovoltaic roofs and their energy consumption. These countries have been ranked according to the number of published articles and their centrality, as shown in Table 4. In terms of publication volume, China has the largest number of articles, accounting for 15.01% of the total, followed by the United States, Spain, and Italy, all of which have implemented photovoltaic promotion policies. Betweenness centrality, which is measured by the number of shortest paths passing through a node, indicates the importance of a node; a centrality over 0.1 qualifies as a key node. The higher the betweenness centrality, the closer the connection of the node with other nodes [15] and the greater the country’s contribution to the field; the United States, Spain, Italy, and Germany not only have a high volume of publications but also exhibit high centrality, with values of 0.42, 0.32, 0.32, 0.31, and 0.20, respectively, indicating strong influence in the field. Considering both publication volume and centrality, China’s theoretical research currently dominates in this area, followed by the United States and Spain, although China’s practical application in this area still needs strengthening.
3.2.2. Author Analysis
This paper has compiled and analyzed the top 20 most active authors in the field of solar photovoltaic roofs, as shown in Table 5. The most active authors include Andreas Athienitis from Concordia University and Ursula Eicker from Universidad de Sevilla. Hachem et al. [16] primarily focus on energy-saving methods for multi-story residential buildings, demonstrating through simulations that optimizing rooftop design for solar energy generation can reduce total energy consumption by approximately 96% in a three-story building. Recently, they have also explored how model predictive control (MPC) can improve the operation of an open-loop air-based BIPV/T system connecting various thermal applications [17]. Recent studies by James et al. [18] used TRNSYS 17.2 software to simulate the annual energy performance of existing homes and low-energy homes with photovoltaic roofs in Quebec, Canada. The results indicate that by retrofitting 16% or rebuilding 12% of single-family homes, enough electricity can be generated to decarbonize the heating energy use of existing greenhouses. Authors with high activity levels have recently focused on using computer simulation technologies to predict and optimize the energy reduction capabilities of existing solar photovoltaic roofs. Research on solar PV roofs began earlier in Europe, and countries that started later, such as China and India, need to delve further into the results of earlier European studies, necessitating enhanced contact and academic cooperation among scholars from different countries.
3.3. Research Direction and Hotspot
3.3.1. Research Direction Analysis
According to the subject classification in the Web of Science database, over the past 30 years, research related to energy-saving solar photovoltaic roofs has encompassed 62 different academic disciplines [19]. The top 10 disciplines by publication volume are shown in Figure 5. Energy fuels, green sustainable science, and construction building technology dominate this field. The energy fuels discipline has published the most papers, totaling 177, as solar photovoltaic roofs convert sunlight into electrical energy through solar panels. This application in the energy sector involves energy production, transformation, and utilization, which aligns closely with the focus of energy fuels on energy extraction, transformation, utilization, and the study and application of related technologies. Solar photovoltaic roofs are a clean energy technology that does not produce greenhouse gases like carbon dioxide, making them environmentally friendly; thus, publications in the green sustainable science discipline rank second. Moreover, solar photovoltaic panels on roofs need to be integrated into the building structure; hence, the design, installation, and maintenance of solar photovoltaic roofs involve technical issues related to building materials, structural design, and construction processes, making the construction building technology discipline significantly impactful. This demonstrates that as technology advances and societal demands for sustainable development grow, solar photovoltaic roof technology holds tremendous potential in the energy, environmental, and architectural sectors, necessitating further exploration by people.
3.3.2. Analysis of High-Frequency Co-Cited Literature
The frequency of citations of co-cited references can illustrate an author’s academic capabilities and their contribution and impact on research topics. Analyzing highly cited documents can provide insights into current research hotspots. Using CiteSpace for co-citation analysis in the database, a network was constructed with 532 nodes and 1416 links, having a density of 0.01, as shown in Figure 6. The top ten most frequently cited articles were statistically organized and summarized to extract the research directions of most interest to current scholars, as displayed in Table 6.
From Table 6, it is evident that the most cited article, published in 2009, “A review on photovoltaic/thermal hybrid solar technology” has received 900 citations. This article reviews the research and development trends in photovoltaic thermal (PV/T) solar collectors and their applications in solar heating, solar greenhouses, photovoltaic thermal solar heat pumps/air conditioning systems, and building-integrated PV/thermal systems [20]. The second most cited article, published in 2019, “A high-resolution geospatial assessment of the rooftop solar photovoltaic potential in the European Union”, which combines satellite data, statistical data, and machine learning, performs a reliable assessment of the technical potential for rooftop solar photovoltaic power across the EU with a spatial resolution of 100 m [21].
Recent years have seen high citation frequencies for articles such as Zhong et al. [22] in 2021, which developed a deep learning-based method to automatically extract rooftop areas through image semantic segmentation to estimate the solar photovoltaic potential at the urban scale, showing that Nanjing has significant rooftop photovoltaic installation potential. Li et al. [23] studied the characteristics of photovoltaic panel semantic segmentation from a computer vision perspective, finding that photovoltaic panel image data have uniform texture and heterogeneous color features, as well as effective semantic segmentation resolution thresholds. This illustrates that applying computer technologies like deep learning to solar photovoltaic roofs is a recent research hotspot, and the evaluation of rooftop solar photovoltaic potential has consistently maintained research interest. Applications of technology, resource assessments, deep learning, and image processing are among the topics receiving considerable attention within the field of solar photovoltaic roofs, showcasing the diversity and cutting-edge nature of this area.
Figure 6Literature co-citation analysis [20,21,22].
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Table 6The top 10 most commonly cited articles.
| NO. | Title | Journals | Time | DOI | Authors | Citation Frequency |
|---|---|---|---|---|---|---|
| 1 | A review of photovoltaic/thermal hybrid solar technology | Applied Energy | 2009 | | Chow, TT | 900 |
| 2 | A high-resolution geospatial assessment of the rooftop solar photovoltaic potential in the European Union | Renewable and Sustainable Energy Reviews | 2019 | | Bódis, K | 687 |
| 3 | Photovoltaic self-consumption in buildings: A review | Applied Energy | 2015 | | Luthander, R | 672 |
| 4 | A method for predicting city-wide electricity gains from photovoltaic panels based on LiDAR and GIS data combined with hourly Daysim simulations | Solar Energy | 2013 | | Jakubiec, JA | 203 |
| 5 | Improved photovoltaic self-consumption with appliance scheduling in 200 single-family buildings | Applied Energy | 2014 | | Widén, J | 134 |
| 6 | Development of a method for estimating the rooftop solar photovoltaic (PV) potential by analyzing the available rooftop area using Hillshade analysis | Applied Energy | 2017 | | Hong, T | 129 |
| 7 | Simulation and analysis of a solar-assisted heat pump system with two different storage types for high levels of PV electricity self-consumption | Solar Energy | 2014 | | Thygesen, R | 89 |
| 8 | A cooperative net zero energy community to improve load matching | Renewable Energy | 2016 | | Lopes, RA | 82 |
| 9 | Review of geographic information system-based rooftop solar photovoltaic potential estimation approaches at urban scales | Applied Energy | 2021 | | Gassar, AAA | 79 |
| 10 | Urban solar utilization potential mapping via deep learning technology: A case study of Wuhan, China | Applied Energy | 2019 | | Huang, ZJ | 71 |
3.3.3. Keyword Co-Occurrence Analysis
Using CiteSpace’s keyword co-occurrence analysis feature, an analysis was conducted on the database of articles, revealing the research field’s hot topics and future development trends. The keywords “solar photovoltaic roof” and “energy consumption”, used as search terms in setting up the database, were excluded from the analysis. The established keyword co-occurrence network consists of 376 nodes and 1495 links, with a network density of 0.0212, indicating very tight connections between nodes, as shown in Figure 7. After statistical organization, the top 15 keywords by frequency and centrality were identified, as listed in Table 7. According to Figure 7 and Table 7, the keywords “solar energy”, “performance”, “energy”, and “renewable energy” had the highest occurrence frequency, while the top four keywords by centrality were “buildings”, “energy”, “performance”, and “solar energy”, each with a centrality exceeding 0.1. This indicates that research on solar photovoltaic roofs primarily focuses on assessing the performance of photovoltaic systems, including evaluations of power output, economic benefits, and environmental impacts. Secondary research interests include the performance of photovoltaic components, particularly their thermal, lighting, and power generation capabilities. Lastly, the feasibility studies of photovoltaic installations, such as simulations of solar radiation on building facades, power generation, and installation potential, are also prominent areas of research.
Based on keyword co-occurrence analysis, a keyword clustering analysis map was obtained, as shown in Figure 8. A cluster silhouette value greater than 0.5 indicates reasonable clustering, while a value greater than 0.7 suggests convincing clustering. Keywords were divided into 12 categories, numbered from 0 to 11, as listed in Table 8. The smaller the cluster number, the larger its size, with each cluster composed of closely related keywords [24]. All 12 obtained clusters have silhouette values significantly greater than 0.7, demonstrating a high credibility of the clustering results. Another critical value to consider in the clustering network is the Q value; the Q value of this study’s keyword clustering network is 0.908, far exceeding 0.3, indicating a very significant clustering structure. Further analysis will be conducted on these credible and large-scale clusters.
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Renewable Energy
Solar photovoltaic (PV) roofs play a significant role in the utilization of renewable energy in buildings. This cluster, the largest among all, comprises 51 documents and is primarily associated with the keywords renewable energy, building envelope, passive design, tropical developing country, and domestic residential power. A comprehensive analysis of research on solar PV roofs reveals that integrating PV components with building elements (roofs, sunshades, and louvers) is a common form in practical applications. The design challenge lies in finding a balance between the original functionality of the components and the added photovoltaic performance. Jhumka et al. [25] employed a novel approach through validated simulation modeling, combining thermal finite element analysis (FEA) with dynamic whole-building simulation to assess the heat transfer characteristics of photovoltaic panels on the facades or roofs of typical office buildings in Mauritius as well as the resultant energy consumption. Elghamry et al. [26] conducted a parametric study on the impact of solar cells on buildings’ power output, energy consumption, comfort conditions (indoor temperature, relative humidity, discomfort hours, and lighting), and carbon dioxide emissions, considering factors like unit positioning on the facade, orientation, and location (wall and roof). Photovoltaic (PV) roofs have been widely promoted across various types of buildings, thanks in large part to the extensive adoption of PV roofs in residential buildings, which has laid a foundation for their development. Countries such as Germany, the United States, and Japan have implemented different policies to drive the energy transformation of residential buildings. In recent years, there has been increasing interest in the benefits and returns of PV roofs after the support period ends. Klamka et al. [27] conducted an analysis of the energy-saving potential of PV roofs in German residential buildings, finding that self-consumed PV energy occupies a significant proportion and can yield personal profit, which is advantageous for the promotion of renewable energy.
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Green Roofs
Green roofs, although they do not directly generate energy, contribute to thermal insulation, sound insulation, and heat retention, reducing indoor temperatures in the summer and thus decreasing the energy consumption of air conditioning systems, indirectly reducing the demand for non-renewable energy sources. The primary keywords associated with this cluster include solar energy, rural energy, deep learning, rooftop solar photovoltaic, and power density. Solar photovoltaic (PV) roofs utilize solar energy for electricity production, helping to reduce the dependence on conventional fossil fuels and thereby lessen environmental pollution. In some cases, building rooftops can accommodate both green roofs and solar PV installations, achieving dual benefits. Zheng and Weng [28] tested the potential mitigative effects of green roofs and photovoltaic systems on the increased building energy demand caused by climate change in Los Angeles County, California. Movahhed et al. [29] used the net present value (NPV) method to study the impact of green roofs and rooftop photovoltaic panels on the energy efficiency of typical buildings, considering three types of vegetation cover and three types of commercial solar panels. The presence of green roofs reduced energy consumption by about 0.1%, while photovoltaic systems could generate 26 megawatt-hours annually, with a payback period of 6.5 to 7.5 years.
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Office Buildings
Office buildings present significant potential for the installation of solar photovoltaic roofs. This cluster includes key terms such as building shape, residential energy model, efficient design, HVAC demand, and building energy simulation. In addition to the performance of photovoltaic components, the design significantly influences the overall performance of photovoltaic buildings. For BAPV systems, common design focuses include the positioning, inclination, and orientation of photovoltaic panels. For BIPV systems, photovoltaic components are integrated with building materials or structures, participating more closely in the interaction between the building envelope and the indoor and outdoor environments, considering factors such as building lighting, thermal environment, and aesthetics.
Flexible and controllable parametric methods have been proven viable for enhancing photovoltaic building performance. Esfahani et al. [30] optimized solar radiation reception by adjusting the building layout, orientation, and roof shape (aspect ratio, slope, and lateral tilt). Miao et al. [31] studied and compared the balance between solar energy collection and energy consumption and savings under different geometric roof shapes in a subtropical climate due to uncontrolled daylight admission, glare, and solar heat gains. Current research uses a grid node method to control building shapes, meshing the building shape and then controlling node coordinates [32], where more nodes require higher flexibility in the parametric model, and fewer nodes might underexploit photovoltaic potential [33]. The control process is constrained by parameters such as building footprint dimensions, local orientation, and tilt angles.
Additionally, energy simulation plays a critical role in the design of photovoltaic roofs, facilitating the assessment of system performance across various conditions, including energy output, heat output, and comfort. Designers leverage simulation to optimize photovoltaic roof system designs, including the layout, angle, and orientation of panels, providing data-driven and scientifically analyzed decision support to the design team. Table 9 summarizes recent studies on architectural photovoltaic roof design, including input variables, output results, tools, and conclusions. Based on parametric model analysis, evaluation, and optimization, the routine process for photovoltaic performance design and optimization, as Table 9 shows, commonly used tool combinations include Revit and Dynamo, and Rhino and Grasshopper, the latter favored by architects and researchers for its user-friendly interface, high openness, and rich ecosystem. Current studies typically target 2–3 optimization objectives, extending beyond photovoltaic performance to include building energy consumption, lighting environment, and thermal environment. Many studies have optimized economic performance with a diverse set of evaluation metrics; however, optimization of carbon emissions is less common, with more studies focusing on balancing electricity generation and building energy consumption. Currently, building design optimization primarily focuses on spatial efficiency, while assessments of architectural aesthetics are typically considered only during the final case selection stage and are not effectively quantified.
3.3.4. Research Trends and Outburst Word Analysis
To better illustrate the evolution of research on solar photovoltaic roofs, a keyword visualization timeline has been established, as shown in Figure 9. The “renewable energy” cluster first appeared in 2005, with a significant increase in research output starting in 2009. From 2015 to 2020, there was a significant increase in the volume of related research within this cluster, forming a large-scale research direction; however, post-2020, there was a decline in the number of studies. The cluster for “building integrated photovoltaics” appeared later, in 2015, but immediately experienced an explosive growth in research quantity. Factors contributing to this growth include improvements in solar cell efficiency and enhancements in the performance of photovoltaic materials, making BIPV systems more mature and reliable. The “economic analysis” cluster, one of the earliest to form, saw growth only in 2009, with fewer studies and reduced attention in other years.
Based on keyword cluster analysis, the top 25 emerging terms were identified and are displayed in Figure 10. The years in which these keywords appeared are highlighted in deep blue. Red-highlighted years indicate periods during which these keywords had a significant impact and were research hotspots. The “intensity” label indicates the degree of emergence, with higher numbers signifying greater intensity. Keywords in the solar PV roof field often appear with strong intensity, highlighting their cutting-edge nature. “Economic analysis” and "life cycle assessment” have had a prolonged period of strong influence and prominence, although interest in these topics has recently declined. Keywords such as “PV”, “generation”, “rooftop photovoltaic”, and “efficiency” have shown strong forefront relevance from 2021 and continue to be prominent research foci.
Figure 9Evolution view of keyword co-occurrence network.
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Figure 10Top 25 keywords with the strongest citation bursts.
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This article, supported by Figure 9 and Figure 10, examines research trends in solar photovoltaic roof technologies over various time periods. Research on solar PV rooftop technology began early but was limited by technological and economic conditions. Before 2002, the number of publications was quite limited and mostly confined to energy-related fields. From 2002 to 2015, there was a surge in research on photovoltaic cells, particularly regarding their performance and energy storage. Photovoltaic cells, primarily based on silicon wafers, were categorized into monocrystalline silicon and polycrystalline silicon. Before 2015, polycrystalline cells almost monopolized the market. However, with breakthroughs in monocrystalline silicon production processes and the commercial application of passivated emitter and rear cell (PERC) technology in p-type monocrystalline silicon cells, the shipment of monocrystalline cells surpassed that of polycrystalline. This period laid a solid foundation for future research on materials such as cadmium telluride and copper indium gallium selenide cells. The economic impact of these technologies was also a focal point during this time.
From 2015 to 2020, keywords such as behavior, energy performance, and BIPV became prevalent. Scholars increasingly focused on the performance and energy efficiency of solar photovoltaic roofs. Due to the increase in operating temperature of photovoltaic (PV) modules, which leads to a decrease in power generation efficiency, there has been significant attention on how to effectively reduce the operating temperature of rooftop PV systems to minimize efficiency losses, especially in high-temperature regions. Existing PV cooling technologies include natural convection, air cooling, water cooling, evaporative cooling, phase change materials (PCM), and heat pipe cooling. These technologies are continuously evolving to improve the overall efficiency of rooftop PV systems. Bevilacqua et al. [40] conducted a comparative analysis of a spray cooling system operating on the back of PV modules and a forced ventilation system within a cavity. The results demonstrated that the simple spray cooling system exhibited excellent performance. In addition to studying the optimization of photovoltaic cell materials to enhance power generation, many researchers have employed simulation techniques to explore the optimal arrangement parameters of photovoltaic modules for maximizing electricity output. The primary goal was to enhance the electricity output and economic potential of rooftop photovoltaic modules. In 2015, Madessa et al. [41] studied a flat-roof residential building in Oslo using PVsyst 6.6.3 software, finding that the spacing and angle of photovoltaic arrays significantly affected electricity generation. That same year, Martinez-Rubio et al. [42] proposed a method for determining the optimal orientation of PV modules based on their effective sunlight hours or maximum electricity generation. This method could be applied to rooftops of various types and geographical locations to design the orientation, tilt angle, and size of the PV arrays.
From 2020 to the present, there has been an explosive increase in keywords such as solar collector, solar cells, PV, generation, and efficiency. Research has increasingly integrated with computer simulation techniques, incorporating neural networks and deep learning to reduce energy consumption and enhance the energy efficiency of photovoltaic buildings. Zhou et al. [43] developed a high-resolution assessment framework that combines top–down and bottom–up approaches to evaluate the zero-energy potential of photovoltaic systems in urban buildings, designing energy solutions based on the location of solar panels and the window-to-wall ratio. Various algorithms have also been incorporated into research, such as a multi-objective method based on the gravitational search algorithm (GSA) for sizing and distributing distributed generators (DG) and shunt capacitors (SCs) in distribution systems integrated with rooftop PV systems [44]. This demonstrates the widespread application of optimization algorithms driven by the need for improved performance in photovoltaic architecture.
3.4. Development Overview and Future Research Prospects
Based on the analysis of co-cited literature, research hotspots, emerging terms, and developmental trends across different periods, as discussed in previous sections, this paper outlines the chronological development of photovoltaic (PV) rooftops within the field of building energy consumption and provides a forecast for future research, as shown in Figure 11. From 1993 to 2023, photovoltaic roofs have seen continuous development in the energy-saving field, with 2013 emerging as a pivotal year. The number of studies on photovoltaic roofs surged significantly that year and has maintained a strong growth trajectory ever since, leading to substantial advancements in both the depth and breadth of research.
From 1993 to 2004, the preparatory phase of PV rooftop research focused primarily on the study of PV components and their economic aspects. To shift from the high cost and market monopoly of first-generation polycrystalline silicon cells, breakthroughs in monocrystalline silicon technology significantly increased its installation volume. Building on this, continuous innovation in cell materials led to the development of second-generation thin-film cells, which are more flexible and translucent, common products being flexible PV tiles [45]. Today, thin-film cells are predominantly cadmium telluride (CdTe) and copper indium gallium selenide (CIGS) cells, but due to their low actual conversion efficiency, their application has been limited. To enhance conversion rates, third-generation PV cells have improved in cell structure and expanded in type, including organic PV cells, perovskite PV cells, and copper zinc tin sulfide PV cells. These third-generation cells offer better translucency and color variety, improving the aesthetics of PV components. Despite their different eras of inception, all three generations are used today, with monocrystalline silicon cells still being mainstream in contemporary architecture.
From 2004 to 2011, the initiation phase, spurred by PV subsidy policies introduced by various countries, saw a significant increase in PV installations. Researchers gradually shifted focus towards forecasting solar energy potential and improving energy efficiency. Bergamasco et al. [46] analyzed existing GIS data to calculate rooftop areas suitable for solar applications and predicted the PV potential in the Piedmont region. Jo et al. [47] combined object-oriented image analysis, geographic information systems, and remote sensing data to quantify rooftop areas applicable for solar uses, predicting the potential benefits of urban-scale PV system implementation. Hachem et al. [48] developed design guidelines and methods for solar-optimized communities in northern mid-latitudes based on the impact of design parameters on energy performance. For high-density communities, additional units could reduce heating and cooling energy consumption while maximizing rooftop surfaces to integrate solar collectors. It is recommended that the distance between buildings be at least 85% of the minimum required to avoid shading to minimize the shading effect.
Figure 11The development of rooftop photovoltaics in the field of building energy consumption between 1993 and 2022.
[Figure omitted. See PDF]
From 2011 to 2013, a period of adjustment was marked by European countries, leading to a reduction in government subsidies, which caused a decline in the returns on photovoltaic (PV) investments and a slowdown in related industry development. Scholars once again focused their research on economic viability. Yu et al. [49] conducted a life cycle sustainability assessment (LCSA) of a 1.2-megawatt flat rooftop PV solar cell array, UQ Solar, which showed favorable environmental performance. Solano et al. [50] coupled the thermal equipment of an office building in two Ecuadorian cities with a grid-connected PV system installed on the building’s rooftop. The payback period for this facility ranges between 10 and 30 years, while the electricity costs vary from 4 to 24 cents per kilowatt-hour. Researchers strive to enhance energy utilization and solar conversion rates to improve return on investment, reduce the cost per unit of energy, and shorten the payback period for PV investments. The life cycle assessment (LCA) of solar PV rooftops remains one of the main challenges in enhancing their performance, as assessing their economic viability involves complex and diverse evaluation methods.
From 2013 to 2015, the incubation period saw continual upgrades in the PV industry with ongoing reductions in costs. The integration of PV with building maintenance structures was explored from an architectural design perspective. In order to reduce energy consumption and increase electricity generation, the consideration of the form, slope, and orientation of rooftops also needed to account for the placement, size, and angles of PV components. Mutan and Todeschi [51] investigated the cost, self-sufficiency, and self-consumption benefits of integrating PV technology on residential rooftops of varying orientations. To improve overall building efficiency, rooftop photovoltaic cooling technologies have diversified. Photovoltaic-thermal (PV/T) systems achieve dual energy utilization by capturing the heat dissipated by photovoltaic modules for building heating. PV rooftops can also be combined with various passive energy-saving technologies, including green roofs. However, most research has focused on urban and residential settings, neglecting the solar potential of rural areas.
Since 2015, the photovoltaic (PV) industry has entered a phase of stable development. Governments worldwide are increasingly emphasizing renewable energy, leading to the grid parity stage for PV power generation. Advanced digital techniques are being applied extensively in the performance simulation and optimization design of PV rooftops. Energy modeling and data analysis technologies continue to impact this field of research significantly [52]. Research on PV rooftop performance covers several branches, primarily focusing on the thermal, lighting, and power generation performance of PV modules. Vats et al. [53] conducted a quantitative study on PV rooftops in cold regions, examining the impact of ventilation systems, PV system types, and installation positions on the indoor thermal environment. Their analysis compared the effects of opaque and semi-transparent PV modules installed on building facades and rooftops, both with and without ventilation systems. Singh et al. [54] utilized a fuzzy genetic algorithm to optimize the parameters of hybrid double-channel semi-transparent photovoltaic thermal (PVT) modules and analyzed the performance of these PV modules in four Indian cities. The results indicated an improvement in power generation efficiency for the optimized modules. Performance simulations enhance the thermal environment within buildings equipped with PV rooftops, reducing energy consumption and carbon emissions. Additionally, neural networks and deep learning techniques have been introduced to further enhance the energy-saving potential of PV buildings.
In summary, potential research hotspots can be categorized into four areas: PV cell components, the economic viability of PV rooftops, PV rooftop design, and PV performance simulation. Future research trends will focus on efficiently integrating PV with architecture to enhance the conversion efficiency of PV components, thereby achieving energy-saving and emission reduction in buildings. The application of PV rooftops will expand beyond urban and low-latitude areas to include rural and high-latitude regions. A shift from focusing solely on PV cell performance to considering the overall performance of rooftops and their impact on the built environment is anticipated. By adjusting the photothermal performance parameters of PV rooftops, indoor environmental conditions can be regulated and optimized, effectively controlling indoor temperatures, reducing energy consumption, and improving the comfort of living or workspaces. The use of environmentally friendly materials in PV rooftops to reduce their overall environmental footprint and the tight integration of PV technology with architectural design to optimize rooftop performance while considering its comprehensive impact on the built environment represent important future directions for PV rooftop research.
4. Conclusions
The analysis indicates a notable advancement in the field of solar photovoltaic (PV) roofing over the past three decades, characterized by a consistent surge in published papers, reflecting a phase of comprehensive development. The development of this field can be segmented into five principle stages: the inception stage spanning from the early 1980s to 2004, followed by the initiation stage from 2004 to 2011, an adjustment stage from 2011 to 2013, a brewing stage from 2013 to 2015, and a stable development stage from 2015 to the present.
Notably, journals such as Energies, Solar Energy, and Energy and Buildings have wielded significant academic impact through the publication of articles related to the energy benefits of solar PV roofs. Among these, Applied Energy stands out as a preeminent journal within the solar PV roofing domain, considering its impact factors and h-index over the past five years.
The analysis of the current state of solar PV roof research by country reveals that China leads in recent theoretical studies on PV, followed by Spain and the USA. Despite China’s prominence in theoretical research, there is a need for enhancement in practical applications. Athienitis, Andreas, and Eicker, Ursula are among the most active researchers, often utilizing computer simulation techniques to predict and optimize the energy-saving potential of solar PV roofs.
Co-citation and keyword clustering indicate that research hotspots in the solar PV roof domain primarily include the following:
The integration of renewable energy with building energy efficiency, especially the incorporation of solar PV roofs;
The dual benefits of green roofs and PV systems, which, while not directly generating energy, provide insulation, soundproofing, and reduced energy consumption when combined with PV roofs;
The design and application of building-integrated PV, focusing on roof design aspects such as form, slope, and the placement and orientation of solar panels;
Optimization methods using parametric models, where energy simulation plays a crucial role in performance assessment, helping design teams with plan optimization and decision support.
Despite advancements in PV roof research within energy-efficient building frameworks, several challenges remain: (1) Economic viability issues: while theoretically reducing energy costs, the high installation costs and long payback periods limit widespread adoption in residential and commercial buildings. (2) Architectural integration and aesthetics: many PV roof designs do not align with architectural styles, potentially affecting the overall appearance and acceptance of buildings. (3) Limited installation regions: research is predominantly focused on urban areas and regions with abundant solar resources, overlooking rural and high-latitude areas that lack targeted studies.
In recent years, solar PV roof research has undergone rapid evolution, transitioning from broad energy-related topics to more nuanced investigations into PV cell performance and storage technologies. Presently, there is a notable shift towards examining the holistic performance of PV roofs and their influence on building environments. Moreover, studies increasingly incorporate advanced computer simulation technologies, leveraging neural networks and deep learning approaches to augment the energy-saving capabilities of PV buildings. As solar PV roof technology continues to advance, future research is likely to concentrate on several key trends:
Efficient integration of PV with building maintenance structures, promoting BIPV development, and developing customizable, modular PV solutions to fit various roof structures;
Performance optimization of PV systems, integrating advanced monitoring and management systems, and utilizing machine learning and AI algorithms to optimize performance and dynamically adjust system operations;
Enhancement of energy utilization efficiency by combining PV roofs with other energy-efficient technologies such as green roofs and high-efficiency HVAC systems, strategically arranging solar panels to reduce heating and cooling demands;
Optimization of the overall performance of PV roofs to improve building environments, adjusting the thermal performance parameters of PV roofs to regulate indoor light and thermal environments, thereby enhancing comfort levels in living or working spaces. This also includes adjusting the positioning and angle of solar panels to reduce the heat island effect.
Conceptualization, Q.Y.; methodology, A.L.; software, A.L.; validation, C.H.; resources, Q.Y.; data curation, A.L.; writing—original draft preparation, Q.Y. and A.L.; writing—review and editing, Q.Y. and A.L; and project administration, C.H. All authors have read and agreed to the published version of the manuscript.
The original contributions presented in the study are included in the article.
The authors declare no conflicts of interest.
Footnotes
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.
Enlarge this image.Figure 2. Annual trend in the number of relevant files. Note: Statistics are up to December 2023. (a) Number of annual publications; (b) Frequency of citations to annual publications from 1993 to 2023.
Enlarge this image.Figure 5. Statistical graph of the top 10 disciplines in terms of publication volume.
The development status of the solar photovoltaic roof industry in various countries.
| Country | Years | Development Status |
|---|---|---|
| Germany | 1995 | The German government first proposed the “photovoltaic roof plan” |
| 1991–1995 | 1000 PV roof projects were implemented | |
| 1998 | Proposed “100,000 photovoltaic roof Plan” | |
| 2001–2004 | Successfully completed the 100,000 roof plan, and the total installed capacity was expanded by 300 MW | |
| 2004 | Amended the Renewable Energy Law to complete the 600 MW rooftop photovoltaic project | |
| 2016 | The German government passed an amendment to the Renewable Energy Act (RESA), bidding for rooftop photovoltaic projects [ | |
| 2021 | Germany added 5.76 GW of PV installed capacity, and the cumulative PV installed capacity was 59.66 GW | |
| 2023 | The “German Photovoltaic Strategy” set a target of achieving a photovoltaic installed capacity of 215 GW by 2030 | |
| Japan | 1993 | Started the New Sunshine program |
| 1997 | Announced the implementation of the “70,000 Solar Roof PV Program” | |
| 2004 | The cumulative installed capacity of rooftop PV has reached 1100 MW, making it the country with the largest installed PV capacity in the world | |
| 2020 | In preparation for the Tokyo Olympic Games, the scale of photovoltaic roof construction was expanded by more than four times | |
| 2021 | Japan’s Ministry of Economy, Trade and Industry (METI) released the 6th edition of the draft Strategic Energy Plan, and solar and wind photovoltaic power generation was positioned as a major energy source in the future in the national strategy | |
| America | 1997 | US government proposed Million Solar Roofs’ |
| 2010 | The US Senate Energy Committee passed the “ten million solar roof proposal” | |
| 2018 | About 2 million homes in the United States have photovoltaic installations | |
| 2023 | The United States added 35.3 GW of new photovoltaic capacity, an increase of 52% compared to last year [ | |
| China | 2005 | The Chinese government issued the “Medium and Long-Term Development Plan for Renewable Energy”, which sets a clear target for solar thermal and photovoltaic utilization: by 2010, the total solar power capacity will reach 300 MW |
| 2009 | The Chinese government put forward the “solar roof program” and also proposed corresponding subsidy policies | |
| 2012 | The Chinese government issued the 12th Five-Year Plan for the Development of solar power generation, which further increased the installed capacity target to 21 GW in 2015 and 50 GW in 2020 | |
| 2018 | The Chinese government issued the “Smart Photovoltaic Industry Development Action Plan (2018–2020)” to promote the installation of photovoltaic on the roofs of urban buildings | |
| 2021 | China added 54.88 GW of PV installed capacity, accounting for 31.6% of the world’s new PV installed capacity | |
| 2022 | In the “Action Plan for Carbon Peak before 2030”, it is proposed to accelerate the optimization of building energy structures and carry out building rooftop photovoltaic action | |
| 2023 | The cumulative installed capacity of solar power generation nationwide is approximately 610 GW |
Data parameters used for article retrieval.
| Parameters | Value |
|---|---|
| Data Base | Web of Science |
| Title | (TS = (solar photovoltaic roof)) and (TS = (energy consumption)) |
| Type | Articles or Comments |
| Time range | 1993–2023 |
| Subject classification | Energy fuel, green and sustainable science and technology, building technology, environmental science, engineering and electrical, etc. |
| Search quantity | 333 |
The top 12 publications by number of publications.
| NO. | Source Title | N | N (%) | IF-(Five Years) | H Index | Country |
|---|---|---|---|---|---|---|
| 1 | Energies | 24 | 7.20 | 3.3 | 132 | Switzerland |
| 2 | Solar Energy | 23 | 6.90 | 6.3 | 210 | USA |
| 3 | Energy and Buildings | 20 | 6.00 | 6.6 | 214 | Holland |
| 4 | Applied Energy | 16 | 4.80 | 11.0 | 264 | UK |
| 5 | Renewable Energy | 13 | 3.90 | 8.4 | 232 | UK |
| 6 | Sustainability | 11 | 3.30 | 4.0 | 136 | Switzerland |
| 7 | Sustainable Cities and Society | 11 | 3.30 | 10.6 | 103 | The Netherlands |
| 8 | Energy Conversion and Management | 8 | 2.40 | 10.3 | 232 | UK |
| 9 | Journal of Cleaner Production | 6 | 1.80 | 6.5 | 268 | UK |
| 10 | Journal of Building Engineering | 5 | 1.50 | 6.5 | 72 | The Netherlands |
| 11 | Journal of Green Building | 5 | 1.50 | 1.4 | 27 | USA |
| 12 | Science of the Total Environment | 4 | 1.20 | 9.6 | 317 | The Netherlands |
Top 10 countries by degree of centrality and number of publications.
| NO. | Country | Centrality | NO. | Country | Publications |
|---|---|---|---|---|---|
| 1 | China | 0.42 | 1 | China | 50 |
| 2 | USA | 0.32 | 2 | USA | 32 |
| 3 | Spain | 0.32 | 3 | Spain | 29 |
| 4 | Italy | 0.31 | 4 | Italy | 23 |
| 5 | Germany | 0.20 | 5 | India | 22 |
| 6 | Saudi Arabia | 0.13 | 6 | Germany | 18 |
| 7 | The Netherlands | 0.13 | 7 | Canada | 15 |
| 8 | Japan | 0.11 | 8 | Japan | 13 |
| 9 | Iran | 0.10 | 9 | Australia | 12 |
| 10 | Norway | 0.10 | 10 | Saudi Arabia | 10 |
Top 20 active authors.
| NO. | Authors | Quantity | Country | Publications |
|---|---|---|---|---|
| 1 | Athienitis, Andreas | Concordia University | Canada | 3 |
| 2 | Eicker, Ursula | Universidad de Sevilla | Spain | 3 |
| 3 | Zambelli, E | University of Chemistry and Technology, Prague | Italy | 2 |
| 4 | Juaidi, Adel | Politecnico di Milano | Italy | 2 |
| 5 | Reddy, Srikanth K | MNIT Jaipur | India | 2 |
| 6 | Elmore, Ryan | Natl Renewable Energy Lab | USA | 2 |
| 7 | Bambara, James | Concordia University | Canada | 2 |
| 8 | Alim, Mohammad A | Western Sydney University | Australia | 2 |
| 9 | Keypour, Reza | Semnan University | Iran | 2 |
| 10 | Biswas, Wahidul K | Curtin University | Australia | 2 |
| 11 | Horn, Sebastian | Technische Universität Dresden | Germany | 2 |
| 12 | Mendez-santo, Pablo | Universidad De Cuenca | Spain | 2 |
| 13 | Zalamea-leon, Esteban | Universidad De Cuenca | Spain | 2 |
| 14 | Awad, Hadia | University of Alberta | Canada | 2 |
| 15 | Gagnon, Pieter | Natl Renewable Energy Lab | USA | 2 |
| 16 | Goutham, Sai G | MNIT Jaipur | India | 2 |
| 17 | Panwar, Lokesh Kumar | MNIT Jaipur | India | 2 |
| 18 | Chen, Wujun | Shanghai Jiao Tong University | China | 2 |
| 19 | Fazio, Paul | Concordia University | Canada | 2 |
| 20 | Hachem, Caroline | Concordia University | Canada | 2 |
Top 15 keywords by frequency and centrality.
| NO. | Keywords | Frequeney | NO. | Keywords | Centrality |
|---|---|---|---|---|---|
| 1 | solar energy | 50 | 1 | buildings | 0.27 |
| 2 | performance | 48 | 2 | energy | 0.26 |
| 3 | energy | 36 | 3 | performance | 0.19 |
| 4 | renewable energy | 34 | 4 | solar energy | 0.16 |
| 5 | buildings | 29 | 5 | model | 0.12 |
| 6 | systems | 28 | 6 | design | 0.11 |
| 7 | system | 27 | 7 | optimization | 0.10 |
| 8 | optimization | 26 | 8 | city | 0.10 |
| 9 | model | 25 | 9 | system | 0.09 |
| 10 | design | 24 | 10 | PV | 0.08 |
| 11 | PV | 16 | 11 | renewable energy | 0.07 |
| 12 | energy efficiency | 15 | 12 | systems | 0.07 |
| 13 | city | 14 | 13 | demand | 0.06 |
| 14 | generation | 14 | 14 | generation | 0.05 |
| 15 | simulation | 14 | 15 | simulation | 0.05 |
Keyword clustering data statistics.
| Cluster ID | Cluster Label (LLR) | Size | Silhouette | Mean Year | Top Keywords |
|---|---|---|---|---|---|
| Cluster #0 | Renewable energy | 51 | 0.745 | 2017 | renewable energy; building envelope; passive design; tropical developing country; domestic residential power |
| Cluster #1 | Green roofs | 40 | 0.774 | 2017 | solar energy; rural energy; deep learning; rooftop solar photovoltaic; power density |
| Cluster #2 | Office buildings | 39 | 0.761 | 2017 | building shape; residential energy model; efficient design; HVAC demand; building energy simulation |
| Cluster #3 | Buildings integrated photovoltaics | 38 | 0.771 | 2018 | energy efficiency; real-world driving; innovative technologies; photovoltaic roofs; co2 emissions |
| Cluster #4 | Net metering | 37 | 0.8 | 2015 | building-integrated photovoltaics; building retrofits; energy efficiency; heating electrification; greenhouse gas emissions |
| Cluster #5 | Economic analysis | 35 | 0.926 | 2012 | economic analysis; PV energy modeling; java applet format; photovoltaic buildings simulation; software tools |
| Cluster #6 | Levelized cost of energy | 30 | 0.87 | 2017 | analytical model; neural network; artificial intelligence; PV production forecasting; nonlinear autoregressive exogenous |
| Cluster #7 | Solar energy | 27 | 0.827 | 2013 | solar energy; economic analysis; life cycle cost; swimming pools heating; multi-story buildings |
| Cluster #8 | Own consumption | 17 | 0.827 | 2019 | curtain wall; energy saving regulation; own consumption; multiple thermal applications; Saudi Arabia |
| Cluster #9 | Building-integrated renewable energy | 8 | 0.964 | 2012 | urban energy consumption; house-gas emissions; energy modeling; solar energy systems; renewable energy |
Photovoltaic roof design research summary.
| Authors | Input Variable | Output Result | Tools/Platforms | Conclusions |
|---|---|---|---|---|
| Saleh Kaji Esfahani, Ali Karrech, Robert Cameron et al. [ | Control point coordinates, azimuth angle, inclination angle, plane aspect ratio | Solar radiation exposure | Designbuilder + Rh/Gh + Ladybug + Galapagos | The shape of the roof has a great influence on the solar energy receiving performance. In addition to the inclination angle, the inclination angle is the most effective parameter for the roof to obtain solar energy, which is higher than the roof aspect ratio and azimuth angle |
| W.M. Pabasara Upalakshi Wijeratne, Tharushi Imalka Samarasinghalage, Rebecca Jing Yang et al. [ | Angle of roof inclination | Power generation, BIPV life cycle cost, life cycle consumption, carbon reduction, net present value, payback period, and energy cost | Revit + Python 3.7.1 | The roof shape of low-rise buildings has a significant impact on the photovoltaic potential of buildings |
| Faridaddin Vahdatikhaki, Negar Salimzadeh, Amin Hammad. [ | Position and tilt angle of photovoltaic panel | Solar radiation reception, power generation, | Revit + Dynamo + Refinery | Photovoltaic selection can effectively improve power generation revenue and reduce life cycle costs |
| Faridaddin Vahdatikhaki, Meggie | Position, size, tilt angle of the photovoltaic panel, size, and direction of the panel above the photovoltaic panel | Solar radiation reception | Revit + Dynamo | The random forest algorithm is effective in predicting photovoltaic potential, and the position and occlusion of photovoltaic panels cannot be simplified |
| Oufan Zhao, Wei Zhang, Lingzhi Xie et al. [ | Double-sided photovoltaic module coverage | Temperature distribution, PMV, UDI | STARCCM + Energyplus + Radiance | Photovoltaic coverage and reflectivity of photovoltaic backplane can effectively regulate indoor thermal environment |
| Abel Groenewolt, Jack Bakker, Johannes Hofer et al. [ | Photovoltaic arrangement, flexible photovoltaic size | Solar radiation reception | Rh/Gh + C# + DIVA + LuxRender | The amount of solar radiation is almost linearly related to the area of the panel |
| er de Sousa Freitas, Joára Cronemberger, Raí Mariano Soares et al. [ | Position and tilt angle of photovoltaic panel | Solar radiation reception | Rh/Gh + Ladybug | BIPV requires evaluation at the design stage |
References
1. Mangherini, G.; Diolaiti, V.; Bernardoni, P.; Andreoli, A.; Vincenzi, D. Review of Façade Photovoltaic Solutions for Less Energy-Hungry Buildings. Energies; 2023; 16, 6901. [DOI: https://dx.doi.org/10.3390/en16196901]
2. Oliver, M.; Jackson, T. Energy and economic evaluation of building-integrated photovoltaics. Energy; 2001; 26, pp. 431-439. [DOI: https://dx.doi.org/10.1016/S0360-5442(01)00009-3]
3. Fry, B.A. Simulation of Grid-Tied Building Integrated Photovoltaic Systems. Master’s Thesis; University of Wisconsin-Madison: Madison, WI, USA, 1998.
4. Jager-Waldau, A.; Altenhoefer-Pflaum, G.; Bernsen, O.; Breyer, C.; Donoso, J.; Fechner, H.; Bournonville, K.H.; Hole, J.; Kaizuka, I.; Koschier, L. et al. Towards an Annual Terrawatt Photovoltaics Market—Comparison of the social acceptance in various IEA PVPS countries. Proceedings of the IEEE 50th Photovoltaic Specialists Conference (PVSC); San Juan, PR, USA, 11–16 June 2023.
5. Hamid, T.; Al-Jumeily, D.; Mustafina, J. Evaluation of the Dynamic Cybersecurity Risk Using the Entropy Weight Method. Technology for Smart Futures; Springer: Cham, Switzerland, 2018; pp. 271-287.
6. Smith, D.A.; Araujo-Pradere, E.A.; Minter, C.; Fuller-Rowell, T. A comprehensive evaluation of the errors inherent in the use of a two-dimensional shell for modeling the ionosphere. Radio Sci.; 2008; 43, pp. 93-115. [DOI: https://dx.doi.org/10.1029/2007RS003769]
7. Sagar, G.V.; Preeti, G.; Rajesh, T.; Kumar, S.A. Effect of various materials used for semitransparent solar panel fitted at roof top of a building. Mater. Today Proc.; 2022; 57, pp. 2067-2072.
8. Maghrabie, H.M.; Abdelkareem, M.A.; Al-Alami, A.H.; Ramadan, M.; Mushtaha, E.; Wilberforce, T.; Olabi, A.G. State-of-the-Art Technologies for Building-Integrated Photovoltaic Systems. Buildings; 2021; 11, 383. [DOI: https://dx.doi.org/10.3390/buildings11090383]
9. Schardt, J.; te Heesen, H. Performance of roof-top PV systems in selected European countries from 2012 to 2019. Sol. Energy; 2021; 217, pp. 235-244. [DOI: https://dx.doi.org/10.1016/j.solener.2021.02.001]
10. Weerasinghe, R.P.N.P.; Yang, R.J.; Wakefield, R.; Too, E.; Le, T.; Corkish, R.; Chen, S.; Wang, C. Economic viability of building integrated photovoltaics: A review of forty-five (45) non-domestic buildings in twelve (12) western countries. Renew. Sustain. Energy Rev.; 2021; 137, 110622. [DOI: https://dx.doi.org/10.1016/j.rser.2020.110622]
11. Abdalazeem, M.E.; Hassan, H.; Asawa, T.; Mahmoud, H. Review on integrated photovoltaic-green roof solutions on urban and energy-efficient buildings in hot climate. Sustain. Cities Soc.; 2022; 82, 103919.
12. Ma, H.Q.; Chen, H.Y.; Hou, J.L.; Ge, J.Y.; Lv, W. CiteSpace-based visualization analysis on mappings of alkali-activated cementi-tious materials in the field of engineering research. Case Stud. Constr. Mater.; 2020; 12, e00350.
13. Donthu, N.; Kumar, S.; Mukherjee, D.; Pandey, N.; Lim, W.M. How to conduct a bibliometric analysis: An overview and guidelines. J. Bus. Res.; 2021; 133, pp. 285-296. [DOI: https://dx.doi.org/10.1016/j.jbusres.2021.04.070]
14. Oteng, D.; Zuo, J.; Sharifi, E. A scientometric review of trends in solar photovoltaic waste management research. Sol. Energy; 2021; 224, pp. 545-562. [DOI: https://dx.doi.org/10.1016/j.solener.2021.06.036]
15. Li, Y.; Li, M.; Sang, P. A bibliometric review of studies on construction and demolition waste management by using CiteSpace. Energy Build.; 2022; 258, 111822. [DOI: https://dx.doi.org/10.1016/j.enbuild.2021.111822]
16. Hachem, C.; Athienitis, A.; Fazio, P. Energy performance enhancement in multistory residential buildings. Appl. Energy; 2014; 116, pp. 9-19. [DOI: https://dx.doi.org/10.1016/j.apenergy.2013.11.018]
17. Sigounis, A.-M.; Vallianos, C.; Athienitis, A. Model predictive control of air-based building integrated PV/T systems for optimal HVAC integration. Renew. Energy; 2023; 212, pp. 655-668. [DOI: https://dx.doi.org/10.1016/j.renene.2023.05.059]
18. Bambara, J.; Athienitis, A.K.; Eicker, U. Decarbonizing Local Mobility and Greenhouse Agriculture through Residential Building Energy Upgrades: A Case Study for Quebec. Energies; 2021; 14, 6820. [DOI: https://dx.doi.org/10.3390/en14206820]
19. Web of Science. Web of Science Core Collection Summary. Clarivate. Available online: https://webofscience.clarivate.cn/wos/woscc/summary/a58f878e-ad19-46a2-8a56-ef1040315928-0106ba3d13/relevance/1 (accessed on 5 September 2024).
20. Chow, T.T. A review on photovoltaic/thermal hybrid solar technology. Appl. Energy; 2009; 87, pp. 365-379. [DOI: https://dx.doi.org/10.1016/j.apenergy.2009.06.037]
21. Bódis, K.; Kougias, I.; Jäger-Waldau, A.; Taylor, N.; Szabó, S. A high-resolution geospatial assessment of the rooftop solar photovoltaic potential in the European Union. Renew. Sustain. Energy Rev.; 2019; 114, 109309. [DOI: https://dx.doi.org/10.1016/j.rser.2019.109309]
22. Zhong, T.; Zhang, Z.; Chen, M.; Zhang, K.; Zhou, Z.; Zhu, R.; Wang, Y.; Lu, G.; Yan, J. A city-scale estimation of rooftop solar photovoltaic potential based on deep learning. Appl. Energy; 2021; 298, 117132. [DOI: https://dx.doi.org/10.1016/j.apenergy.2021.117132]
23. Li, P.; Zhang, H.; Guo, Z.; Lyu, S.; Chen, J.; Li, W.; Song, X.; Shibasaki, R.; Yan, J. Understanding rooftop PV panel semantic segmentation of satellite and aerial images for better using machine learning. Adv. Appl. Energy; 2021; 4, 100057. [DOI: https://dx.doi.org/10.1016/j.adapen.2021.100057]
24. Chen, C. The Citespace Manual; College of Computing and Informatic: Lafayette, LA, USA, 2014; Volume 1, pp. 1-84.
25. Jhumka, H.; Yang, S.L.; Gorse, C.; Wilkinson, S.; Yang, R.; He, B.J.; Prasad, D.; Fiorito, F. Assessing heat transfer characteristics of building envelope deployed BIPV and resultant building energy consumption in a tropical climate. Energy Build.; 2023; 298, 24. [DOI: https://dx.doi.org/10.1016/j.enbuild.2023.113540]
26. Elghamry, R.; Hassan, H.; Hawwash, A.A. A parametric study on the impact of integrating solar cell panel at building envelope on its power, energy consumption, comfort conditions, and CO2 emissions. J. Clean. Prod.; 2020; 249, 119374. [DOI: https://dx.doi.org/10.1016/j.jclepro.2019.119374]
27. Klamka, J.; Wolf, A.; Ehrlich, L.G. Photovoltaic self-consumption after the support period: Will it pay off in a cross-sector perspective?. Renew. Energy; 2020; 147, pp. 2374-2386. [DOI: https://dx.doi.org/10.1016/j.renene.2019.10.033]
28. Zheng, Y.; Weng, Q. Modeling the Effect of Green Roof Systems and Photovoltaic Panels for Building Energy Savings to Mitigate Climate Change. Remote Sens.; 2020; 12, 2402. [DOI: https://dx.doi.org/10.3390/rs12152402]
29. Movahhed, Y.; Safari, A.; Motamedi, S.; Khoshkhoo, R.H. In Simultaneous use of PV system and green roof: A techno-economic study on power generation and energy consumption. Proceedings of the Applied Energy Symposium and Summit—Renewable Energy Integration with Mini/Microgrid (REM); Rhodes, Greece, 28–30 September 2019; pp. 478-483.
30. Esfahani, S.K.; Karrech, A.; Cameron, R.; Elchalakani, M.; Tenorio, R.; Jerez, F. Optimizing the solar energy capture of residential roof design in the southern hemisphere through Evolutionary Algorithm. Energy Built Environ.; 2021; 2, pp. 406-424. [DOI: https://dx.doi.org/10.1016/j.enbenv.2020.09.004]
31. Miao, Y.; Liu, P.; Fu, I.C.S.; Lei, Q.; Lau, S.S.Y.; Tao, Y. The Study of Architectural Geometry and Shape in the Energy Balance of Glazed Roofs. Buildings; 2022; 12, 1550. [DOI: https://dx.doi.org/10.3390/buildings12101550]
32. Youssef, A.M.A.; Zhai, Z.J.; Reffat, R.M. Genetic algorithm based optimization for photovoltaics integrated building envelope. Energy Build.; 2016; 127, pp. 627-636. [DOI: https://dx.doi.org/10.1016/j.enbuild.2016.06.018]
33. Keskas, I.; Bourbia, F.; Mohammadi, M.; Calautit, J. Geo-solar segmentation mechanism: An early design stage method for building solar morphing. Sol. Energy; 2022; 246, pp. 302-319. [DOI: https://dx.doi.org/10.1016/j.solener.2022.09.028]
34. Wijeratne, W.M.P.U.; Samarasinghalage, T.I.; Yang, R.J.; Wakefield, R. Multi-objective optimisation for building integrated photovoltaics (BIPV) roof projects in early design phase. Appl. Energy; 2022; 309, 118476. [DOI: https://dx.doi.org/10.1016/j.apenergy.2021.118476]
35. Vahdatikhaki, F.; Salimzadeh, N.; Hammad, A. Optimization of PV modules layout on high-rise building skins using a BIM-based generative design approach. Energy Build.; 2022; 258, 111787. [DOI: https://dx.doi.org/10.1016/j.enbuild.2021.111787]
36. Vahdatikhaki, F.; Barus, M.V.; Shen, Q.; Voordijk, H.; Hammad, A. Surrogate modelling of solar radiation potential for the design of PV module layout on entire façade of tall buildings. Energy Build.; 2023; 286, 112958. [DOI: https://dx.doi.org/10.1016/j.enbuild.2023.112958]
37. Zhao, O.; Zhang, W.; Xie, L.; Wang, W.; Chen, M.; Li, Z.; Li, J.; Wu, X.; Zeng, X.; Du, S. Investigation of indoor environment and thermal comfort of building installed with bifacial PV modules. Sustain. Cities Soc.; 2022; 76, 103463. [DOI: https://dx.doi.org/10.1016/j.scs.2021.103463]
38. Groenewolt, A.; Bakker, J.; Hofer, J.; Nagy, Z.; Schlüter, A. Methods for modelling and analysis of bendable photovoltaic modules on irregularly curved surfaces. Int. J. Energy Environ. Eng.; 2016; 7, pp. 261-271. [DOI: https://dx.doi.org/10.1007/s40095-016-0215-3]
39. Freitas, J.d.S.; Cronemberger, J.; Soares, R.M.; Amorim, C.N.D. Modeling and assessing BIPV envelopes using parametric Rhinoceros plugins Grasshopper and Ladybug. Renew. Energy; 2020; 160, pp. 1468-1479. [DOI: https://dx.doi.org/10.1016/j.renene.2020.05.137]
40. Bevilacqua, P.; Morabito, A.; Bruno, R.; Ferraro, V.; Arcuri, N. Seasonal performances of photovoltaic cooling systems in different weather conditions. J. Clean. Prod.; 2020; 272, 12. [DOI: https://dx.doi.org/10.1016/j.jclepro.2020.122459]
41. Madessa, H.B. Performance analysis of roof-mounted photovoltaic systems—The case of a Norwegian residential building. Energy Procedia; 2015; 83, pp. 474-483. [DOI: https://dx.doi.org/10.1016/j.egypro.2015.12.167]
42. Martinez-Rubio, A.; Sanz-Adan, F.; Santamaria, J. Optimal design of photovoltaic energy collectors with mutual shading for pre-existing building roofs. Renew. Energy; 2015; 78, pp. 666-678. [DOI: https://dx.doi.org/10.1016/j.renene.2015.01.043]
43. Zhou, X.; Huang, Z.; Scheuer, B.; Lu, W.; Zhou, G.; Liu, Y. High-resolution spatial assessment of the zero energy potential of buildings with photovoltaic systems at the city level. Sustain. Cities Soc.; 2023; 93, 104526. [DOI: https://dx.doi.org/10.1016/j.scs.2023.104526]
44. Roy, A.; Verma, V.; Gampa, S.R.; Bansal, R.C. Planning of distribution system considering residential roof top photovoltaic systems, distributed generations and shunt capacitors using gravitational search algorithm. Comput. Electr. Eng.; 2023; 111, 108960. [DOI: https://dx.doi.org/10.1016/j.compeleceng.2023.108960]
45. Tossa, A.K.; Soro, Y.M.; Thiaw, L.; Azoumah, Y.; Sicot, L.; Yamegueu, D.; Lishou, C.; Coulibaly, Y.; Razongles, G. Energy performance of different silicon photovoltaic technologies under hot and harsh climate. Energy; 2016; 103, pp. 261-270. [DOI: https://dx.doi.org/10.1016/j.energy.2016.02.133]
46. Bergamasco, L.; Asinari, P. Scalable methodology for the photovoltaic solar energy potential assessment based on available roof surface area: Application to Piedmont Region (Italy). Sol. Energy; 2011; 85, pp. 1041-1055. [DOI: https://dx.doi.org/10.1016/j.solener.2011.02.022]
47. Jo, J.H.; Otanicar, T.P. A hierarchical methodology for the mesoscale assessment of building integrated roof solar energy systems. Renew. Energy; 2011; 36, pp. 2992-3000. [DOI: https://dx.doi.org/10.1016/j.renene.2011.03.038]
48. Hachem, C.; Fazio, P.; Athienitis, A. Solar optimized residential neighborhoods: Evaluation and design methodology. Sol. Energy; 2013; 95, pp. 42-64. [DOI: https://dx.doi.org/10.1016/j.solener.2013.06.002]
49. Yu, M.; Halog, A. Solar Photovoltaic Development in Australia—A Life Cycle Sustainability Assessment Study. Sustainability; 2015; 7, pp. 1213-1247. [DOI: https://dx.doi.org/10.3390/su7021213]
50. Solano, J.; Olivieri, L.; Caamano, E. HVAC systems using PV technology: Economic feasibility analysis in commercial buildings of Ecuador. IEEE Lat. Am. Trans.; 2016; 14, pp. 767-772. [DOI: https://dx.doi.org/10.1109/TLA.2016.7437221]
51. Mutani, G.; Todeschi, V. Optimization of Costs and Self-Sufficiency for Roof Integrated Photovoltaic Technologies on Residential Buildings. Energies; 2021; 14, 4018. [DOI: https://dx.doi.org/10.3390/en14134018]
52. Cristino, T.M.; Neto, A.F.; Wurtz, F.; Delinchant, B. The Evolution of Knowledge and Trends within the Building Energy Efficiency Field of Knowledge. Energies; 2022; 15, 691. [DOI: https://dx.doi.org/10.3390/en15030691]
53. Vats, K.; Tomar, V.; Tiwari, G.N. Effect of packing factor on the performance of a building integrated semitransparent photovoltaic thermal (BISPVT) system with air duct. Energy Build.; 2012; 53, pp. 159-165. [DOI: https://dx.doi.org/10.1016/j.enbuild.2012.07.004]
54. Singh, S.; Agrawal, S. Efficiency maximization and performance evaluation of hybrid dual channel semitransparent photovoltaic thermal module using fuzzyfied genetic algorithm. Energy Convers. Manag.; 2016; 122, pp. 449-461. [DOI: https://dx.doi.org/10.1016/j.enconman.2016.06.010]
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