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SPAN: An open-source plugin for photovoltaic potential estimation of individual roof segments using point cloud data
Abstract
Decentralized solar PhotoVoltaic (PV) is one of the most promising energy sources for cities and individuals pursuing energy self-sufficiency. Especially, the already available rooftop surfaces are a major contributor to push for rooftop mounted PV systems. However, accurate PV potential estimation of individual buildings is still a challenging task since many parameters must be considered such as meteorological factors, panel technology, geographical location, available roof surface area, surface azimuth and tilt angle. In this study, we created an efficient approach that can be used for roof surface's PV potential estimation based on point cloud data and capable of processing various scales from single building to city scale. In the proposed approach, each roof surface's features were utilized for PV potential estimation by employing the PVGIS database. PV potential estimation was carried out on daily, monthly, and annual periods to provide a better estimation. Also, we developed a flexible and easy to use open-source plugin based on the QGIS software for rooftop mounted PV potential estimation capable of estimating every roof surface's PV potential. The method was tested on 80 buildings selected from ROOFN3D dataset. The proposed approach achieved an overall accuracy of 84% and an F1 score of 0.92.
... Among the non-commercial instruments relatively new tool that allows solar potential analysis is the Solar Potential Analyzer, introduced by Ozdemir et al., in 2023 [62]. This tool is available as an open-source plug-in to QGIS software and, using point cloud data, allows to estimate rooftop PV potential for different spatial scales (from a building scale detailing the individual roof areas to an entire city) and different time frames. ...
... Globally, it is clear that some cities are of particularly high interest, as indicated by the number of studies that have been published. The largest number, nine papers, was for Wuhan, China [75][76][77][78][79][80][81][82][83] followed by Geneva, Switzerland, with eight papers [84][85][86][87][88][89][90][91] onwards Lisbon, Portugal [92][93][94][95][96][97][98] and New York, USA [62,[99][100][101][102][103][104]: seven papers each. 117 cities, in contrast, were the subject of singular studies: Vasteras, Sweden [105]; Plovdiv, Bulgaria [106]; Riyadh, Saudi Arabia [107] and others. ...
Current trends in the global energy market focus on gradually increasing the share of renewable energy sources in the overall energy mix. In recent years, there has been growing interest within the scientific community in assessing the suitability of cities for implementing solar energy solutions. This work discusses various research directions on the solar potential of urban areas, with a particular focus on the role of Geographic Information System (GIS) tools in support of spatial analyses. The main aim of the study was to update the current state of the research based on the analysis of previous works. An attempt was made to assess the role of GIS in research on the solar potential of cities in the context of the overall investigation process. A total of 201 case studies published between 1999 and 2024 (year to date) were analysed, among which articles from 2019–24 were examined in detail. The analysis revealed a wide variation in the approaches regarding the spatial scale of studies and the sources of key data, such as shading and solar radiation. It was shown that one of the key challenges in current analyses is the lack of universality of the methodologies used, leading to divergent, and sometimes challenging to compare final results. In the research aspect, a global urban solar potential was estimated for cities with more than 1 million inhabitants, which amounted to 33.7 PW h annually.
... The time taken for the pulse to travel allows the estimation of the distance to the surface, resulting in a 3D point cloud [3]. Over the years, various methods have been developed that utilize point cloud data derived from LiDAR or an unmanned aerial vehicle (UAV) to assess the solar or PV potential of 3D surfaces using solar irradiance simulation or deep learning approaches [4][5][6][7]. Moreover, newer methods leverage 3D surface information for the optimal placement of hypothetical PV systems [8,9]. ...
Maximizing the energy output of photovoltaic (PV) systems is becoming increasingly important. Consequently, numerous approaches have been developed over the past few years that utilize remote sensing data to predict or map solar potential. However, they primarily address hypothetical scenarios, and few focus on improving existing installations. This paper presents a novel method for optimizing the tilt angles of existing PV arrays by integrating Very High Resolution (VHR) satellite imagery and airborne Light Detection and Ranging (LiDAR) data. At first, semantic segmentation of VHR imagery using a deep learning model is performed in order to detect PV modules. The segmentation is refined using a Fine Optimization Module (FOM). LiDAR data are used to construct a 2.5D grid to estimate the modules’ tilt (inclination) and aspect (orientation) angles. The modules are grouped into arrays, and tilt angles are optimized using a Simulated Annealing (SA) algorithm, which maximizes simulated solar irradiance while accounting for shadowing, direct, and anisotropic diffuse irradiances. The method was validated using PV systems in Maribor, Slovenia, achieving a 0.952 F1-score for module detection (using FT-UnetFormer with SwinTransformer backbone) and an estimated electricity production error of below 6.7%. Optimization results showed potential energy gains of up to 4.9%.
... A good application of this can be cited as the Javanmardi et al., [9] study, which uses LIDAR data to identify rooftops with high solar energy potential and then perform EVCS site selection in areas containing suitable rooftops. Similar to this research, suitable rooftops, parking areas, and empty areas with high solar radiation potential can be obtained with preliminary processes [57] via high spatial resolution (under 1 m) LiDAR or photogrammetric-based 3D sources and then optimum site selection for EVCS can be carried out. Optimum site selection for EVCS has generally been carried out for city centres. ...
Transport electrification and renewable energy integration are essential for transitioning to a zero-carbon society. Electric vehicles (EVs) are seen as a solution to cut transport emissions, but the existing charging station network is insufficient, and the electricity is often largely supplied by fossil fuels. Therefore, a key question is how to design optimally located charging stations supported by renewable energy. Geographical information system (GIS) and multi-criteria decision-making (MCDM) have proven to be powerful methods for site selection as they help manage geographical data, local characteristics and stakeholder preferences. These approaches have been successfully applied for solar or EV charging station site selection, but their use for solar-energy-assisted electric vehicle charging stations (SE-EVCS) is limited. As SE-EVCSs are of quickly increasing importance, this study developed a generic approach using GIS and MCDM to identify optimal locations for SE-EVCSs. A systematic literature review was performed to identify the relevant site selection criteria and MCDMs used so far. The proposed approach considers the most relevant criteria and their application in practice, analysing different use cases for city centres and urban areas. These criteria consist of solar irradiance, accessibility (roads and amenities), land availability/type, existing charging network, population densities, economic KPIs and technical energy factors, but their importance depends on the local context. The findings are expected to help city planners , plot owners, private charging operators and energy companies to select optimal locations for SE-EVCSs, and help researchers and practitioners design methods and criteria for tools supporting these site selection processes.
... This would allow for more complex studies in terms of longer time periods and even larger urban areas. In addition, these studies can also be performed from point cloud data [48,49], which should enhance the precision but extend the computational time considerably. The results could be directly related to the power consumption of the urban area [50,51], which should improve the general estimation of the PV impact in the urban area; however, it is questionable how to properly estimate the selected area's consumption, and if the area is too wide, it could even be impossible to include. ...
Highlights
This study highlights the needs and means of estimating open-source photovoltaic power output in future urban areas. The proposed methodology includes the building shadow analysis as the first of the key inputs. The second key input is the statistical analysis based on the PVGIS tool, which consists of 16-year data processing and the evaluation of the photovoltaic installation aspect, slope, and, therefore, general efficiency of such systems in the specified sample area in Bratislava, Slovakia. The results from the proposed methodology are presented, and future research in open science in this area is discussed.
What are the main findings? The photovoltaic power estimation in predominantly urban areas without building shadow analysis is not a good approximation, especially in winter, when the building shadows cover the largest areas. This aspect is often neglected in studies with very large sample areas.
Many current methodologies for photovoltaic power output estimation are of a proprietary nature, resulting in limited use in academic, public, or other non-commercial use.
What is the implication of the main finding? Urban areas need to be examined with proper tools and algorithms, with the most precise knowledge of meteorological, geospatial, and geographical data.
There is a strong need for a completely open-source methodology for various aspects of urban planning, including smart city concepts, energy efficiency, and low-carbon footprint engineering in general.
Abstract
This paper aims to effectively estimate urban-scale rooftop photovoltaic potential using strictly open-source software and publicly available GIS data. This approach is often neglected; however, its importance is significant regarding technology transfer and general commercial or academic ease of use. A complete methodology is introduced, including the building shadow analysis. Although many papers are published in similar areas, very few reveal the specific steps and functions in the software used, or the computational core of some part of the estimation is a “black box” of a commercial service. Detailed irradiation parameters can be obtained using the proposed methodologies, and the maximum photovoltaic (PV) power output in the area can be estimated. The great advantage of this model is its scalability and the easy way of modifying every computational parameter. The results and limitations of the proposed methodology are discussed, and further development is suggested. The presented model is based on a sample location in Bratislava, Slovakia, with an area of circa 2.5 km².
... This would allow for more complex studies in terms of longer time periods and even larger urban areas. In addition, these studies can also be performed from point cloud data (that is, the tool from [15]), and the results could be directly related to the power consumption of the urban area or other known factors such as in [16]- [18] or the modeling of photovoltaic systems with more precision such as in [19], [20]. ...
This paper is designed to offer a straightforward yet efficient approximation of the potential of photovoltaic systems on urban rooftops, utilizing only open-source software and publicly accessible data. The proposed approach is demonstrated in a representative location in Bratislava, Slovakia. Despite the numerous publications in related fields, only a handful disclose the precise procedures and functions employed in the software, or the computational heart of a portion of the estimation is a paid feature. By applying our approach, it is feasible to acquire fundamental irradiation parameters and photovoltaic production in the region. A key strength of this model is its adaptability and the simplicity of altering any computational parameter. The extension of the team's previous efforts in this area is mostly in the building shadow implementation into the process. The results and constraints of the suggested approach are examined, and recommendations for future improvements are made.
... For this reason, many authors have focused their efforts on the analysis of solar potential in cities around the world [8][9][10][11] through the development of novel methodologies [12,13] and tools to carry out accurate analyses [14,15]. Most solar potential studies make use of either web or desktop tools based on solar radiation databases. ...
The presence of shadows is one of the main disadvantages of photovoltaic solar panel installations in urban environments. This article analyses their effect on solar installations performed on urban elements where the use of solar energy can be considered novel: bus shelters. For this purpose, the PVGIS tool is used in combination with a new methodology for the extraction of the shadow horizon profile from LiDAR point clouds. The results show a 29.90% and 37% reduction of calculated solar radiation using horizon profiles derived from LiDAR point clouds of 0.5 pts/m2 and 1 pt/m2, respectively, versus no horizon profile. By taking shading into account, thanks to this study it is possible to make a more realistic prediction of the use of the electrical energy generated by the sun to cover urban energy consumption in bus shelters. Results show that the energy surplus produced overall allows these elements to be converted into charging points for light electric vehicles, allowing up to 35 units per day/bus shelter to be charged. The use of bus shelters as a place to generate clean energy through solar energy and charge light vehicles makes it possible to move towards sustainable and environmentally friendly cities.
Locating rooftop solar photovoltaic (PV) installations in densely populated urban areas is a daunting task because the shadows of surrounding structures vary and depend on the location of the PV installation. Real time shadow dynamics are not properly accounted for at the traditional PV placement, hence they can result in a suboptimal energy production and a decrease of the system efficiency. As a way to address this issue, we argue in this research to develop a system which takes advantage of real time shadow analysis for the placement of solar panels on rooftops in order to optimize with regard to maximal energy yield. Based on this, the proposed system uses Mask R-CNN for accurate roof area segmentation and mapping areas that are shadowed by the environmental factors or close by buildings on the rooftops. This approach used Google Earth Pro images to train the shadow-based height estimation model by minimizing the global error across all sample buildings. Author selected different urban and suburban areas that have different geographical conditions from around the world to train the model. A typical urban area located in the city center of Shanghai, China with an area of around 90 km2 was selected to validate the proposed method. In total 15,966 buildings were successfully extracted and the results indicated that the estimated building heights have high accuracy with an absolute mean error of 4.08 m. By integrating this shadow data with a solar energy simulation tool (this tool is our solar energy simulation tool), we model and forecast the performance of the panel under different conditions. Finally, optimization algorithms are used to determine the location at which the panels will be placed such that the shadow impacts will be minimized and the maximum energy will be produced at any moment in the day and in all seasons. This approach promises major improvement of the efficiency in urban rooftop solar PV installations.
This study presents two methods for rapidly and effectively determining the photovoltaic (PV) potential of building roofs in urban areas using aerial photographs and point cloud data. In the first method, the Segment Anything Model (SAM) and Contrastive Language Image Pre-Training (CLIP) models are used to detect roof surfaces and obstacles from aerial photographs. In the second method, the Random Sample Consensus (RANSAC) and Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithms are employed to identify roof surfaces from Light Detection and Ranging (LiDAR) point clouds. Through the first proposed method, the performance of current deep learning approaches in 2.5D PV potential analysis is investigated, while the second approach examines the performance of 3D PV potential analysis compared to the 2D approach. In PV potential analysis, the Photovoltaic Geographical Information System (PVGIS) Application Programming Interface (API) was utilized. The analysis is conducted based on roof parameters obtained through both proposed methods. In building detection, the first approach achieved an Intersection over Union (IoU) score of 94.29%, whereas the second approach attained an IoU score of 91.23%.
With the growing need for sustainable urban energy solutions, rooftop solar photovoltaic (PV) systems can play a pivotal role. However, the effective integration of solar energy into urban landscapes faces challenges in spatial planning, resource optimisation, and stakeholder engagement. This literature review addresses the existing gaps by systematically evaluating Geographic Information System (GIS)-based methodologies for PV potential assessment and rooftop solar energy planning. It aims to provide the knowledge necessary for supporting efficient and sustainable solar energy adoption in cities. This chapter involved a systematic review and synthesised the diverse body of knowledge regarding city-scale rooftop solar energy planning. The goal was to identify the technical challenges, highlight the trends, and reveal opportunities for future research initiatives with respect to the evolving needs of sustainable energy planning. Employing a dual-method systematic approach, this review utilised both quantitative and qualitative analysis techniques. Through the quantitative survey, a generic corpus (805 studies) of Scientometrics using CiteSpace was built, ensuring a broad representation of the existing literature. In parallel, a qualitative survey, evaluating literature associated with solar rooftop PV assessment using GIS, modelling techniques, planning implications, and recommendations, led to a critical corpus (76 studies) using the Preferred Reporting Items for Systematic Reviews (PRISMA) method. Spanning the past two decades and drawing from databases of Scopus and Web of Science (WoS), the synthesised corpora formed the foundation for a comprehensive and informed evaluation. Additionally, analytical assessment criteria were developed to provide insights into future research agendas and framework. The literature synthesis unveiled the challenges within urban energy planning, covering issues of data accuracy, model validation, and imperative stakeholder engagement. Emerging trends in GIS models, especially the integration of machine learning and three-dimensional (3D) mapping, emphasise the dynamic nature of the urban environment. Furthermore, applications of GIS in real-world urban contexts provide spatially explicit insights for future implementations. Finally, this literature review proposed a research agenda for advancing GIS-based rooftop solar energy planning, with a particular focus on developing a spatial digital twin environment incorporating three-dimensional (3D) models, real-time data integration, and decision support systems tailored for city-scale applications. Spatial digital twin technologies enable modelling of different energy production scenarios. Such an integrated approach will underpin effective strategies to accommodate the complexities within urban environments, thus providing a roadmap for researchers, urban planners, and policymakers to collaboratively develop sustainable solutions for rooftop solar energy adoption at a city scale.
Industry 4.0 has ushered in a new era of technological advancements, particularly in smart production, using technologies like the Internet of Things, big data analytics, and artificial intelligence. While much attention has been focused on the technological and economic aspects of this transformation, the concept of social sustainability within smart production remains underexplored. This paper explores the intersection of technology and social sustainability in the context of smart production in China. Machine learning, especially in hybrid models, is examined as a tool to integrate social sustainability into smart production. These algorithms can analyze vast datasets, predict social disruptions, inform policymaking, and tailor technological solutions. The paper presents a comprehensive analysis of the performance of various machine learning models in forecasting solar PV energy production, with a focus on different photovoltaic technologies and emission scenarios. The results highlight the robustness of certain photovoltaic technologies, such as p-Si and m-Si, in the face of climate variability. The study introduces the MLP-CARIMA-GPM model as a benchmark in predicting solar PV energy output, challenging the traditional belief that composite models always offer superior results. Theoretical and policy implications are discussed, emphasizing the importance of aligning solar PV energy production with Sustainable Development Goals. The research underscores the pivotal role of sophisticated, hybrid machine learning models in ensuring sustainable energy production and offers valuable insights for policymakers, industry leaders, and stakeholders navigating the challenges of energy demands, climate change, and technological advancements. This study serves as a roadmap for achieving sustainable smart production, where technology and sustainability coalesce to illuminate possibilities for the future.
UAVs provide critical geospatial intelligence for a wide variety of industries and applications. One of the areas with continued UAV use is the renewable energy industry, specifically commercial energy. The expansion of consumer-level solar energy installations has the potential to reduce household energy use, offset carbon footprints, ensure a reliable energy source, and, perhaps most importantly, offset electricity costs. However, not all households can take advantage of this technology due to various limitations, leading to energy inequities across urban areas. While many studies have explored how these inequities have materialized from a purely socioeconomic standpoint, we explore how these inequities develop due to the structural limitations of the household. In this chapter, we explore the impact of structural limitations across neighborhoods of varying socioeconomic status on rooftop solar energy output by leveraging high-resolution UAV data. We demonstrate how structural differences across neighborhoods limit the economic viability of rooftop solar when explicitly considering the cost of electricity, solar energy output, and solar system costs.
Accurate evaluation of rooftop solar potential is increasingly important in sustainable urban development. However, accurately evaluating the solar photovoltaic (PV) potential of rooftops in urban areas is a challenge due to the diversity of urban rooftop outlines and rooftop obstacles. This study proposes a generic framework for evaluating the potential of urban rooftop solar PV that integrates deep learning and geographic information systems (GIS). GIS is used to extract information about land use types and classify buildings based on land use types. A deep learning-based method for calculating the rooftop available area of multi-type buildings is proposed. To validate the proposed methodology, an area in Wuhan containing a variety of building features was used, combined with the utilization of the available rooftop area to estimate the solar photovoltaic potential, and solar irradiance measurement experiments were set up for validation. The results show that the classification of rooftops by building type can effectively improve the accuracy of rooftop availability identification, and the classification process improved the identification of rooftops by 12.68% and obstacles by 12.42% overall. The annual solar potential of urban rooftops in Hanyang District is 8864.37 GWh/year.
Rooftop solar photovoltaics (PV) play increasing role in the global sustainable energy transition. This raises the challenge of accurate and high-resolution geospatial assessment of PV technical potential in policymaking and power system planning. To address the challenge, we propose a general framework that combines multi-resource satellite images and deep learning models to provide estimates of rooftop PV power generation. We apply deep learning based inversion model to estimate hourly solar radiation based on geostationary satellite images, and automatic segmentation model to extract building footprint from high-resolution satellite images. The framework enables precise survey of available rooftop resources and detailed simulation of power generation process on an hourly basis with a spatial resolution of 100 m. The case study in Jiangsu Province demonstrates that the framework is applicable for large areas and scalable between precise locations and arbitrary regions across multiple temporal scales. Our estimates show that rooftop resources across the province have a potential installed capacity of 245.17 GW, equivalent to an annual power generation of 290.66 TWh. This highlights the huge space for carbon emissions reduction through developing rooftop PVs.
A comprehensive understanding of failure modes of solar photovoltaic (PV) modules is key to extending their operational lifetime in the field. In this review, first, specific failure modes associated with mature PV technologies, such as crystalline silicon (c‐Si), copper indium gallium selenide (CIGS) and cadmium telluride (CdTe), are framed by sources of specific failure modes, their development from the early‐developmental stages onwards and their impact upon long term performance of PV modules. These failure modes are sorted by both PV technology and location of occurrence in PV modules, such as substrate, encapsulant, front and rear electrode, absorber and interlayers. The second part of the review is focused on emerging PV technologies, such as perovskites solar cells, dye sensitised and organic PVs, where due to their low to medium technology readiness levels, specific long‐term degradation mechanisms have not fully emerged, and most mechanisms are only partially understood. However, an in‐depth summary of the known stability challenges associated with each emerging PV technology is presented. Finally, in this paper, lessons learned from mature PV technologies are reviewed, and considerations are given in to how these might be applied to the further development of emerging technologies. Namely, any emerging PV technology must eventually pass industry‐standard qualification tests, while warranties for the lifetime of modern c‐Si‐based modules might be extended beyond the existing warranted life of 25 years.
The revolution towards Industry 4.0 in the AECO Industry has taken Building Information Modelling (BIM) as one of its central points. BIM abilities for automatization, interoperability and sustainability play a key role in this change. In this paper, a literature review about BIM adoption for the structural project is presented. The aim of the presented review is to clearly establish the current state of knowledge of the implementation of the BIM methodology in the field of structural analysis. Papers related to these two topics simultaneously, BIM and structure analysis, during the last 10 years have been selected. The literature has been analysed from two different approaches. First, bibliometric analysis has been performed, studying the production on the topic. Secondly, 81 representative papers have been selected and analysed, establishing thematic areas via cluster analysis. The articles have also been classified upon several categorizations based on the structural life cycle and their aim. Finally, a SWOT analysis is performed from this data to create a complete framework that shows the state of the integration of the structural project in BIM environments and possible future developments and risks. This set of studies shows a tendency towards design tools and new buildings. While automatization and computer-aided design have been a trend in the research for several years, a research gap on the structural analysis via BIM for existing and heritage buildings has been pointed out, showing its ability to improve the analysis of existing buildings and its maintenance.
There have been significant advances in the shift from fossil-based energy systems to renewable energies in recent years. Decentralized solar photovoltaic (PV) is one of the most promising energy sources because of the availability of rooftop areas, ease of installation, and reduced cost of PV panels. The current modeling method using remote sensing data based on a geographic information system (GIS) is objective and accurate, but the analysis processes are complicated and time-consuming. In this study, we developed a method to estimate the rooftop solar power potential over a wide area using globally available solar radiation data from Solargis combined with a building polygon. Our study also utilized light detection and ranging (LiDAR) data and AW3D to estimate rooftop solar power potential in western Aichi, Japan, and the solar radiation was calculated using GIS. The estimation using LiDAR data took into account the slope and azimuth of rooftops. A regression analysis of the estimated solar power potential for each roof between the three methods was conducted, and the conversion factor 0.837 was obtained to improve the accuracy of the results from the Solargis data. The annual rooftop solar power potential of 3,351,960 buildings in Aichi Prefecture under Scenario A, B, and C was 6.92 × 107, 3.58 × 107, and 1.27 × 107 MWh/year, estimated using Solargis data after the adjustment. The estimated solar power potential under Scenario A could satisfy the total residential power demand in Aichi, revealing the crucial role of rooftop solar power in alleviating the energy crisis. This approach of combining Solargis data with building polygons can be easily applied in other parts of the world. These findings can provide useful information for policymakers and contribute to local planning for cleaner energy.
Crystalline silicon (c-Si) photovoltaics has long been considered energy intensive and costly. Over the past decades, spectacular improvements along the manufacturing chain have made c-Si a low-cost source of electricity that can no longer be ignored. Over 125 GW of c-Si modules have been installed in 2020, 95% of the overall photovoltaic (PV) market, and over 700 GW has been cumulatively installed. There are some strong indications that c-Si photovoltaics could become the most important world electricity source by 2040–2050. In this Review, we survey the key changes related to materials and industrial processing of silicon PV components. At the wafer level, a strong reduction in polysilicon cost and the general implementation of diamond wire sawing has reduced the cost of monocrystalline wafers. In parallel, the concentration of impurities and electronic defects in the various types of wafers has been reduced, allowing for high efficiency in industrial devices. Improved cleanliness in production lines, increased tool automation and improved production technology and cell architectures all helped to increase the efficiency of mainstream modules. Efficiency gains at the cell level were accompanied by an increase in wafer size and by the introduction of advanced assembly techniques. These improvements have allowed a reduction of cell-to-module efficiency losses and will accelerate the yearly efficiency gain of mainstream modules. To conclude, we discuss what it will take for other PV technologies to compete with silicon on the mass market. Crystalline silicon solar cells are today’s main photovoltaic technology, enabling the production of electricity with minimal carbon emissions and at an unprecedented low cost. This Review discusses the recent evolution of this technology, the present status of research and industrial development, and the near-future perspectives.
The accurate prediction of the solar energy that can be generated using the rooftops of buildings is an essential tool for many researchers, decision makers, and investors for creating sustainable cities and societies. This study is focused on the development of an automated method to extract the useable areas of rooftops and optimize the solar PV panel layout based on the given electricity loading of a building. In this context, the authors of this article developed two crucial methods. First, a special pixel-based rooftop recognition methodology was developed to analyze detailed and complex rooftop types while avoiding the challenges associated with the nature of the particular building rooftops. Second, a multi-objective enveloped min–max optimization algorithm was developed to maximize solar energy generation and minimize energy cost in terms of payback based on the marginal price signals. This optimization algorithm facilitates the optimal integration of three controlled variables—tilt angle, azimuth angle, and inter-row spacing—under a non-linear optimization space. The performance of proposed algorithms is demonstrated using three campus buildings at the University of Alberta, Edmonton, Alberta, Canada as case studies. It is shown that the proposed algorithms can be used to optimize PV panel distribution while effectively maintaining system constraints.
Achieving sustainability through solar energy has become an increasingly accessible option in the United States (US). Nationwide, universities are at the forefront of energy efficiency and renewable generation goals. The aim of this study was to determine the suitability for the installation of photovoltaic (PV) systems based on their solar potential and corresponding electricity generation potential on a southern US university campus. Using Auburn University located in the southern US as a case study, freely available geospatial data were utilized, and geographic information system (GIS) approaches were applied to characterize solar potential across the 1875-acre campus. Airborne light detection and ranging (lidar) point clouds were processed to extract a digital surface model (DSM), from which slope and aspect were derived. The area and total solar radiation of campus buildings were calculated, and suitable buildings were then determined based on slope, aspect, and total solar radiation. Results highlighted that of 443 buildings, 323 were fit for solar arrays, and these selected rooftops can produce 27,068,555 kWh annually. This study demonstrated that Auburn University could benefit from rooftop solar arrays, and the proposed arrays would account for approximately 21.07% of annual electricity requirement by buildings, equivalent to 14.43% of total campus electricity for all operations. Given increasing open and free access to high-resolution lidar data across the US, methods from this study are adaptable to institutions nationwide, for the development of a comprehensive assessment of solar potential, toward meeting campus energy goals.
The evaluation of rooftop solar energy potential in cities has a fundamental role in the development and utilization of solar energy. The irradiance-based approach of evaluation is a repetitive calculation process combined with the complex geometry of urban forms. This research paper proposes a framework that utilizes urban streetscapes to quickly estimate the urban solar energy potential using a simplified solar energy yield model based on two indicators: sky view factor (SVF) and sun coverage factor (SCF). First, 327 Google Street View images were converted into fisheye images, which were semantically segmented based on the deep-learning Full Convolutional Network (FCN). Then, the SVF and SCF were calculated based on the method of pixel statistics. Finally, a model with the simulated solar energy yield was established. Furthermore, a validation study was conducted using rooftop solar production data, which showed that the maximum estimation error of the proposed model was less than 8% and the average relative error was 6.6%. Generally, the proposed model reduced the required computation time while preserving a satisfactory degree of accuracy.
Green energy is increasingly used due to the lack of traditional resources and the increase in environmental pollution, which badly affects our planet in all aspects of life including air, plant life, seas, oceans, etc. In this context, buildings’ rooftops extraction approach for photovoltaic (PV) potential estimation is presented into two main phases. First, rooftops detection from satellite images using image pre-processing techniques and a machine learning algorithm. The pre-processing steps include gamma correction, shadow, vegetation masking, kmeans, and connected components. Support Vector Machine (SVM) algorithm is then applied to extract rooftops. Second, using two GIS-based methods, PVGIS and Solar Analyst Tool in ArcGIS, for PV estimation. Satellite images for a part of Madinaty city in Egypt were used to evaluate our approach. The accuracy assessment of SVM expressed by the precision and recall were 95.7% and 90%, respectively. The identifiable rooftops in the image were 112 rooftops with a total area of 26,131 m2. The annual PV potential area was estimated to be 9.3 and 8.7 MWh/year using PVGIS and Solar Analyst Tool, respectively. PVGIS was more accurate as it uses more recent data from solar databases that exist in Africa. On the other hand, Solar Analyst Tool was less accurate as it depends on a digital elevation model with a resolution of 30 m. According to our calculations, the electric energy and the amount of CO2 emission were compensated by an annual average value of 48% for using solar panels instead of the traditional sources of energy.
This data-rich report summarises global and regional trends in renewable energy development in cities.
This study presents a method to estimate the solar energy potential based on 3D data taken from unmanned aerial devices. The solar energy potential on the roof of a building was estimated before the placement of solar panels using photogrammetric data analyzed in a geographic information system, and the predictions were compared with the data recorded after installation. The areas of the roofs were chosen using digital surface models and the hemispherical viewshed algorithm, considering how the solar radiation on the roof surface would be affected by the orientation of the surface with respect to the sun, the shade of trees, surrounding objects, topography, and the atmospheric conditions. The results show that the efficiency percentages of the panels and the data modeled by the proposed method from surface models are very similar to the theoretical efficiency of the panels. Radiation potential can be estimated from photogrammetric data and a 3D model in great detail and at low cost. This method allows the estimation of solar potential as well as the optimization of the location and orientation of solar panels.
This study presents a method to estimate the solar energy potential based on 3D data taken from unmanned aerial devices. The solar energy potential on the roof of a building was estimated before the placement of solar panels using photogrammetric data analyzed in a geographic information system, and the predictions were compared with the data recorded after installation. The areas of the roofs were chosen using digital surface models and the hemispherical viewshed algorithm, considering how the solar radiation on the roof surface would be affected by the orientation of the surface with respect to the sun, the shade of trees, surrounding objects, topography, and the atmospheric conditions. The results show that the efficiency percentages of the panels and the data modeled by the proposed method from surface models are very similar to the theoretical efficiency of the panels. Radiation potential can be estimated from photogrammetric data and a 3D model in great detail and at low cost. This method allows the estimation of solar potential as well as the optimization of the location and orientation of solar panels.
A quick-scan yield prediction method has been developed to assess rooftop photovoltaic (PV) potential. The method has three main parts. For each roof, first (i) virtual 3D roof segments were reconstructed using aerial imagery, then, (ii) PV modules were automatically fitted onto roof segments using a fitting algorithm and finally, (iii) expected annual yield was calculated. For each roof, the annual yield was calculated by three different quick yield calculation approaches. Two approaches are commercial software packages of Solar Monkey (SM) and Photovoltaic Geographical Information System (PVGIS) whereas the other one is the simplified skyline-based approach developed in photovoltaic material and devices (PVMD) group of Delft University of Technology. To validate the quick-scan method, a set of 145 roofs and 215 roof segments were chosen in urban areas in the Netherlands. For the chosen roofs, the number of fitted modules and calculated yield were compared with the actual modular layout and the measured yield of existing PV systems. Results showed a satisfactory agreement between the quick-scan yield prediction and measured annual yield per roof, with relative standard deviations of 7.2%, 9.1%, and 7.5% respectively for SM, PVGIS, and PVMD approaches. It was concluded that the obstacle-including approaches (e.g. SM and PVMD) outperformed the approaches which neglect the shading by surrounding obstacles (e.g. PVGIS). Results also showed that 3D roof segments had added value as input for the quick-scan PV yield prediction methods since the precision of yield prediction was significantly lower using only 2D land register data of buildings.
Available renewable energy resources play a vital role in fulfilling the energy demands of the increasing global population. To create a sustainable urban environment with the use of renewable energy in human habitats, a precise estimation of solar energy on building roofs is essential. The primary goal of this paper is to develop a procedure for measuring the rooftop solar energy photovoltaic potential over a heterogeneous urban environment that allows the estimation of solar energy yields on flat and pitched roof surfaces at different slopes and in different directions, along with multi-segment roofs on a single building. Because of the complex geometry of roofs, very high-resolution data, such as ortho-rectified aerial photography (orthophotos), and LiDAR data have been used to generate a new object-based algorithm to classify buildings. An overall accuracy index and a Kappa index of agreement (KIA) of 97.39% and 0.95, respectively, were achieved. The paper also develops a new model to create an aspect-slope map, which combines slope orientation with the gradient of the slope and uses it to demonstrate the collective results. This study allows the assessment of solar energy yields through defining solar irradiances in units of pixels over a specific time period. It might be beneficial in terms of more efficient measurements for solar panel installations and more accurate calculations of solar radiation for residents and commercial energy investors.
With the growing demand of economically feasible, clean, and renewable energy, the use of solar photovoltaic (PV) systems is increasing. The PV panel performance to generate electrical energy depends on many factors among which tilt angle is also a crucial one. Among hundreds of research work performed pertinent to solar PV panels performance, this work critically reviews the role of tilt angles and particularly locating the optimum tilt angle using different methods. The past data collected for analysis can be categorized mainly into mathematical model based, experimental based, simulation based, or combination of any of these. Single-axis tracking, dual-axis tracking, simple glass cover, hydrophobic glass cover, soiled glass, clean glass, partial shadow, use of phase-change material, computational fluid dynamic analysis, etc., are the novel methods found in the literature for analysis and locating the optimum tilt angle. For illustration purpose, few figures are provided in which the optimum tilt angle obtained on monthly, seasonally, and annual basis is shown. Research works are growing in the field of computations and simulations using online software and codes. Pure mathematical-based calculations are also reported but the trend is to combine this method with the simulation method. As the PV panel performance is found to be affected by number of parameters, their consideration in any single study is not reported. In future, work is required to carry out the experiment or simulation considering the effect of soiling, glass material, temperature, and surrounding ambience on the location of optimum tilt angle. As a whole, the optimum tilt angles reported for locations exactly on the equator line, i.e., 0° latitude, ranges between − 2.5° and 2.5°, for locations just above the equator line, i.e., latitude 2.6°–30° N ranges between 5° and 28°, for 40°–70° N, it is 29°–40°, and for 71°–90° N, it is 41°–45°. For locations at 2.6°–30° S, optimum tilt angles range between − 4° and − 32°, 30°–46° S, it is − 33° to − 36°, 47°–65° S, it is − 34° to − 50°, and for 66°–90° S it is − 51° to − 62°.
Rooftop solar photovoltaic (PV) systems can make a significant contribution to Europe's energy transition. Realising this potential raises challenges at policy and electricity system planning level. To address this, the authors have developed a geospatially explicit methodology using up-to-date spatial information of the EU building stock to quantify the available rooftop area for PV systems. To do this, it combines satellite-based and statistical data sources with machine learning to provide a reliable assessment of the technical potential for rooftop PV electricity production with a spatial resolution of 100 m across the European Union (EU). It estimates the levelised cost of electricity (LCOE) using country-specific parameters and compares it to the latest household electricity prices. The results show that the EU rooftops could potentially produce 680 TWh of solar electricity annually (representing 24.4% of current electricity consumption), two thirds of which at a cost lower than the current residential tariffs. Country aggregated results illustrate existing barriers for cost-effective rooftop systems in countries with low electricity prices and high investment interest rates, as well as provide indications on how to address these.
https://www.sciencedirect.com/science/article/pii/S1364032119305179
Solar energy is the most clean renewable energy source and has good prospects for future sustainable development. Installation of solar photovoltaic (PV) systems on building rooftops has been the most widely applied method for using solar energy resources. In this study, we developed an approach to simulate the monthly and annual solar radiation on rooftops at an hourly time step to estimate the solar PV potential, based on rooftop feature retrieval from remote sensing images. The rooftop features included 2D rooftop outlines and 3D rooftop parameters retrieved from high-resolution remote sensing image data (obtained from Google Maps) and digital surface model (DSM, generated from the Pleiades satellite), respectively. We developed the building features calculation method for five rooftop types: flat rooftops, shed rooftops, hipped rooftops, gable rooftops and mansard rooftops. The parameters of the PV modules derived from the building features were then combined with solar radiation data to evaluate solar photovoltaic potential. The proposed method was applied in the Chao Yang District of Beijing, China. The results were that the number of rooftops available for PV systems was 743, the available rooftop area was 678,805 m², and the annual PV electricity potential was 63.78 GWh/year in the study area, which has great solar PV potential. The method to perform precise calculation of specific rooftop solar PV potential developed in this study will guide the formulation of energy policy for solar PV in the future.
Machine learning methods have gained in importance through the latest development of artificial intelligence and computer hardware. Particularly approaches based on deep learning have shown that they are able to provide state-of-the-art results for various tasks. However, the direct application of deep learning methods to improve the results of 3D building reconstruction is often not possible due, for example, to the lack of suitable training data. To address this issue, we present RoofN3D which provides a new 3D point cloud training dataset that can be used to train machine learning models for different tasks in the context of 3D building reconstruction. It can be used, among others, to train semantic segmentation networks or to learn the structure of buildings and the geometric model construction. Further details about RoofN3D and the developed data preparation framework, which enables the automatic derivation of training data, are described in this paper. Furthermore, we provide an overview of other available 3D point cloud training data and approaches from current literature in which solutions for the application of deep learning to unstructured and not gridded 3D point cloud data are presented.
The local generation of renewable electricity through roof-mounted photovoltaic (PV) systems on buildings in urban areas provides huge potentials for the mitigation of greenhouse gas emissions. This contribution presents a new method to provide local decision makers with tools to assess the remaining PV potential within their respective communities. It allows highly detailed analyses without having to rely on 3D city models, which are often not available. This is achieved by a combination of publicly available geographical building data and aerial images that are analyzed using image recognition and machine learning approaches. The method also employs sophisticated algorithms for irradiance simulation and power generation that exhibit a higher accuracy than most existing PV potential studies. The method is demonstrated with an application to the city of Freiburg, for which a technical PV electricity generation potential of about 524 GWh/a is identified. A validation with a 3D city model shows that the correct roof azimuth can be determined with an accuracy of about 70% and existing solar installations can be detected with an accuracy of about 90%. This demonstrates that the method can be employed for spatially and temporally detailed PV potential assessments in arbitrary urban areas when only public geographical building data is available instead of exact 3D city model data. Future work will focus on methodological improvements as well as on the integration of the method within an urban energy system modeling framework.
Plane segmentation is a basic task in the automatic reconstruction of indoor and urban environments from unorganized point clouds acquired by laser scanners. As one of the most common plane-segmentation methods, standard Random Sample Consensus (RANSAC) is often used to continually detect planes one after another. However, it suffers from the spurious-plane problem when noise and outliers exist due to the uncertainty of randomly sampling the minimum subset with 3 points. An improved RANSAC method based on Normal Distribution Transformation (NDT) cells is proposed in this study to avoid spurious planes for 3D point-cloud plane segmentation. A planar NDT cell is selected as a minimal sample in each iteration to ensure the correctness of sampling on the same plane surface. The 3D NDT represents the point cloud with a set of NDT cells and models the observed points with a normal distribution within each cell. The geometric appearances of NDT cells are used to classify the NDT cells into planar and non-planar cells. The proposed method is verified on three indoor scenes. The experimental results show that the correctness exceeds 88.5% and the completeness exceeds 85.0%, which indicates that the proposed method identifies more reliable and accurate planes than standard RANSAC. It also executes faster. These results validate the suitability of the method.
Recently, photovoltaic systems have been developed and production of electricity directly from the sunlight has gained importance and become widespread. Photovoltaic systems will obviously be more important in the near future with their low cost, compared to other energy production methods. For example, in some European countries, municipalities produce solar potential maps to determine suitable spots for photovoltaic systems. However, in Turkey, there is no production of the solar potential maps for this purpose.
Every rooftop in an area may not be suitable for installation of photovoltaic systems. This is because there are some parameters (i.e. insolation time and shadow effect) affecting the efficiency of energy production via photovoltaic panels. Therefore, performance of solar potential analysis may be of help before the installation of photovoltaic panels. In this study, Karadeniz Technical University campus, which is situated in the city of Trabzon, has been selected as the study area. Solar analysis module (r.sun) of open source GIS software GRASS has been used to determine solar energy potential. Solar analysis has been performed using a very highresolution DEM (Digital Elevation Model) and DSM (Digital Surface Model), which were produced by using the photos taken from an Unmanned Aerial Vehicle (UAV). Global solar irradiation maps have been generated to specify suitable areas for installation of photovoltaic panels.
Karadeniz Technical University campus has been found to have solar energy potential. We believe that solar potential maps are significant for urban planning and these maps must be taken into consideration in urban planning processes.
In this paper, we propose a real-time image superpixel segmentation method with 50fps by using the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm. In order to decrease the computational costs of superpixel algorithms, we adopt a fast two-step framework. In the first clustering stage, the DBSCAN algorithm with color-similarity and geometric restrictions is used to rapidly cluster the pixels, and then small clusters are merged into superpixels by their neighborhood through a distance measurement defined by color and spatial features in the second merging stage. A robust and simple distance function is defined for obtaining better superpixels in these two steps. The experimental results demonstrate that our real-time superpixel algorithm (50fps) by the DBSCAN clustering outperforms the state-of-the-art superpixel segmentation methods in terms of both accuracy and efficiency.
Solar energy is environmentally safe, efficient and sustainable energy source. Recently, photovoltaic systems have been developed and production of electricity directly from the sunlight has gained importance and become widespread. Photovoltaic systems will obviously be more important in the near future with their low cost compared to other energy production methods and continuously increasing efficiency. For example, in some European countries, municipalities produce solar potential maps to determine the suitable spots for the photovoltaic systems. However, In Turkey, there is no production of the solar potential maps for this purpose.
Every building in an area may not be suitable for photovoltaic systems installed on the rooftops. Because some parameters like insolation time and shadow effect may affect the efficiency of energy production via photovoltaic panels. Therefore, solar potential analysis must be performed before the installation of photovoltaic panels. This kind of analysis can be implemented through solar modules that are integrated in Geographic Information Systems (GIS). In this study, Karadeniz Technical University Campus, which is situated in the city of Trabzon, has been selected as the study area. Digital Elevation Model (DEM) based solar analysis module (r.sun) of open source GIS software GRASS has been used in this study. Solar analysis has been performed using very high-resolution DEM and DSM (Digital Surface Model) which are produced by using the photos taken from an Unmanned Aerial Vehicle. Also analysis considered shadowing effect in the implementation process. Suitable areas have been determined by producing beam irradiation, diffuse irradiation, reflected irradiation and insolation time raster maps. The work is underway for generating solar potential maps of some other cities in Turkey.
This study has shown that Karadeniz Technical University campus has some solar energy potential. We believe that solar potential maps are significant for urban planning work and therefore these maps must be taken into consideration in all kinds of planning processes.
A methodology for estimating the rooftop solar photovoltaic potential for a region has been described. The methodology has been applied and illustrated for the Indian city of Mumbai (18.98°N, 72.83°E). It uses high-granularity land use data available in the public domain and GIS-based image analysis of sample satellite images to estimate values of the Building Footprint Area (BFA) Ratio. Photovoltaic-Available Roof Area (PVA) Ratio has been estimated by simulations in PVSyst and has been compared with relevant values from the literature. Solar irradiance (DNI and DHI) and ambient temperature data have been taken from Climate Design Data 2009 ASHRAE Handbook. Liu Jordan transposition model has been used for estimating the plane-of-array insolation. Effect of tilt angle on the plane-of-array insolation received has been studied to make an optimum choice for the tilt angle. Micro-level simulations in PVSyst have been used to estimate effective sunshine hours for the region of interest. The installed capacity, annual and daily generation profiles and capacity factor have been estimated for PV panels with different rated solar cell efficiency and power-temperature coefficient values.
Sustainable solar energy is of the interest for the city of San Francisco to meet their renewable energy initiative. Buildings in the downtown area are expected to have great photovoltaic (PV) potential for future solar panel installation. This study presents a comprehensive method for estimating geographical PV potential using remote sensed LiDAR data for buildings in downtown San Francisco. LiDAR derived DSMs and DTMs were able to generate high quality building footprints using the object-oriented classification method. The GRASS built-in solar irradiation model (r.sun) was used to simulate and compute PV yields. Monthly and yearly maps, as well as an exquisite 3D city building model, were created to visualize the variability of solar irradiation across the study area. Results showed that monthly sum of solar irradiation followed a one-year cycle with the peak in July and troughs in January and December. The mean yearly sum of solar irradiation for the buildings in the study area was estimated to be 1675 kWh/m2. A multiple regression model was used to test the significance of building height, roof area and roof complexity against PV potential. Roof complexity was found to be the dominant determinant. Uncertainties of the research are mainly from the inherent r.sun limitations, boundary problems, and the LiDAR data accuracy in terms of both building footprint extraction and 3D modeling. Future work can focus on a more automated process and segment rooftops of buildings to achieve more accurate estimation of PV potential. The outcome of this research can assist decision makers in San Francisco to visualize building PV potential, and further select ideal places to install PV systems. The methodology presented and tested in this research can also be generalized to other cities in order to meet contemporary society's need for renewable energy.
The search for solar buildings is one of the primary challenges in urban planning, especially when developing self-sustainable cities. This work uses an evolutionary approach for finding the optimal building model based on airborne Light Detection And Ranging (LiDAR) laser-scanned data, regarding solar potential. The method considers self-adaptive differential evolution for solving the constrained optimisation problem. In the experiments, the effect of different buildings' layouts and design parameters were analysed regarding solar irradiance. Rectangular, T and L-shaped buildings were considered with various design parameters: position, building rotation, facades' height, roof's height and slope. The experiments confirmed that the method can efficiently find the solar building design with maximum solar potential within constrained optimisation space.
Solar photovoltaic (PV) technology now become a new trend for the home builders and buyers of the twentieth century, in order to have not only good in structure, but also more environmentally friendly. Understanding the rooftop PV potential is important for planning, accommodate grid capacity, and many more, this paper demonstrate the technique utilizing the LiDAR data merge with the Geographical Information System to determine the suitable location to locate the PV solar panels. There are five criteria that had been used to select the best location which is generate the ground mask, aspect mask, slope mask, human mask and high radiation layer. Based on the output of the suitable location, the estimating number of solar panel can be generate based on the calculation of one-and half square area meter was used as a required area for each solar panel. This new understanding of roof area distribution and potential PV outputs will guide energy policy formulation in Georgetown area and will inform future research in solar PV deployment and its geographical potential.
This study presents a comprehensive methodology to evaluate plants that integrate renewable energy sources and hydrogen generation devices. The paper focuses on presenting the methods for devices’ operation assessment taking into account the annual operation. Multiple effectiveness indices have been presented. On the basis of experimental investigation with the hydrogen generator, the methods for assessing its operation during start-up phase and sudden change in the supply current were proposed. The results of the experiments and the provided mathematical models show that dynamics of the hydrogen generator should be taken into account when selecting the suitable device for cooperation with variable renewable energy. It is especially important for multiple start-ups throughout the day due to significant differences in the amount of hydrogen produced by devices characterized by the same efficiency, yet various time constants. Methodology for selecting the optimal nominal power for hydrogen generator to cooperate with given renewable sources was developed. It was proven the optimal power depends on the type of the renewable source and minimal load of the hydrogen generator. Several case studies, including the integration of wind and solar energy farms to yield a 10 MW renewable energy farm were considered and the minimal load of the hydrogen generator impacts the annual operation of the device has been presented. The paper provides a set of tools to contribute to the development of sustainable energy plants. The methods proposed in this paper are universal and can be used for various renewable energy sources.
This paper proposes a comprehensive framework for estimating the regional rooftop photovoltaic (PV) potential. The required rooftop information is extracted from Gao Fen-7 satellite images. In particular, the rooftop area is obtained using a semantic segmentation network. The azimuth and inclination angles are calculated based on the digital surface model. In addition, to improve the accuracy of the economic evaluation, buildings are divided into commercial and industrial buildings and residential buildings. Based on the difference in the roof inclination, the rooftops can be divided into flat roofs, on which the PV panels are installed with the optimal inclination angle, and sloped rooftops, on which the PV panels are installed in a lay-flat manner. The solar irradiation on the plane-of-array is calculated using the isotropic sky translocation model. Then, the available installed capacity and generation potential of the rooftop PV is obtained. Finally, the net present value, dynamic payback period, and internal rate of return are used to evaluate the economic efficiency of the rooftop PV project. The proposed framework is applied in the Da Xing district of Beijing, China, with a total area of 546.84 km². The results show that the rooftop area and available installed capacity of PV are 25.63 km² and 1487.45 MWp, respectively. The annual rooftop PV generation potential is 2832.23 GWh, with significant economic returns.
Recent years have seen a substantial increase in energy produced by renewable sources. The International Energy Agency (IEA) expects a large portion of future growth in renewable energy to come from solar, especially rooftop photovoltaic (PV) systems. Studies focused on estimating rooftop solar energy potential generally use the total area available for PV installation as determined by solar irradiance availability. This process can lead to substantial over- or under-estimation of energy estimates. Only a few studies have incorporated the spatial layout of PV panels in the solar energy generation estimates, and none have simultaneously considered PV panel size, orientation, and rooftop structure. We address this limitation with a new spatially explicit optimization framework to enhance the accuracy of rooftop solar energy assessments. We consider both the roof's structural configuration and the shape and size of the panels in a novel maximum cover spatial optimization model. After applying the framework to three different types of rooftops (flat roof, pitched roof, and complex roof), we find that conventional methods can lead to a nearly 60% overestimation of energy potential compared to the optimized panel layout. Our work illustrates the importance of considering panel size and rooftop characteristics and offers a mechanism for designing more efficient rooftop PV systems.
Rooftop photovoltaic (PV) power generation is an important form of solar energy development, especially in rural areas where there is a large quantity of idle rural building roofs. Existing methods to estimate the spatial distribution of PV power generation potential are either unable to obtain spatial information or are too expensive to be applied in rural areas. Herein, we propose a novel approach to estimate the spatial distribution of the general potential of rural rooftop power from publicly available satellite images. We divide rural building roofs into three categories based on their orientation and roof angle and propose a revised U-Net deep learning network to extract roof images from satellite images at the macro level. Based on the rooftop detection, a calculation method for the potential area of the installed PV panel at the micro level was developed, considering different types of PV panels and their maintenance methods. By combining the above results and setting the solar radiation parameters and PV system efficiency, we can obtain the spatial distribution of the rooftop PV power generation potential in rural areas. This method is applied in northern China on a village and a town scale, and the overall accuracy of the revised U-Net model can reach over 92%. The spatial distribution information was analyzed and displayed. The annual average PV power generation potential ranges from 26.5 to 36.2 MWh per household and from 7.3 to 10 GWh per village.
In cities seeking energy self-sufficiency, one of the trends is to pursue nearly zero-energy buildings (nZEB). To achieve this target, reducing energy consumption as well as replacing the traditional energy by renewable energy are two major strategies. The former is limited by the difficulty in lowering energy consumption in daily life, so the latter has more potential. Solar photovoltaic systems are a popular means to reach the goal of self-sufficiency in cities, and those on rooftops have the highest efficiency. Shadow from surrounding buildings affects the energy generation but this research found that the impact of shadow is generally limited. Only buildings of less than 3 storeys might suffer serious shadow covering and those higher than this mainly reduce their productivity by only about 1%. However, although lower buildings experience shadow effects, they are the main energy generators due to their low energy self-consumption. For example, the energy shortage of the tallest building in this case study could be covered by a 1-storey building with 10.33 times area. This research indicated that a fully energy self-sufficient environment can be achieved when the city pattern is designed with consideration of a well-balanced building height arrangement.
There are multiple approaches of estimating solar power generation by rooftop solar photovoltaic (PV) systems. Methods processed using GIS as well as 3D models provide the most reliable and accurate results. However, with the restriction of a limited detail 3D model area, estimations on a regional scale are hard to achieve. The regional power potential can be predicted by identifying the correlation of rooftop areas and the shadowing effect changing between the Level of Detail (LOD)1 and LOD2 models in the case study areas. This research considers the aspects and angles of the rooftops, to try and not only figure out the optimal PV installation conditions but also to make a general estimation for optimal angle, 2-axis solar tracking as well as total solar radiation received scenarios. The results of the research indicate that the optimal annual angle is 177° and 13° in aspect and slope degree, respectively, and the improvements of power generation under those three scenarios increase from about 2 to 16% in the test area, respectively. This research also showed that the power potential in partial small areas had the same tendency as the whole region even though each part of the small area had uneven city pattern. Additionally, different from most of the study, north-facing rooftops had been proved to have the strong potential to be developed as a solar power PV panel site and should not be neglected from the whole PV system.
The current study develops a hydrogen map concept where renewable energy sources are considered for green hydrogen production and specifically investigates the solar energy-based hydrogen production potential in Turkey. For all cities in the country, the available onshore and offshore potentials for solar energy are considered for green hydrogen production. The vacant areas are calculated after deducting the occupied areas based on the available governmental data. Abundant solar energy as a key renewable energy source is exploited by photovoltaic cells. To obtain the hydrogen generation potential, monocrystalline and polycrystalline type solar cells are considered, and the generated renewable electricity is directed to electrolysers. For this purpose, alkaline, proton exchange membrane (PEM), and solid oxide electrolysers (SOEs) are considered to obtain the green hydrogen. The total hydrogen production potential for Turkey is estimated to be between 415.48 and 427.22 Million tons (Mt) depending on the type of electrolyser. The results show that Erzurum, Konya, Sivas, and Van are found to be the highest hydrogen production potentials. The main idea is to prepare hydrogen map in detail for each city in Turkey, based on the solar energy potential. This, in turn, can be considered in the context of the current policies of the local communities and policy-makers to supply the required energy of each country.
Accurate rooftop solar energy potential characterization is critically important for promoting the wide penetration of renewable energy in high-density cities. However, it has been a long-standing challenge due to the complex building shading effects and diversified rooftop availabilities. To overcome the challenge, this study proposed a novel 3D-geographic information system (GIS) and deep learning integrated approach, in which a 3D-GIS-based solar irradiance analyzer was developed to predict dynamic rooftop solar irradiance by taking shading effects of surrounding buildings into account. A deep learning framework was developed to identify the rooftop availabilities. Experimental validations have shown their high accuracies. As a case study, a real urban region of Hong Kong was used. The results showed that the annual solar energy potential of the entire building group was reduced by 35.7% due to the shading effect and the reduced rooftop availability. The reductions of individual buildings varied from 13.4% to 74.5%. In spite of the substantial reductions of the annual solar energy, the shading effect could only slightly reduce the peak solar power. In fact, the annual solar energy reduction could be five times higher than the peak solar power reduction. Further analysis showed that simple addition of the respective solar energy potential reductions, caused by the shading effect and the rooftop availability, tends to highly overestimate the total reduction by up to 26%. For this reason, their impacts cannot be considered separately but as joint effects. The integrated approach provides a viable means to accurately characterize rooftop solar energy potential in urban regions, which can help facilitate solar energy applications in high-density cities.
Usage of solar energy is increasing steadily especially in the rooftop installations of buildings in large cities. This study contains calculations of electrical energy produced by photovoltaic panels placed on roofs of buildings for city of Istanbul using building data and verify calculated results by a mobile measurement system. Three dimensional city model utilizes light detection and ranging data that covers an area of 5400 km² for the whole city. The main object classes such as ground, building, vegetation were derived, buildings are vectorized and a digital elevation model are used for the generation of a second level of detail. Using the geometric data of the buildings in Istanbul as input to the developed software, the electricity production of panels is calculated on the suitable roofs of the buildings considering the observed climatic conditions and solar radiation for the city. A photovoltaic weather mobile vehicle measurement system was designed and applied for verification of developed model in eight different areas of Istanbul for stationing 50 days at each location. Results of difference between measurements and calculations of solar irradiation and electrical energy production are 2.44% and 14.70%, respectively. Batch processing was performed on 39 districts of Istanbul with 1.3 million buildings and annual electrical energy production from the roofs of all buildings is calculated to be 30.8 TWh at the point of common coupling to the utility. Total rooftop electricity production of Istanbul has a potential to meet 67% of the total electricity consumption for the year 2019.
The photovoltaic (PV) industry boom and increased PV applications call for better planning based on accurate and updated data on the installed capacity. Compared with the manual statistical approach, which is often time-consuming and labor-intensive, using satellite/aerial images to estimate the existing PV installed capacity offers a new method with cost-effective and data-consistent features. Previous studies investigated the feasibility of segmenting PV panels from images involving machine learning technologies. However, due to the particular characteristics of PV panel semantic-segmentation, the machine learning tools need to be designed and applied with careful considerations of the issue formulation, data quality, and model explainability. This paper investigated the characteristics of PV panel semantic-segmentation from the perspective of computer vision. The results reveal that the PV panel image data has several specific characteristics: highly class-imbalance and non-concentrated distribution; homogeneous texture and heterogenous color features; and the notable resolution threshold for effective semantic-segmentation. Moreover, this paper provided recommendations for data obtaining and model design, aiming at each observed character from the viewpoints of recent solutions in computer vision, which can be helpful for future improvement of the PV panel semantic-segmentation.
The estimation of rooftop solar photovoltaic (PV) potential is crucial for policymaking around sustainable energy plans. But it is difficult to accurately estimate the availability of rooftop area for solar radiation on a city-scale. In this study, a generic framework for estimating the rooftop solar PV potential on a city-scale using publicly available high-resolution satellite images is proposed. A deep learning-based method is developed to extract the rooftop area with image semantic segmentation automatically. A spatial optimization sampling strategy is developed to solve the labor-intensive problem when training the rooftop extraction model based on prior knowledge of urban and rural spatial layout and land use. In the case study of Nanjing, China, the labor cost on preparing the dataset for training the rooftop extraction model has been reduced by about 80% with the proposed spatial optimization sampling strategy. Meanwhile, the robustness of the rooftop extraction model in districts with different architectural styles and land use has been improved. The total rooftop area extracted was 330.36 km², and the overall accuracy reached 0.92. The estimation results show that Nanjing has significant potential for rooftop-mounted PV installations, and the potential installed capacity reached 66 GW. The annual rooftop solar PV potential was approximately 311,853 GWh, with a corresponding estimated power generation of 49,897 GWh in 2019.
In urban environments, decentralized energy systems from renewable photovoltaic resources, clean and available, are gradually replacing conventional energy systems as an attractive source for electricity generation. Especially with the availability of unexploited rooftop areas and the ease of installation, along with technological development and permanent cost reductions of photovoltaic panels. However, the optimal use of these systems requires accurate estimates of supply (rooftop solar photovoltaic potential) and the design of an intelligent distributed-system integrated with power grids. Geographic information systems (GISs)-based estimation is justified as a promising approach for estimating rooftop solar photovoltaic potential, in particular, the possibility of combining GISs with LiDAR (Lighting-Detection-And-Ranging) to build robust approaches leading to accurate estimates of the rooftop solar photovoltaic potential. Accordingly, this study aims to present a comprehensive review of GISs-based rooftop solar photovoltaic potential estimation approaches that have been applied at different scales, including countries. The study classified GISs-based approaches into sampling, geostatistics, modeling, and machine learning. The applications, advantages, and disadvantages of each approach were reviewed and discussed. The results revealed that GISs-based rooftop solar photovoltaic potential estimation approaches, can be applied to the large-scale spatial-temporal assessment of future energy systems with decentralized electrical energy grids. Assessment results can be employed to propose effective-policies for rooftop photovoltaic integration in built environments. However, the development of a new methodology that integrates GISs with machine learning to provide an accurate and less computationally demanding alternative to LiDAR-based approaches, will contribute significantly to large-scale estimates of the solar photovoltaic potential of building rooftops.
This study analyzed the impacts from multi-crystalline silicon (m-Si), organic thin-film (OPV), and perovskite thin-film (PSC) panels over each products’ lifetime using a cradle-to-grave system model. The rate of panel installation each year was modeled to account for efficiency, functional lifetime, and degradation. Landfill and recycling scenarios were used to compare end-of-life impacts and the overall environmental impacts were determined using life cycle impact assessment at the midpoint and endpoint levels. Impact calculations revealed that the production and use of m-Si panels resulted in the worst impacts for all categories. OPV panels produced drastically lower impacts comparatively, with PSC designs falling at mid-range. Recycling lowered the impacts for all module types and showed the largest decrease in the impacts of m-Si panels. Although moderately sensitive to the energy production mix, the results can be applied to other regions for the comparison between panel types.
Deep decarbonization pathways (DDPs) can be cost-effective for carbon mitigation, but they also have environmental co-benefits and economic impacts that cannot be ignored. Despite many empirical studies on the co-benefits of NDCs at the national or sectoral level, there is lack of integrated assessment on DDPs for their energy, economic, and environmental impact. This is due to the limitations of bottom-up and top-down models when used alone. This paper aims to fill this gap and link the bottom-up MAPLE model with a top-down CGE model to evaluate China's DDPs' comprehensive impacts. First, results show that carbon dioxide emissions can be observed to peak in or before 2030, and non-fossil energy consumption in 2030 is around 27%, which is well above the NDC target of 20%. Second, significant environmental co-benefits can be expected: 7.1 million tons of SO2, 3.96 million tons of NOx, and 1.02 million tons of PM2.5 will be reduced in the DDP scenario compared to the reference scenario. The health co-benefits demonstrated with the model-linking approach is around 678 billion RMB, and we observe that the linked model results are more in accordance with the conclusions of existing studies. Third, after linking, we find the real GDP loss from deep decarbonization is reduced from 0.92% to 0.54% in 2030. If the environmental co-benefits are considered, the GDP loss is further offset by 0.39%. The primary innovation of this study is to give a full picture of DDPs' impact, considering both environmental co-benefits and economic losses. We aim to provide positive evidence that developing countries can achieve targets higher than stated in the NDCs through DDP efforts, which will have clear environmental co-benefits to offset the economic losses.
Remotely sensed data provide many opportunities for enhancing our understanding of the built and natural environment. Representations of the urban landscape from light detection and ranging (LiDAR) sensors and digital orthophotography from unmanned aerial systems (UAS) are quickly becoming essential for examining and maintaining infrastructure systems, estimating risk from extreme events, and improving urban sustainability. This includes community efforts toward energy resilience and the development of alternative energy systems, such as solar and wind. While LiDAR provides the means to model key characteristics of the urban landscape for solar energy planning, including slope, aspect and elevation, issues of spatial uncertainty and error persist in LiDAR data and have the potential to reduce the fidelity of solar energy assessments. In this paper, we use extremely high-resolution UAS data to improve solar energy audits and mitigate uncertainties associated with LiDAR data. The results suggest improvements in aggregate irradiation estimates by as much as 36% when using digital orthophotos from a UAS when compared to LiDAR. This paper concludes with a detailed discussion of potential strategies for improving solar energy estimates for both researchers and practitioners.
Free eprints: https://www.tandfonline.com/eprint/ZAT9BDNMBXPESXXNHP2V/full?target=10.1080/2150704X.2019.1649735 Photovoltaic (PV) installations on rooftops in urban environments
have a significant potential to reduce human environmental impact.
However, the quality of remote sensing data available for solar
potential estimates in different regions varies and regional authorities
or policy makers need to know if the available data are suitable
for solar potential estimates and/or whether to invest into expensive
data collection. Published studies often mention the importance of
identification of small disturbing structures on the roof (e.g. chimneys),
which is however questionable due to the fact that estimated
energy yields are much more dependent on the variability of annual
solar irradiation. We used two different models: (i) Photovoltaic
Geographical Information System based on coarse data and (ii)
ArcGIS Area Solar Radiation tool based on digital surface models at
very high resolutions (≤1 m) acquired from UAV photogrammetry.
We compared solar irradiation estimates with ground-truth data from
a PV system installed on the roof. We show that the effect of adopted
model and resolution on estimated irradiation is negligible in comparison
with the year-to-year variation of meteorological conditions.
We suggest that accurate predictions can be made with relatively
coarse building data (e.g. simple roof shapes that do not include
dormers and chimneys).
This study examines the performance of the estimated solar radiation components obtained via the Meteorological Radiation Model, satellite-based data sets (CAMS, PVGIS-CMSAF-SARAH) and reanalysis (PVGIS-ERA5) against ground measurements taken with the Sunshine-Pyranometer at Methoni station, Greece. MRM shows satisfactory simulations for the global solar irradiation (R2=0.97, RMSE=11.5%, MBE=-2.5%) at 15-min time-intervals, while for the diffuse larger biases are found (R2=0.57, RMSE=45%). Solar irradiation estimates via CAMS at 15-min intervals reveal RMSE values of 19.5%, 38% and 28% for the global, diffuse and direct radiations, respectively. Biases are progressively reduced for hourly, daily and monthly data sets. PVGIS databases simulate the global irradiance reasonably well (R2=0.82-0.92), exhibiting high uncertainties for the diffuse (R2=0.39-0.49) and direct (R2=0.75-0.87), regarding instantaneous measurements. Simulations under clear-sky conditions of all components are found to be significantly improved, from both MRM, satellite-based retrievals and reanalysis. Overcast and partially cloudy skies result in large uncertainties, especially for the diffuse and direct irradiations, since the satellite sensors may detect clouds at time intervals of unobstructed Sun disk by clouds. In addition, broken bright clouds near to the Sun's disk may increase significantly the measured diffuse irradiance, leading to large biases in the simulations from both MRM and satellite databases.
This study presents a novel approach to detect the city-wide solar potential which utilizes image segmentation with deep learning technology unlike traditional methods. In order to study the solar energy potential in the urban scale, there exists a requirement to quantify the roof area of buildings which are available to receive solar radiation, calculate the total solar radiation obtained within the region based on the meteorological conditions, and determine the total solar energy potential with carbon emissions savings and the economic recovery period. However, obtaining the overall roof area of a city is an existing difficulty when considering the quantification of solar potential in the urban scale. This study utilizes the U-Net of deep learning technology, and a large range of satellite maps to identify the building roof, in order to estimate the city's solar potential. This research established that the urban roofs of Wuhan have an annual photovoltaic electricity generation potential of 17292.30 × 10 ⁶ kWh/year.
In urban areas with dense buildings, it is expected that the building-integrated photovoltaic (BIPV) system, will become widespread. Especially, the solar photovoltaic blinds (SPB), which can block the sunlight coming into the room and produce electricity, is emerging as a new technology trend. To facilitate the installation of the SPB, this study analyzed the techno-economic performance of the smart SPB considering the PV panel type and solar tracking method used. Towards this end, this study conducted experiments using the developed smart SPB, as well as a comparative analysis in terms of the techno-economic aspects based on the experiment results. The analysis results of this study were as follows: at the same cost, (i) the monocrystalline silicon (mono-Si) PV panel generated 350.5% more electricity than the amorphous silicon (a-Si) PV panel; and (ii) the direct solar tracking system generated 12.9% more electricity than the indirect solar tracking method. Accordingly, the mono-Si PV panel and the direct solar tracking method were selected for the optimal smart SPB. The installation of the smart SPB with the proposed optimal design on the south-facing window of buildings can be helpful for raising the electricity self-sufficiency rate of buildings by up to 20.3%.
Buildings are responsible for 40% of energy consumption and 36% of CO₂ emissions in the European Union. To bring these levels down, governments are striving to promote a more efficient use of energy resources and an increase adoption of renewable energy technologies, as photovoltaic panels and solar collectors on the building envelopes. To fully exploit the potential of these technologies, a detailed analysis of the incident solar radiation on buildings roofs and facades is mandatory taking into account the geographical and urban environments. Three solar radiation tools, in association with two different modelling approaches (2.5D and 3D) handled by a 3D GIS tool, were applied to a city block of downtown Lisbon for both the winter and summer solstices and for different levels of detail of the surrounding context. The study showed that both built surroundings and topographic relief have an important impact on solar potential of buildings in urban areas. An average difference of about 30% in the results was observed between the simulations with and without the geographical and the urban environments included. The study also showed that the 3D approach has high potential to fully evaluate solar access in complex urban layouts, for accounting the irradiation of all sun-exposed surfaces of the buildings.
https://www.sciencedirect.com/science/article/pii/S0378778818309587
abstract
Energy production and consumption is a key element in future development which is influenced both by the technical possibilities available and by decision makers. Sustainability issues are closely linked in with energy policy, given the desire to increase the proportion of renewable energy. According to the Horizon 2020 climate and energy package, European Union (EU) member countries have to reduce the amount of greenhouse gases they emit by 20%, to increase the proportion of renewable energy to 20% and to improve energy efficiency by 20% by 2020. In this study we aim to assess the opportunities available to exploit solar radiation on roofs with Light Detection And Ranging (LiDAR) and photogram- metry techniques. The surveyed areawas in Debrecen, the second largest city in Hungary. An aerial LiDAR
survey was conducted with a density of 12 points/m2, over a 7 ? 1.8 km wide band. We extracted the building and roof models of the buildings from the point cloud. Furthermore, we applied a low-cost drone (DJI Phantom with a GoPro camera) in a smaller area of the LiDAR survey and also created a 3D model: buildings and roof planes were identified with multiresolution segmentation of the digital sur- face models (DSM) and orthophoto coverages. Building heights and building geometry were also extracted and validated in field surveys. 50 buildings were chosen for the geodetic survey and the results of the accuracy assessment were extrapolated to other buildings; in addition to this, 100 building heights were measured.We focused primarily on the roofs, as these surfaces offer possible locations for thermal and photovoltaic equipment. We determined the slope and aspect of roof planes and calculated the incoming solar energy according to roof planes before comparing the results of the point cloud pro- cessing of LiDAR data and the segmentation of DSMs. Extracted roof geometries showed varying degrees of accuracy: the research proved that LiDAR-based roof-modelling is the best choice in residential areas, but the results of the drone survey did not differ significantly. Generally, both approaches can be applied, because the solar radiation values calculated were similar. The aerial techniques combined with the multiresolution processing demonstrated can provide a valuable tool to estimate potential solar energy.