Rooftop Solar Photovoltaic Potential Research Articles

Harnessing rooftop solar photovoltaic potential in Islamabad, Pakistan: A remote sensing and deep learning approach

Solar energy shines as a beacon for sustainable development, with rooftop solar photovoltaic (PV) installations playing a crucial role. This study proposes a novel framework to precisely assess citywide existing solar power generation and analyze future potential under various rooftop utilization scenarios (10–50 %). To illustrate the methodology, the existing solar PV yield and future potential of Islamabad are explored as a case study. Employing open-source satellite data and deep learning (DL), this study quantified the electricity generation from current solar infrastructure and projected future possibilities. DL models like Mask R–CNN and Deeplab-v3 are trained on extensive custom datasets for solar panels and rooftops extraction. The study delineated 19,000 solar arrays, covering an area of 0.8 sqkm, and extracted 75.08 sqkm of suitable rooftops for future installations. The analysis revealed, current solar infrastructure generates 141.42 GWh electricity, satisfying 6.34 % of Islamabad's annual energy demand. Utilizing 50 % rooftop area could generate 6578 GWh annually, meeting 294 % of the city's electricity needs. This research contributes to the strategic planning for solar energy infrastructure, demonstrating the substantial role rooftop solar can play in meeting urban energy needs. This can inform policy decisions and guide investment opportunities for expanding clean energy in the city.

Estimation of Rooftop Solar Photovoltaic Potential Based on High-Resolution Images and Digital Surface Models

Buildings are important components of urban areas, and the construction of rooftop photovoltaic systems plays a critical role in the transition to renewable energy generation. With rooftop solar photovoltaics receiving increased attention, the problem of how to estimate rooftop photovoltaics is under discussion; building detection from remote sensing images is one way to address it. In this study, we presented an available approach to estimate a building’s rooftop solar photovoltaic potential. A rapid and accurate rooftop extraction method was developed using object-based image classification combining normalized difference vegetation index (NDVI) and digital surface models (DSMs), and a method for the identification of suitable rooftops for solar panel installation by analysing the geographical restrictions was proposed. The approach was validated using six scenes from Beijing that were taken using Chinese Gaofen-2 (GF-2) satellite imagery and Pleiades imagery. A total of 176 roofs in six scenarios were suitable for PV installation, and the estimated photovoltaic panel area was 205,827 m2. The rooftop photovoltaic potential was estimated to total 22,551 GWh. The results indicated that the rooftop photovoltaic potential estimation method performs well.

A novel approach for assessing rooftop-and-facade solar photovoltaic potential in rural areas using three-dimensional (3D) building models constructed with GIS

Characterization of solar photovoltaic (PV) potential is crucial for promoting renewable energy in rural areas, where there are a large number of roofs and facades ideal for PV module installation. However, accurately estimating solar PV potential on three-dimensional (3D) rural surfaces has been challenging due to the lack of 3D building models. To address this issue, we proposed a novel approach, which for the first time constructs rural 3D building models from publicly available satellite images and vector maps. Based on these models, it precisely evaluates the solar PV potential of rural rooftops and facades. The approach was validated against two realistic 3D village models and on-site solar radiation measurements. Using the validated approach, case studies in a village and on a large-scale island were conducted, respectively. The results showed that rural rooftops facing south and north, and facades facing south and west, have the highest PV potential ranks. North-facing rooftops with a slope of 30° represent 32.7% of the total rooftop solar PV potential, therefore, they should not be neglected in future evaluations. The proposed approach is cost-effective and valid for accurately assessing micro- and macro-scale rural solar PV potential that can facilitate rural renewable energy penetration.

Fast and accurate estimation of solar irradiation on building rooftops in Hong Kong: A machine learning-based parameterization approach

Harvesting solar energy on rooftops can be a promising solution for providing affordable energy. This requires accurately estimating spatio-temporal solar photovoltaic (PV) potential on urban surfaces. However, it is still a challenge to obtain a fast and accurate estimation of rooftop solar PV potential over large urban built-up areas. Thus, this study proposes a parametric-based method to estimate annual rooftop solar irradiation at a fine spatial resolution. Specifically, seven parameters (Digital Surface Model, Sky View Factor, shadow from buildings, shadow from terrain, building volume to façade ratio, slope, and aspect) are determined that having great importance in modeling rooftop solar irradiation. Three machine learning methods (Random Forest (RF), Gradient Boost Regression Tree (GBRT), and AdaBoost) trained by the selected parameters are cross-compared based on R2, Mean Absolute Error (MAE), and computation time. As a case study in Hong Kong, China, the RF outperformed GBRT and AdaBoost, with R2=0.77 and MAE=22.83kWh/m2/year. The time for training and prediction of rooftop solar irradiation is within 13 h, achieving a 99.32% reduction in time compared to the physical-based hemispherical viewshed algorithm. These results suggest that the proposed method can provide an accurate and fast estimation of rooftop solar irradiation for large datasets.

Geospatial assessment of rooftop solar photovoltaic potential using multi-source remote sensing data

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 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, corresponding to an annual power generation of 290.66 TWh. This highlights the huge space for carbon emissions reduction through developing rooftop PVs.

Power to the city: Assessing the rooftop solar photovoltaic potential in multiple cities of Ecuador

Abstract Solar energy plays a crucial role in helping cities to decentralize energy production and thus decarbonize the energy mix. Reliable resource assessments are needed to support the deployment of solar power systems, especially in cities of developing countries where large solar potential remains untapped. The aim of this work is to assess the potential of rooftop solar photovoltaic (PV) in three populated cities in Ecuador’s mainland (Quito, Guayaquil and Cuenca) and in the Galapagos Islands. The assessment involves (i) the estimation of the available rooftop area based on geographic information system data, (ii) the calculation of energy yield based on hourly satellite-derived irradiance and meteorological data, and (iii) the economic feasibility assessment in terms of levelized cost of electricity (LCOE) compared to representative electricity tariffs. In addition, a sensitivity analysis is carried out to assess the variability of the estimated technical and economic potential with respect to changes in the input parameters. The results reveal a total available rooftop area of about 144 km2, mainly concentrated in urban parishes. The estimated energy yield is 16.94 ± 3.38 TWh/a, which could cover almost twice the annual energy consumption in 2019 of the study areas. The economic assessment shows that the LCOE ranges between 7.65 and 21.12 USD cents/kWh. However, the comparison of LCOE against representative residential tariff suggests that rooftop PV technology is not cost-competitive under most of the financial scenarios. The findings from this study will be of interest for local authorities and other decision makers to design policies and financing strategies to increase the penetration of rooftop PV and thus exploiting the large potential assessed in the study areas. The described methodology can be used for assessing the potential in other regions of Ecuador and thereby support the diversification and decarbonization of the energy mix in the country.

Utilising high-fidelity 3D building model for analysing the rooftop solar photovoltaic potential in urban areas

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.

A city-scale estimation of rooftop solar photovoltaic potential based on deep learning

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.

Review of geographic information systems-based rooftop solar photovoltaic potential estimation approaches at urban scales

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.

Exploiting the rooftop solar photovoltaic potential of a tropical island state: case of the Mascarene Island of Mauritius

Mauritius relies heavily on fossil fuel imports to satisfy the energy requirements of its population. Urged by policies to address climate change at the national and international levels, the government is committed to promote sustainable development, with emphasis laid on reducing greenhouse gas emissions. Consequently, we provide an assessment of rooftop PV potential at the national level. A sampling strategy is used to acquire reduction coefficients, which are thereafter applied to energy-based equations. The result suggests that the rooftop PV potential at the country-scale is estimated at 20.04 GW with an annual energy output of 32,512 GWh. A novel architectural layout for a Solar City to be located at an identified optimum site is proposed. A shading analysis is conducted to verify the appropriateness of the city’s structural design to harness energy from the sun. An energy analysis performed, reveals that the Solar City has a solar power potential of 13.55 MW and an annual energy generation capacity equivalent to 21.99 GWh. Construction of the Solar City would help avert the emissions of 13,744 tons of CO2 annually.

Economic and technical assessment of rooftop solar photovoltaic potential in Brownsville, Texas, U.S.A

Localized assessment of solar energy economic feasibility will benefit the structuring of residential solar energy deployment globally. In the U.S. growing interest in rooftop residential solar among city managers has spurred the development of photovoltaic (PV) feasibility maps of the technical and economic solar potential within cities. The City of Brownsville, Texas was interested in evaluating solar feasibility for their city but lacked information to make informed policy decisions on PV development. This paper presents novel and systems approaches for determining the technical and economic feasibility of solar development for homes in the Brownsville using LiDAR and local information. Residential technical and economic potential was assessed by optimizing the internal rate of return (IRR) and an average residential building demand profile to determine ideal size and placement of solar arrays. Results showed that residential structures in Brownsville have the technical potential to generate approximately 11% of the total energy provided by the local utility; however, average IRR was only 2.9% with a payback period of over 15 years. Five neighborhoods in the City of Brownsville were identified with spatially clustered homes that had relatively higher IRRs compared with other areas in the city. Despite the high technical potential, modeled results indicate that perspective home owners interested in solar development may require additional incentives to improve the economic feasibility of PV in Brownsville. This study provides a demonstration of an interdisciplinary systems approach and methodology that can be adopted internationally to evaluate the feasibility of solar development in other areas.

A high-resolution geospatial assessment of the rooftop solar photovoltaic potential in the European Union

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.

A bottom-up approach for estimating the economic potential of the rooftop solar photovoltaic system considering the spatial and temporal diversity

To successfully deploy distributed solar generation in urban environments, it is essential to investigate the potential to generate electricity from the rooftop solar photovoltaic (PV) system within a region. While various interpretations are possible for the rooftop solar PV potential, most of the previous studies focused on estimating the technical potential, not considering the economic viability and market dynamics. Therefore, it is necessary to estimate the economic potential of the rooftop solar PV system to quantify the amount of economically viable solar PV energy within a region and to evaluate the impact of the various factors affecting market access. Towards this end, this study proposed a bottom-up approach for estimating the economic potential of the rooftop solar PV system considering the market dynamics by adoption year. Accordingly, the economic potential of the rooftop solar PV system was estimated for the Gangnam district in Seoul, South Korea from 2008 to 2016. In terms of power capacity, it was analyzed that as of 2016, the actual installed capacity of the solar PV system in the Gangnam district was only 3% of the maximum economic potential of the rooftop solar PV system (i.e., economic potential for electricity business purposes), showing a high potential for additional rooftop solar PV adoption. In terms of electricity generation, it was shown that as of 2016, the annual economic potential of the rooftop solar PV system could supply up to 4.48% of the annual electricity consumption in the Gangnam district, while only 0.12% could be supplied from the annual electricity generation of the actual installed solar PV system. This study has significant contributions in that it took one step farther towards the rooftop solar PV potential estimation process, from the technical potential to the economic potential, considering the spatial and temporal diversity of the solar PV technology.

Development of a rooftop solar photovoltaic rating system considering the technical and economic suitability criteria at the building level

The successful deployment of distributed solar generation in urban areas involves the evaluation of how suitable a building is for installing the rooftop solar photovoltaic (PV) system within a region. When evaluating the rooftop solar PV suitability of a building, it is crucial to consider not only its technical but also its economic performance as the difference in technical performance can lead to different financial returns for rooftop solar PV adopters. Towards this end, this study aimed to propose a method for developing a rooftop solar PV rating system using cluster analysis based on the technical and economic suitability criteria. As a result, a rooftop solar PV rating system was developed for 21,681 buildings in the Gangnam district in Seoul, South Korea by dividing them into four grades according to their rooftop solar PV potential, return on investment, and payback period. The effectiveness of the proposed rating system was validated by comparing it with the rating systems developed using the classification method and suitability criteria from the previous studies. This study has significant contributions in that it can provide the rooftop solar PV suitability information on a grade scale for intuitive decision-making based on scientific evidence and reasonable criteria.

Rooftop solar photovoltaic potential in cities: how scalable are assessment approaches?

Distributed photovoltaics (PV) have played a critical role in the deployment of solar energy, currently making up roughly half of the global PV installed capacity. However, there remains significant unused economically beneficial potential. Estimates of the total technical potential for rooftop PV systems in the United States calculate a generation comparable to approximately 40% of the 2016 total national electric-sector sales. To best take advantage of the rooftop PV potential, effective analytic tools that support deployment strategies and aggressive local, state, and national policies to reduce the soft cost of solar energy are vital. A key step is the low-cost automation of data analysis and business case presentation for structure-integrated solar energy. In this paper, the scalability and resolution of various methods to assess the urban rooftop PV potential are compared, concluding with suggestions for future work in bridging methodologies to better assist policy makers.

Quantifying rooftop photovoltaic solar energy potential: A machine learning approach

The need for reduction in CO2 emissions to mitigate global warming has resulted in increasing use of renewable energy sources. In urban areas, solar photovoltaic (PV) deployment on existing rooftops is one of the most viable sustainable energy resources. The present study uses a combination of support vector machines (SVMs) and geographic information systems (GIS) to estimate the rooftop solar PV potential for the urban areas at the commune level (the smallest administrative division) in Switzerland. The rooftop solar PV potential for a total 1901 out of 2477 communes in Switzerland is estimated. Following a 6-fold cross validation, the root-mean-square error (also normalized) is used to estimate the accuracy of the different SVM models. The results show that, on average, 81% of the total ground floor area of each building corresponds to the available roof area for the PV installation. Also considering the total available roof area for PV installation, that is, 328km2 and roof orientations within ±90° of due south, the annual potential PV electricity production for the urban areas in Switzerland is estimated at 17.86TWh (assumed 17% efficiency and 80% performance ratio). This amount corresponds to 28% of Switzerland’s electricity consumption in 2015.

Impact of Solar Panel Orientation on Large Scale Rooftop Solar Photovoltaic Scenario for Mumbai

Abstract Application of solar photovoltaic systems at a large scale is becoming increasingly interesting for researchers, policymakers and investors. Singh and Banerjee (2015) [1] have presented a methodology for estimation of the rooftop solar photovoltaic potential of a city. The methodology has been applied and illustrated for the Indian city of Mumbai (18.980N, 72.830E). In this paper, different orientations of the solar panels to be used for the Mumbai rooftop solar photovoltaic scenario have been analyzed and compared in terms of their impact on the generation from the scenario. The three possible orientations studied and compared in this study are – fixed tilt orientation, two-point system orientation, and horizontal N-S axis E-W tracking. The results show that for the fixed tilt orientation, the best tilt angle for year-round optimal performance is same as the latitude of the place, i.e. 190. However, optimization of seasonal output would warrant another tilt angle. Further, it has been found that, as compared to the fixed tilt at 190, the two-point system gives 2.21% higher annual plane-of-array insolation; and horizontal N-S axis E-W tracking gives 10.18% higher annual plane-of-array insolation. However, in terms of the annual generation from the large scale Mumbai rooftop scenario, the gain is only 1.97% with the two-point system, and 9.62% with the horizontal N-S axis E-W tracking, as compared to the fixed tilt (at 190) orientation. Incremental cost-benefit analysis for the excess capital expenditure on the tracking equipment has also been done. For this analysis, the Average Unit Cost of Power Supply for India has been taken into account, which is found to have a CAGR of 8.57% over the last decade; discount rate has been estimated as per the Renewable Energy Tariff Regulations passed by the Central Electricity Regulatory Commission (CERC), and has been found to be 10.76%. The ensuing analysis gives a discounted payback period of around 13 years for the excess capital expenditure due to the tracking equipment costs.

Development of a method for estimating the rooftop solar photovoltaic (PV) potential by analyzing the available rooftop area using Hillshade analysis

The solar photovoltaic (PV) system is known as one of the most outstanding new renewable energy systems for achieving the nearly zero energy building (nZEB). For the continuous deployment of the solar PV system in urban environments, it is crucial to estimate the rooftop solar PV potential. Urban areas, however, where high-rise buildings abound, are not always suitable for solar PV installation. Therefore, it is important to accurately estimate the available rooftop area considering the shadows from the surrounding buildings for reliable rooftop solar PV potential estimation. Therefore, this study proposed a method for estimating the rooftop solar PV potential by analyzing the available rooftop area through Hillshade analysis. Toward this end, the rooftop solar PV potential was estimated through the following hierarchical process: (i) calculation of the physical potential; (ii) calculation of the geographic potential; and (iii) calculation of the technical potential. For accurate estimation of the rooftop solar PV potential, the geographic potential (i.e., the available rooftop area) was explored in detail by analyzing the shadow based on the location of the sun through Hillshade analysis. By applying the proposed method to the Gangnam district in Seoul, South Korea, this study estimated the physical, geographic, and technical potentials on hourly, monthly, and annual bases. Overall, the physical, geographic, and technical potentials in the Gangnam district were found to be 9,287,982MWh, 4,964,118m2, and 1,130,371MWh, respectively. These rooftop solar PV potential results can be used in establishing solar policies by analyzing the different levels of the rooftop solar PV potential on hourly, monthly, and annual bases.

Estimation of rooftop solar photovoltaic potential using geo-spatial techniques: A perspective from planned neighborhood of Karachi – Pakistan

With an ever increasing population and significant solar resource availability throughout the whole year, Karachi metropolis hold a promising rooftop solar photovoltaic (PV) potential considering its millions of urban households. This research work highlights techniques to combine geographic information systems (GIS) and object-based image recognition approach to identify the available rooftop area for PV deployment in a small scale region of DHA Phase 7 Karachi. A six step methodology has been adopted for the estimation of rooftop PV potential which involves geographic division of high resolution satellite imagery; sampling and rooftop feature extraction using FE tool of ENVI EX software; extrapolation of rooftop areas for the entire ROI using roof area-population relationship; visual inspection to analyze different rooftop factors such as building orientation, shading effect from trees and nearby buildings and other roof uses; a comparison of extracted rooftops to the physically measured sample rooftops, reduction for shading and other uses; and finally conversion to energy and power outputs. A relationship of total roof area and population of 13 m2/capita ±5% has been found. With higher efficiency rooftop PV panels, 12.24 MW of solar power can be generated which is 122.4% of peak power demand of DHA Phase 7.

Estimation of rooftop solar photovoltaic potential of a city

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.The results show a potential of 2190MW for Mumbai city with median efficiency panels, at an annual average capacity factor of 14.8%. Daily and monthly variation of the generation from the Rooftop PV Systems has been studied. Comparison with sample daily load profiles shows that large scale deployment of Rooftop Solar Photovoltaic Systems can provide 12.8–20% of the average daily demand and 31–60% of the morning peak demand for different months, even with median conversion efficiency panels. This method can be used to obtain the PV potential for any region.