The Urban Rooftop Photovoltaic Potential Determination
Abstract
:1. Introduction
2. Materials and Methods
2.1. Sub-Potentials and Their Essential Factors
2.1.1. Physical Potential
2.1.2. Geographic (Urban) Potential
2.1.3. The Technical (Electricity Generation) Potential
2.1.4. The Economic Potential
2.2. Methodologies and Their Approaches
2.2.1. Statistical Sampling Approach
2.2.2. Mathematical Approach
2.2.3. Digital Modeling Approach and Commercial Software Packages
2.2.4. Optimization Approach
2.2.5. Artificial Intelligence in Commercial Software Packages Approach
2.2.6. Artificial Intelligence Approach
3. Results
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Ref | Data | Location | Location Area (km2) | Roof Area (km2) | Annual Generated Electricity | Demand Coverage (%) | Error (%) | Year |
---|---|---|---|---|---|---|---|---|
[2] | GIS Statistical | Spain | 505,990 | 571 | 32 | 2008 | ||
[10] | Google Earth Statistical | Andalusia (Spain) | 87,597 | 265.52 | 9.73 GWh | 78.89 | 10 | 2010 |
Ref | Tools | Data | Location | Location Area (km2) | Roof Area (km2) | Annual Generated Electricity | Demand Coverage (%) | Error (%) | Year |
---|---|---|---|---|---|---|---|---|---|
[24] | MATLAB and GIS Software | GIS Statistical | Piedmont (Italy) | 25,000 | 43 | 6900 GWh | 28.16 | 1.7 | 2011 |
[25] | Ortho-image analysis | Georeferenced images | Turin (Italy) | 858 GWh | 10 | 2011 | |||
[26] | estimate rooftop from ground floor area | Statistical on-site information | Hong Kong | 54 | 5981 GWh | 14.2 | 15 | 2013 | |
[27] | Nightlight intensity | Statistical Nighttime satellite images | Riyadh (Saudi Arabia) | 185,000 rooftop | 0.7 TWh | 2020 | |||
[28] | Interpolation algorithm with Matlab ArcGIS—PVGIS | National geographical—Cartographical | Spain | 505,990 | 1134 | 291 TWh | 45 | 2020 | |
[29] | GIS image analysis micro–macro synthesis | Statistical Google earth | Mumbai (India) | 458.27 | 2190 MW | 20 | 19.4 | 2015 | |
[30] | mathematical model micro-level simulations in PVSyst | Statistical—land-use and building stock | 13 Indian cities | 17.8 GWp | 2020 |
Ref | Tools | Data | Location | Location Area (km2) | Roof Area (km2) | Annual Generated Electricity | Demand Coverage (%) | Error (%) | Year |
---|---|---|---|---|---|---|---|---|---|
[31] | Rating rooftops Digital elevation model | LiDAR geospatial | Maribor (Slovenia) | 1 | 2.6 | 2013 | |||
[15] | ArcGIS solar analyst extension tool | LIDAR Statistical | Lisbon (Portugal) | 538 rooftop | 11.5 GWh | 48 | 2012 | ||
[7] | digital surface model digital elevation model ArcGIS | LiDAR—GIS | Lethbridge (Canada) | 124.3 | 2.73 | 3011 GWh | 38 | 2019 | |
[20] | 3D city model ArcGIS Radiance Software | Real Estate Office 3D model | Karlsruhe (Germany) | 173 | 930 GWh | 2015 | |||
[32] | modular simulation INSEL model 3D model | LIDAR | Stuttgart (Germany) | 1.5 | 35 | 2012 | |||
[33] | INSEL model 3D model SimStadt platform | CityGML | Ludwigsburg (Germany) | 700 | 22.26 | 1318 GWh | 77 | 2017 | |
[34] | 3D model Daysim simulation | GIS—LiDAR | Cambridge (USA) | 4881.3 kWh | 5.3 | 2013 | |||
[35] | Normalized Digital Surface Model Digital Terrain Model Digital Surface Model | LiDAR Statistical | Erie County (USA) | 271 | 2020 | ||||
[36] | Digital surface model ArcGIS | LiDAR statistical | Auckland (New Zealand) | 1364 kWh/m2 | 2017 | ||||
[37] | Hillshade tool Polygon to Raster tool ArcGIS | Building elevation Statistical | Gangnam-Seoul (South Korea) | 4.903 | 2016 | ||||
[38] | Hillshade tool from ArcGIS | Building elevation Statistical | Gangnam-Seoul (South Korea) | 4.964 | 1,130,371 MWh | 150 | 2017 | ||
[39] | quick-scan yield prediction | Aerial imagery GIS-LIDAR | Eindhoven (Netherlands) | 145 rooftop | 145 rooftop | 1070 kWh/kWp | 2020 | ||
[40] | Digital elevation model | UAS-LIDAR | Phoenix (U.S) | 0.265 | 0.027 | 5089 GWh | 2020 | ||
[14] | nonlinear efficiency characteristics model | LIDAR | Maribor (Slovenia) | 0.5 | 12 | 2014 | |||
[41] | GIS-based method Esri ArcGIS software | Urban Planning and Municipalities building-shape data | Khalifa—Zayed (Abu Dhabi) | 23 (Khalifa) | 2 (Khalifa) | 206 GWh (Khalifa) | 20 (Khalifa) | 2020 |
Ref | Tools | Data | Location | Location Area (km2) | Roof Area (km2) | Annual Generated Electricity | Demand Coverage (%) | Error (%) | Year |
---|---|---|---|---|---|---|---|---|---|
[42] | GIS-based optimization model | Statistical on-site information | Seoul Busan-Daejeon (South Korea) | 275.33 kWh per panel (busan) | 2014 | ||||
[43] | Optimization model | statistical | Austria | 10 GWp | 100 | 2020 |
Ref | Tools | Data | Location | Location Area (km2) | Roof Area (km2) | Annual Generated Electricity | Demand Coverage (%) | Error (%) | Year |
---|---|---|---|---|---|---|---|---|---|
[44] | Feature analyst extraction in ArcGIS | GIS Statistical | Part of Ontario (Canada) | 48,000 | 25 | 6909 GWh | 5 | 15 | 2010 |
[45] | DeepSolar GIS software | National datasets Statistical | 4 US cities | 395,387 roof | 2020 |
Ref | Tools | Data | Location | Location Area (km2) | Roof Area (km2) | Annual Generated Electricity | Demand Coverage (%) | Error (%) | Year |
---|---|---|---|---|---|---|---|---|---|
[6] | Supervised learning Support Vector Machine algorithm | LiDAR CORINE Land Cover Statistical | Switzerland | 1901 communes | 328 | 17.86 TWh | 28 | 2017 | |
[4] | Support Vector Machine classification MATLAB Solar radiation GIS | LIDAR | Geneva (Switzerland) | 66,811 roof | 2018 | ||||
[21] | Machine Learning Random Forest model | LiDAR Digital Orthophoto | Switzerland | 41,285 | 252 | 16.29 TWh | 25.3 | 2017 | |
[9] | Data mining Machine Learning | LiDAR statistical | Switzerland | 9,600,000 rooftops | 267 | 24 TWh | 40 | 2019 | |
[1] | U-Net Deep learning | Google Earth | Wuhan (China) | 961 | 17.3 TWh | 9.51 | 2019 | ||
[47] | Image recognition Machine learning | Geographical building Aerial images | Freiburg (Germany) | 49,573 building | 524 GWh | 2017 | |||
[48] | Machine learning | Statistical Satellite | EU | 680 TWh | 24.4 | 2019 | |||
[49] | Artificial intelligence | Statistical Satellite image | Hanoi (Vietnam) | 3359 | 139.4 | 37,591 GWh | 2020 | ||
[50] | Segmentation Hough transformation | Satellite image | Beijing (China) | 0.678 | 63.78 GWh | 2018 |
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Fakhraian, E.; Alier, M.; Valls Dalmau, F.; Nameni, A.; Casañ Guerrero, M.J. The Urban Rooftop Photovoltaic Potential Determination. Sustainability 2021, 13, 7447. https://doi.org/10.3390/su13137447
Fakhraian E, Alier M, Valls Dalmau F, Nameni A, Casañ Guerrero MJ. The Urban Rooftop Photovoltaic Potential Determination. Sustainability. 2021; 13(13):7447. https://doi.org/10.3390/su13137447
Chicago/Turabian StyleFakhraian, Elham, Marc Alier, Francesc Valls Dalmau, Alireza Nameni, and Maria José Casañ Guerrero. 2021. "The Urban Rooftop Photovoltaic Potential Determination" Sustainability 13, no. 13: 7447. https://doi.org/10.3390/su13137447