A quick-scan method to assess photovoltaic rooftop potential based on aerial imagery and LiDAR
| Autor: | Joris Bronkhorst, Tim N.C. de Vries, Olindo Isabella, Miro Zeman, Hesan Ziar, Martijn Vermeer, Sven A. Briels, Jaap C.B. Donker |
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| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Yield (engineering)
Urban PV 020209 energy 02 engineering and technology Standard deviation Module fitting PV systems PV potential 0202 electrical engineering electronic engineering information engineering General Materials Science Automatic PV system design Roof Remote sensing Skyline Commercial software Rooftop PV Renewable Energy Sustainability and the Environment business.industry Photovoltaic system Modular design 021001 nanoscience & nanotechnology Quick-scan Yield prediction Annual energy yield Lidar Environmental science 0210 nano-technology business |
| Zdroj: | Solar Energy, 209 |
| ISSN: | 0038-092X |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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