Predicting the Solar Potential of Rooftops using Image Segmentation and Structured Data

• DOI: 10.48550/arxiv.2106.15268
• Publication Date: 2021-01-01
• Authors: Soares, Daniel de...; Andrieux, François; Hell, Bastien; Lenhardt, Julien; Badosa, Jordi; Gavoille, Sylvain; Gaïffas, Stéphane; Bacry, Emmanuel
• Keywords: FOS: Computer and information sciences; Computer Science - Machine Learning; Computer Vision and Pattern Recognition (cs.CV); Computer Science - Computer Vision and Pattern Recognition; 0202 electrical engineering, electronic engineering, information engineering; 02 engineering and technology; [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing; 7. Clean energy; [SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing; Machine Learning (cs.LG)

Estimating the amount of electricity that can be produced by rooftop photovoltaic systems is a time-consuming process that requires on-site measurements, a difficult task to achieve on a large scale. In this paper, we present an approach to estimate the solar potential of rooftops based on their location and architectural characteristics, as well as the amount of solar radiation they receive annually. Our technique uses computer vision to achieve semantic segmentation of roof sections and roof objects on the one hand, and a machine learning model based on structured building features to predict roof pitch on the other hand. We then compute the azimuth and maximum number of solar panels that can be installed on a rooftop with geometric approaches. Finally, we compute precise shading masks and combine them with solar irradiation data that enables us to estimate the yearly solar potential of a rooftop.