A simplified evaluation method of rooftop solar energy potential based on image semantic segmentation of urban streetscapes

Abstract

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.