Deep-Learning-Based Evaluation of Rooftop Photovoltaic Deployment in Tianjin, China

Abstract

Rooftop photovoltaics (RPVs) are crucial in addressing energy shortages and environmental concerns caused by fossil fuel combustion. To promote the optimal deployment of RPVs in Tianjin, a region with abundant solar resources and dense buildings, this study proposes a framework that integrates building vector data with a deep learning model to extract currently installed RPVs from remote sensing images, and further estimate the development potential of RPVs. A total of 86,363 RPV polygons were extracted, covering an area of 10.34 km2. More than 70% of these RPVs are concentrated on large and low-rise buildings, and a similar proportion is found in industrial buildings, as these buildings offer favorable installation conditions. Combining solar radiation and construction land development planning, we further determined the potential deployment zone of RPVs covering about 13% of the Tianjin’s land area, which represents 31.31 TWh per year of power generation potential. In the future, it is recommended to prioritize RPV installation on large and low-rise buildings or industrial buildings in the potential deployment zone, which could provide higher power generation and contribute significantly to environmental emission reduction goals. The proposed research framework can also be applied to other cities.

Keywords

• rooftop photovoltaics
• deep learning
• remote sensing
• installed RPVs
• RPV development assessment