High resolution photovoltaic power generation potential assessments of rooftop in China

Abstract

Rooftop photovoltaic system plays an important role in solar energy power generation especially in urban. In this paper, we present an assessment method for the PV power generation potential of rooftop in China. Using machine learning model processes the big data that consists of the gross domestic product, building footprint, road length and population, at a high geographic resolution of 10 km by 10 km. The result shows that the rooftop generation potential in China is 3.27×109 MWh annually, which is close to half of the total electricity generation of China mainland in 2020, and will contribute to 2.41×109 tons of CO2 emission reduction per year. The regional results are counted by provinces and cities, showing Shandong province is the one with highest potential of 0.275×109 MWh. On the whole, the western region covers a large area with sufficient solar radiation, while the eastern region has greater photovoltaic power generation potential because of its available roof resources. The development potential of first tier, new first tier and second tier cities is generally greater than that of third and fourth tier cities.

Keywords

• Rooftop photovoltaic
• Geographic data
• Machine learning
• Photovoltaic potential
• CO2 emission reduction