Deep learning method for evaluating photovoltaic potential of urban land-use: A case study of Wuhan, China

Abstract

To strengthen the synergy between urban photovoltaic development and urban planning, which can help to promote photovoltaic and renewable energy development in cities, a workflow based on a deep-learning method by using neural networks and urban satellite images is constructed, which is applied to study the relationship between urban rooftop photovoltaic potential and urban land use. A Chinese city, Wuhan, has been considered the subject and divided into 5184 units, which is a 1201.2 km2 area in central urban space. The result shows that, three types of urban land use type have the highest rooftop photovoltaic potential, which are Continuous Urban area, Discontinuous Dense Urban area and Industrial, commercial, public and education unit. The annual photovoltaic potential of these three land use types have reached 1818.41 GWh/year, 1957.32 GWh/year and 2022.71 GWh/year, and the average photovoltaic power generation per unit area of these three types have reached 11.23 GWh/km2·year, 9.99 GWh/km2·year and 13.07 GWh/km2·year. In addition, these three land use types contributed 71.4% of the city’s total rooftop photovoltaic potential. When considering the coordination of roof photovoltaic development and urban planning, these three types of land use should be given priority. The method and findings can provide urban planning authorities with tools and data to reference when developing urban master plan and PV development plans.