Assessment of rooftop photovoltaic power generation potentials by using multisource remote sensing data
Rooftop solar photovoltaic(PV)systems are becoming increasingly critical in the global shift toward sustainable energy.Despite their importance,the fragmented and small-scale spatial distribution of rooftop PV systems poses significant challenges to accurate and detailed regional potential assessments.This study aims to deal with these challenges by developing a comprehensive assessment framework that integrates multisource remote sensing data and advanced artificial intelligence algorithms.The objective is to provide a robust methodology for evaluating the potential of rooftop PV systems on a large scale.The assessment framework developed in this study leverages a combination of geostationary meteorological satellite imagery and deep learning inversion models to estimate hourly surface solar radiation.To accurately extract building outlines,high-resolution remote sensing images are processed using advanced image segmentation models.Furthermore,the framework integrates a geometric optical model to simulate the PV generation process.This holistic approach enables the precise revelation of spatial and temporal variations in solar energy resources.It also facilitates the investigation of the total available rooftop resources and the determination of PV power generation potential at meter-level resolution and hourly scales.The effectiveness of the framework was validated through a case study conducted in Jiangsu Province,China.The results demonstrated the scalability and applicability of the framework across different geographic locations and multiple temporal scales.The estimation results revealed that rooftop resources in Jiangsu Province could support a PV installed capacity of 236.25 GW,with an annual power generation potential of 303.81 TWh.This substantial output could meet 41.1％of the province's total electricity consumption.The case study highlights the framework's ability to provide detailed and accurate assessments of rooftop PV potential on a large scale.This study illustrates the feasibility and effectiveness of integrating multisource remote sensing observations for the spatiotemporal assessment of rooftop PV potential.The developed framework offers robust tools and technical support for advancing the transition to sustainable energy.By providing insights into the spatial and temporal variability of solar resources,this framework paves the way for the optimized utilization of rooftop PV systems.This research contributes to the broadening effort of achieving sustainable energy goals by enabling more precise and large-scale assessments of rooftop PV potential.
renewable energyrooftop photovoltaicsremote sensing image segmentationsurface solar radiation inversioncarbon reduction
万方数据
维普
