First estimation of high-resolution solar photovoltaic resource maps over China with Fengyun-4A satellite and machine learning

Abstract

Fengyun-4A (FY-4A), which is the latest-generation Chinese geostationary meteorological satellite, measures solar reflection and thermal emission with high temporal, spatial, and spectral resolutions. It is expected to be highly beneficial for solar resource assessment and forecasting in China. This study is the first to estimate, using FY-4A and a random forest model, the global horizontal irradiance (GHI) at a 4-km–15-min spatio-temporal resolution over China, as a means to arrive at a solar photovoltaic (PV) resource map. In terms of GHI estimates, the root mean square error and mean bias error between hourly measured and retrieved values are 147.02 (35.2%), −5.64 W/m2 (−1.4%), respectively, whereas the values of daily estimates are 29.20 (18.0%), −2.97 W/m2 (−1.3%). The retrieval accuracy is found much better for instances with solar zenith angles smaller than 60°. Relatively larger errors are found at locations in the Sichuan Basin and northeastern China, which can be attributed to bright surfaces and/or strong cloud transients. With the retrieved irradiance, PV resource is derived through a physical model chain. The annual mean PV resource map suggests that, over most of the west areas, the annual mean effective irradiance exceeds 1700 kWh/m2, with the highest value found in Tibet (around 2000 kWh/m2 per annum). Eastern China has an annual effective irradiance of only 1300–1500 kWh/m2. The region with poorest solar resource is the Sichuan Basin (less than 1100 kWh/m2 per annum).

Citation

Hongrong Shi, Dazhi Yang, Wenting Wang, Disong Fu, Ling Gao, Jinqiang Zhang, Bo Hu, Yunpeng Shan, Yingjie Zhang, Yuxuan Bian, Hongbin Chen, Xiangao Xia, First estimation of high-resolution solar photovoltaic resource maps over China with Fengyun-4A satellite and machine learning, Renewable and Sustainable Energy Reviews, Volume 184, 2023, 113549, https://doi.org/10.1016/j.rser.2023.113549.

Acknowledgments

This work is supported by the National Natural Science Foundation of China [Nos., 42030608] and the Open Grants of the State Key Lab- oratory of Severe Weather [Nos., 2022LASW-B17]. We thank the National Satellite Meteorological Center (NSMC) for providing access to and the guidance on the Fengyun-4 AGRI dataset.

Data availability

Data will be made available on request (shihrong@mail.iap.ac.cn)