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FY-4A Satellite and Machine Learning Model Advance Solar Photovoltaic Resource Mapping in China

Jul 26, 2023

A joint research team from the Institute of Atmospheric Physics (IAP) of the Chinese Academy of Sciences, Harbin Institute of Technology (HIT) and China Meteorological Administration have made progress in solar resource assessment, a critical element for utilizing photovoltaic (PV) energy efficiently. 

Using data from the Advanced Geostationary Radiation Imager onboard the Fengyun-4A (FY-4A) satellite, a random forest model, and a physical model chain that converts irradiance to PV power, the researchers generated a detailed PV resource map, shedding new light on China's solar energy potential.
The findings were published in Renewable and Sustainable Energy Reviews on July 13.

FY-4A satellite (Image by China Meteorological Administration)
FY-4A, the first of the latest generation of Chinese geostationary satellites, significantly enhances solar resourcing and forecasting with its high-resolution capabilities. The satellite's wider field-of-view has improved the reliability of the current solar radiation product over China, compared to using measurements from Himawari or Meteosat satellites, especially towards the edge of the satellite disk.
"Indeed, what sets this research apart is the integration of a highly advanced workflow known as the physical model chain," said Prof. XIA Xiang'ao from IAP, corresponding author of the study. "By leveraging a sequence of energy meteorology models in cascade, we achieved remarkably accurate estimates of in-plane irradiance, a crucial factor for precise solar resource assessment for PV applications.
"The solar PV resource map resulting from our research is of immense value to those involved in designing, planning, and operating solar energy systems," said Prof. YANG Dazhi from HIT, co-corresponding author of the study.
Contact

LIN Zheng

Institute of Atmospheric Physics

E-mail:

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

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