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AI and satellite imagery transform solar energy potential mapping in China

The FY-4A satellite, the first of its kind in China's new generation of geostationary satellites, has been instrumental in improving solar resourcing and forecasting. GAO Ling from NSMC underscored the advantages of the satellite's broader field-of-view in enhancing the reliability of solar radiation data over China, in contrast to measurements from other satellites like Himawari or Meteosat.
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AI and satellite imagery transform solar energy potential mapping in China

by Simon Mansfield
Sydney, Australia (SPX) Jul 26, 2023
China's pursuit of carbon neutrality has made a substantial advancement following a successful collaboration among leading research and meteorological organizations. The Chinese Academy of Sciences' Institute of Atmospheric Physics (IAP/CAS), the Harbin Institute of Technology (HIT), and the National Satellite Meteorological Center (MSMC) of the China Meteorological Administration have collectively made significant strides in the field of solar resource assessment.

The team harnessed data from the Advanced Geostationary Radiation Imager on the Fengyun-4A (FY-4A) satellite, coupled with a random forest model and a physical model chain. This sophisticated combination enabled the creation of a comprehensive photovoltaic (PV) resource map, highlighting untapped solar energy potential across China.

The FY-4A satellite, the first of its kind in China's new generation of geostationary satellites, has been instrumental in improving solar resourcing and forecasting. GAO Ling from NSMC underscored the advantages of the satellite's broader field-of-view in enhancing the reliability of solar radiation data over China, in contrast to measurements from other satellites like Himawari or Meteosat.

Moving beyond the conventional, Prof. XIA Xiang'ao from IAP/CAS noted that the team's work expands on typical global horizontal irradiance (GHI) methods. Instead, their research incorporates effective irradiance, a vital factor for accurate solar resource assessment for PV applications.

YANG Dazhi, HIT professor and co-author of the research paper, acknowledged that this shift from focusing on irradiance to PV resourcing typifies the contemporary energy-meteorology-style of solar resource assessment.

A defining feature of the research is the use of the physical model chain, an advanced workflow that links a series of energy meteorology models. This innovative method allowed the team to produce impressively accurate estimates of in-plane irradiance, offering significant prospects for the future of solar resource evaluation.

The resulting solar PV resource map will be an invaluable tool for stakeholders involved in the design, planning, and operation of solar energy systems. It provides a detailed understanding of China's solar energy landscape, enabling informed decision-making towards a sustainable and green energy future.

The research, first authored by Dr. SHI Hongrong from IAP/CAS, has been published in the journal Renewable and Sustainable Energy Reviews.

This collaboration between FY-4A satellite technology and artificial intelligence stands as a landmark in China's journey toward carbon neutrality. By setting a new precedent in solar resource mapping, it contributes an innovative approach to the global quest for renewable energy.

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


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    Summary:

    This article details the collaboration between leading research and meteorological organizations in China that has enabled successful advancements in the field of solar resource assessment. Specifically, the team harnessed data from the Advanced Geostationary Radiation Imager on the Fengyun 4A satellite coupled with a random forest model and a physical model chain to create a comprehensive photovoltaic resource map that highlights untapped solar energy potential across the country. Furthermore, the article discusses the advantages of the satellite’s broader field of view in improving solar resourcing and forecasting as well as the shift from focusing on irradiance to PV resourcing. Overall, the research has resulted in a more accurate estimate of AI and satellite imagery which has transformed solar energy potential mapping in China.

    This AI report is generated by a sophisticated prompt to a ChatGPT API. Our editors clean text for presentation, but preserve AI thought for our collective observation. Please comment and ask questions about AI use by Spacedaily. We appreciate your support and contribution to better trade news.


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