Cloud fraction nowcasting method for PV applications


Researchers from China have developed a novel way to nowcast the cloud coverage of PV plants. It consists of a new algorithm that uses machine learning techniques for processing and taking geostationary satellite images as input.

“We are committed to developing a daytime hourly intra-day cloud fraction (CF) prediction algorithm for small areas over PV plants,” they explained. “Our primary objective is to develop an innovative and easy-to-promote algorithm or system based on the key framework of the PredRNN++ model.”

The “NCP_CF” method receives input radiance data from the Himawari-8/9 satellite. It can reportedly process data centered at PV plants with spatio-temporal resolutions of 4 km. It then transforms these details into tiles, which are used for a prediction model.

The model is also said to be able to learn to predict long-term future imageries in various spatio-temporal tasks using recurrent-neural networks-based (RNNs) long short-term memory (LSTM) algorithm framework. Modified for cloud forecasting, the novel system updates every hour, showing the most recent cloud images for the following four hours. Then, a cloud mask algorithm calculates the number of cloudy pixels over the PV plants.

Diagram of the system

Image: Sun Yat-sen University and Southern Marine Science and Engineering Guangdong Laboratory, Nature Communications, CC BY 4.0 DEED

The novel prediction system was tested on five PV plants located in China from 2019 to 2022. The results were then compared to observations from twelve ground-based manual stations and three all-sky imager stations.

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As for the predicted cloud fraction (CF) data, the scientists found their system to have root mean square errors (RMSEs) of 0.21, 0.25, 0.3, and 0.35 for the forecast leading time of one hour, two hours, three hours, and four hours, respectively. The mean bias errors (MBEs) or the same leading times were found at −0.09, −0.08,−0.07, and −0.03 for the forecast.

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Image: Sun Yat-sen University and Southern Marine Science and Engineering Guangdong Laboratory, Nature Communications, CC BY 4.0 DEED

“Our future mission is to further promote applications and improve the accuracy of this cloud cover nowcasting technique, especially for the forecast leading time of more than two hours, by using higher spatial-resolution satellite data and combining the short-term forecast data from a rapidly updated regional high resolution numerical weather prediction,” the academics concluded.

The research was conducted by scientists from Sun Yat-sen University, Southern Marine Science and Engineering Guangdong Laboratory, China Meteorological Administration, China General Nuclear Power Group, and the Beijing Keytec Technology. They presented their results in “Accurate nowcasting of cloud cover at solar photovoltaic plants using geostationary satellite images,” which was recently published in Nature Communications.

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