Ultra Short-Term Distributed Photovoltaic Power Prediction Based on Satellite Remote Sensing
Liu Xiaoyan1,2, Wang Jue1,2, Yao Tiechui1,2, Zhang Pei3, Chi Xuebin1,2
1. Computer Network Information Center Beijing 100190 China; 2. University of Chinese Academy of Sciences Beijing 100040 China; 3. East China Jiaotong University Nanchang 330013 China
Abstract：Photovoltaic (PV) output prediction is of great significance for power grid dispatching. In this paper, an ultra short-term distributed PV power prediction method based on satellite remote sensing is proposed for the distributed PV power station without irradiance measurement device. Firstly, the SWR grid is spatio-temporal predicted based on Res-UNet model, and then the predicted SWR grid is spatially interpolated to obtain the future irradiance of the ground distributed stations. Finally, the LSTM model with codec is constructed to predict the PV output. Res-UNet can fully learn the spatio-temporal correlation of the SWR grid, and LSTM can better learn the annual and daily periodicity of irradiance by introducing daily coding and time coding. The power experiments on real PV power stations show that, compared with the PV power prediction method that takes the irradiance of numerical weather forecast as the input, the PV power prediction method that takes the irradiance predicted by the Res-UNet+ interpolation as the input realizes ultra short term power prediction with higher accuracy.
刘晓艳, 王珏, 姚铁锤, 张沛, 迟学斌. 基于卫星遥感的超短期分布式光伏功率预测[J]. 电工技术学报, 2022, 37(7): 1800-1809.
Liu Xiaoyan, Wang Jue, Yao Tiechui, Zhang Pei, Chi Xuebin. Ultra Short-Term Distributed Photovoltaic Power Prediction Based on Satellite Remote Sensing. Transactions of China Electrotechnical Society, 2022, 37(7): 1800-1809.
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2022, Vol. 37 
