Photovoltaic power prediction based on G-L-R model
Liu Qiong1, Tian Xiaoli1, Hua Lingfeng2, Liu Xiaoqiang2
1. State Grid Dingxi Electric Power Company, Dingxi, Gansu 743000; 2. State Grid Telecommunications Industry Group Anhui Jiyuan Software Co., Ltd, Hefei 230088
Abstract Photovoltaic power prediction is one of the key technologies for distributed photo- voltaics. Based on gradient boosting decision tree (GBDT) and light gradient boosting machine (LightGBM) algorithm, the Ridge algorithm with L2 regular term is used as the fusion strategy, and the G-L-R model is designed. The short-term prediction and ultra-short-term prediction of photovoltaic power prediction are accurately carried out. The experiment was carried out on the data of a photovoltaic station in Anhui in 2018. The results show that with reference to the East China regional assessment standard, the accuracy of photovoltaic power prediction is 91.31%, among which, the accuracy of sunny prediction is 96.34%, and the accuracy of sudden weather prediction is 87.22%.
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2020, Vol. 21 
