A hybrid RBF neural network based model for day-ahead prediction of photovoltaic plant power output

被引:0
|
作者
Zhang, Qipei [1 ]
Tang, Ningkai [1 ]
Lu, Jixiang [1 ]
Wang, Wei [1 ]
Wu, Lin [1 ]
Kuang, Wenteng [1 ]
机构
[1] NARI Technol Co Ltd, NARI Res Inst, Nanjing, Jiangsu, Peoples R China
来源
关键词
photovoltaic power plant; solar energy forecasting; radial basis function neural network; whale optimization algorithm; low carbon; RENEWABLE ENERGY-RESOURCES; SOLAR-FORECASTING METHODS;
D O I
10.3389/fenrg.2023.1338195
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
Renewable energy resources like solar power contribute greatly to decreasing emissions of carbon dioxide and substituting generators fueled by fossil fuels. Due to the unpredictable and intermittent nature of solar power production as a result of solar radiance and other weather conditions, it is very difficult to integrate solar power into conventional power systems operation economically in a reliable manner, which would emphasize demand for accurate prediction techniques. The study proposes and applies a revised radial basis function neural network (RBFNN) scheme to predict the short-term power output of photovoltaic plant in a day-ahead prediction manner. In the proposed method, the linear as well as non-linear variables in the RBFNN scheme are efficiently trained using the whale optimization algorithm to speed the convergence of prediction results. A nonlinear benchmark function has also been used to validate the suggested scheme, which was also used in predicting the power output of solar energy for a well-designed experiment. A comparison study case generating different outcomes shows that the suggested approach could provide a higher level of prediction precision than other methods in similar scenarios, which suggests the proposed method can be used as a more suitable tool to deal such solar energy forecasting issues.
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页数:8
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