Research on short-term power prediction of wind power generation based on WT-CABC-KELM - Jin-ning Shan; Hong-zhe Wang; Gen Pei; Shuang Zhang; Wei-hao Zhou (Energy Reports, 2022)
Under the framework of the gradual scarcity of fossil energy, how to make better use of wind power to provide a safe and effective guarantee for the power grid has become a hot topic today. Aiming at the problems of existing short-term wind power prediction models that are easy to fall into local optimum, slow training speed, and poor accuracy, this paper proposes an improved artificial bee colony algorithm based on wavelet transform (WT) combined with a nuclear extreme learning machine. Power prediction method (WT-CABC-KELM). First, aiming at the volatility of historical wind power data, it is proposed to use wavelet transform to extract the hidden main features at each frequency to improve the prediction accuracy. Then, because the two parameters C and λ of the KELM model have a greater impact on the prediction accuracy, The improved artificial bee colony algorithm (CABC) is used to optimize the parameters of its model to achieve the purpose of improving the prediction accuracy, and the WT-CABC-KELM short-term wind power prediction model is established. In order to prove the effectiveness of the method proposed in this paper, 4 Each season is forecasted separately, and compared with traditional SVM and BP neural network. Finally, the results are compared and analyzed by 4 kinds of error indicators. The experimental results show that the short-term wind power prediction method (WT-CABC-KELM) mentioned in this article can effectively improve the short-term power prediction accuracy of wind power.