张旭,张宏立,王聪.基于数据分析和改进Chebyshev神经网络的风速时间序列预测[J].电测与仪表,2020,57(22):33-39. zhangxu,Zhang Hongli,Wang Cong.Time Series Prediction of Wind Speed Based on Data Analysis and Improved Chebyshev Neural Network Zhang Xu1 , Zhang Hongli1 , Fan Wenhui2,Wang Cong1[J].Electrical Measurement & Instrumentation,2020,57(22):33-39.
基于数据分析和改进Chebyshev神经网络的风速时间序列预测
Time Series Prediction of Wind Speed Based on Data Analysis and Improved Chebyshev Neural Network Zhang Xu1 , Zhang Hongli1 , Fan Wenhui2,Wang Cong1
In order to improve the prediction accuracy of wind speed time series, based on the randomness and fluctuation of wind speed time series, a hybrid wind speed time series prediction model (CEEMD-OPSO-Chebyshev) based on Chebyshev basis function neural network is proposed by using complementary set empirical mode decomposition and orthogonal particle swarm optimization. The original wind speed time series is decomposed into finite intrinsic modal components by CEEMD, which avoids the residual problem of redundant noise in traditional decomposition signal reconstruction. At the same time, permutation entropy is introduced to analyze the intrinsic characteristics of each component for clustering, and a Chebyshev neural network wind speed prediction model based on orthogonal particle swarm optimization algorithm is proposed, which uses orthogonal particle swarm optimization to predict the weight of the network to further improve the prediction accuracy. Through forecasting and analyzing the actual wind speed time series of wind farms, the results show that the proposed hybrid forecasting model can obtain higher forecasting accuracy than the traditional forecasting model.
