Short term wind speed prediction modeling based on error correction and VMD-ICPA-LSSVM
Accurate wind speed prediction is the key to large-scale application of wind energy in power system,but the randomness and volatility of wind speed sequence make it difficult to predict.Herein,strategies of Logistic chaotic mapping,adaptive parameter adjustment,and the introduction of mutation are used to improve the Carnivo-rous Plant Algorithm(CPA),and a short-term wind speed prediction model based on error correction and VMD-IC-PA-LSSVM is proposed.First,meteorological factors are used as inputs for Least Squares Support Vector Machine(LSSVM)to predict wind speed and obtain an error sequence.Then,K-L divergence is used to adaptively determine the parameters of Variational Mode Decomposition(VMD)and decompose the error sequence.Then the Improved Carnivorous Plant Algorithm(ICPA)is combined to optimize the adjustable parameters of LSSVM to predict the decomposed subsequences.The prediction results of each subsequence are stacked and error correction is performed on the original prediction sequence to obtain the final wind speed prediction values.The experimental results show that the proposed model has excellent prediction accuracy and generalization performance.
万方数据
维普
