Short-term Forecasting for Wind Speed Based on Wavelet Decomposition and LMBP Neural Network
In this paper,a wind speed forecasting method based on wavelet decomposition and BP neural network with Levenberg-Marquardt algorithm (LMBP) is proposed.Firstly,original wind speed seires is decomposed into one low-frequency component and several high-frequency components by wavelet decomposition method.Then different LMBP neural networks are built for the forecasting of every component respectively.Finally the predictions of components are reconstructed to obtain the prediction of original wind speed.As for the problem that the convergence rate is limited by the inversion calculation of largescale matrix in the training process of LMBP network,supermemory gradient algorithm solving large linear equations is introduced to adjust weights and thresholds of the network.Meanwhile the structure of hidden layer neurons is optimized by least squares network pruning method.At the end of the paper,actual wind speed data from certain wind farm is used to verify the forecasting model and the results indicate that the model develops the precision of wind speed forecasting effectively.
wavelet decomposition:LMBP neural network:wind speed forecasting:super-memory gradient algorithm:least squares network pruning
Wei WEI Guilian WU Minghai YANG Yongwu ZHANG Shengxiao QIU Aiming SUN
Key Laboratory of Smart Grid of Ministry of Education Tianjin University Tianjin,China Electric Power Dispatching Center Weifang Electric Power Bureau Weifang,China
国际会议
威海
英文
1126-1131
2011-07-06（万方平台首次上网日期，不代表论文的发表时间）