Short-term Hybrid Forecasting of Wind Speed Based on Attribute Reduction and Weighting Optimal Hierarchical Clustering
Accurate wind speed prediction is an important guarantee to improve the accuracy of wind power prediction.Therefore, a hybrid wind speed forecasting method of outlier robust extreme learning machine (ORELM) based on mutual information (MI) attribute reduction and weighted optimal hierarchical clustering (WOHC) is proposed. On this basis, the maximum correlation minimum redundancy (MRMR) and WOHC algorithms are applied to reduce wind speed attributes and cluster the wind speed sample set. The optimal cluster number is determined by the cluster optimization on the preprocessing stage (COPS) method. Then, ORELM is used to train different sample data sets, and the ORELM wind speed hybrid prediction model is established. Calculate the Euclidean distance between the point to be predicted and each cluster center, and select the matching ORELM model to predict the wind speed. Finally, the effectiveness and accuracy of the proposed prediction method are verified by the measured data of a wind farm in Northeast China. The results show that the new method has good prediction accuracy and can meet the needs of wind speed prediction of actual wind farms.
国家科技期刊平台
NSTL
万方数据
维普
