Short Term Wind Speed Prediction Based on Multi-feature Data and Mixed Model
In order to improve the accuracy and reliability of short-term wind speed prediction, a new hybrid prediction model considering multi feature data was proposed. The model was built based on Stacking integration algorithm integrating adaptive fuzzy neural network, data grouping prediction model and random forest regression model. At the same time, combined with the improved mode decomposition of time-varying filter and adaptive noise mode decomposition, the data depth secondary decomposition was completed. Firstly, the multidimensional subsequence matrix was obtained by preprocessing the original data with multiple features. Then, the subsequence array entropy was calculated to reconstruct the subsequence matrix. Finally, the mixed model integrated by Stacking integration algorithm was used to predict sequences in different frequency domains. The comparison with the classical model shows that the proposed hybrid model considering multi feature data has greater advantages in prediction accuracy and model stability.
short term wind speed predictionstacking integration algorithmdeep learning network mixed model
万方数据
维普
