1. Multistep Prediction of Wind Speed Based on Grey Wolf Algorithm and Extreme Learning Machine.
- Author
-
ZHANG Wenyu, MA Keke, GUO Zhenhai, ZHAO Jing, and QIU Wenzhi
- Abstract
In order to improve the multi-step prediction of wind speed, a hybrid prediction model based on data signal decomposition and grey wolf optimization algorithm was proposed to optimize extreme learning machine. Firstly, the original wind speed time series was decomposed into several intrinsic mode functions and a residual sequence using the complete ensemble empirical mode decomposition with adaptive noise, and the partial autocorrelation function model input. Then, the model was built and the prediction was made on the decomposition subsequence. An extreme learning machine neural network with multi-input-multi-output strategy was constructed, and grey wolf algorithm was used to solve the weight and bias of the optimal hidden layer. Finally, the subsequence was reconstructed and the final prediction result was obtained. Simulation experiments were conducted using multiple sets of measured data with a time resolution of 15 minutes. The root mean square errors of the proposed model in the three wind farms were 0.859, 0.925, and 0.927, respectively, which were lower than other comparative models, verifying the effectiveness of the model in predicting wind speed in the next four hours, i. e. 16 steps prediction. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF