1. Wind Power Prediction Model Based on Wavelet Neural Network under Missing Data
- Author
-
Jing Lu, Yan Qing Zhao, Chao Ying Yang, Yu Hong Zhao, and Jun Yi Zhao
- Subjects
Engineering ,Wind power ,Artificial neural network ,business.industry ,Basis function ,General Medicine ,computer.software_genre ,Missing data ,Transfer function ,Power (physics) ,Wavelet ,Data mining ,business ,computer ,Interpolation - Abstract
Wind power prediction is a key problem in optimizing power dispatching. This paper builds a wind power prediction model based on wavelet neural network which substitutes wavelet basis function for the transfer function of hidden layer. A missing data interpolation strategy is also given to improve the applicability of the model. With the wind farm data from southeast coast, the model works and the wind power in the next 30 hours is predicted. In the sense of the mean square errors this paper compared the prediction results of the model and BP neural network model, the results shows the models have a better accuracy.
- Published
- 2015