Wind Power Prediction Model Based on Wavelet Neural Network under Missing Data

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.

• Periodical: Applied Mechanics and Materials (Volume 737)
• Pages: 76-80
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.737.76
• Online since: March 2015
• Authors: Jing Lu, Yan Qing Zhao, Yu Hong Zhao*, Jun Yi Zhao, Chao Ying Yang
• Keywords: Interpolation Scheme Data, Short-Term Power Prediction, Wavelet Neural Network

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