Research Article
Short Term Wind Power Prediction Based on Wavelet Transform and BP Neural Network
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@INPROCEEDINGS{10.1007/978-3-030-62483-5_26, author={Shuang Zheng and Zhaoju Jia and Ziwei Zhang and Fugang Liu and Long Han}, title={Short Term Wind Power Prediction Based on Wavelet Transform and BP Neural Network}, proceedings={Green Energy and Networking. 7th EAI International Conference, GreeNets 2020, Harbin, China, June 27-28, 2020, Proceedings}, proceedings_a={GREENETS}, year={2020}, month={11}, keywords={BP neural network Wavelet transform Wind power prediction}, doi={10.1007/978-3-030-62483-5_26} }- Shuang Zheng
Zhaoju Jia
Ziwei Zhang
Fugang Liu
Long Han
Year: 2020
Short Term Wind Power Prediction Based on Wavelet Transform and BP Neural Network
GREENETS
Springer
DOI: 10.1007/978-3-030-62483-5_26
Abstract
Wind power generation has great randomness because of its randomness and uncontrollability. Due to the instability of wind energy, the power system access to large-scale wind power will pose a serious threat to the system. The accuracy of wind power prediction is very important to the security and stability. In this paper, a prediction model of electric power based on wavelet and BP neural network is proposed. The wavelet can further refine the periodic and nonlinear characteristics of electric power, and it solves many uncontrollable features when testing with BP neural network alone. The simulation shows that the prediction results of this method is better than that of BP neural network.
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