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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Acknowledgements
This work has been partially supported by the National Natural Science Foundation of China project (51674109) and 2017 scientific research project of basic scientific research business expenses of provincial colleges and universities in Heilongjiang Province.
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Zheng, S., Jia, Z., Zhang, Z., Liu, F., Han, L. (2020). Short Term Wind Power Prediction Based on Wavelet Transform and BP Neural Network. In: Jiang, X., Li, P. (eds) Green Energy and Networking. GreeNets 2020. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 333. Springer, Cham. https://doi.org/10.1007/978-3-030-62483-5_26
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DOI: https://doi.org/10.1007/978-3-030-62483-5_26
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