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Short-Term Wind Power Prediction Based on Combinatorial Neural Networks

Tusongjiang Kari1, Sun Guoliang2, Lei Kesong1, Ma Xiaojing1,*, Wu Xian1

1 School of Electrical Engineering, Xinjiang University, Urumqi, 830017, China
2 Anhui Nari Jiyuan Electric Power System Tech Co. LTD, Hefei, 230088, China

* Corresponding Author: Ma Xiaojing. Email: email

Intelligent Automation & Soft Computing 2023, 37(2), 1437-1452.


Wind power volatility not only limits the large-scale grid connection but also poses many challenges to safe grid operation. Accurate wind power prediction can mitigate the adverse effects of wind power volatility on wind power grid connections. For the characteristics of wind power antecedent data and precedent data jointly to determine the prediction accuracy of the prediction model, the short-term prediction of wind power based on a combined neural network is proposed. First, the Bi-directional Long Short Term Memory (BiLSTM) network prediction model is constructed, and the bi-directional nature of the BiLSTM network is used to deeply mine the wind power data information and find the correlation information within the data. Secondly, to avoid the limitation of a single prediction model when the wind power changes abruptly, the Wavelet Transform-Improved Adaptive Genetic Algorithm-Back Propagation (WT-IAGA-BP) neural network based on the combination of the WT-IAGA-BP neural network and BiLSTM network is constructed for the short-term prediction of wind power. Finally, comparing with LSTM, BiLSTM, WT-LSTM, WT-BiLSTM, WT-IAGA-BP, and WT-IAGA-BP&LSTM prediction models, it is verified that the wind power short-term prediction model based on the combination of WT-IAGA-BP neural network and BiLSTM network has higher prediction accuracy.


Cite This Article

T. Kari, S. Guoliang, L. Kesong, M. Xiaojing and W. Xian, "Short-term wind power prediction based on combinatorial neural networks," Intelligent Automation & Soft Computing, vol. 37, no.2, pp. 1437–1452, 2023.

cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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