Short-term wind prediction based on mRMR and SVMD-TPA-BiSTM

XING Peiyu; WEI Yunbing; HU Hua; ZHANG Wenhu

doi:10.12299/jsues.23-0217

Volume 38 Issue 4

Dec. 2024

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Article Navigation > Journal of Shanghai University of Engineering Science > 2024 > 38(4): 414-421

XING Peiyu, WEI Yunbing, HU Hua, ZHANG Wenhu. Short-term wind prediction based on mRMR and SVMD-TPA-BiSTM[J]. Journal of Shanghai University of Engineering Science, 2024, 38(4): 414-421. doi: 10.12299/jsues.23-0217

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XING Peiyu, WEI Yunbing, HU Hua, ZHANG Wenhu. Short-term wind prediction based on mRMR and SVMD-TPA-BiSTM[J]. Journal of Shanghai University of Engineering Science, 2024, 38(4): 414-421. doi: 10.12299/jsues.23-0217

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Short-term wind prediction based on mRMR and SVMD-TPA-BiSTM

doi: 10.12299/jsues.23-0217

1.
School of Electronic and Electrical Engineering ，Shanghai University of Engineering Science, Shanghai 201620, China
2.
Zhejiang Intellectual Property Protection Center, Hangzhou 310012, China

Received Date: 2023-10-15
Publish Date: 2024-12-31

Abstract

Abstract

A short-term wind power prediction method based on mRMR and SVMD-TPA-BiLSTM was proposed. Firstly, successive variational modal decomposition (SVMD) was adopted to reduce the dimensionality of wind power sequences, and the decomposed subsequences were combined with the key meteorological feature data selected maximum correlation minimum redundancy (mRMR) to form a training set. Secondly, a combined model based on the temporal pattern attention (TPA) mechanism to improve the Bi-directional long short-term memory neural network (BiLSTM) was established, and TPA mechanism was used to capture the correlation of different time sequence data. Finally, the prediction component results were superimposed to obtain the final prediction results. Taking the dataset of a certain wind farm as an example, different models were subjected to single step and multi-step prediction experiments to compare the accuracy, and it was proved that the prediction method can effectively portray the component characteristics of wind power and improve the prediction accuracy of wind power.

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References(19)

References

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