ULTRA SHORT TREM WIND POWER PREDICTION BASED ON IAOA-VMD-LSTM

Xiao Liexi; Zhang Yu; Zhou Hui; Zhao Guanhao

doi:10.19912/j.0254-0096.tynxb.2022-1054

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Acta Energiae Solaris Sinica ›› 2023, Vol. 44 ›› Issue (11) : 239-246. DOI: 10.19912/j.0254-0096.tynxb.2022-1054

ULTRA SHORT TREM WIND POWER PREDICTION BASED ON IAOA-VMD-LSTM

Xiao Liexi¹, Zhang Yu^1,2, Zhou Hui¹, Zhao Guanhao¹

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Abstract

In order to accurately predict wind power, an ultra-short-term wind power prediction model was proposed based on improved arithmetic optimization algorithm （IAOA）, variational modal decomposition （VMD） and long short-term memory network （LSTM）. The IAOA algorithm was used to optimize the key decomposition parameters k and α of VMD, and the inherent modal functions （IMF） obtained were periodic, which could improve the prediction accuracy of LSTM. Meanwhile, the IAOA algorithm was used to optimize the LSTM network parameters. Through the prediction analysis of wind power data, the results show that the IAOA-VMD-LSTM prediction model has higher prediction accuracy than other models.

Key words

wind power forecast / variational modal decomposition / long short-term memory / arithmetic optimization algorithm

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Xiao Liexi, Zhang Yu, Zhou Hui, Zhao Guanhao. ULTRA SHORT TREM WIND POWER PREDICTION BASED ON IAOA-VMD-LSTM[J]. Acta Energiae Solaris Sinica. 2023, 44(11): 239-246 https://doi.org/10.19912/j.0254-0096.tynxb.2022-1054

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