SHORT TERM WIND POWER FORECASTING BASED ON VMD-WPE AND SSA-ELM

Liu Dong; Wei Xia; Wang Weiqing; Ye Jiahao

doi:10.19912/j.0254-0096.tynxb.2021-0629

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Acta Energiae Solaris Sinica ›› 2022, Vol. 43 ›› Issue (12) : 360-367. DOI: 10.19912/j.0254-0096.tynxb.2021-0629

SHORT TERM WIND POWER FORECASTING BASED ON VMD-WPE AND SSA-ELM

Liu Dong, Wei Xia, Wang Weiqing, Ye Jiahao

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Abstract

Aiming at the nonlinear and non-stationary characteristics of wind power series, a hybrid wind power prediction model based on variational mode decomposition （VMD）, weighted permutation entropy （WPE） and sparrow algorithm （SSA）-optimized extreme learning machine （ELM） is proposed. Firstly, the original sequence is decomposed into multiple intrinsic mode components by VMD technology, and then each component is reconstructed into several subsequences with different complexity by WPE technology. Then, a new heuristic SSA algorithm is used to optimize the parameters of ELM, and the wind power prediction optimization model is established. Finally, the actual data of a wind farm in Northwest China is used to verify the proposed model. The results show that the prediction performance of the model is improved compared with other models.

Key words

wind power forecasting / variational mode decomposition / weighted permutation entropy / sparrow search algorithm / extreme learning machine

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Liu Dong, Wei Xia, Wang Weiqing, Ye Jiahao. SHORT TERM WIND POWER FORECASTING BASED ON VMD-WPE AND SSA-ELM[J]. Acta Energiae Solaris Sinica. 2022, 43(12): 360-367 https://doi.org/10.19912/j.0254-0096.tynxb.2021-0629

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