RESEARCH ON MULTI-STEP WIND SPEED FORECAST BASED ON CEEMDAN SECONDARY DECOMPOSITION AND LSTM

Xiang Ling; Liu Jianing; Su Hao; Hu Aijun; Zhu Zening

doi:10.19912/j.0254-0096.tynxb.2020-1410

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Acta Energiae Solaris Sinica ›› 2022, Vol. 43 ›› Issue (8) : 334-339. DOI: 10.19912/j.0254-0096.tynxb.2020-1410

RESEARCH ON MULTI-STEP WIND SPEED FORECAST BASED ON CEEMDAN SECONDARY DECOMPOSITION AND LSTM

Xiang Ling, Liu Jianing, Su Hao, Hu Aijun, Zhu Zening

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Abstract

In order to improve the accuracy of wind speed prediction, a multi-step wind speed forecast method based on CEEMDAN secondary decomposition and long short-term memory （LSTM） network is proposed. Firstly, variational mode decomposition （VMD） is utilized to decompose the original wind speed series. The obtained residual components are subjected to secondary decomposition by using adaptive noise complete empirical mode decomposition （CEEMDAN） method. Then all the decomposed subsequences are input to LSTM models for training and predicting. Finally, the outputs of all subsequence models are superimposed to obtain the predicted wind speed. Taking the measured data of a wind farm in Inner Mongolia as an example, the results indicate that the proposed multi-step wind speed prediction model has higher prediction accuracy and has the feasibility of practical application.

Key words

wind speed / forecasting / long short-term memory network / secondary decomposition / CEEMDAN

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Xiang Ling, Liu Jianing, Su Hao, Hu Aijun, Zhu Zening. RESEARCH ON MULTI-STEP WIND SPEED FORECAST BASED ON CEEMDAN SECONDARY DECOMPOSITION AND LSTM[J]. Acta Energiae Solaris Sinica. 2022, 43(8): 334-339 https://doi.org/10.19912/j.0254-0096.tynxb.2020-1410

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