WIND SPEED PREDICTION SYNERGISTICALLY BASED ON DEEP LEARNING AND GENERALIZED S TRANSFORM

Zhu Zhexuan; Ma Ruwei; Cao Liyuan; Li Chunxiang

doi:10.19912/j.0254-0096.tynxb.2023-0416

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Acta Energiae Solaris Sinica ›› 2024, Vol. 45 ›› Issue (7) : 664-671. DOI: 10.19912/j.0254-0096.tynxb.2023-0416

WIND SPEED PREDICTION SYNERGISTICALLY BASED ON DEEP LEARNING AND GENERALIZED S TRANSFORM

Zhu Zhexuan, Ma Ruwei, Cao Liyuan, Li Chunxiang

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Abstract

A hybrid wind speed prediction model based on deep learning and time-frequency analysis is proposed, aiming at the non-stationary characteristics of wind speed. Firstly, empirical mode decomposition （EMD） is used to decompose the wind speed into several sub layers and summarized into a trend component and a fluctuating component to reduce the nonlinearity. According to the time-frequency characteristics of the two components, long short term memory （LSTM） is used to deal with the trend component while extreme learning machine （ELM） with the fluctuating component. Then, generalized S transform （GST） is innovatively introduced to obtain the time-frequency characteristics of the prediction process. Improved grey wolf algorithm （IGWO） is used to optimize the parameters of GST, LSTM and ELM at the same time. Finally, the proposed model is validated with the actual data of a wind farm in Inner Mongolia, and the results show that the model has accuracy.

Key words

wind farm / wind speed / prediction / long short-term memory / extreme learning machine / generalized S transform

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Zhu Zhexuan, Ma Ruwei, Cao Liyuan, Li Chunxiang. WIND SPEED PREDICTION SYNERGISTICALLY BASED ON DEEP LEARNING AND GENERALIZED S TRANSFORM[J]. Acta Energiae Solaris Sinica. 2024, 45(7): 664-671 https://doi.org/10.19912/j.0254-0096.tynxb.2023-0416

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