SHORT-TERM WIND POWER PREDICTION BASED ON CEEMDAN-AsyHyperBand-MultiTCN

Liu Fan; Li Handong; Qin Tao

doi:10.19912/j.0254-0096.tynxb.2022-1427

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Acta Energiae Solaris Sinica ›› 2024, Vol. 45 ›› Issue (1) : 151-158. DOI: 10.19912/j.0254-0096.tynxb.2022-1427

SHORT-TERM WIND POWER PREDICTION BASED ON CEEMDAN-AsyHyperBand-MultiTCN

Liu Fan, Li Handong, Qin Tao

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Abstract

In order to improve the utilization efficiency and accuracy of short-term wind power, this paper proposes a method based on complete ensemble empirical mode decomposition with adaptive noise and multi-layer temporal convolution networks （CEEMDAN-AsyHyperBand-MultiTCN）. Firstly, determine the number of sequence components, and then decompose the time series of wind power as training dataset using CEEMDAN. Secondly, apply the Deep Residual Cascade （DRnet） to build a multi-layer Temporal Convolutional Networks （TCN） model for each component, and the AsyHyperband algorithm is used to optimize the hyperparameters for the components model. Finally, the final prediction result is obtained after reconstructing the prediction results of each component model. The experimental results show that the proposed method can effectively reduce the wind power prediction error compared with other methods.

Key words

wind power / forecasting / neural networks / multilayers / complete ensemble empirical mode decomposition with adaptive noise / hyperparameter search

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Liu Fan, Li Handong, Qin Tao. SHORT-TERM WIND POWER PREDICTION BASED ON CEEMDAN-AsyHyperBand-MultiTCN[J]. Acta Energiae Solaris Sinica. 2024, 45(1): 151-158 https://doi.org/10.19912/j.0254-0096.tynxb.2022-1427

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