Multistep Wind Power Prediction Using Time-Varying Filtered Empirical Modal Decomposition and Improved Adaptive Sparrow Search Algorithm-Optimized Phase Space Reconstruction–Echo State Network

Chao Tan (), Wenrui Tan, Yanjun Shen and Long Yang
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Chao Tan: School of Electrical and New Energy, China Three Gorges University, Yichang 443000, China
Wenrui Tan: Hubei Provincial Research Center on Microgrid Engineering Technology, China Three Gorges University, Yichang 443000, China
Yanjun Shen: School of Electrical and New Energy, China Three Gorges University, Yichang 443000, China
Long Yang: School of Electrical and New Energy, China Three Gorges University, Yichang 443000, China

Sustainability, 2023, vol. 15, issue 11, 1-17

Abstract: Accurate wind power prediction is vital for improving grid stability. In order to improve the accuracy of wind power prediction, in this study, a hybrid prediction model combining time-varying filtered empirical modal decomposition (TVFEMD), improved adaptive sparrow search algorithm (IASSA)-optimized phase space reconstruction (PSR) and echo state network (ESN) methods was proposed. First, the wind power data were decomposed into a set of subsequences by using TVFEMD. Next, PSR was used to construct the corresponding phase space matrix for sequences, which were then divided into training sets, validation sets, and testing sets. Then, ESN was used for subsequence prediction. Finally, the predicted values of all the subseries were used to determine the final predicted power. To enhance the model performance, the sparrow search algorithm was improved in terms of the discoverer position update strategy, the follower position update strategy, and the population structure. IASSA was employed to synchronously optimize multiple parameters of PSR-ESN. The results revealed that the proposed model has higher applicability and prediction accuracy than existing models.

Keywords: wind power prediction; time-varying filtering empirical modal decomposition; echo state network; sparrow search algorithm (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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