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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Chao Tan & Wenrui Tan & Yanjun Shen & Long Yang, 2023. "Multistep Wind Power Prediction Using Time-Varying Filtered Empirical Modal Decomposition and Improved Adaptive Sparrow Search Algorithm-Optimized Phase Space Reconstruction–Echo State Network," Sustainability, MDPI, vol. 15(11), pages 1-17, June.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:11:p:9107-:d:1164210
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    References listed on IDEAS

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    1. Weihua Tian & Yan Bao & Wei Liu & Naeem Jan, 2022. "Wind Power Forecasting by the BP Neural Network with the Support of Machine Learning," Mathematical Problems in Engineering, Hindawi, vol. 2022, pages 1-10, April.
    2. Hu, Huanling & Wang, Lin & Tao, Rui, 2021. "Wind speed forecasting based on variational mode decomposition and improved echo state network," Renewable Energy, Elsevier, vol. 164(C), pages 729-751.
    3. Xiangyue Wang & Ji Li & Lei Shao & Hongli Liu & Lei Ren & Lihua Zhu, 2023. "Short-Term Wind Power Prediction by an Extreme Learning Machine Based on an Improved Hunter–Prey Optimization Algorithm," Sustainability, MDPI, vol. 15(2), pages 1-14, January.
    4. Fu, Wenlong & Zhang, Kai & Wang, Kai & Wen, Bin & Fang, Ping & Zou, Feng, 2021. "A hybrid approach for multi-step wind speed forecasting based on two-layer decomposition, improved hybrid DE-HHO optimization and KELM," Renewable Energy, Elsevier, vol. 164(C), pages 211-229.
    5. Zhang, Chu & Ma, Huixin & Hua, Lei & Sun, Wei & Nazir, Muhammad Shahzad & Peng, Tian, 2022. "An evolutionary deep learning model based on TVFEMD, improved sine cosine algorithm, CNN and BiLSTM for wind speed prediction," Energy, Elsevier, vol. 254(PA).
    6. Dongyu Wang & Xiwen Cui & Dongxiao Niu, 2022. "Wind Power Forecasting Based on LSTM Improved by EMD-PCA-RF," Sustainability, MDPI, vol. 14(12), pages 1-23, June.
    7. Mohsen Vahidzadeh & Corey D. Markfort, 2019. "Modified Power Curves for Prediction of Power Output of Wind Farms," Energies, MDPI, vol. 12(9), pages 1-19, May.
    8. Fang, Ping & Fu, Wenlong & Wang, Kai & Xiong, Dongzhen & Zhang, Kai, 2022. "A compositive architecture coupling outlier correction, EWT, nonlinear Volterra multi-model fusion with multi-objective optimization for short-term wind speed forecasting," Applied Energy, Elsevier, vol. 307(C).
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