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Neural Networks with Transfer Learning and Frequency Decomposition for Wind Speed Prediction with Missing Data

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  • Xiaoou Li

    (Departamento de Computacion, CINVESTAV-IPN (National Polytechnic Institute), Mexico City 07360, Mexico)

  • Yingqin Zhu

    (Departamento de Control Automatico, CINVESTAV-IPN (National Polytechnic Institute), Mexico City 07360, Mexico)

Abstract

This paper presents a novel data-driven approach for enhancing time series forecasting accuracy when faced with missing data. Our proposed method integrates an Echo State Network (ESN) with ARIMA (Autoregressive Integrated Moving Average) modeling, frequency decomposition, and online transfer learning. This combination specifically addresses the challenges missing data introduce in time series prediction. By using the strengths of each technique, our framework offers a robust solution for handling missing data and achieving superior forecasting accuracy in real-world applications. We demonstrate the effectiveness of the proposed model through a wind speed prediction case study. Compared to the existing methods, our approach achieves significant improvement in prediction accuracy, paving the way for more reliable decisionmaking in wind energy operations and management.

Suggested Citation

  • Xiaoou Li & Yingqin Zhu, 2024. "Neural Networks with Transfer Learning and Frequency Decomposition for Wind Speed Prediction with Missing Data," Mathematics, MDPI, vol. 12(8), pages 1-20, April.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:8:p:1137-:d:1373109
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    References listed on IDEAS

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    1. 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.
    2. Tang, Zhenhao & Zhao, Gengnan & Ouyang, Tinghui, 2021. "Two-phase deep learning model for short-term wind direction forecasting," Renewable Energy, Elsevier, vol. 173(C), pages 1005-1016.
    3. Tian, Zhongda & Chen, Hao, 2021. "Multi-step short-term wind speed prediction based on integrated multi-model fusion," Applied Energy, Elsevier, vol. 298(C).
    4. Bai, Yulong & Liu, Ming-De & Ding, Lin & Ma, Yong-Jie, 2021. "Double-layer staged training echo-state networks for wind speed prediction using variational mode decomposition," Applied Energy, Elsevier, vol. 301(C).
    5. Zuluaga, Carlos D. & Álvarez, Mauricio A. & Giraldo, Eduardo, 2015. "Short-term wind speed prediction based on robust Kalman filtering: An experimental comparison," Applied Energy, Elsevier, vol. 156(C), pages 321-330.
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    1. Zhu, Yingqin & Liu, Yue & Wang, Nan & Zhang, ZhaoZhao & Li, YuanQiang, 2025. "Real-time Error Compensation Transfer Learning with Echo State Networks for Enhanced Wind Power Prediction," Applied Energy, Elsevier, vol. 379(C).

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