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DMPR: A novel wind speed forecasting model based on optimized decomposition, multi-objective feature selection, and patch-based RNN

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  • Cai, Chenhao
  • Zhang, Leyao
  • Zhou, Jianguo

Abstract

As the global demand for sustainable energy continues to grow, accurate wind speed prediction becomes crucial for optimizing the allocation of wind energy resources and enhancing the efficiency of wind power generation. This paper presents a composite prediction model that integrates Optimized Decomposition, Multi-Objective Feature Selection (MOFS), Patch-Based Recurrent Neural Networks (RNNs), and Cross-Attention Mechanisms. Initially, the wind speed time series is decomposed using RIME-optimized Variational Mode Decomposition (VMD), and sample entropy of each component is calculated to reconstruct high, medium, and low-frequency components. Subsequently, these components are patched to serve as inputs to the RNN layers. The third step involves the incorporation of exogenous variables, where MOFS is employed for feature selection, followed by a decomposition and reconstruction through RIME-optimized VMD. This process applies Variate-wise Cross-Attention Mechanism on the high, medium, and low-frequency components of both endogenous and exogenous variables. Finally, the outputs from all processing steps are integrated through a normalization layer and a feed-forward layer to produce the final prediction results. Experimental results demonstrate that our proposed model, through meticulous preprocessing and advanced architectural design, significantly improves the accuracy of wind speed predictions.

Suggested Citation

  • Cai, Chenhao & Zhang, Leyao & Zhou, Jianguo, 2024. "DMPR: A novel wind speed forecasting model based on optimized decomposition, multi-objective feature selection, and patch-based RNN," Energy, Elsevier, vol. 310(C).
    • Handle: RePEc:eee:energy:v:310:y:2024:i:c:s0360544224030536
    DOI: 10.1016/j.energy.2024.133277
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    1. Liu, Wenhui & Bai, Yulong & Yue, Xiaoxin & Wang, Rui & Song, Qi, 2024. "A wind speed forcasting model based on rime optimization based VMD and multi-headed self-attention-LSTM," Energy, Elsevier, vol. 294(C).
    2. Lv, Sheng-Xiang & Wang, Lin, 2023. "Multivariate wind speed forecasting based on multi-objective feature selection approach and hybrid deep learning model," Energy, Elsevier, vol. 263(PE).
    3. 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.
    4. Zhang, Ziyuan & Wang, Jianzhou & Wei, Danxiang & Luo, Tianrui & Xia, Yurui, 2023. "A novel ensemble system for short-term wind speed forecasting based on Two-stage Attention-Based Recurrent Neural Network," Renewable Energy, Elsevier, vol. 204(C), pages 11-23.
    5. Cassola, Federico & Burlando, Massimiliano, 2012. "Wind speed and wind energy forecast through Kalman filtering of Numerical Weather Prediction model output," Applied Energy, Elsevier, vol. 99(C), pages 154-166.
    6. Yang, Qiuling & Deng, Changhong & Chang, Xiqiang, 2022. "Ultra-short-term / short-term wind speed prediction based on improved singular spectrum analysis," Renewable Energy, Elsevier, vol. 184(C), pages 36-44.
    7. Mengran Zhou & Tianyu Hu & Kai Bian & Wenhao Lai & Feng Hu & Oumaima Hamrani & Ziwei Zhu, 2021. "Short-Term Electric Load Forecasting Based on Variational Mode Decomposition and Grey Wolf Optimization," Energies, MDPI, vol. 14(16), pages 1-17, August.
    8. Aasim, & Singh, S.N. & Mohapatra, Abheejeet, 2019. "Repeated wavelet transform based ARIMA model for very short-term wind speed forecasting," Renewable Energy, Elsevier, vol. 136(C), pages 758-768.
    9. Sun, Zexian & Zhao, Mingyu & Zhao, Guohong, 2022. "Hybrid model based on VMD decomposition, clustering analysis, long short memory network, ensemble learning and error complementation for short-term wind speed forecasting assisted by Flink platform," Energy, Elsevier, vol. 261(PB).
    10. Hu, Yahui & Guo, Yingshi & Fu, Rui, 2023. "A novel wind speed forecasting combined model using variational mode decomposition, sparse auto-encoder and optimized fuzzy cognitive mapping network," Energy, Elsevier, vol. 278(PA).
    11. Zhang, Jinliang & Wei, Yiming & Tan, Zhongfu, 2020. "An adaptive hybrid model for short term wind speed forecasting," Energy, Elsevier, vol. 190(C).
    12. Lu Peng & Sheng‐Xiang Lv & Lin Wang, 2024. "Explainable machine learning techniques based on attention gate recurrent unit and local interpretable model‐agnostic explanations for multivariate wind speed forecasting," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(6), pages 2064-2087, September.
    13. Erdem, Ergin & Shi, Jing, 2011. "ARMA based approaches for forecasting the tuple of wind speed and direction," Applied Energy, Elsevier, vol. 88(4), pages 1405-1414, April.
    14. Kuriqi, Alban & Pinheiro, António N. & Sordo-Ward, Alvaro & Bejarano, María D. & Garrote, Luis, 2021. "Ecological impacts of run-of-river hydropower plants—Current status and future prospects on the brink of energy transition," Renewable and Sustainable Energy Reviews, Elsevier, vol. 142(C).
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