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Short-Term Wind Power Prediction Based on Data Decomposition and Combined Deep Neural Network

Author

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  • Xiaomei Wu

    (Department of Electrical Engineering, School of Automation, Guangdong University of Technology, Guangzhou 510006, China)

  • Songjun Jiang

    (Department of Electrical Engineering, School of Automation, Guangdong University of Technology, Guangzhou 510006, China)

  • Chun Sing Lai

    (Department of Electrical Engineering, School of Automation, Guangdong University of Technology, Guangzhou 510006, China
    Brunel Interdisciplinary Power Systems Research Centre, Department of Electronic and Electrical Engineering, Brunel University London, London UB8 3PH, UK)

  • Zhuoli Zhao

    (Department of Electrical Engineering, School of Automation, Guangdong University of Technology, Guangzhou 510006, China)

  • Loi Lei Lai

    (Department of Electrical Engineering, School of Automation, Guangdong University of Technology, Guangzhou 510006, China)

Abstract

A hybrid short-term wind power prediction model based on data decomposition and combined deep neural network is proposed with the inclusion of the characteristics of fluctuation and randomness of nonlinear signals, such as wind speed and wind power. Firstly, the variational mode decomposition (VMD) is used to decompose the wind speed and wind power sequences in the input data to reduce the noise in the original signal. Secondly, the decomposed wind speed and wind power sub-sequences are reconstructed into new data sets with other related features as the input of the combined deep neural network, and the input data are further studied for the implied features by convolutional neural network (CNN), which should be passed into the long and short-term memory neural network (LSTM) as input for prediction. At the same time, the improved particle swarm optimization algorithm (IPSO) is adopted to optimize the parameters of each prediction model. By superimposing each predicted sub-sequence, the predicting wind power could be obtained. Simulations based on a short-term power prediction in different months with huge weather differences is carried out for a wind farm in Guangdong, China. The simulated results validate that the proposed model has a high prediction accuracy and generalization ability.

Suggested Citation

  • Xiaomei Wu & Songjun Jiang & Chun Sing Lai & Zhuoli Zhao & Loi Lei Lai, 2022. "Short-Term Wind Power Prediction Based on Data Decomposition and Combined Deep Neural Network," Energies, MDPI, vol. 15(18), pages 1-16, September.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:18:p:6734-:d:915162
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    References listed on IDEAS

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    1. Wang, Yun & Zou, Runmin & Liu, Fang & Zhang, Lingjun & Liu, Qianyi, 2021. "A review of wind speed and wind power forecasting with deep neural networks," Applied Energy, Elsevier, vol. 304(C).
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    7. Xiaomei Wu & Chun Sing Lai & Chenchen Bai & Loi Lei Lai & Qi Zhang & Bo Liu, 2020. "Optimal Kernel ELM and Variational Mode Decomposition for Probabilistic PV Power Prediction," Energies, MDPI, vol. 13(14), pages 1-21, July.
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    Cited by:

    1. Kai-Hung Lu & Qianlin Rao, 2023. "Enhancing the Dynamic Stability of Integrated Offshore Wind Farms and Photovoltaic Farms Using STATCOM with Intelligent Damping Controllers," Sustainability, MDPI, vol. 15(18), pages 1-21, September.
    2. Zhaozhi Wang & Shemeng Wu & Kai-Hung Lu, 2022. "Improvement of Stability in an Oscillating Water Column Wave Energy Using an Adaptive Intelligent Controller," Energies, MDPI, vol. 16(1), pages 1-15, December.
    3. Wen-Chang Tsai & Chih-Ming Hong & Chia-Sheng Tu & Whei-Min Lin & Chiung-Hsing Chen, 2023. "A Review of Modern Wind Power Generation Forecasting Technologies," Sustainability, MDPI, vol. 15(14), pages 1-40, July.

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