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Wind and Photovoltaic Power Generation Forecasting for Virtual Power Plants Based on the Fusion of Improved K-Means Cluster Analysis and Deep Learning

Author

Listed:
  • Zhichao Qiu

    (College of Information Science and Engineering, Northeastern University, Shenyang 110819, China)

  • Ye Tian

    (Institute of Electric Power Science, State Grid Liaoning Electric Power Co., Ltd., Shenyang 110000, China)

  • Yanhong Luo

    (College of Information Science and Engineering, Northeastern University, Shenyang 110819, China)

  • Taiyu Gu

    (Institute of Electric Power Science, State Grid Liaoning Electric Power Co., Ltd., Shenyang 110000, China)

  • Hengyu Liu

    (Institute of Electric Power Science, State Grid Liaoning Electric Power Co., Ltd., Shenyang 110000, China)

Abstract

Virtual power plants (VPPs) have emerged as an innovative solution for modern power systems, particularly for integrating renewable energy sources. This study proposes a novel prediction approach combining improved K-means clustering with Time Convolutional Networks (TCNs), a Bi-directional Gated Recurrent Unit (BiGRU), and an attention mechanism to enhance the forecasting accuracy of wind and photovoltaic power generation in VPPs. The proposed TCN-BiGRU-Attention model demonstrates superior predictive performance compared to traditional models, achieving high accuracy and robustness. These results provide a reliable basis for optimizing VPP operations and integrating renewable energy sources effectively.

Suggested Citation

  • Zhichao Qiu & Ye Tian & Yanhong Luo & Taiyu Gu & Hengyu Liu, 2024. "Wind and Photovoltaic Power Generation Forecasting for Virtual Power Plants Based on the Fusion of Improved K-Means Cluster Analysis and Deep Learning," Sustainability, MDPI, vol. 16(23), pages 1-24, December.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:23:p:10740-:d:1538661
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    References listed on IDEAS

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    1. Rita Teixeira & Adelaide Cerveira & Eduardo J. Solteiro Pires & José Baptista, 2024. "Advancing Renewable Energy Forecasting: A Comprehensive Review of Renewable Energy Forecasting Methods," Energies, MDPI, vol. 17(14), pages 1-30, July.
    2. Wang, Yuqing & Fu, Wenjie & Wang, Junlong & Zhen, Zhao & Wang, Fei, 2024. "Ultra-short-term distributed PV power forecasting for virtual power plant considering data-scarce scenarios," Applied Energy, Elsevier, vol. 373(C).
    3. Dukhwan Yu & Wonik Choi & Myoungsoo Kim & Ling Liu, 2020. "Forecasting Day-Ahead Hourly Photovoltaic Power Generation Using Convolutional Self-Attention Based Long Short-Term Memory," Energies, MDPI, vol. 13(15), pages 1-17, August.
    4. Sameer Al-Dahidi & Manoharan Madhiarasan & Loiy Al-Ghussain & Ahmad M. Abubaker & Adnan Darwish Ahmad & Mohammad Alrbai & Mohammadreza Aghaei & Hussein Alahmer & Ali Alahmer & Piero Baraldi & Enrico Z, 2024. "Forecasting Solar Photovoltaic Power Production: A Comprehensive Review and Innovative Data-Driven Modeling Framework," Energies, MDPI, vol. 17(16), pages 1-38, August.
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    Cited by:

    1. Arkadiusz Małek & Agnieszka Dudziak & Andrzej Marciniak & Tomasz Słowik, 2025. "Designing a Photovoltaic–Wind Energy Mix with Energy Storage for Low-Emission Hydrogen Production," Energies, MDPI, vol. 18(4), pages 1-23, February.

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