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Vision Transformer-Based Photovoltaic Prediction Model

Author

Listed:
  • Zaohui Kang

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

  • Jizhong Xue

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

  • Chun Sing Lai

    (Department of Electrical Engineering, 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)

  • Yu Wang

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

  • Haoliang Yuan

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

  • Fangyuan Xu

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

Abstract

Sensing the cloud movement information has always been a difficult problem in photovoltaic (PV) prediction. The information used by current PV prediction methods makes it challenging to accurately perceive cloud movements. The obstruction of the sun by clouds will lead to a significant decrease in actual PV power generation. The PV prediction network model cannot respond in time, resulting in a significant decrease in prediction accuracy. In order to overcome this problem, this paper develops a visual transformer model for PV prediction, in which the target PV sensor information and the surrounding PV sensor auxiliary information are used as input data. By using the auxiliary information of the surrounding PV sensors and the spatial location information, our model can sense the movement of the cloud in advance. The experimental results confirm the effectiveness and superiority of our model.

Suggested Citation

  • Zaohui Kang & Jizhong Xue & Chun Sing Lai & Yu Wang & Haoliang Yuan & Fangyuan Xu, 2023. "Vision Transformer-Based Photovoltaic Prediction Model," Energies, MDPI, vol. 16(12), pages 1-14, June.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:12:p:4737-:d:1172061
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    References listed on IDEAS

    as
    1. Qu, Yinpeng & Xu, Jian & Sun, Yuanzhang & Liu, Dan, 2021. "A temporal distributed hybrid deep learning model for day-ahead distributed PV power forecasting," Applied Energy, Elsevier, vol. 304(C).
    2. Jaeik Jeong & Hongseok Kim, 2019. "Multi-Site Photovoltaic Forecasting Exploiting Space-Time Convolutional Neural Network," Energies, MDPI, vol. 12(23), pages 1-14, November.
    3. Shang, Chuanfu & Wei, Pengcheng, 2018. "Enhanced support vector regression based forecast engine to predict solar power output," Renewable Energy, Elsevier, vol. 127(C), pages 269-283.
    4. Korkmaz, Deniz, 2021. "SolarNet: A hybrid reliable model based on convolutional neural network and variational mode decomposition for hourly photovoltaic power forecasting," Applied Energy, Elsevier, vol. 300(C).
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    Cited by:

    1. Hanif, M.F. & Mi, J., 2024. "Harnessing AI for solar energy: Emergence of transformer models," Applied Energy, Elsevier, vol. 369(C).
    2. Niu, Yunbo & Wang, Jianzhou & Zhang, Ziyuan & Luo, Tianrui & Liu, Jingjiang, 2024. "De-Trend First, Attend Next: A Mid-Term PV forecasting system with attention mechanism and encoder–decoder structure," Applied Energy, Elsevier, vol. 353(PB).

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