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Prediction of Photovoltaic Power by the Informer Model Based on Convolutional Neural Network

Author

Listed:
  • Ze Wu

    (College of Resource and Environment, Anhui Agriculture University, Hefei 230036, China)

  • Feifan Pan

    (College of Resource and Environment, Anhui Agriculture University, Hefei 230036, China)

  • Dandan Li

    (College of Resource and Environment, Anhui Agriculture University, Hefei 230036, China)

  • Hao He

    (College of Resource and Environment, Anhui Agriculture University, Hefei 230036, China)

  • Tiancheng Zhang

    (College of Resource and Environment, Anhui Agriculture University, Hefei 230036, China)

  • Shuyun Yang

    (College of Resource and Environment, Anhui Agriculture University, Hefei 230036, China
    Hefei Agricultural Environmental Science Observation and Experiment Station, Ministry of Agriculture and Rural Affairs, Hefei 230036, China)

Abstract

Accurate prediction of photovoltaic power is of great significance to the safe operation of power grids. In order to improve the prediction accuracy, a similar day clustering convolutional neural network (CNN)–informer model was proposed to predict the photovoltaic power. Based on correlation analysis, it was determined that global horizontal radiation was the meteorological factor that had the greatest impact on photovoltaic power, and the dataset was divided into four categories according to the correlation between meteorological factors and photovoltaic power fluctuation characteristics; then, a CNN was used to extract the feature information and trends of different subsets, and the features output by CNN were fused and input into the informer model. The informer model was used to establish the temporal feature relationship between historical data, and the final photovoltaic power generation power prediction result was obtained. The experimental results show that the proposed CNN–informer prediction method has high accuracy and stability in photovoltaic power generation prediction and outperforms other deep learning methods.

Suggested Citation

  • Ze Wu & Feifan Pan & Dandan Li & Hao He & Tiancheng Zhang & Shuyun Yang, 2022. "Prediction of Photovoltaic Power by the Informer Model Based on Convolutional Neural Network," Sustainability, MDPI, vol. 14(20), pages 1-16, October.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:20:p:13022-:d:939484
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    1. Xiao, Zenan & Huang, Xiaoqiao & Liu, Jun & Li, Chengli & Tai, Yonghang, 2023. "A novel method based on time series ensemble model for hourly photovoltaic power prediction," Energy, Elsevier, vol. 276(C).
    2. Cao, Yisheng & Liu, Gang & Luo, Donghua & Bavirisetti, Durga Prasad & Xiao, Gang, 2023. "Multi-timescale photovoltaic power forecasting using an improved Stacking ensemble algorithm based LSTM-Informer model," Energy, Elsevier, vol. 283(C).
    3. Haobo Shi & Yanping Xu & Baodi Ding & Jinsong Zhou & Pei Zhang, 2023. "Long-Term Solar Power Time-Series Data Generation Method Based on Generative Adversarial Networks and Sunrise–Sunset Time Correction," Sustainability, MDPI, vol. 15(20), pages 1-19, October.

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