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Multiple Load Forecasting of Integrated Renewable Energy System Based on TCN-FECAM-Informer

Author

Listed:
  • Mingxiang Li

    (School of Electrical Engineering and Automation, Henan Polytechnic University, Jiaozuo 454003, China)

  • Tianyi Zhang

    (School of Electrical and Information Engineering, Changsha University of Science and Technology, Changsha 410114, China)

  • Haizhu Yang

    (School of Electrical Engineering and Automation, Henan Polytechnic University, Jiaozuo 454003, China)

  • Kun Liu

    (Tianjin Eco-Environmental Monitoring Center, Tianjin 300191, China)

Abstract

In order to solve the problem of complex coupling characteristics between multivariate load sequences and the difficulty in accurate multiple load forecasting for integrated renewable energy systems (IRESs), which include low-carbon emission renewable energy sources, in this paper, the TCN-FECAM-Informer multivariate load forecasting model is proposed. First, the maximum information coefficient (MIC) is used to correlate the multivariate loads with the weather factors to filter the appropriate features. Then, effective information of the screened features is extracted and the frequency sequence is constructed using the frequency-enhanced channel attention mechanism (FECAM)-improved temporal convolutional network (TCN). Finally, the processed feature sequences are sent to the Informer network for multivariate load forecasting. Experiments are conducted with measured load data from the IRES of Arizona State University, and the experimental results show that the TCN and FECAM can greatly improve the multivariate load prediction accuracy and, at the same time, demonstrate the superiority of the Informer network, which is dominated by the attentional mechanism, compared with recurrent neural networks in multivariate load prediction.

Suggested Citation

  • Mingxiang Li & Tianyi Zhang & Haizhu Yang & Kun Liu, 2024. "Multiple Load Forecasting of Integrated Renewable Energy System Based on TCN-FECAM-Informer," Energies, MDPI, vol. 17(20), pages 1-16, October.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:20:p:5181-:d:1501102
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    References listed on IDEAS

    as
    1. Chen, Haoyu & Huang, Hai & Zheng, Yong & Yang, Bing, 2024. "A load forecasting approach for integrated energy systems based on aggregation hybrid modal decomposition and combined model," Applied Energy, Elsevier, vol. 375(C).
    2. Shi, Jian & Teh, Jiashen & Alharbi, Bader & Lai, Ching-Ming, 2024. "Load forecasting for regional integrated energy system based on two-phase decomposition and mixture prediction model," Energy, Elsevier, vol. 297(C).
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    4. Tian, Zhirui & Liu, Weican & Jiang, Wenqian & Wu, Chenye, 2024. "CNNs-Transformer based day-ahead probabilistic load forecasting for weekends with limited data availability," Energy, Elsevier, vol. 293(C).
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