IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v17y2024i20p5181-d1501102.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/17/20/5181/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/17/20/5181/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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).
    2. 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).
    3. 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).
    4. Hu, Jiaxiang & Hu, Weihao & Cao, Di & Sun, Xinwu & Chen, Jianjun & Huang, Yuehui & Chen, Zhe & Blaabjerg, Frede, 2024. "Probabilistic net load forecasting based on transformer network and Gaussian process-enabled residual modeling learning method," Renewable Energy, Elsevier, vol. 225(C).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Wang, Danhao & Peng, Daogang & Huang, Dongmei & Zhao, Huirong & Qu, Bogang, 2025. "MMEMformer: A multi-scale memory-enhanced transformer framework for short-term load forecasting in integrated energy systems," Energy, Elsevier, vol. 322(C).
    2. Hu, Rong & Zhou, Kaile & Lu, Xinhui, 2025. "Integrated loads forecasting with absence of crucial factors," Energy, Elsevier, vol. 322(C).
    3. 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).
    4. Yin, Linfei & Ju, Linyi, 2025. "ShuffleTransformerMulti-headAttentionNet network for user load forecasting," Energy, Elsevier, vol. 322(C).
    5. Xie, Xiangmin & Ding, Yuhao & Sun, Yuanyuan & Zhang, Zhisheng & Fan, Jianhua, 2024. "A novel time-series probabilistic forecasting method for multi-energy loads," Energy, Elsevier, vol. 306(C).
    6. Ren, Xiaoxiao & Tian, Xin & Wang, Kai & Yang, Sifan & Chen, Weixiong & Wang, Jinshi, 2025. "Enhanced load forecasting for distributed multi-energy system: A stacking ensemble learning method with deep reinforcement learning and model fusion," Energy, Elsevier, vol. 319(C).
    7. Cheng, Fang & Liu, Hui, 2024. "Multi-step electric vehicles charging loads forecasting: An autoformer variant with feature extraction, frequency enhancement, and error correction blocks," Applied Energy, Elsevier, vol. 376(PB).
    8. Tian, Zhirui & Liu, Weican & Zhang, Jiahao & Sun, Wenpu & Wu, Chenye, 2025. "EDformer family: End-to-end multi-task load forecasting frameworks for day-ahead economic dispatch," Applied Energy, Elsevier, vol. 383(C).
    9. Royal, Emily & Bandyopadhyay, Soutir & Newman, Alexandra & Huang, Qiuhua & Tabares-Velasco, Paulo Cesar, 2025. "A statistical framework for district energy long-term electric load forecasting," Applied Energy, Elsevier, vol. 384(C).
    10. Shi, Jian & Teh, Jiashen & Lai, Ching-Ming, 2025. "Wind power prediction based on improved self-attention mechanism combined with Bi-directional Temporal Convolutional Network," Energy, Elsevier, vol. 322(C).
    11. Gou, Tong & Xu, Yinliang & Sun, Hongbin, 2025. "Carbon-aware day-ahead optimal dispatch for integrated power grid thermal systems with aggregated distributed resources," Applied Energy, Elsevier, vol. 389(C).
    12. Liu, Mao & Kong, Xiangyu & Xiong, Kaizhi & Wang, Jimin & Lin, Qingxiang, 2025. "Multi-scale spatio-temporal transformer: A novel model reduction approach for day-ahead security-constrained unit commitment," Applied Energy, Elsevier, vol. 380(C).
    13. Altan Unlu & Malaquias Peña, 2025. "Comparative Analysis of Hybrid Deep Learning Models for Electricity Load Forecasting During Extreme Weather," Energies, MDPI, vol. 18(12), pages 1-27, June.
    14. Hu, Likun & Cao, Yi & Yin, Linfei, 2024. "Long-term price guidance mechanism for integrated energy systems based on gated recurrent unit - vision transformer prediction and fractional-order stochastic dynamic calculus control," Energy, Elsevier, vol. 312(C).
    15. Peng, Daogang & Liu, Yu & Wang, Danhao & Zhao, Huirong & Qu, Bogang, 2024. "Multi-energy load forecasting for integrated energy system based on sequence decomposition fusion and factors correlation analysis," Energy, Elsevier, vol. 308(C).
    16. Pantoš, Miloš & Lukas, Lucija, 2025. "Enhancing power system reliability through demand flexibility of Grid-Interactive Efficient Buildings: A thermal model-based optimization approach," Applied Energy, Elsevier, vol. 381(C).
    17. Wang, Wenhao & Tang, Aihong & Wei, Feng & Yang, Huiyuan & Xinran, Li & Peng, Jiao, 2025. "Electric vehicle charging load forecasting considering weather impact," Applied Energy, Elsevier, vol. 383(C).
    18. Dai, Shuangfeng & Mansouri, Seyed Amir & Huang, Shoujun & Alharthi, Yahya Z. & Wu, Yongfei & Bagherzadeh, Leila, 2024. "A multi-stage techno-economic model for harnessing flexibility from IoT-enabled appliances and smart charging systems: Developing a competitive local flexibility market using Stackelberg game theory," Applied Energy, Elsevier, vol. 373(C).
    19. Xu, Huifeng & Hu, Feihu & Liang, Xinhao & Zhao, Guoqing & Abugunmi, Mohammad, 2024. "A framework for electricity load forecasting based on attention mechanism time series depthwise separable convolutional neural network," Energy, Elsevier, vol. 299(C).
    20. Tian, Chenlu & Liu, Yechun & Zhang, Guiqing & Yang, Yalong & Yan, Yi & Li, Chengdong, 2024. "Transfer learning based hybrid model for power demand prediction of large-scale electric vehicles," Energy, Elsevier, vol. 300(C).

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:17:y:2024:i:20:p:5181-:d:1501102. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.