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A multi-task learning method for multi-energy load forecasting based on synthesis correlation analysis and load participation factor

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  • Tan, Mao
  • Liao, Chengchen
  • Chen, Jie
  • Cao, Yijia
  • Wang, Rui
  • Su, Yongxin

Abstract

Multi-energy load forecasting is the prerequisite for energy management and optimal scheduling of integrated energy systems (IES). Considering the randomness and coupling of multi-energy load demands, this paper proposes an IES multi-task learning (MTL) method for multi-energy load forecasting based on synthesis correlation analysis (SCA) and load participation factor (LPF). Firstly, SCA is proposed to screen multi-level features to maximize the key information in strong features and remove the prediction noise in weak features. Secondly, the LPF is proposed to describe the involvement of different loads in the total load, and the LPF application criteria of the factor are analyzed. Lastly, a MTL-LSTM integrated model considering total load forecasting is designed to deeply explore the hidden dynamic coupling information among different types of loads. The prediction model was tested on four seasons with data obtained from real-world scenarios and compared with existing prediction methods. The results show that the forecasting method constructed in this paper exhibits superior performance relative to other methods, and the average prediction accuracy achieve 97.18% for electricity, cooling, and heat loads, and 97.85% for the total load.

Suggested Citation

  • Tan, Mao & Liao, Chengchen & Chen, Jie & Cao, Yijia & Wang, Rui & Su, Yongxin, 2023. "A multi-task learning method for multi-energy load forecasting based on synthesis correlation analysis and load participation factor," Applied Energy, Elsevier, vol. 343(C).
    • Handle: RePEc:eee:appene:v:343:y:2023:i:c:s030626192300541x
    DOI: 10.1016/j.apenergy.2023.121177
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    References listed on IDEAS

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    1. Wang, Yongli & Wang, Yudong & Huang, Yujing & Li, Fang & Zeng, Ming & Li, Jiapu & Wang, Xiaohai & Zhang, Fuwei, 2019. "Planning and operation method of the regional integrated energy system considering economy and environment," Energy, Elsevier, vol. 171(C), pages 731-750.
    2. Jieyun Zheng & Linyao Zhang & Jinpeng Chen & Guilian Wu & Shiyuan Ni & Zhijian Hu & Changhong Weng & Zhi Chen, 2021. "Multiple-Load Forecasting for Integrated Energy System Based on Copula-DBiLSTM," Energies, MDPI, vol. 14(8), pages 1-14, April.
    3. Lianhui Li & Hongguang Wang, 2018. "A VVWBO-BVO-based GM (1,1) and its parameter optimization by GRA-IGSA integration algorithm for annual power load forecasting," PLOS ONE, Public Library of Science, vol. 13(5), pages 1-20, May.
    4. Niu, Dongxiao & Yu, Min & Sun, Lijie & Gao, Tian & Wang, Keke, 2022. "Short-term multi-energy load forecasting for integrated energy systems based on CNN-BiGRU optimized by attention mechanism," Applied Energy, Elsevier, vol. 313(C).
    5. Songyao Wang & Zhisheng Zhang, 2021. "Short-Term Multiple Load Forecasting Model of Regional Integrated Energy System Based on QWGRU-MTL," Energies, MDPI, vol. 14(20), pages 1-13, October.
    6. Wang, Jianxiao & Zhong, Haiwang & Ma, Ziming & Xia, Qing & Kang, Chongqing, 2017. "Review and prospect of integrated demand response in the multi-energy system," Applied Energy, Elsevier, vol. 202(C), pages 772-782.
    7. Ruijin Zhu & Weilin Guo & Xuejiao Gong, 2019. "Short-Term Load Forecasting for CCHP Systems Considering the Correlation between Heating, Gas and Electrical Loads Based on Deep Learning," Energies, MDPI, vol. 12(17), pages 1-18, August.
    8. Wang, Shaomin & Wang, Shouxiang & Chen, Haiwen & Gu, Qiang, 2020. "Multi-energy load forecasting for regional integrated energy systems considering temporal dynamic and coupling characteristics," Energy, Elsevier, vol. 195(C).
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    Cited by:

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    3. Li, Ke & Mu, Yuchen & Yang, Fan & Wang, Haiyang & Yan, Yi & Zhang, Chenghui, 2023. "A novel short-term multi-energy load forecasting method for integrated energy system based on feature separation-fusion technology and improved CNN," Applied Energy, Elsevier, vol. 351(C).

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