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Multi-task learning based multi-energy load prediction in integrated energy system

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Abstract

Accurate multi-energy load prediction plays a very crucial role in integrated energy system management. To address the load characteristics of strong relational coupling, volatility and uncertainty in user-side integrated energy systems, this paper proposes a multi-task learning based short-term multi-energy load prediction method. Load participation factor is proposed to portray the proportion of different loads in the total load demand, and multi-task learning method is introduced to deeply explore the coupling relationships among them. Then, the proposed method is validated on a real-world dataset. The results show that the method has higher prediction accuracy than the existing methods, and the prediction accuracy is improved by at least 1.3%.

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Data Availability

The data that support the findings of this study are openly available at http://cm.asu.edu/, or from the corresponding author upon request.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (61873222) and the Project of Hunan National Center for Applied Mathematics (2020ZYT003).

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Authors and Affiliations

  1. Hunan National Center for Applied Mathematics, Xiangtan University, Yuhu District, Xiangtan, 411100, China

    Lulu Wang & Mao Tan

  2. Hunan Engineering Research Center of Multi-energy Cooperative Control Technology, Xiangtan University, Yuhu District, Xiangtan, 411100, China

    Mao Tan

  3. School of Automation and Electronic Information, Xiangtan University, Yuhu District, Xiangtan, 411100, China

    Jie Chen & Chengchen Liao

Authors
  1. Lulu Wang
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  2. Mao Tan
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  3. Jie Chen
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  4. Chengchen Liao
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Correspondence to Mao Tan.

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Wang, L., Tan, M., Chen, J. et al. Multi-task learning based multi-energy load prediction in integrated energy system. Appl Intell 53, 10273–10289 (2023). https://doi.org/10.1007/s10489-022-04054-6

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