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Joint forecasting of source-load-price for integrated energy system based on multi-task learning and hybrid attention mechanism

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  • Li, Ke
  • Mu, Yuchen
  • Yang, Fan
  • Wang, Haiyang
  • Yan, Yi
  • Zhang, Chenghui

Abstract

In integrated energy systems (IESs), reliable planning and operation are challenging owing to significant uncertainties in energy production, utilization, and trading. To this end, this paper proposes a multi-task joint forecasting method that enables joint source-load-price forecasting. First, three uncertain variables in an IES, namely, renewable energy, the multi-energy load, and the energy price, were investigated and the complex coupling relationships among them were validated. Second, to cope with the redundant noise resulting from various inputs, multi-channel feature extraction and a hybrid attention mechanism were combined to enable separate extraction and unified fusion of features. Additionally, considering the unique one-dimensional input in the prediction domain, a sequential convolution attention module (SCAM) with a hybrid channel and temporal attention mechanism was proposed to guide multi-channel feature fusion. Finally, facing the challenge of multi-layer coupling information learning, a multi-task learning (MTL) integrated shared layer was designed. Based on the coordinated with MTL, multi-column convolutional neural network, SCAM and long short-term memory network, joint forecasting of source-load-price was realized. The simulation results showed that the average mean absolute percentage error of the proposed model was as low as 4.10% in source-load-price long-term forecasting, while that of winter short-term forecasting could reach 3.14%. In addition, the here proposed model was found to be superior to others in terms of computational efficiency and result stability.

Suggested Citation

  • Li, Ke & Mu, Yuchen & Yang, Fan & Wang, Haiyang & Yan, Yi & Zhang, Chenghui, 2024. "Joint forecasting of source-load-price for integrated energy system based on multi-task learning and hybrid attention mechanism," Applied Energy, Elsevier, vol. 360(C).
    • Handle: RePEc:eee:appene:v:360:y:2024:i:c:s0306261924002046
    DOI: 10.1016/j.apenergy.2024.122821
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    References listed on IDEAS

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