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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

    as
    1. Huang, Qian & Li, Jinghua & Zhu, Mengshu, 2020. "An improved convolutional neural network with load range discretization for probabilistic load forecasting," Energy, Elsevier, vol. 203(C).
    2. Marcjasz, Grzegorz & Narajewski, Michał & Weron, Rafał & Ziel, Florian, 2023. "Distributional neural networks for electricity price forecasting," Energy Economics, Elsevier, vol. 125(C).
    3. Xu, Lei & Wang, Shengwei & Tang, Rui, 2019. "Probabilistic load forecasting for buildings considering weather forecasting uncertainty and uncertain peak load," Applied Energy, Elsevier, vol. 237(C), pages 180-195.
    4. Ahmad, Tanveer & Zhang, Dongdong & Huang, Chao, 2021. "Methodological framework for short-and medium-term energy, solar and wind power forecasting with stochastic-based machine learning approach to monetary and energy policy applications," Energy, Elsevier, vol. 231(C).
    5. Zulfiqar, M. & Kamran, M. & Rasheed, M.B. & Alquthami, T. & Milyani, A.H., 2023. "A hybrid framework for short term load forecasting with a navel feature engineering and adaptive grasshopper optimization in smart grid," Applied Energy, Elsevier, vol. 338(C).
    6. Heydari, Azim & Majidi Nezhad, Meysam & Pirshayan, Elmira & Astiaso Garcia, Davide & Keynia, Farshid & De Santoli, Livio, 2020. "Short-term electricity price and load forecasting in isolated power grids based on composite neural network and gravitational search optimization algorithm," Applied Energy, Elsevier, vol. 277(C).
    7. Zhu, Jizhong & Dong, Hanjiang & Zheng, Weiye & Li, Shenglin & Huang, Yanting & Xi, Lei, 2022. "Review and prospect of data-driven techniques for load forecasting in integrated energy systems," Applied Energy, Elsevier, vol. 321(C).
    8. Wang, Xinlin & Yao, Zhihao & Papaefthymiou, Marios, 2023. "A real-time electrical load forecasting and unsupervised anomaly detection framework," Applied Energy, Elsevier, vol. 330(PA).
    9. Dedinec, Aleksandra & Filiposka, Sonja & Dedinec, Aleksandar & Kocarev, Ljupco, 2016. "Deep belief network based electricity load forecasting: An analysis of Macedonian case," Energy, Elsevier, vol. 115(P3), pages 1688-1700.
    10. Yin, Chen & Mao, Shuhua, 2023. "Fractional multivariate grey Bernoulli model combined with improved grey wolf algorithm: Application in short-term power load forecasting," Energy, Elsevier, vol. 269(C).
    11. Ma, Zhengjing & Mei, Gang, 2022. "A hybrid attention-based deep learning approach for wind power prediction," Applied Energy, Elsevier, vol. 323(C).
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