IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v14y2021i20p6555-d654418.html
   My bibliography  Save this article

Short-Term Multiple Load Forecasting Model of Regional Integrated Energy System Based on QWGRU-MTL

Author

Listed:
  • Songyao Wang

    (College of Electrical Engineering, Qingdao University, Qingdao 266071, China)

  • Zhisheng Zhang

    (College of Electrical Engineering, Qingdao University, Qingdao 266071, China)

Abstract

In order to improve the accuracy of the multiple load forecasting of a regional integrated energy system, a short-term multiple load forecasting model based on the quantum weighted GRU and multi-task learning framework is proposed in this paper. Firstly, correlation analysis is carried out using a maximum information coefficient to select the input of the model. Then, a multi-task learning architecture is constructed based on the quantum weighted GRU neural network, and the coupling information among multiple loads is learned through the sharing layer in order to improve the prediction accuracy of multiple loads. Finally, the PSO algorithm is used to optimize the parameters of the quantum weighted GRU. The simulation data of a regional integrated energy system in northern China are used to predict the power and cooling loads on summer weekdays and rest days, and the results show that, compared with the LSTM, GRU and single task learning QWGRU models, the proposed model is more effective in the multiple load forecasting of a regional integrated energy system.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:20:p:6555-:d:654418
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/14/20/6555/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/14/20/6555/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Denis Sidorov & Fang Liu & Yonghui Sun, 2020. "Machine Learning for Energy Systems," Energies, MDPI, vol. 13(18), pages 1-6, September.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Peng Song & Zhisheng Zhang, 2023. "Research on Multiple Load Short-Term Forecasting Model of Integrated Energy Distribution System Based on Mogrifier-Quantum Weighted MELSTM," Energies, MDPI, vol. 16(9), pages 1-13, April.
    2. Bo Hu & Jian Xu & Zuoxia Xing & Pengfei Zhang & Jia Cui & Jinglu Liu, 2022. "Short-Term Combined Forecasting Method of Park Load Based on CEEMD-MLR-LSSVR-SBO," Energies, MDPI, vol. 15(8), pages 1-14, April.
    3. Fang Gao & Guojian Wu, 2023. "Application of Quantum Computing in Power Systems," Energies, MDPI, vol. 16(5), pages 1-3, February.
    4. 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).
    5. 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).
    6. Mehmet Türker Takcı & Tuba Gözel, 2022. "Effects of Predictors on Power Consumption Estimation for IT Rack in a Data Center: An Experimental Analysis," Sustainability, MDPI, vol. 14(21), pages 1-19, November.

    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. He, Zhaoyu & Guo, Weimin & Zhang, Peng, 2022. "Performance prediction, optimal design and operational control of thermal energy storage using artificial intelligence methods," Renewable and Sustainable Energy Reviews, Elsevier, vol. 156(C).
    2. Zhou, Yuekuan & Zheng, Siqian, 2020. "Multi-level uncertainty optimisation on phase change materials integrated renewable systems with hybrid ventilations and active cooling," Energy, Elsevier, vol. 202(C).
    3. Gohar Gholamibozanjani & Mohammed Farid, 2021. "A Critical Review on the Control Strategies Applied to PCM-Enhanced Buildings," Energies, MDPI, vol. 14(7), pages 1-39, March.
    4. Amaral, C. & Silva, T. & Mohseni, F. & Amaral, J.S. & Amaral, V.S. & Marques, P.A.A.P. & Barros-Timmons, A. & Vicente, R., 2021. "Experimental and numerical analysis of the thermal performance of polyurethane foams panels incorporating phase change material," Energy, Elsevier, vol. 216(C).
    5. Ding, Zhixiong & Wu, Wei & Leung, Michael, 2021. "Advanced/hybrid thermal energy storage technology: material, cycle, system and perspective," Renewable and Sustainable Energy Reviews, Elsevier, vol. 145(C).
    6. Qin, Di & Liu, Zhengxuan & Zhou, Yuekuan & Yan, Zhongjun & Chen, Dachuan & Zhang, Guoqiang, 2021. "Dynamic performance of a novel air-soil heat exchanger coupling with diversified energy storage components—modelling development, experimental verification, parametrical design and robust operation," Renewable Energy, Elsevier, vol. 167(C), pages 542-557.
    7. Abokersh, Mohamed Hany & Gangwar, Sachin & Spiekman, Marleen & Vallès, Manel & Jiménez, Laureano & Boer, Dieter, 2021. "Sustainability insights on emerging solar district heating technologies to boost the nearly zero energy building concept," Renewable Energy, Elsevier, vol. 180(C), pages 893-913.
    8. Xiaotong Qie & Rui Zhang & Yanyong Hu & Xialing Sun & Xue Chen, 2021. "A Multi-Criteria Decision-Making Approach for Energy Storage Technology Selection Based on Demand," Energies, MDPI, vol. 14(20), pages 1-29, October.
    9. Hana Charvátová & Aleš Procházka & Martin Zálešák, 2020. "Computer Simulation of Passive Cooling of Wooden House Covered by Phase Change Material," Energies, MDPI, vol. 13(22), pages 1-15, November.
    10. Mukhamet, Tileuzhan & Kobeyev, Sultan & Nadeem, Abid & Memon, Shazim Ali, 2021. "Ranking PCMs for building façade applications using multi-criteria decision-making tools combined with energy simulations," Energy, Elsevier, vol. 215(PB).
    11. Prabha Bhola & Alexandros-Georgios Chronis & Panos Kotsampopoulos & Nikos Hatziargyriou, 2023. "Business Model Selection for Community Energy Storage: A Multi Criteria Decision Making Approach," Energies, MDPI, vol. 16(18), pages 1-30, September.
    12. Olga Zhironkina & Sergey Zhironkin, 2023. "Technological and Intellectual Transition to Mining 4.0: A Review," Energies, MDPI, vol. 16(3), pages 1-37, February.

    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:14:y:2021:i:20:p:6555-:d:654418. 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.