IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0343511.html

MTMF-Grid: A multi-task multi-modal fusion model for operational forecasting and decision support in power grids

Author

Listed:
  • Dongyu Zhang
  • Biao Shen
  • Peng Li
  • Pengcheng Wang
  • Yang Sheng
  • Yuqi Bing

Abstract

Power grid strategic emerging business investment features multi-objective coupling and multi-source heterogeneous data. It requires simultaneous completion of regression and classification tasks, making traditional single-task or single-modal assessment methods inadequate for precise decision-making. This study proposes a multi-task multi-modal fusion model (MTMF-Grid) for operational forecasting and decision support in strategic emerging power grid investments. MTMF-Grid leverages operational data proxies to support investment decision-making, rather than directly predicting financial returns. MTMF-Grid adopts a modular architecture with three core mechanisms: a task-adaptive Transformer to balance general and task-specific feature expression, a Cross-Fusion Gating Mechanism (CFGM) for dynamic multi-modal fusion and robustness to modal missing scenarios, and a loss variance-based mechanism to dynamically adjust task weights and alleviate gradient conflicts. Experiments on SEWA (Sharjah Electricity and Water Authority dataset) and OPSD (Open Power System Data) datasets show MTMF-Grid outperforms mainstream baseline models. It achieves 3.06% MAPE (Mean Absolute Percentage Error) for hourly electricity price prediction, 0.915 accuracy for load fluctuation risk classification. This study presents a comprehensive framework for supporting strategic decision-making in power grid investment through operational forecasting and multi-modal data integration.

Suggested Citation

  • Dongyu Zhang & Biao Shen & Peng Li & Pengcheng Wang & Yang Sheng & Yuqi Bing, 2026. "MTMF-Grid: A multi-task multi-modal fusion model for operational forecasting and decision support in power grids," PLOS ONE, Public Library of Science, vol. 21(3), pages 1-24, March.
    • Handle: RePEc:plo:pone00:0343511
    DOI: 10.1371/journal.pone.0343511
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0343511
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0343511&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0343511?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Mobarak Abumohsen & Amani Yousef Owda & Majdi Owda, 2023. "Electrical Load Forecasting Using LSTM, GRU, and RNN Algorithms," Energies, MDPI, vol. 16(5), pages 1-31, February.
    Full references (including those not matched with items on IDEAS)

    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. Vasileios Laitsos & Georgios Vontzos & Paschalis Paraschoudis & Eleftherios Tsampasis & Dimitrios Bargiotas & Lefteri H. Tsoukalas, 2024. "The State of the Art Electricity Load and Price Forecasting for the Modern Wholesale Electricity Market," Energies, MDPI, vol. 17(22), pages 1-37, November.
    2. Xie, Kexin & Giacomoni, Anthony & Deng, Xinwei & Wu, Yinghua, 2025. "Bias calibration and error propagation adjustment for ML-based time series forecasting: A systematic study for PJM’s electricity load forecast amid Virginia’s data center surge," Energy, Elsevier, vol. 336(C).
    3. Jun Li & Xingzhao Zhang & Qingsong Hu & Fuxi Zhang & Oleg Gaidai & Leilei Chen, 2024. "Data Augmentation Technique Based on Improved Time-Series Generative Adversarial Networks for Power Load Forecasting in Recirculating Aquaculture Systems," Sustainability, MDPI, vol. 16(23), pages 1-17, December.
    4. Yuyang Zhang & Lei Cui & Wenqiang Yan, 2025. "Integrating Kolmogorov–Arnold Networks with Time Series Prediction Framework in Electricity Demand Forecasting," Energies, MDPI, vol. 18(6), pages 1-18, March.
    5. Tae-Geun Kim & Sung-Guk Yoon & Kyung-Bin Song, 2025. "Very Short-Term Load Forecasting Model for Large Power System Using GRU-Attention Algorithm," Energies, MDPI, vol. 18(13), pages 1-22, June.
    6. Ramos, Paulo Vitor B. & Villela, Saulo Moraes & Silva, Walquiria N. & Dias, Bruno H., 2023. "Residential energy consumption forecasting using deep learning models," Applied Energy, Elsevier, vol. 350(C).
    7. Zhuoqun Zou & Jing Wang & Ning E & Can Zhang & Zhaocai Wang & Enyu Jiang, 2023. "Short-Term Power Load Forecasting: An Integrated Approach Utilizing Variational Mode Decomposition and TCN–BiGRU," Energies, MDPI, vol. 16(18), pages 1-17, September.
    8. Akpinar, Muhammed Halil & Atila, Orhan & Sengur, Abdulkadir & Salvi, Massimo & Acharya, U.R., 2025. "A novel uncertainty-aware liquid neural network for noise-resilient time series forecasting and classification," Chaos, Solitons & Fractals, Elsevier, vol. 193(C).
    9. Xinjian Xiang & Tianshun Yuan & Guangke Cao & Yongping Zheng, 2024. "Short-Term Electric Load Forecasting Based on Signal Decomposition and Improved TCN Algorithm," Energies, MDPI, vol. 17(8), pages 1-21, April.
    10. Aydin Zaboli & Swetha Rani Kasimalla & Kuchan Park & Younggi Hong & Junho Hong, 2024. "A Comprehensive Review of Behind-the-Meter Distributed Energy Resources Load Forecasting: Models, Challenges, and Emerging Technologies," Energies, MDPI, vol. 17(11), pages 1-27, May.
    11. Serdal Atiç & Ercan Izgi, 2024. "Smart Reserve Planning Using Machine Learning Methods in Power Systems with Renewable Energy Sources," Sustainability, MDPI, vol. 16(12), pages 1-20, June.
    12. George Kandilogiannakis & Paris Mastorocostas & Athanasios Voulodimos & Constantinos Hilas, 2023. "Short-Term Load Forecasting of the Greek Power System Using a Dynamic Block-Diagonal Fuzzy Neural Network," Energies, MDPI, vol. 16(10), pages 1-20, May.
    13. Omar Farhan Al-Hardanee & Hüseyin Demirel, 2024. "Hydropower Station Status Prediction Using RNN and LSTM Algorithms for Fault Detection," Energies, MDPI, vol. 17(22), pages 1-23, November.
    14. Wenting Zhao & Haoran Xu & Peng Chen & Juan Zhang & Jing Li & Tingting Cai, 2025. "Elastic Momentum-Enhanced Adaptive Hybrid Method for Short-Term Load Forecasting," Energies, MDPI, vol. 18(13), pages 1-25, June.
    15. Akash Mahajan & Srijita Das & Wencong Su & Van-Hai Bui, 2024. "Bayesian-Neural-Network-Based Approach for Probabilistic Prediction of Building-Energy Demands," Sustainability, MDPI, vol. 16(22), pages 1-21, November.
    16. Huiqun Yu & Haoyi Sun & Yueze Li & Chunmei Xu & Chenkun Du, 2024. "Enhanced Short-Term Load Forecasting: Error-Weighted and Hybrid Model Approach," Energies, MDPI, vol. 17(21), pages 1-22, October.
    17. Wang, Zhongrui & Chen, Liang & Wang, Chunbo, 2026. "Weekly periodicity feature-driven CNN-GRU and LightGBM hybrid model for short-term multi-step building load forecasting," Energy, Elsevier, vol. 355(C).
    18. Tulin Guzel & Hakan Cinar & Mehmet Nabi Cenet & Kamil Doruk Oguz & Ahmet Yucekaya & Mustafa Hekimoglu, 2023. "A Framework to Forecast Electricity Consumption of Meters using Automated Ranking and Data Preprocessing," International Journal of Energy Economics and Policy, International Journal of Energy Economics and Policy, vol. 13(5), pages 179-193, September.
    19. Piotr Obrycki & Krzysztof Perlicki & Marek Stawowy, 2025. "Peak Shaving Strategy in the Context of the Charging Process of a Battery Energy Storage System in the Railway Microgrid," Energies, MDPI, vol. 18(11), pages 1-21, May.
    20. Abbas, Muhammad & Che, Yanbo & Khan, Inam Ullah, 2025. "A novel stacked ensemble framework with the Kolmogorov-Arnold Network for short-term electric load forecasting," Energy, Elsevier, vol. 332(C).

    More about this item

    Statistics

    Access and download statistics

    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:plo:pone00:0343511. 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: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    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.