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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
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    References listed on IDEAS

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