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

Dongyu Zhang, Biao Shen, Peng Li, Pengcheng Wang, Yang Sheng and Yuqi Bing

PLOS ONE, 2026, vol. 21, issue 3, 1-24

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.

Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0343511

DOI: 10.1371/journal.pone.0343511

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