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, Yuqi Bing
TL;DR
The paper introduces MTMF-Grid, a model for power grid investment decisions using multi-task and multi-modal data fusion.
Contribution
A novel multi-task multi-modal fusion model (MTMF-Grid) is proposed for operational forecasting and decision support in power grids.
Findings
MTMF-Grid outperforms baseline models on SEWA and OPSD datasets.
Achieves 3.06% MAPE for electricity price prediction and 0.915 accuracy for load fluctuation risk classification.
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…
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Taxonomy
TopicsEnergy Load and Power Forecasting · Stock Market Forecasting Methods · Electricity Theft Detection Techniques