Beyond Information Redundancy: Expanding Cross-Modal Knowledge Representation for Power Load Time Series Forecasting

Abstract

Load forecasting is pivotal for stable power systems. Conventional uni-modal methods suffer from representation drift under data scarcity. While recent multi-modal approaches attempt to alleviate this, they exhibit severe information redundancy, merely recycling time series data via superficial intra-modal transformations. In this paper, we argue that the essence of multi-modal time series learning should expand representation manifolds via complementary cross-modal knowledge enrichment rather than duplicating redundant information, especially for few-shot scenarios prevalent in power systems. To this end, we propose KEMM-Net, a Knowledge-Enriched Multi-Modal Network for power load forecasting. KEMM-Net first constructs textual and visual embeddings to strengthen load time series representations from different knowledge perspectives. It then introduces a Partial Information Decomposition (PID)-guided cross-modal contrastive learning mechanism to achieve cross-modal semantic alignment and balance redundant, synergistic, and unique information for forecasting. Extensive experiments on real-world public datasets demonstrate that KEMM-Net consistently outperforms strong deep learning and multi-modal baselines, particularly in few-shot settings. Our code is available at https://anonymous.4open.science/r/KEMM-Net-2898.