PrismNet: Viewing Time Series Through a Multi-Modal Prism for Interpretable Power Load Forecasting

TL;DR

PrismNet is an interpretable multi-modal framework that enhances power load forecasting by integrating text and image data, improving few-shot learning and providing domain-specific semantic insights.

Contribution

It introduces a novel multi-modal augment module and a PID-guided contrastive learning mechanism for better interpretability and performance in load forecasting.

Findings

Outperforms existing methods on real-world datasets.

Effective in few-shot learning scenarios.

Provides interpretable insights into modality interactions.

Abstract

Load forecasting plays a pivotal role in the safe and stable operation of power systems. Conventional deep learning methods often struggle to adapt to few-shot scenarios frequently encountered in industrial applications. Existing multi-modal approaches typically overlook domain-specific cross-modal semantic alignment and lack sufficient mechanism interpretability. To address these challenges, this study proposes PrismNet, an interpretable multi-modal framework for power load forecasting. First, a multi-modal augment module integrates text and image modalities to strengthen load time series representations, empowering the model with few-shot learning capabilities. Subsequently, we design a Partial Information Decomposition (PID) guided multi-modal contrastive learning (CL) mechanism to achieve domain-specific cross-modal semantic alignment. This process elucidates the intrinsic interactions among modalities and offers a new lens for interpretability. Extensive experiments on real-world public datasets demonstrate that PrismNet outperforms strong deep learning and multi-modal baselines, particularly in few-shot settings, while providing a trustworthy and interpretable solution for safety-critical electric load scenarios. Our code is available at https://anonymous.4open.science/r/PrismNet-9DFC.