GRAFT: Grid-Aware Load Forecasting with Multi-Source Textual Alignment and Fusion

TL;DR

GRAFT is a novel grid-aware load forecasting model that aligns and fuses multi-source textual data with load data, significantly improving accuracy and interpretability across various regions and time scales.

Contribution

This paper introduces GRAFT, a new method that integrates multi-source textual information into load forecasting with alignment, fusion, and interpretability features, supported by a comprehensive benchmark.

Findings

GRAFT outperforms strong baselines across multiple regions and horizons.

The model is robust in event-driven scenarios.

It enables temporal localization and source-level interpretation of effects.

Abstract

Electric load is simultaneously affected across multiple time scales by exogenous factors such as weather and calendar rhythms, sudden events, and policies. Therefore, this paper proposes GRAFT (GRid-Aware Forecasting with Text), which modifies and improves STanHOP to better support grid-aware forecasting and multi-source textual interventions. Specifically, GRAFT strictly aligns daily-aggregated news, social media, and policy texts with half-hour load, and realizes text-guided fusion to specific time positions via cross-attention during both training and rolling forecasting. In addition, GRAFT provides a plug-and-play external-memory interface to accommodate different information sources in real-world deployment. We construct and release a unified aligned benchmark covering 2019--2021 for five Australian states (half-hour load, daily-aligned weather/calendar variables, and three categories of external texts), and conduct systematic, reproducible evaluations at three scales -- hourly, daily, and monthly -- under a unified protocol for comparison across regions, external sources, and time scales. Experimental results show that GRAFT significantly outperforms strong baselines and reaches or surpasses the state of the art across multiple regions and forecasting horizons. Moreover, the model is robust in event-driven scenarios and enables temporal localization and source-level interpretation of text-to-load effects through attention read-out. We release the benchmark, preprocessing scripts, and forecasting results to facilitate standardized empirical evaluation and reproducibility in power grid load forecasting.