GRAFT：基于多源文本对齐与融合的网格感知负载预测
GRAFT: Grid-Aware Load Forecasting with Multi-Source Textual Alignment and Fusion
AI总结：
GRAFT通过多源文本对齐与融合改进STanHOP，实现网格感知预测和文本干预，构建统一基准并验证模型在不同时间尺度和区域的优越性。
摘要：
电力负载受天气、日历节奏、突发事件和政策等外源因素在多个时间尺度上共同影响。本文提出GRAFT（GRid-Aware Forecasting with Text），改进STanHOP以更好地支持网格感知预测和多源文本干预。具体而言，GRAFT严格对齐每日聚合新闻、社交媒体和政策文本与半小时负载，并通过交叉注意力在训练和滚动预测中实现文本引导的融合。此外，GRAFT提供插件式外部内存接口，以适应实际部署中的不同信息源。我们构建并发布了覆盖2019-2021年五个澳大利亚州的统一对齐基准（包含半小时负载、每日对齐的天气/日历变量和三类外部文本），并在统一协议下进行系统、可重复的评估，分别在小时、日和月三个时间尺度上进行比较。实验结果表明，GRAFT显著优于强基线模型，并在多个区域和预测时间范围内达到或超越最先进水平。此外，模型在事件驱动场景中表现出鲁棒性，并通过注意力读取实现时间局部化和源级解释。我们发布了基准、预处理脚本和预测结果，以促进电力电网负载预测的标准化经验评估和可重复性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
