评估协变量信息下的电网负荷预测：基于时间序列基础模型
Assessing Covariate-Informed Grid Load Forecasting with a Time-Series Foundation Model
- Artificial Intelligence Team GE Vernova Advanced Research Center（GE Vernova先进研究中心人工智能团队）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本研究评估时间序列基础模型Chronos-2在电网负荷预测中的表现，发现其经任务特定微调后短时域预测性能优异，但零样本准确性不及任务特定模型，误差随预测步长增长更快。
AI中文摘要：
现代电力系统随着整合多种发电来源以满足日益增长的需求而变得越来越复杂，这使得准确的负荷预测变得具有挑战性。时间序列基础模型（TSFMs）的最新进展在零样本单变量负荷预测任务中展现了有前景的性能。然而，现实世界中的负荷预测通常涉及多个目标变量，并且需要整合外生变量，这引发了关于TSFMs在实际场景中实用性的重要问题。在本研究中，我们将Chronos-2（亚马逊最近开发的一个模型）定位为代表性的多通道TSFM，它支持单变量、多变量和协变量信息预测，并系统性地调查了此类模型如何用于现实世界的负荷预测。虽然先前的工作在零样本设置下对Chronos-2在有限数量的能源相关任务上进行了评估，但其相对于已建立的任务特定深度学习模型的性能以及在使用任务特定历史数据进行适配时的行为仍未被充分理解。在本工作中，我们在两个真实世界的公用事业数据集（ISO新英格兰和ENTSO-E）上评估了Chronos-2，并将其与广泛使用的任务特定深度学习模型进行基准比较。我们的结果表明，Chronos-2从任务特定的微调中获益显著，并实现了强大的短时域预测性能，但其零样本准确性落后于任务特定模型，且其预测误差随着预测步长的增加而增长更快。总体而言，本研究详细描述了Chronos-2等TSFMs在电网负荷预测中的优势和局限性，并为如何有效适配预训练TSFM以用于运营负荷预测应用提供了实用见解。
英文摘要：
Modern power systems are growing increasingly complex as they integrate diverse generation sources to meet rising demand, making accurate load forecasting challenging. Recent advances in time-series foundation models (TSFMs) resulted in promising performance in zero-shot univariate load forecasting tasks. However, real-world load forecasting often involves multiple target variables and requires the integration of exogenous variables, raising important questions about the utility of TSFMs in realistic settings. In this study, we position Chronos-2, a recently developed model by Amazon, as a representative multi-channel TSFM that supports univariate, multivariate, and covariate-informed forecasting, and conduct a systematic investigation of how such models can be used for real-world load forecasting. While prior work has evaluated Chronos-2 on a limited number of energy-related tasks in a zero-shot setting, its performance relative to established task-specific deep learning models and its behavior when adapted using task-specific historical data remains insufficiently understood. In this work, we evaluate Chronos-2 on two real-world utility datasets, ISO New England and ENTSO-E, and benchmark it against widely used task-specific deep learning models. Our results show that Chronos-2 benefits substantially from task-specific fine-tuning and achieves strong short-horizon forecasting performance, but its zero-shot accuracy lags behind task-specific models and its forecasting error grows more rapidly with increasing forecast steps. Overall, this study provides a detailed characterization of the strengths and limitations of TSFMs such as Chronos-2 in grid load forecasting and offers practical insights into how a pretrained TSFM can be effectively adapted for operational load forecasting applications.
