用时序基础模型提升低压负荷峰值预测精度，更贴合电网实际需求。
Probabilistic Low-Voltage Peak Load Forecasting with Time Series Foundation Models Evaluated on Application-Oriented Metrics
- 采用时序基础模型（如Chronos-2）实现自动化低电压负荷预测。
- 在200条真实馈线数据上，Chronos-2显著优于传统基线模型。
- 提出新评估指标，关联预测能力与电网资产规划成本和风险。
低电压负荷预测是高电气化、分布式发电场景下能源系统的关键环节。然而现有方法依赖大量人工干预，常缺乏不确定性估计和精准峰值预测，且评估未充分契合电网实际需求。本研究对200条真实低压馈线的短时净负荷预测进行了全面评估，重点考察快速发展的时序基础模型。对比Chronos-Bolt、Chronos-2、TabPFN-TS与六种基线模型，结果表明Chronos-2表现最优。消融实验显示，即使不使用气象协变量，时序基础模型仍能适应更高不确定性，尽管气象信息仍有价值。研究提出一种面向应用的新指标，将峰值预测能力与电网资产规划中成本降低和故障风险最小化的权衡直接关联。
原文摘要 · Abstract (English)
Low-voltage load forecasting is an important component in current and future energy systems with a high degree of electrification and decentralized generation. However, current forecasting methods require significant manual effort, often lack uncertainty estimation and proper peak prediction, and they are often not adequately evaluated in terms of grid requirements. In the present study, we provide an extensive evaluation of short-term net load forecasts of 200 real-world low-voltage feeders with a focus on the rapidly evolving time series foundation models. Our study compares Chronos-Bolt, Chronos-2 and TabPFN-TS to six baseline models and demonstrates superior performance, in particular for Chronos-2. An ablation study, in which weather covariates are omitted, shows that time series foundation models adapt to increased uncertainty, despite the importance of weather information. A novel application-oriented metric links the model's forecasting capabilities in peak prediction to the trade-off in grid asset planning and operation between cost reduction and minimizing the risk of failure.
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