这篇论文用41天实测数据证明，一个小型开源库spotforecast2-safe能打过ENTSO-E官方预测，甚至敢跟上亿参数的chronos-2比划比划。
论文报告了一项为期41天的实时挑战赛，用于评估面向德国输电电网总负荷的短期负荷预测（STLF）流水线。该流水线基于开源Python库spotforecast2-safe，按欧盟AI法案要求设计，输出目标日24个时点的负荷预测值，并与ENTSO-E官方日前预测进行对比。结果显示，spotforecast2-safe流水线在准确性上超过了ENTSO-E基线。作者指出，透明的本地模型（macl2l）能与参数量超1亿的预训练基础模型（如chronos-2）竞争。
Short-term load forecasting under EU-AI Act Requirements in Safety-Critical Environments: Results from a 41-day live challenge on the aggregated German transmission-grid load
Short-term load forecasting (STLF) play a vital role in the electric power industry. It serves infrastructure that European and German law designate as critical. Determinism, reproducibility, and auditability are engineering requirements rather than optional extras. STLF is no longer purely an accuracy problem. It is also a software-engineering and compliance problem. This paper describes results from a 41-day live challenge that evaluated a complete STLF pipeline for the aggregated German transmission-grid load. The pipeline is based on the open-source Python library spotforecast2-safe, which implements the EU-AI Act Requirements in Safety-Critical Environments by design. The pipeline predicts the 24 hourly load values of a target day from European Network of Transmission System Operators for Electricity (ENTSO-E) data. It includes anomaly detection and gap-aware data preparation, calendar and weather covariates, a recursive multi-step forecasting algorithm, and hyperparameter tuning. Forecast accuracy is measured against the official ENTSO-E day-ahead forecast. The EU-AI act compliant spotforecast2-safe pipeline beats the ENTSO-E baseline. In-context models show competitive performance. Transparent, low-cost, and auditable local models (referred to as macl2l in this paper) are competitive with more than 100-million-parameter large, energy-intensive pre-trained foundation models such as chronos-2. The challenge infrastructure, the complete submission history of all teams, and the frozen final leaderboard are publicly available.