安全关键环境下符合欧盟AI法案要求的短期负荷预测：德国输电电网聚合负荷41天在线挑战赛结果
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
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中文总结 AI 辅助
本文通过41天在线挑战赛验证，符合欧盟AI法案要求的spotforecast2-safe短期负荷预测流程，在德国输电电网聚合负荷预测中优于ENTSO-E基线，其本地模型性能可与超1亿参数的chronos-2等大模型媲美。
中文摘要 AI 辅助
短期负荷预测（STLF）在电力行业中至关重要，它服务于欧洲及德国法律指定为关键的基础设施。确定性、可复现性和可审计性是工程要求，而非可选附加项，STLF不再纯粹是精度问题，也是软件工程与合规性问题。本文描述了一项为期41天的在线挑战赛结果，该挑战赛针对德国输电电网聚合负荷评估了一套完整的STLF流程。该流程基于开源Python库spotforecast2-safe，其设计符合欧盟AI法案在安全关键环境下的要求，可根据欧洲输电系统运营商网络（ENTSO-E）的数据预测目标日的24小时负荷值。该流程包含异常检测与间隙感知数据准备、日历与天气协变量、递归多步预测算法及超参数调优，预测精度以ENTSO-E官方日前预测为基准进行衡量。符合欧盟AI法案要求的spotforecast2-safe流程优于ENTSO-E基线，上下文模型表现具有竞争力，透明、低成本且可审计的本地模型（本文中称为macl2l）可与超过1亿参数的大型高能耗预训练基础模型（如chronos-2）相媲美。挑战赛基础设施、所有团队的完整提交历史及冻结的最终排行榜均已公开。
英文摘要
Short-term load forecasting (STLF) plays a vital role in the electric power industry. It is relevant for critical infrastructure. STLF is no longer purely a performance and accuracy problem, because determinism, fail-safe handling, minimal-attack surface, and no dead code are software-engineering requirements rather than optional extras. This report describes results from a 41-day live challenge that evaluated an STLF pipeline for the aggregated German transmission-grid load. The STLF pipeline predicts the 24 hourly load values of a target day from European Network of Transmission System Operators for Electricity (ENTSO-E) data. It is based on the open-source Python library spotforecast2-safe, which tries to implement the EU-AI Act Requirements in Safety-Critical Environments by design. The STLF pipeline includes gap- and anomaly-aware data preprocessing, calendar and weather covariates, and a forecasting algorithm. Forecast quality is compared to the official ENTSO-E day-ahead forecast. The spotforecast2-safe pipeline beats the ENTSO-E baseline. In-context models show competitive performance. Transparent, deterministic, low-cost, and auditable local models (referred to as macl2l in this report) are competitive with more than 100-million-parameter large, energy-intensive pre-trained foundation models such as chronos-2. The challenge implementation, the submission history of all teams, and the archived leaderboard are publicly available.
发表机构
- THK-AI Research Cluster（THK-AI研究集群）
机构由 AI 辅助整理，请以论文原文为准。
