从基准到生产：将时间序列异常检测方法迁移至电力生产监测
From Benchmarks to Production: Transferring Time Series Anomaly Detection Methods for Electricity Production Monitoring
- EDF Lab Paris Saclay（法国电力集团巴黎萨克雷实验室）
- EDF, DOAAT（法国电力集团，DOAAT）
- Inria, Ecole Normale Supérieure (PSL), CNRS（法国国家信息与自动化研究所，巴黎高等师范学院（PSL），法国国家科学研究中心）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对电力生产预测中的异常检测问题，提出可扩展可解释的TAMIS系统，基于历史模式偏差自动识别异常日，在真实工业数据上实现最佳准确率-效率权衡，并公开数据集。
AI中文摘要：
准确的电力生产预测对于维持能源公用事业公司的运营效率和战略规划至关重要。在工业环境中，此类预测每日生成，以确保供需平衡和发电资产的最优管理。然而，现代电力系统和数据流的日益复杂性对确保这些预测的可靠性和一致性构成了重大挑战。本文针对法国电力公司（EDF）短期生产预测中的异常检测问题，将其表述为识别可能表明数据质量问题或运营异常的非典型日内模式。我们引入了TAMIS，一个可扩展且可解释的系统，该系统分析每日生产时间序列，基于从历史数据中学习的模式偏差自动检测异常日。TAMIS专为人在回路（human-in-the-loop）工作流设计，通过自动化的每日简报呈现排名靠前的异常，从而实现高效的专家审查和持续监控。在真实工业数据上进行的大量实验评估表明，与基线方法相比，TAMIS在准确性和效率之间取得了最佳平衡。为促进进一步研究和可复现性，我们公开发布了研究中使用的匿名化应用数据集。
英文摘要：
Accurate forecasting of electricity production is essential for maintaining the operational efficiency and strategic planning of energy utilities. In industrial settings, such forecasts are generated daily to ensure supply-demand balance and optimal management of production assets. However, the increasing complexity of modern power systems and data flows poses significant challenges for ensuring the reliability and consistency of these forecasts. This paper addresses the problem of anomaly detection in short-term production forecasts at EDF, formulated as identifying atypical intra-day patterns that may signal data quality issues or operational irregularities. We introduce TAMIS, a scalable and interpretable system that analyzes daily production time series to automatically detect anomalous days based on deviations from historical patterns learned from past data. Designed for human-in-the-loop workflows, TAMIS surfaces top-ranked anomalies through an automated daily newsletter, enabling efficient expert review and continuous monitoring. An extensive experimental evaluation on real-world industrial data demonstrates that TAMIS achieves the best accuracy-efficiency trade-off compared to baseline methods. To foster further research and reproducibility, we publicly release the anonymized application datasets used in our study.
