In this paper we present novel methodology for automatic anomaly and switch event filtering to improve load estimation in power grid systems. By leveraging unsupervised methods with supervised optimization, our approach prioritizes interpretability while ensuring robust and generalizable performance on unseen data. Through experimentation, a combination of binary segmentation for change point detection and statistical process control for anomaly detection emerges as the most effective strategy, specifically when ensembled in a novel sequential manner. Results indicate the clear wasted potential when filtering is not applied. The automatic load estimation is also fairly accurate, with approximately 90% of estimates falling within a 10% error margin, with only a single significant failure in both the minimum and maximum load estimates across 60 measurements in the test set. Our methodology's interpretability makes it particularly suitable for critical infrastructure planning, thereby enhancing decision-making processes.

翻译：本文提出了一种新颖的自动异常与开关事件滤波方法，旨在提升电网系统中的负荷估计性能。通过将无监督方法与有监督优化相结合，我们的方法在保证对未见数据具有鲁棒性和泛化性能的同时，优先考虑了模型的可解释性。实验表明，结合用于变点检测的二元分割法与用于异常检测的统计过程控制，并以一种新颖的顺序集成方式组合时，能形成最有效的策略。结果明确指出，未应用滤波时将存在明显的潜力浪费。自动负荷估计也具备相当的准确性，约90%的估计值误差在10%以内，且在测试集60次测量中，最小与最大负荷估计均仅出现一次显著偏差。本方法因其良好的可解释性，特别适用于关键基础设施规划，从而有助于提升决策过程。