AI 中文总结
该研究针对非侵入式负荷监测中用电量时间序列分割的挑战，提出BayesSeg框架，结合双稳态分割准则、复合评估指标与贝叶斯优化，在SustDataED2数据集上实现高效精准的分割，大幅降低优化延迟。
AI 中文摘要
在非侵入式负荷监测（NILM）中，用电量时间序列的自适应分割对电器识别至关重要。然而，现有方法面临启发式参数调优、边界敏感性和指标饱和等挑战。本文提出BayesSeg，一个整合时间序列分割、多维评估和自动参数优化的统一框架。分割层采用基于前序子序列尾值和均值的双稳态准则，结合顺序提取与补集解析策略，实现稳态和过渡态片段的精确无监督划分；评估层将分割结果映射为二值状态序列，构建融合事件级F1分数（event_F1）与归一化互信息（NMI）的复合指标，其中event_F1通过容差匹配量化开关事件的精确率和召回率，NMI捕捉全局结构一致性，共同克服边界敏感性和点式指标区分度有限的问题；优化层将复合得分作为贝叶斯优化的目标函数，构建TPE代理模型以实现高效的全局参数空间探索。在SustDataED2数据集上的实验表明，贝叶斯优化仅需约100次目标评估，即可定位到与 exhaustive 网格搜索最优值偏差在0.35%以内的参数区域；该框架实现了加权复合得分0.7149、event_F1为0.9340，同时将优化延迟从约5300秒降至1秒以内，加速比超过5700倍。BayesSeg实现了分割配置的自动化，为NILM及相关领域的时间序列分析提供了可扩展、高效的解决方案。
英文摘要
In Non-Intrusive Load Monitoring (NILM), adaptive segmentation of electricity consumption time series is critical for appliance recognition. However, prevailing methods face challenges including heuristic parameter tuning, boundary sensitivity, and metric saturation. This paper proposes BayesSeg, a unified framework integrating time-series segmentation, multidimensional evaluation, and automatic parameter optimization. The segmentation layer employs a dual steady-state criterion based on the tail value and mean of preceding subsequences, combined with a sequential extraction and complement-set parsing strategy, to achieve precise unsupervised partitioning of steady-state and transition-state segments. The evaluation layer maps segmentation results to binary state sequences and formulates a composite metric integrating an event-level F1 score (event_F1) with Normalized Mutual Information (NMI). The event_F1 quantifies switching-event precision and recall via tolerance matching, while NMI captures global structural consistency, jointly overcoming the boundary sensitivity and limited discriminability of point-wise metrics. In the optimization layer, the composite score serves as the objective function for Bayesian optimization, which constructs a TPE surrogate model for efficient global parameter-space exploration. Experiments on the SustDataED2 dataset demonstrate that Bayesian optimization requires only ~100 objective evaluations to locate a parameter region within 0.35% deviation of the exhaustive grid-search optimum. The framework achieves a weighted composite score of 0.7149 and an event_F1 of 0.9340 while reducing optimization latency from ~5300 seconds to under 1 second, a speedup exceeding 5700x. BayesSeg automates segmentation configuration and provides a scalable, efficient solution for time-series analysis in NILM and related domains.
Comments23 pages, 4 figures, 5 tables
