School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China.
Sensors (Basel). 2023 Apr 12;23(8):3939. doi: 10.3390/s23083939.
As a low-cost demand-side management application, non-intrusive load monitoring (NILM) offers feedback on appliance-level electricity usage without extra sensors. NILM is defined as disaggregating loads only from aggregate power measurements through analytical tools. Although low-rate NILM tasks have been conducted by unsupervised approaches based on graph signal processing (GSP) concepts, enhancing feature selection can still contribute to performance improvement. Therefore, a novel unsupervised GSP-based NILM approach with power sequence feature (STS-UGSP) is proposed in this paper. First, state transition sequences (STS) are extracted from power readings and featured in clustering and matching, instead of power changes and steady-state power sequences featured in other GSP-based NILM works. When generating graph in clustering, dynamic time warping distances between STSs are calculated for similarity quantification. After clustering, a forward-backward power STS matching algorithm is proposed for searching each STS pair of an operational cycle, utilizing both power and time information. Finally, load disaggregation results are obtained based on STS clustering and matching results. STS-UGSP is validated on three publicly accessible datasets from various regions, generally outperforming four benchmarks in two evaluation metrics. Besides, STS-UGSP estimates closer energy consumption of appliances to the ground truth than benchmarks.
作为一种低成本的需求侧管理应用，非侵入式负荷监测（NILM）无需额外传感器即可提供设备级用电反馈。NILM 的定义是仅通过分析工具从总功率测量中分解负荷。尽管已经通过基于图信号处理（GSP）概念的无监督方法进行了低速率 NILM 任务，但增强特征选择仍然可以有助于提高性能。因此，本文提出了一种新颖的基于无监督 GSP 的 NILM 方法，即带功率序列特征的无监督 GSP（STS-UGSP）。首先，从功率读数中提取状态转移序列（STS），并在聚类和匹配中进行特征化，而不是在其他基于 GSP 的 NILM 工作中使用功率变化和稳态功率序列进行特征化。在聚类过程中，计算 STS 之间的动态时间扭曲距离以进行相似性量化。聚类后，提出了一种正向-反向功率 STS 匹配算法，用于搜索每个操作周期的 STS 对，同时利用功率和时间信息。最后，根据 STS 聚类和匹配结果获得负荷分解结果。STS-UGSP 在来自不同地区的三个公开可用数据集上进行了验证，在两个评估指标上普遍优于四个基准。此外，STS-UGSP 估计的设备能耗比基准更接近实际情况。
