Department of Mechatronics and Biomedical Engineering, Air University, Islamabad 44000, Pakistan.
Sensors (Basel). 2022 May 26;22(11):4036. doi: 10.3390/s22114036.
With widely deployed smart meters, non-intrusive energy measurements have become feasible, which may benefit people by furnishing a better understanding of appliance-level energy consumption. This work is a step forward in using graph signal processing for non-intrusive load monitoring (NILM) by proposing two novel techniques: the spectral cluster mean (SC-M) and spectral cluster eigenvector (SC-EV) methods. These methods use spectral clustering for extracting individual appliance energy usage from the aggregate energy profile of the building. After clustering the data, different strategies are employed to identify each cluster and thus the state of each device. The SC-M method identifies the cluster by comparing its mean with the devices' pre-defined profiles. The SC-EV method employs an eigenvector resultant to locate the event and then recognize the device using its profile. An ideal dataset and a real-world REFIT dataset are used to test the performance of these two techniques. The f-measure score and disaggregation accuracy of the proposed techniques demonstrate that these two techniques are competitive and viable, with advantages of low complexity, high accuracy, no training data requirement, and fast processing time. Therefore, the proposed techniques are suitable candidates for NILM.
随着广泛部署的智能电表，非侵入式能源测量变得可行，这可以通过提供对家电级能耗更好的理解，从而使人们受益。通过提出两种新的技术，即谱聚类均值 (SC-M) 和谱聚类特征向量 (SC-EV) 方法，这项工作在使用图信号处理进行非侵入式负载监测 (NILM) 方面向前迈进了一步。这些方法使用谱聚类从建筑物的总能源曲线中提取单个电器的能源使用情况。在对数据进行聚类后，采用不同的策略来识别每个簇，从而识别每个设备的状态。SC-M 方法通过将其均值与设备的预定义轮廓进行比较来识别簇。SC-EV 方法使用特征向量的结果来定位事件，然后使用其轮廓来识别设备。使用理想数据集和真实世界的 REFIT 数据集来测试这两种技术的性能。所提出的技术的 F1 分数和去混淆准确性表明，这两种技术具有竞争力且可行，具有复杂度低、准确性高、无需训练数据和处理时间快等优点。因此，所提出的技术是 NILM 的合适候选技术。
