Journal Article

Non-Intrusive Load Monitoring of Buildings Using Spectral Clustering

Naseer, Noman, Din, Zia Mohy Ud, Rehman, Hafiz Zia Ur, Ghaffar, Muzzamil, Naved, Muhammad, Sheikh, Shakil R.

2022

Overview

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.

• Publisher: MDPI AG,MDPI
• Subject: Accuracy / Algorithms / Appliances / Classification / Cluster Analysis / demand-side energy management / Energy consumption / energy disaggregation / Energy management / graph signal processing / Humans / Internet of Things / Machine learning / Neural networks / non-intrusive load monitoring / Signal processing / Signal Processing, Computer-Assisted / smart buildings / spectral clustering / Wavelet transforms