Citation: Kligman, A.; Yaniv, A.; Beck, Y. Energy Disaggregation of Type I and II Loads by Means of Birch Clustering and Watchdog Timers. Energies 2023, 16, 3027. https://doi.org/10.3390/en16073027 Academic Editors: Tiago Pinto and Georgios Christoforidis Received: 26 October 2022 Revised: 8 March 2023 Accepted: 10 March 2023 Published: 26 March 2023 Copyright: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). energies Article Energy Disaggregation of Type I and II Loads by Means of Birch Clustering and Watchdog Timers Amitay Kligman, Arbel Yaniv * and Yuval Beck School of Electrical Engineering, Tel-Aviv University, Tel-Aviv 69978, Israel * Correspondence: arbelyaniv@mail.tau.ac.il Abstract: A non-intrusive load monitoring (NILM) process is intended to allow for the separation of individual appliances from an aggregated energy reading in order to estimate the operation of individual loads. In the past, electricity meters specified only active power readings, for billing purposes, thus limiting NILM capabilities. Recent progress in smart metering technology has introduced cost-effective, household-consumer-grade metering products, which can produce multiple features with high accuracy. In this paper, a new method is proposed for applying a BIRCH (balanced iterative reducing and clustering using hierarchies) algorithm as part of a multi-dimensional load disaggregation solution based on the extraction of multiple features from a smart meter. The method uses low-frequency meter reading and constructs a multi-dimensional feature space with adaption to smart meter parameters and is useful for type I as well as type II loads with the addition of timers. This new method is described as energy disaggregation in NILM by means of multi-dimensional BIRCH clustering (DNB). It is simple, fast, uses raw meter sampling, and does not require preliminary training or powerful hardware. The algorithm is tested using a private dataset and a public dataset. Keywords: balanced iterative reducing and clustering using hierarchies (BIRCH); clustering algorithms; load-disaggregation; non-intrusive load monitoring (NILM); smart grid; smart metering 1. Introduction Smart grid technologies have gained increasing attention in the last ten years [1,2], as electricity demands are rising and they encourage energy supply to be efficient. Earlier research had already indicated that power usage efficiency could grow significantly by providing consumers with frequent feedback about their energy consumption [3]. Other works indicated that smart metering would play a big role in enabling adequate feedback to customers [4,5]. Recent improvements in smart metering technology enable deployment of advanced metering products on a large population scale, hence contributing to the realization of the smart grid vision [6,7] while also encouraging governments to invest a great amount of resources in distributing smart meters over a wide range of households and facilities [8,9]. These widely distributed meters offer new opportunities for implementation of power analysis and feedback techniques. Non-intrusive load monitoring (NILM) was initially introduced by G. W. Hart [10–12] as a technique intended for identification of load signatures in a single facility or a household via a single measuring point at the entrance of the facility. This technique disaggregates the total power measure into separate components and determines the energy consumption of individual appliances. As modern smart metering technology developed, several approaches were proposed to implement the NILM idea: the factorial hidden Markov model [13,14], particle filter [15], event window [16], deep learning [17], graph signal processing [18], combinatorial op- timization [19], modified cross-entropy [20], principle component analysis [21], multi-objective optimization [22], quadratic programming [23], evolutionary algorithms [24], and more. Some of the suggested solutions utilize clustering algorithms suitable for big data analysis [25], especially solutions which can easily be adapted for smart meter reading [26], such as K-means [27], fuzzy Energies 2023, 16, 3027. https://doi.org/10.3390/en16073027 https://www.mdpi.com/journal/energies