energies Article Identification of TV Channel Watching from Smart Meter Data Using Energy Disaggregation Pascal A. Schirmer 1, * , Iosif Mporas 1, * and Akbar Sheikh-Akbari 2   Citation: Schirmer, P.A.; Mporas, I.; Sheikh-Akbari, A. Identification of TV Channel Watching from Smart Meter Data Using Energy Disaggregation. Energies 2021, 14, 2485. https:// doi.org/10.3390/en14092485 Academic Editor: Dumitru Baleanu Received: 9 March 2021 Accepted: 19 April 2021 Published: 27 April 2021 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affil- iations. Copyright: © 2021 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/). 1 Communications Intelligent Systems Group, School of Engineering and Computer Science, University of Hertfordshire, Hatfield AL10 9AB, UK 2 Engineering and Computing, School of Built Environment, Leeds Beckett University, Leeds LS1 3HE, UK; a.sheikh-akbari@leedsbeckett.ac.uk * Correspondence: p.schirmer@herts.ac.uk (P.A.S.); i.mporas@herts.ac.uk (I.M.); Tel.: +44-(0)-1707-284195 (I.M.) Abstract: Smart meters are used to measure the energy consumption of households. Specifically, within the energy consumption task, a smart meter must be used for load forecasting, the reduction in consumer bills as well as the reduction in grid distortions. Smart meters can be used to disaggregate the energy consumption at the device level. In this paper, we investigated the potential of identifying the multimedia content played by a TV or monitor device using the central house’s smart meter measuring the aggregated energy consumption from all working appliances of the household. The proposed architecture was based on the elastic matching of aggregated energy signal frames with 20 reference TV channel signals. Different elastic matching algorithms, which use symmetric distance measures, were used with the best achieved video content identification accuracy of 93.6% using the MVM algorithm. Keywords: video content identification; smart meters; load disaggregation 1. Introduction In recent decades, there has been an extensive use of smart meters in residential buildings, with 60% of the houses in the USA [1] and 50% of the houses in Europe [2] having smart meters installed. Smart meters provide residents/consumers with information about their daily energy consumption, and based on this information, residents can manage or reschedule the usage of their devices to reduce electricity bills, e.g., by using some appliances like washing machines at night time, during which electricity costs are usually lower [3]. Apart from measuring a household’s energy consumption, smart meters can also be used to provide more detailed information, as in the case of energy disaggregation where from one smart meter installed at the main inlet of the household, the usage and energy consumption at the device level is extracted using non-intrusive load monitoring (NILM) methods [4]. In NILM, the aggregated signal is split into device signals using source separation methods [5–7] or is processed by machine learning-based models to detect the existence of devices within time sliding frames [8–13]. Specifically, variants of HMMs [14], CNNs [11] and LSTM [15] architectures, have been utilized in order to achieve accurate disaggregation. Furthermore, elastic matching algorithms have also been proven to work successfully [16,17]. By breaking down the energy consumption information at the device level, consumers can be informed about the distribution of energy consumption across home appliances and manage them, or rearrange the schedule of their operation in a more efficient way [18,19]. Furthermore, smart meters have been utilized for other energy-related tasks, e.g., load forecasting, for the reduction in consumer bills [20] or the reduction in grid distortions [21]. Moreover, additional information, e.g., the weather condition [22] or socio–economic information [23,24], has been used and combined with the measurements of the smart Energies 2021, 14, 2485. https://doi.org/10.3390/en14092485 https://www.mdpi.com/journal/energies