**1. Introduction**

Domestic energy consumption is estimated to account for 30–40% of worldwide generation [1] and it is expected to increase as more appliances and electronic devices are utilized. Moreover, buildings contribute to one-third of worldwide final CO2 emissions, thus urging a substantial reduction of both consumption and emissions in this sector [2]. Therefore, energy efficiency has become one of the most important challenges. According to statistics [3], customer engagement and awareness of better energy-consumption habits make a considerable difference in energy reduction [4]. Consumers should be given individual load-level consumption information rather than aggregated consumption information [5]. By raising customer knowledge on the usage habits of domestic appliances, appliance-level energy information can significantly contribute to reducing energy consumption [6]. Home energy-management systems (HEMS) can be an effective solution [7] by enabling a set of long-term smart energy-saving applications [8]. They provide visual feedback to consumers

**Citation:** Caldera, M.; Hussain, A.; Romano, S.; Re, V. Energy-Consumption Pattern-Detecting Technique for Household Appliances for Smart Home Platform. *Energies* **2023**, *16*, 824. https://doi.org/10.3390/en16020824

Academic Editors: Antonio Cano-Ortega and Francisco Sánchez-Sutil

Received: 30 November 2022 Revised: 22 December 2022 Accepted: 23 December 2022 Published: 11 January 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/).

in the form of energy-use statistics, utility-driven automation and control, load forecasting, and optimum load-scheduling strategies [9]. With smart meters and sensors that can measure real-time power consumption, monitoring power consumption has become easier with the help of signal-processing methods [10]. The Internet of things (IoT) adds a new degree of machine-to-machine communication to the interactions between humans and apps. The advancement of new communication technologies and smart devices that function by parsing signals has increased the ability to manage different equipment. Smart home (SH) devices can store data, respond to users' prompts, provide feedbacks to users, and issue alarms. SH-IoT promises to create an energy-optimized environment by connecting devices, in addition to providing flexibility in home management and monitoring [11].

Unlike previous studies in which HEMS relies on raw data collected by energy meters from which models disaggregate desired appliances by relying on classification methods based on the prior information of appliance energy signatures, in this paper we propose a model based on 15-min aggregated raw data that relies on descriptive data-mining techniques. The model is quite simple and computationally less complex, yet effective. It can detect energy-consumption patterns, the appliance operation (i.e., on–off, standby), and the duration period in which the appliance is operative for all household appliances. Moreover, it provides consumers with the information on the total number of cycles, disaggregates cycles in close sequence, identifies cycles according to their duration (i.e., short, medium, and long cycles), and calculates energy consumption during customised time periods. The model has been calibrated and validated on two datasets collected by ENEA by real-time monitoring of Italian dwellings equipped with IoT devices. The former dataset contains measurements of 58 domestic appliances from 14 homes over a period of 2.2 years, and the latter contains measurements of 48 appliances from 10 homes over a period of 6 months. The key contributions of this work are as follows.

