Energy-Consumption Pattern-Detecting Technique for Household Appliances for Smart Home Platform

Abstract

Rising electricity prices and the greater penetration of electricity consumption in end-uses have prompted efforts to set up data-driven methodologies to optimise energy consumption and foster user engagement in demand-side management strategies. The performance of energy-management systems is greatly affected by the consumer behaviors and the adopted energy-management methodology. Consequently, it is necessary to develop appliance-level, detailed energy-consumption information models to inform citizens to improve behaviors toward energy use. The goal of the Home Energy Management System (HEMS) is to foster an ecosystem that is energy-optimized and can manage Internet of things (IoT) equipment over its network. HEMS allows consumers to reduce energy costs by adapting their consumption to variable pricing over the day. With the use of descriptive data-mining techniques, we have developed a numerical model that gives consumers access to information on their domestic appliances with regard to the number and duration of operations, cycles disaggregation for appliances that have cyclic operation (e.g., washing machine, dishwasher), and energy consumption throughout various time periods basing on 15-min monitoring data. The model has been calibrated and validated on two datasets collected by ENEA by real-time monitoring of Italian dwellings and has been tested over several appliances showing effective analysis of the energy-consumption patterns. Therefore, it has been integrated in the DHOMUS IoT platform, developed by ENEA to monitor and analyse the energy consumption in dwellings in order to increase citizens’ engagement and awareness of their energy consumption. The results indicate that the developed model is sufficiently accurate, and that it is possible to promote a more virtuous and sustainable use of energy by end users, as well as to reduce the energy demand as required by the current European Council Regulation (EU) 2022/1854.

Model for Identification of Electrical Appliance and Determination of Patterns Using High-Resolution Wireless Sensor NETWORK for the Efficient Home Energy Consumption Based on Deep Learning

“…The massification of the meters of accountants, smart devices, low-cost sensors and smart home appliances over the last decade has encouraged a favorable environment for the development of new management strategies of demand response, which include communication, decision-making and the interaction between users, devices and the electrical grid. Therefore, there is a problem of interest in smart networks or smart grids: obtaining, storing and analyzing hard data on the electrical consumption of residential users alone is not sufficient to provide root cause elements of demand response and what it can do to reduce and/or optimize it [1]. Additionally, the more we want to delve into this last aspect, the higher the resolution of the data needed for a precise energy analysis, which implies, as a final consequence, overcoming these technological obstacles by automating the operations of the electrical network.…”

Digital and Sustainable Transition in Textile Industry through Internet of Things Technologies: A Pakistani Case Study

“…One important direction is the development of advanced data analytics techniques specifically tailored for textile manufacturing [77]. This includes applying machine learning algorithms to improve process optimization, quality control, and predictive maintenance [78,79]. Additionally, the scalability and interoperability of IoT and remote sensing systems in the textile industry require attention [80].…”

Energy Conservation at Home: A Critical Review on the Role of End-User Behavior

“…Therefore, great effort has been put into developing digital and/or smart tools to support the optimization of energy demand in the last decades. Home energy management systems (HEMSs) have been extensively discussed in the literature, as well as the potential of utility apps and feedback in energy bills to enable or activate users' conservation practices [66,[82][83][84][85]. However, up to date, most of the experts in the field agree that effective feedback from such devices is essential not only to activate one-shot changes but especially to engage users in the long run [31,61,[86][87][88][89][90][91][92][93][94][95][96][97][98][99].…”