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

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