Appliances Operation Modes Identification Using States Clustering

The increasing cost, energy demand, and environmental issues have led many researchers to find approaches for energy monitoring, and hence energy conservation. The emerging technologies of the Internet of Things (IoT) and Machine Learning (ML) deliver techniques that have the potential to conserve energy and improve the utilization of energy consumption efficiently. Smart Home Energy Management Systems (SHEMSs) have the potential to contribute to energy conservation through the application of Demand Response (DR) in the residential sector. In this paper, the aPpliances opeRation mOdes idenTification using statEs ClusTering (PROTECT) is proposed, a SHEMS analytical component that utilizes the sensed residential disaggregated power consumption in supporting DR by providing consumers with the opportunity to select lighter Appliance Operation Modes (AOMs). The states of an appliance’s Single Usage Profile (SUP) are extracted and reformed into features in terms of clusters of states. These features are then used to identify the AOM used in every occurrence using K-Nearest Neighbors (KNN). AOM identification is considered a basis for many potential smart DR applications within SHEMS, contributing to up to 78% energy reduction for some appliances.