  The increasing cost, energy demand, and environmental issues has led many
researchers to find approaches for energy monitoring, and hence energy
conservation. The emerging technologies of Internet of Things (IoT) and Machine
Learning (ML) deliver techniques that have the potential to efficiently
conserve energy and improve the utilization of energy consumption. Smart Home
Energy Management Systems (SHEMSs) have the potential to contribute in energy
conservation through the application of Demand Response (DR) in the residential
sector. In this paper, we propose appliances Operation Modes Identification
using Cycles Clustering (OMICC) which is SHEMS fundamental approach that
utilizes the sensed residential disaggregated power consumption in supporting
DR by providing consumers the opportunity to select lighter appliance operation
modes. The cycles of the Single Usage Profile (SUP) of an appliance are
extracted and reformed into features in terms of clusters of cycles. These
features are then used to identify the operation mode used in every occurrence
using K-Nearest Neighbors (KNN). Operation modes identification is considered a
basis for many potential smart DR applications within SHEMS towards the
consumers or the suppliers
