Appliance Operation Modes Identification Using Cycles Clustering

Abstract

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.

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 works [14][15][16][17] show the development of an intelligent system that analyzes the periodic use of electrical appliances in a home to extract and determine the behavioral patterns of residential users in an IoT environment. Particularly, in [15,16] there is an additional objective within their research which consists of reducing the excessive electricity consumption of appliances through an alert system.…”

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

“…They show that the suggested secure appliance scheduling for a flexible and efficient energy-consumption method, termed SAFE, significantly decreases power costs while protecting users' privacy through an intensive simulation research by using realworld datasets. Appliances' operating mode identification, which uses the cycle-clustering technique proposed in [12] is a smart home energy-management system primary approach based on sensed power consumption values, which enables DR by allowing users to employ energy-saving appliance operation modes. Cycles from an appliance single-usage profile, are extracted and reshaped into characteristics in the form of clusters of cycles.…”