Electrical Load Identification for Household Appliances

Abstract

 Electrical energy is one of the leading sources of production and of crucial significance in nowadays life. Energy saving is a key element and hence, efficient management of energy in buildings is pivotal to reduce electricity consumption. For this motive, it is essential to provide individual appliance energy consumption data to homeowners with the use of efficient assets and thus, the overall energy consumption obtained from the house main circuits must be disaggregated into separate device. Electrical load identification helps determine the type of load, operating conditions and electricity consumption of electrical appliances. This work examines different identification techniques based on power signature of household electrical appliances obtained from REDD system which uses a single electricity sensor connected to a building's main circuit to measure aggregated energy consumption. Each distinct algorithm extracts dissimilar features for analysis and classification. Various sets of samples were generated for simulation purposes to evaluate the proposed methods. From results obtained, we were able to identify the appliances chosen for use within a certain level of inaccuracy. The Load Switching Transient (LST), Mean Steady State (MSS) and Discrete Fourier Transform (DFT) algorithms have been determined to have overall accuracy of 99.8%, 97.1% and 100% respectively based on the samples generated for simulations. Finally, the DFT method was deemed to be unsuitable for use in practise to its limitations with the other two method preferred despite their drawbacks and lower precision percentages.