An efficient hybrid model for appliances classification based on time series features

Abstract

Today, depending on the increasing population and technological developments, household appliances have significantly increased. This situation causes an ever-increasing energy demand, especially in modern societies. However, efficient energy use is also critical due to limited energy resources. With the Non-intrusive load monitoring methods, the usage of home appliances can be controlled, and behaviours can be adjusted for the energy saving of users. The control of home appliances is only possible with the effective detection of these appliances. In this study, a new hybrid model including a sliding window approach, long short-term memory (LSTM) network, and classifier is proposed to detect house appliances. Unlike traditional feature extraction, the proposed model uses time-series features to determine device features. For this purpose, each time series is divided into windows that do not overlap with the sliding window approach. The mean, standard deviation, median, and multiscale entropy values are calculated from each window. These values are combined and applied to the LSTM network for deep feature extraction. It is applied to k nearest neighbor (k-NN), Ensemble Learning (EBT), and support vector machine (SVM) classifiers to detect residential appliances from deep features retrieved from the LSTM network. The proposed model's effectiveness has been tested with the high-resolution profiles of the residential appliances dataset. In experimental studies, residential appliances were detected in LSTM based k-NN, EBT, and SVM classifiers with an accuracy of 98.25%, 97.81%, and 97.38%, respectively.