Appliance Recognition in an OSGi-Based Home Energy Management Gateway

Abstract

The rational use and management of energy is considered a key societal and technological challenge. Home energy management systems (HEMS) have been introduced especially in private home domains to support users in managing and controlling energy consuming devices. Recent studies have shown that informing users about their habits with appliances as well as their usage pattern can help to achieve energy reduction in private households. This requires instruments able to monitor energy consumption at fine grain level and provide this information to consumers. While the most existing approaches for load disaggregation and classification require high-frequency monitoring data, in this paper we propose an approach that exploits low-frequency monitoring data gathered by meters (i.e., Smart Plugs) displaced in the home. Moreover, while the most existing works dealing with appliance classification delegate the classification task to a remote central server, we propose a distributed approach where data processing

Automated Non-Intrusive Load Monitoring System Using Stacked Neural Networks and Numerical Integration

“…F − score = 2 * P rec * Recall P rec + Recall (16) Other ways to visualize the results of the classification task are the AUC-ROC curve and the confusion matrix.…”

“…Considering appliance identification and load disaggrega-tion, different techniques have been proposed in literature, such as: Discrete Fourier Transform [12]; Decision trees [13]; Principal Component Analysis (PCA) [14], [15], Genetic Programming [16], Artificial Neural Networks (ANN) [17]- [19], Deep Artificial Neural Networks (DNN) [20]- [26], Hidden Markov Models (HMM) [27]- [31], Integer and Quadratic Programming [32]- [34], Transfer learning [23], among others. Many of these techniques make use of several consumption parameters obtained from data collected at high frequency, which require expensive meters, that are not viable for residential use.…”