Energy Consumption Management of Residential Appliances Based on Load Signatures Decomposition

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Nowadays, energy consumption management techniques in the residential side have gained significant importance due to their considerable influence on the control of power flow in distribution networks and especially the possibility of managing a huge part of domestic electrical demand during peak-load hours. Since the customer’s data are recorded in an aggregated form, therefore, in order to apply control approaches, it is necessary to use load pattern evaluation techniques (load signature). These methods capable to decomposition effective features that help control approaches to implement with more accuracy. In this article, features of residential loads have been extracted by using the signature of the aggregated consumer’s demand. Then these features have been evaluated by methods such as logistic regression, k-nearest neighborhood, and decision tree. By assessing the results, it was determined that among the extracted features, the first two features (consumed power and injected harmonics) covered more than 89% of the variance of the entire set, and with the help of using the principal component analysis method, it was determined that by reducing the number of features to 2, a considerable amount of computation is reduced and only about 4% of the accuracy is reduced. Also, the convolutional neural network approach was used to estimate the type of load, and by identifying controllable loads and applying remote home energy management methods, it was found that by increasing participation up to 80%, more than 41% of peak-load consumption could be shifted to off-peak hours.