Feature Extraction using Apparent Power and Real Power for Smart Home Data Classification

The goal of this paper is to perform an experimental research and show that simple statistical predictors can reveal usage patterns of the electrical appliances from smart meter and sensor readings. We used an open data set of Smart* project and its real power and apparent power variability to accomplish this goal. We generated the predictors using block-based statistical information of the real power and apparent power associated with each appliance class type. We constructed five machine learning models using these predictors and evaluated them using random forest classification and the qualitative measures - classification accuracy, out-of-bag error, and misclassification error. Our finding is that the simple statistical predictors that reveal smart home occupants appliance usage patterns and energy consumption details can be obtained through smart home data analytics. Our finding includes that the statistical predictors generated from apparent power can improve the accuracy of the significantly-imbalanced smart home data classification.