Classification of home appliance by using Probabilistic KNN with sensor data

To date, many researchers have been conducted studies to control an electrical power to construct a smart home system which automatically manipulates individuals. One of the recent topics is NILM(Non-intrusive Load Monitoring) system to infer the devices states. In NILM, the approaches have been focused on dealing only with the feature of the electrical power signals to identify the states of the running devices. However, it is hard to classify all of devices with such traditional approaches. To solve and increase the accuracy, we propose a new method to infer the device states by electrical power signal from the home appliances and also sensor data including temperature and humidity. In this paper, we compare the performance among PKNN(Probabilistic K-Nearest Neighbor) and other algorithms. We apply the three methods in PKNN and analyze the comparison through AUC(Area Under the ROC). Finally, we can find the optimized parameters for accurate classification in each method.