Applying power meters for appliance recognition on the electric panel

Recognition of appliances states is an import building block for making energy-efficiency schemes and providing energy-saving advice and performing automatic control. Several existing approachs use smart outlets or detectors to acquire the information of individual appliance and recognize the operating state. However, such approachs have to install numerous devices if they want to monitor the states of all appliances. This will increase the cost and complexity of installation and maintenance. Therefore, we develop an appliance recognition system which minimizing the scope of deployment. We install smart meters at single-point, distribution board, to measure the power consumption at circuit-level. In addition, to improve the recognition accuracy of our system and detect the state changes in real time, We use dynamic baysian network to take user behavior into account and Bayes filter to perform online inference. Finally, we design several experiments to compare our approach with some commonly used classifiers, such as Naive Bayes, k-Nearest Neighbor (kNN) and Support Vector Machine (SVM). Results shows that our model outperforms these classifiers and the accuracies of all appliances are greater than 92%. Furthermore, we also compare the results of Bayes filter with Viterbi algorithm, which is an offline inference method. The difference in accuracy of every appliance between Bayes filter and Viterbi algorithm is less than 1%.