LAUSR

Appliance-level data is a prerequisite for establishing friendly two-way interactions between customers and the power company, and this data is now mainly obtained by non-intrusive load monitoring. However, as the number of loads increases, the number of possible appliances state combinations tends to grow exponentially, leading to a significant increase in the time of load identification. In order to reduce the search range of the load state combinations and shorten the algorithm response time, a non-intrusive load monitoring method based on the time-segmented state probability is proposed in this paper. Firstly, the affinity propagation (AP) clustering algorithm is introduced to obtain the power templates of the load, and then the power templates are used to count the time-segmented state probabilities. Secondly, a number of appliance state matrices are generated using the probabilities, and the optimal matrix is selected by the function as the identification result of the appliance state. Finally, the performance of the algorithm is tested on the public NILM dataset and compared to several state-of-the-art techniques. The results illustrate that the proposed method achieves an accuracy of 96% for load state identification and 89% for power decomposition of the load, and is able to meet the real-time application requirements.