Non-Intrusive Load Identification Method Based on Improved KM Algorithm

To achieve load disaggregation in non-intrusive load monitoring (NILM) system, a load event matching method based on graph theory is proposed, which is built on the improved Kuhn-Munkras algorithm. In this method, firstly, an adaptive fitting method using time window is applied to detect the load whether it is switched on and/or off. Particularly, to avoid the fluctuation of load signatures, the kernel density estimation is then built by a number of the independent features of the load switching on, including the active and reactive power signatures. The distribution of load signatures is thereby obtained, allowing the load event to be classified by its features. The load matching, which is based on the improved KM algorithm, is then utilized to resolve the matrix formed by the matching probability of the load event. Similarly, load identification can also be realized by matching the features of events with the signatures in the database. Finally, the experimental results using datasets of our lab and REDD demonstrate that the proposed method can obtain the desirable result for load event matching, and promote the performance in load identification.