Supervised household's loads pattern recognition

The deployment of smart meters is a promising innovation that comes to enhance the energy efficiency measures in the smart grid. The smart meter enables distributors to better understand the electrical network and reduce complexity of the management operations. It offers to households monitoring and control possibilities to their everyday energy consumption through the distribution of detailed information on household consumption and its evolution. This involves disaggregation of individual household loads in term of their individual energy consumption known as Non intrusive loads monitoring. In this paper, we present a supervised NILM approach based on dynamic fuzzy c-means events clustering and KNN label matching. First, a filtering method is involved to enhance the edge/events detection step. Then we perform a dynamic Fuzzy c-means clustering procedures to build appliances signature data based on active and reactive power measurements taking into account the time of day usage. The data base is further refined to map potential clusters centers that best identify the different appliances. A performance evaluation of the proposed approach is conducted showing a recognition rate over 90% for high consumption loads and promising results for low consumption loads.