Applications of fuzzy classification with fuzzy c-means clustering and optimization strategies for load identification in NILM systems

Due to global warming and climate changes, it is very important to use and conserve the power energy effectively. Monitoring the electrical consumption of consumers is one of the methods that can improve the energy usage efficiency. In this paper, a Non-Intrusive Load Monitoring (NILM) system, which applies a fuzzy classifier with the Fuzzy C-Means (FCM) clustering and optimization algorithms to identify the energizing and de-energizing statuses of each appliance, is proposed. Load energizing and de-energizing transient features are extracted, and the fuzzy classifier performs load identification based on these features. A two-stage fuzzy classifier is used in this paper. For the first stage, the FCM clustering is used to coarsely determine the parameters of the fuzzy classifier. Following this stage, two optimization algorithms, Error Back-Propagation Algorithm (EBPA) and Genetic Algorithm (GA), are employed to fine tune those parameters. As the classification results obtained from different realistic experimental environments, the proposed system is confirmed that it is able to identify the operational status of each appliance.