An Extended Factorial Hidden Markov Model for Non-Intrusive Load Monitoring Based on Density Peak Clustering

Non-Intrusive Load Monitoring (NILM) has received widespread attention as an energy-saving technology. The method based on Hidden Markov Model (HMM) is very popular in this domain because of its relatively small demand for computing resources. However, the traditional HMM-based methods need additional information such as the working states of appliance to train the model. In this paper, we proposed a non-parameter model (IC-FHMM) to alleviate the problem that require prior knowledge. Experiments are conducted on three open-access datasets, and the results indicate that the proposed model is superior to the four state-of-the-art models on the metrics of Accuracy and F-measure.