An Online Transient-Based Electrical Appliance State Tracking Method Via Markov Chain Monte Carlo Sampling

Abstract

This paper presents an online transient-based electrical appliance state tracking method for nonintrusive load monitoring (NILM). The proposed Factorial Particle based Hidden Markov Model (FPHMM) method takes advantage of transient features (TF) in high-resolution data to infer states in the transient process and conducts steady state verification (SSV) to rectify falsely identified appliances. The FPHMM method can overcome the common feature similarity problem in NILM by combining particle filter method and Markov Chain Monte Carlo (MCMC) sampling method, and by mining the intra-relationship of states inside a single appliance and the inter-relationship of states among multiple appliances. The FPHMM method is tested on the LIFTED dataset with appliance-level details and high sampling rates. Testing results demonstrate that the FPHMM method can resolve the feature similarity problem thus achieving high accuracy.