Time feature based hidden Markov model for NILM

In Non-Intrusive Load Monitoring (NILM), the commonly used methods based on hidden Markov model (HMM) often neglect time feature of load states, leading to increased bias in the estimation of HMM parameters. To address this issue, this paper proposes a time feature based hidden Markov model for NILM. It employs an improved K-means algorithm to identify appliance states at different times slots and establishes the correspondence between state combinations and hyper-states through binary encoding. Finally, a simplified Viterbi algorithm is used for state estimation. Experimental results on the AMPDS2 dataset show that this method can enhance the monitoring accuracy of HMM in NILM, providing more precise identification of individual load states and power consumption.