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Time feature based hidden Markov model for NILM
- 1 School of Electrical Engineering and Automation, Fuzhou University, Fuzhou, China
* Author to whom correspondence should be addressed.
Abstract
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.
Keywords
hidden Markov model, NILM, time feature based
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Cite this article
Sun,Y. (2025). Time feature based hidden Markov model for NILM. Theoretical and Natural Science,95,80-86.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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Volume title: Proceedings of the 2nd International Conference on Applied Physics and Mathematical Modeling
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