Non-intrusive load monitoring plays a significant role in supporting applications such as load forecasting and demand response. To address the challenge of independently decomposing sub-equipment in industrial load us-ers with production lines,a non-intrusive load monitoring (NILM) scheme is proposed,using the production line as the decomposition unit,based on the interlinked operation of equipment within the line. A factorized hidden Markov model (FHMM),based on Gaussian mixture model (GMM),is employed to achieve a fine-grained representation of load at the production line level. Additionally,a time-segmented state transition probability decomposition model is developed,leveraging the stable overall load patterns of industrial production lines,to further enhance load monitor-ing accuracy. Experimental results demonstrate that the proposed model significantly improves performance in both multi-state modeling and time segmentation,with load monitoring error metrics on some production lines ultimately reduced by nearly 20％.
non-intrusive load monitoringindustrial production lineFHMMGMMstate transition probability
维普
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