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％.
关键词
非介入式负荷辨识/工业产线/因子化隐马尔可夫模型/高斯混合模型/状态转移概率
Key words
non-intrusive load monitoring/industrial production line/FHMM/GMM/state transition probability
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