Non-intrusive Load Monitoring (NILM), or load disaggregation, aims to decompose aggregate power consumption into appliance components. Factors such as noise power affect algorithm performance, reducing accuracy and increasing complexity. While existing literature often relies on standard machine learning metrics to report noise power proportion in aggregate consumption, these are overall measures that do not specify the ratio between appliance consumption and noise power. This paper proposes a noise metric that assesses the proportion of dataset noise relative to an appliance’s consumption in NILM. The proposed metric’s sensitivity and applicability are assessed for different data scenarios using a real-world dataset. Additionally, the paper explores a potential association between the proposed metric and disaggregation performance is also inspected. Furthermore, the proposed metric is compared to existing noise metrics to highlight its unique contributions. The results demonstrate that the proposed metric effectively quantifies noise proportion with respect to specific appliances across various data scenarios. It exhibits robustness to suitable granularity variations and complements other noise metrics in interpreting NILM experiments.