Advances in non-intrusive type I load monitoring using R-statistic steady-state detection and subtractive clustering

This paper introduces a novel unsupervised method that uses advanced clustering techniques based on power and time features to identify Type 1 electrical load profiles within aggregated power measurements. The adoption of the R-statistic algorithm for the detection of ON/OFF events enhances the algorithm's performance, enabling it to capture and accurately reconstruct both slow and fast dynamic loads. A double clustering approach also guarantees that signals exhibiting identical power levels but different durations are distinctly recognised, allowing for accurate identification of individual appliances within aggregated power data. This way, the combination of clustering techniques with R-statistic improves the granularity of load profile analysis and overcomes traditional barriers in power consumption monitoring. Both simulation and experimental results are presented to evaluate and compare the performance of the proposed method to existing approaches from the literature.