Active power load modeling based on uncertainties for Non Intrusive Load Monitoring

Although a diversity of approaches towards Non-Intrusive Load Monitoring (NILM) are now available, complementary studies must be carried out in order to improve load modeling and consequently increase load recognition accuracy. This paper presents a study on active power appliances' modeling when only low-frequency granularities are considered. The approach analyzes the distribution of power changes in finite-state loads using an effective feature transform to stabilize power variance. Subtractive clustering method is utilized for detection of power transition recurrences. It allows identifying most likely power changes presented in individual load profiles using a prior knowledge of power uncertainties. The principal aim of this paper is to create a probabilistic device-specific model of household resistive loads, which can be used in event-based load recognition. The results using real data prove the efficacy of the proposed method.