Sequential Clustering-Based Event Detection for Non-Intrusive Load Monitoring

Abstract

The problem of change-point detection has been well studied and adopted in many signal processing applications.In such applications, the informative segments of the signal are the stationary ones before and after the change-point.However, for some novel signal processing and machine learning applications such as Non-Intrusive Load Monitoring (NILM), the information contained in the non-stationary transient intervals is of equal or even more importance to the recognition process.In this paper, we introduce a novel clustering-based sequential detection of abrupt changes in an aggregate electricity consumption profile with accurate decomposition of the input signal into stationary and non-stationary segments.We also introduce various event models in the context of clustering analysis.The proposed algorithm is applied to building-level energy profiles with promising results for the residential BLUED power dataset.