Multivariate event detection methods for non-intrusive load monitoring in smart homes and residential buildings.

• Peformance comparison of four detectors in their multivariate and univariate versions using specific detection metrics. • Setting up of a new Feature Selection Algorithm for Detection Purposes. • Proposal of a new dataset for Non Intrusive Load Monitoring event-based approaches containing labeled and time-stamped power time series of appliances turned on and off. • Application of the feature selection algorithm to a publicily available dataset and to our own dataset. • Multivariate time series help to improve the detection performances with electrical features selected by the proposed algorithm. Non Intrusive Load Monitoring (NILM) approaches refer to the analysis of the aggregated electrical signals of Home Electrical Appliances (HEAs) in order to identify their operating schedules. It has emerged as a promising solution to help residential consumers to reduce their electricity bills through a breakdown of energy consumption. NILM methods are either event-based or non event-based. This categorization depends on whether or not they rely on the detection of HEAs' significant state transitions (e.g., On/Off or state change) in power consumption signals. This paper focuses on event-based approaches and especially in multivariate change detection algorithms. It aims at highlighting the benefits brought by a multivariate approach for change detection using the appropriate electrical features. We first suggest to extend four existing change detection algorithms in the multidimensional case. The studied detection algorithms are first detailed and compared to each other and to their existing scalar versions through numerical simulations. Then, a new feature selection algorithm for change detection is presented and assessed when combined with the most efficient detector among the four investigated ones. Finally, the feature selection method for detection purposes is applied to two different NILM case studies. The first one uses power features derived from the BLUED current and voltage measurements and the second one is based on current and voltage measurements acquired using our own acquisition system. Compared to the classical scalar approach, the results show that the multivariate approach brings a significant performance improvement when the features selected by the proposed algorithm are used. [ABSTRACT FROM AUTHOR]