Multi-apartment residential microgrid monitoring system based on kernel canonical variate analysis

In the residential energy sector there is a growing interest in smart energy management systems able to monitor, manage and minimize energy consumption. A key factor to curb household energy consumption is the amendment of occupant erroneous behaviors and systems malfunctioning. In this scenario energy efficiency benefits can be either amplified or neutralized by, respectively, good or bad practices carried out by end users. Authors propose a diagnostic system for a residential microgrid application able to detect faults and occupant bad behaviors. In particular a nonlinear monitoring method, based on kernel canonical variate analysis, is developed. To overcome the normality assumption regarding the signals probability distribution, Upper Control Limits are derived from the estimated Probability Density Function through Kernel Density Estimation. The proposed method, applied to a smart residential microgrid, is tested on experimental data acquired from July 2012 to October 2013.

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