Automated load disaggregation for residences with electrical resistance heating

Abstract Building-related heating consumes 80% of final energy in the Swiss residential sector. Electrical resistance heating systems consumed 9.3 PJ and 7.9 PJ for space and domestic hot water heating respectively. This is 8.6% of the total electricity consumption in Switzerland. A successful retrofit action requires knowledge of energy consumed by these systems in the pre-retrofit stage. However, it is difficult to deduce the heating energy from the overall electrical load profile of each residence. One of the primary reasons is irregularities in the operation of the heating system. We address this by applying a data mining approach that group data points based on their concentration in the time axis. This is done by using DBSCAN (Density-based spatial clustering of applications with noise) which requires derivation of two clustering parameters, ‘Epsilon’ and ‘MinPts’. We present a method to automatically derive these two parameters based on a combination of histogram analysis and range search method. We also compare our results with those obtained by k-means and density peaks clustering methods. The proposed methodology can effectively disaggregate electrical load into space and domestic hot water heating for residences with an electrical resistance heating system.

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