An Efficient, Scalable Time-Frequency Method for Tracking Energy Usage of Domestic Appliances Using a Two-Step Classification Algorithm

Paula Meehan, Conor McArdle and Stephen Daniels
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Paula Meehan: Energy Design Lab, Faculty of Engineering and Computing, Dublin City University, Glasnevin, Dublin 9, Ireland
Conor McArdle: Energy Design Lab, Faculty of Engineering and Computing, Dublin City University, Glasnevin, Dublin 9, Ireland
Stephen Daniels: Energy Design Lab, Faculty of Engineering and Computing, Dublin City University, Glasnevin, Dublin 9, Ireland

Energies, 2014, vol. 7, issue 11, 1-26

Abstract: Load monitoring is the practice of measuring electrical signals in a domestic environment in order to identify which electrical appliances are consuming power. One reason for developing a load monitoring system is to reduce power consumption by increasing consumers’ awareness of which appliances consume the most energy. Another example of an application of load monitoring is activity sensing in the home for the provision of healthcare services. This paper outlines the development of a load disaggregation method that measures the aggregate electrical signals of a domestic environment and extracts features to identify each power consuming appliance. A single sensor is deployed at the main incoming power point, to sample the aggregate current signal. The method senses when an appliance switches ON or OFF and uses a two-step classification algorithm to identify which appliance has caused the event. Parameters from the current in the temporal and frequency domains are used as features to define each appliance. These parameters are the steady-state current harmonics and the rate of change of the transient signal. Each appliance’s electrical characteristics are distinguishable using these parameters. There are three Types of loads that an appliance can fall into, linear nonreactive, linear reactive or nonlinear reactive. It has been found that by identifying the load type first and then using a second classifier to identify individual appliances within these Types, the overall accuracy of the identification algorithm is improved.

Keywords: current harmonics; transient signal; appliance identification; load monitoring (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2014
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (8)

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