Appliance Identification Through Nonintrusive Load Monitoring in Residences

Christos Gogos () and George Georgiou ()
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Christos Gogos: University of Ioannina
George Georgiou: University of Ioannina

Chapter Chapter 10 in Computational Intelligence and Optimization Methods for Control Engineering, 2019, pp 227-244 from Springer

Abstract: Abstract Residential energy consumption forms a major part of the total energy expenditure. Consumers, power utilities, grid operators, electric appliance manufacturers, government agencies, and others are greatly interested in curbing the energy consumption, expecting in return financial and environmental rewards. Better understanding of how energy is consumed in residences will be crucial in developing trustworthy Demand Side Management (DSM) systems. This work presents state-of-the-art approaches for disaggregating power consumption in residences through nonintrusive load monitoring. Also, it contributes a new dataset of detailed power consumption data that was captured in a residence that was specially set up. The results show that by analyzing overlapping power patterns that electrical appliances generate, and a resident-level energy meter of adequate granularity, appliance identification becomes possible.

Date: 2019
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Persistent link: https://EconPapers.repec.org/RePEc:spr:spochp:978-3-030-25446-9_10

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DOI: 10.1007/978-3-030-25446-9_10

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