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Appliance Identification Through Nonintrusive Load Monitoring in Residences

In: Computational Intelligence and Optimization Methods for Control Engineering

Author

Listed:
  • Christos Gogos

    (University of Ioannina)

  • George Georgiou

    (University of Ioannina)

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.

Suggested Citation

  • Christos Gogos & George Georgiou, 2019. "Appliance Identification Through Nonintrusive Load Monitoring in Residences," Springer Optimization and Its Applications, in: Maude Josée Blondin & Panos M. Pardalos & Javier Sanchis Sáez (ed.), Computational Intelligence and Optimization Methods for Control Engineering, chapter 0, pages 227-244, Springer.
    • Handle: RePEc:spr:spochp:978-3-030-25446-9_10
    DOI: 10.1007/978-3-030-25446-9_10
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