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Open AccessArticle

Real-Time Recognition Non-Intrusive Electrical Appliance Monitoring Algorithm for a Residential Building Energy Management System

1
Department of Electrical Engineering, Kyungpook National University, Daegu 41566, Korea
2
Department of Creative IT Engineering, Pohang University of Science and Technology, Pohang 37673, Korea
*
Author to whom correspondence should be addressed.
Academic Editor: Neville Watson
Energies 2015, 8(9), 9029-9048; https://doi.org/10.3390/en8099029
Received: 10 June 2015 / Revised: 18 August 2015 / Accepted: 19 August 2015 / Published: 26 August 2015
(This article belongs to the Collection Smart Grid)
The concern of energy price hikes and the impact of climate change because of energy generation and usage forms the basis for residential building energy conservation. Existing energy meters do not provide much information about the energy usage of the individual appliance apart from its power rating. The detection of the appliance energy usage will not only help in energy conservation, but also facilitate the demand response (DR) market participation as well as being one way of building energy conservation. However, energy usage by individual appliance is quite difficult to estimate. This paper proposes a novel approach: an unsupervised disaggregation method, which is a variant of the hidden Markov model (HMM), to detect an appliance and its operation state based on practicable measurable parameters from the household energy meter. Performing experiments in a practical environment validates our proposed method. Our results show that our model can provide appliance detection and power usage information in a non-intrusive manner, which is ideal for enabling power conservation efforts and participation in the demand response market. View Full-Text
Keywords: unsupervised disaggregation; demand response (DR); advanced metering infrastructure (AMI); current harmonics; hidden Markov model (HMM) unsupervised disaggregation; demand response (DR); advanced metering infrastructure (AMI); current harmonics; hidden Markov model (HMM)
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MDPI and ACS Style

Agyeman, K.A.; Han, S.; Han, S. Real-Time Recognition Non-Intrusive Electrical Appliance Monitoring Algorithm for a Residential Building Energy Management System. Energies 2015, 8, 9029-9048.

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