real-time recognition non-intrusive electrical appliance monitoring algorithm for a residential building energy management system

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ID: 188674
2015
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Abstract
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.
Reference Key
agyeman2015energiesreal-time Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;Kofi Afrifa Agyeman;Sekyung Han;Soohee Han
Journal acs combinatorial science
Year 2015
DOI
10.3390/en8099029
URL
http://www.mdpi.com/1996-1073/8/9/9029
Keywords Keywords not found

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