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Energies 2018, 11(4), 1008; https://doi.org/10.3390/en11041008

Load Disaggregation via Pattern Recognition: A Feasibility Study of a Novel Method in Residential Building

1
School of Architecture, Kyonggi University, Suwon-si 16227, Gyeonggi-do, Korea
2
Department of Construction Environmental System Engineering, Sungkyunkwan University, Suwon-si 16419, Gyeonggi-do, Korea
3
Department of Fire Safety Research, Korea Institute of Civil Engineering and Building Technology, 283 Goyang-daero, Ilsanseo-gu, Goyang-si 10223, Gyeonggi-do, Korea
*
Author to whom correspondence should be addressed.
Received: 20 February 2018 / Revised: 5 April 2018 / Accepted: 18 April 2018 / Published: 20 April 2018
(This article belongs to the Special Issue Building Energy Use: Modeling and Analysis)
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Abstract

In response to the need to improve energy-saving processes in older buildings, especially residential ones, this paper describes the potential of a novel method of disaggregating loads in light of the load patterns of household appliances determined in residential buildings. Experiments were designed to be applicable to general residential buildings and four types of commonly used appliances were selected to verify the method. The method assumes that loads are disaggregated and measured by a single primary meter. Following the metering of household appliances and an analysis of the usage patterns of each type, values of electric current were entered into a Hidden Markov Model (HMM) to formulate predictions. Thereafter, the HMM repeatedly performed to output the predicted data close to the measured data, while errors between predicted and the measured data were evaluated to determine whether they met tolerance. When the method was examined for 4 days, matching rates in accordance with the load disaggregation outcomes of the household appliances (i.e., laptop, refrigerator, TV, and microwave) were 0.994, 0.992, 0.982, and 0.988, respectively. The proposed method can provide insights into how and where within such buildings energy is consumed. As a result, effective and systematic energy saving measures can be derived even in buildings in which monitoring sensors and measurement equipment are not installed. View Full-Text
Keywords: load disaggregation; pattern recognition; Hidden Markov model; residential building; feasibility study load disaggregation; pattern recognition; Hidden Markov model; residential building; feasibility study
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Kwak, Y.; Hwang, J.; Lee, T. Load Disaggregation via Pattern Recognition: A Feasibility Study of a Novel Method in Residential Building. Energies 2018, 11, 1008.

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