Residential End-Use Energy Estimation Models in Korean Apartment Units through Multiple Regression Analysis

The aim of this study was to develop a mathematical regression model for predicting end-use energy consumption in the residential sector. To this end, housing characteristics were collected through a field survey and in-depth interviews with residents of 71 households (15 apartment complexes) in Seo...

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Bibliographic Details
Published in:Energies (Basel) 2019-06, Vol.12 (12), p.2327
Main Authors: Lee, Soo-Jin, Kim, You-Jeong, Jin, Hye-Sun, Kim, Sung-Im, Ha, Soo-Yeon, Song, Seung-Yeong
Format: Article
Language:English
Subjects:
apartment unit
Architecture
Conflicts of interest
Electric appliances
Electricity
end-use energy consumption
Energy consumption
Energy economics
Energy efficiency
Energy management
Error analysis
estimation model
Hot water heating
Household appliances
Households
Independent variables
International organizations
Mathematical models
Multiple regression analysis
Neural networks
Polls & surveys
Regression analysis
Residential areas
Residential energy
Ventilation
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Summary:The aim of this study was to develop a mathematical regression model for predicting end-use energy consumption in the residential sector. To this end, housing characteristics were collected through a field survey and in-depth interviews with residents of 71 households (15 apartment complexes) in Seoul, South Korea, and annual data on end-use energy consumption were collected from measurement systems installed within each apartment unit. Based on the data collected, correlativity between the field-survey data and end-use energy consumption was analyzed, and effective independent variables from the field-survey data were selected. Regression models were developed and validated for estimating six end uses of energy consumption: heating, cooling, domestic hot water (DHW), lighting, electric appliances, and cooking. Regression analysis for ventilation was not applied, and instead a calculation formula was derived, because the energy-consumption proportion was too low. The adj-R2 of the estimation model ranged from 0.406 to 0.703, and the maximum error between measured and estimated values was around ±30%, depending on the end use.
ISSN:1996-1073
1996-1073
DOI:10.3390/en12122327