Statistical Methodology for the Definition of Standard Model for Energy Analysis of Residential Buildings in Korea

This study was conducted to propose an optimal methodology for deriving a standard model from existing residential buildings. To strategically improve existing residential buildings, it is necessary to identify standard models that can be used as quantitative standards. In this study, a total of six...

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Bibliographic Details
Published in:Energies (Basel) 2020-11, Vol.13 (21), p.5796
Main Authors: Nam, Hye-Ryeong, Kim, Seo-Hoon, Han, Seol-Yee, Lee, Sung-Jin, Hong, Won-Hwa, Kim, Jong-Hun
Format: Article
Language:English
Subjects:
Accuracy
Buildings
Climate change
Cluster analysis
Clustering
Data processing
Emission standards
Energy consumption
Energy efficiency
Energy modeling
energy retrofit
Green buildings
Households
Housing
Low income groups
Methods
Multivariate analysis
Principal components analysis
Public housing
Regional planning
representative building
Residential areas
Residential buildings
Residential energy
Simulation
standard model
Variables
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Summary:This study was conducted to propose an optimal methodology for deriving a standard model from existing residential buildings. To strategically improve existing residential buildings, it is necessary to identify standard models that can be used as quantitative standards. In this study, a total of six methods were established for different algorithms in the dimensionality reduction and clustering stage of the data preprocessing stage. In addition, a total of 22,342 households’ data were analyzed, and a total of 26 variables were used to perform cluster analysis. The process of method 6 (data pre-processing, principal components analysis, clustering [K-medoids], verification) was proposed as a way to derive the standard model from the existing Korean housing. The method proposed in this study is capable of deriving a number of standard models considering all variables (n) in a single analysis. The representative building derived in this study contains a lot of building data, so it can be effectively used for planning and research related to buildings on a regional and national scale. In addition, this process can be applied to various buildings to derive representative buildings.
ISSN:1996-1073
1996-1073
DOI:10.3390/en13215796