TY - JOUR
T1 - Variable Importance Analysis for Urban Building Energy Assessment in the Presence of Correlated Factors
AU - Yang, Song
AU - Tian, Wei
AU - Heo, Yeonsook
AU - Meng, Qingxing
AU - Wei, Lai
N1 - Funding Information:
This research is supported by the Tianjin Research Program of Application Foundation and Advanced Technology (No. 14JCYBJC42600) and the Scientific Research Foundation for the Returned Overseas Chinese Scholars, State Education Ministry of China.
Publisher Copyright:
© 2015 The Authors. Published by Elsevier Ltd.
PY - 2015
Y1 - 2015
N2 - It is becoming urgent to thoroughly understand characteristics of energy use in order to reduce energy use in urban areas. When assessing energy performance in urban buildings, it is likely that explanatory variables are correlated if considering both physical conditions and social economic factors. This research applied three variable importance methods, including Genizi, CAR (Correlation-Adjusted marginal coRrelation), PCC (partial correlation coefficient), to identify key factors from 30 highly correlated variables in London. The results indicate that the land area for domestic buildings is the only dominant variable influencing gas use, while electricity consumption is more affected by the number of electricity meters for Economy 7 (a differential electricity tariff according to the time of day) and the number of households allocated to higher council tax band in London. Moreover, it is confirmed that the SRC (standardized regression coefficient), a commonly used method in building energy analysis, is not suitable for the correlated factors in urban energy assessment.
AB - It is becoming urgent to thoroughly understand characteristics of energy use in order to reduce energy use in urban areas. When assessing energy performance in urban buildings, it is likely that explanatory variables are correlated if considering both physical conditions and social economic factors. This research applied three variable importance methods, including Genizi, CAR (Correlation-Adjusted marginal coRrelation), PCC (partial correlation coefficient), to identify key factors from 30 highly correlated variables in London. The results indicate that the land area for domestic buildings is the only dominant variable influencing gas use, while electricity consumption is more affected by the number of electricity meters for Economy 7 (a differential electricity tariff according to the time of day) and the number of households allocated to higher council tax band in London. Moreover, it is confirmed that the SRC (standardized regression coefficient), a commonly used method in building energy analysis, is not suitable for the correlated factors in urban energy assessment.
KW - Building energy
KW - Correlated variables
KW - Urban environment
KW - Variable importance
UR - http://www.scopus.com/inward/record.url?scp=84957867434&partnerID=8YFLogxK
U2 - 10.1016/j.proeng.2015.08.1069
DO - 10.1016/j.proeng.2015.08.1069
M3 - Conference article
AN - SCOPUS:84957867434
SN - 1877-7058
VL - 121
SP - 277
EP - 284
JO - Procedia Engineering
JF - Procedia Engineering
T2 - 9th International Symposium on Heating, Ventilation and Air Conditioning, ISHVAC 2015 Joint with the 3rd International Conference on Building Energy and Environment, COBEE 2015
Y2 - 12 July 2015 through 15 July 2015
ER -
