Open AccessMultiple regression analysis on the HVAC energy consumption of railway passenger stations
Author(s) -
Zhixun Su,
X Li
Publication year - 2019
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.179
H-Index - 26
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/609/5/052014
Subject(s) - hvac , energy consumption , occupancy , benchmarking , environmental science , air conditioning , transport engineering , electricity , climate zones , regression analysis , ventilation (architecture) , consumption (sociology) , civil engineering , automotive engineering , engineering , computer science , meteorology , business , geography , physical geography , mechanical engineering , social science , electrical engineering , marketing , machine learning , sociology
With the rapid development of high-speed railway transport in the last decade, the number of railway passenger stations in China has surged. Distinguished by its large floor space, high quality of service, long operation time and high occupancy rate throughout the year, the electricity consumption of the heating, ventilation and air conditioning (HVAC) system for current railway stations is much higher than other types of public buildings. Despite of its huge amount of energy consumption, the assessment standard and benchmarking for the railway passenger stations is not completely established. Therefore, it is of great significance to study the energy use level and the associated factors of influence. In this paper, extensive field tests have been conducted to collect detailed energy data of stations in different climate zones in China. Multiple regression model is adopted to determine relevant impacting parameters. The results showed that building area tends to be the primary factor affecting the energy consumed by the HVAC system in each of the four climate zones. Furthermore, for each climate region, regression equations are developed, which serve as an effective standard to evaluate the energy consumption of stations.