Abstract：

This paper takes Heilongjiang Province, a typical severe cold climate zone, as the research object. It addresses a limitation in the current climate zoning standard (GB 50176-2016), which emphasizes winter heating but inadequately characterizes summer cooling demand, and proposes an innovative method to reconstruct cooling climate sub-zones using the cooling degree days (CDD) and outdoor comprehensive temperature. Through field surveys of 67 public institutions, combined with machine learning and statistical testing, the influencing factors of air conditioning configuration are quantitatively analysed. The research reveals that institution type, building age, and floor area are the primary influencing factors, and the influence of climate sub-zone is relatively minor. The chi-square test indicates that climate sub-zone, institution type, and floor area have significant linear effects on the selection of air conditioning types. The cross-analysis shows that with the increase of CDD, the configuration frequency of centralized, variable refrigerant volume, and split air conditioning increases, while the frequency of non-air conditioning decreases. Different institution types exhibit distinct configuration patterns. The larger the floor area, the more the air conditioning system tends to be centralized. Although building age shows no significant linear correlation, its non-linear influence is evident.

Keywords:severe cold climate zone; public institution; air conditioning system; cooling degree days; outdoor comprehensive temperature; energy efficiency optimization; differentiated configuration; cooling climate sub-zone