Building climate zoning in China using supervised classification-based machine learning

The planning, design and construction of buildings are all influenced by climate. Building climate zoning is significant for the formulation of building energy efficiency strategies in various regions. However, there is overlapping among the variables used for building climate zoning (Temp(1) and Temp(7) in zones I and VI, and Temp(1) in zones II and VII) in the existing building climate zoning system in China. In addition, the climate conditions at some stations were not included in either zones due to the thresholds were low for some variables. In this study, based on data acquired by 701 national surface meteorological stations between 1984 and 2013 in China, a supervised classification algorithm was developed for building climate zoning and the Mahalanobis distance was used to assess the climate similarities between two regions. Three main variables from 172 stations were selected as the training samples for the supervised classification algorithm and the accuracy of the training results was 93.6%. The results obtained for 76 stations (10.8% of the total) using the interval judgment method could be considered controversial but only five stations (0.7% of the total) with the supervised classification algorithm. Compared with the interval judgment method, the supervised classification algorithm is advantageous and the classification results are suitable for indicating the climatic characteristics of different zones.