A Novel Approach for Climate Classification Using Agglomerative Hierarchical Clustering

Abstract

Climate classification plays a significant role in the development of building codes and standards. It guides the design of buildings’ envelope and systems by considering their location’s climate conditions. Various methods, such as ASHRAE Standard 169, Köppen, Trewartha utilize climate parameters such as temperature, humidity, solar radiation, precipitation, etc., to classify climates. When establishing requirements in building codes and standards, it is crucial to validate the classification based on the building’s thermal loads.

This paper introduces a novel methodology for classifying cities based on the number of similar days between them. It calculates similarity using daily mean temperature, relative humidity, and solar radiation by applying threshold values. A matrix of similar days is analyzed through agglomerative hierarchical clustering with different thresholds. A scoring system based on building thermal load, where lower scores signify better classification, is employed to select the best method.

The method was tested using U.S. weather data, yielding a lower score of 54.5 compared to ASHRAE Standard 169’s score of 63.09. This suggests that the new approach results in less variation in thermal loads across cluster zones. The study used thresholds of 7 $^\circ$C for daily mean temperature, 45% for daily mean relative humidity, and 35 Wh/m² for daily mean solar radiation, which was found to yield the lowest score.