Abstract
Distance-based selection has been the conventional criterion for weather file selections, although this topographically- and meteorologically-agnostic decision may lead to unreliable Climate-Based Daylight Modelling (CBDM) simulation results. To mitigate this risk, this study explores alternative criteria that may be better predictors of weather file proxies for locations within the tropical climate. Principal Component Analysis (PCA) and k-means clustering were conducted on screened tropical climate files to determine the primary attributes and meteorological clusters. Four Principal Components (PCs) explained 89.1% of the variance in weather files. Sensitivity analysis of Malaysian files determined that the four PCs provided a 78% probability of simulating Spatial Daylight Autonomy (sDA) and Useful Daylight Illuminance (UDI) within a 10% tolerance of the Petaling Jaya (PJ) benchmark. Screening for files within weighted PCA distance (PCAd) outperformed pure geometric proximity selections when evaluated at both Malaysian and Class A levels. Overall, the Climatic Similarity Index (CSI) protocol achieved a 86.67% predictive selection accuracy rate across a proportionate random sampling of all Class A TMYx files. This study proves that a combination of atmospheric and geometric feature matching has the potential to overcome the limitations of local weather file availability for a target site.