New PCA-Based Index Enhances Weather File Selection for Daylight Modeling in Tropical Climates

A new Principal Component Analysis (PCA)-based climatic similarity index has been developed to improve the selection of weather files for climate-based daylight modeling simulations. This index is specifically designed for tropical climates, where accurate weather data is crucial for reliable simulations. The PCA-based approach aims to enhance the criteria used for choosing appropriate weather files, leading to more precise daylighting analysis. This development is important for architects, engineers, and researchers working on sustainable building design in tropical regions. By improving weather file selection, the index contributes to more accurate energy performance predictions and better indoor environmental quality assessments.

This development offers a refined method for selecting weather data, crucial for accurate daylight modeling in tropical regions, impacting sustainable building design.