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Abstract Climate adaptive passive design features such as glazing and shading configurations, often exhibit conflicting behaviour to maintaining indoor visual and thermal environment while minimizing energy consumption. This study employed the MOO approach through SPEA-II and HypE algorithms to find the efficient glazing and shading configurations in four cooling dominant climatic zones of India such as hot and dry, composite, warm and humid, and temperate. A residential building situated in Jaipur city (India) was chosen for the analysis and to demonstrate the effectiveness and reliability of the optimization process. Twenty-six design variables including wall window ratio, louvers depth, louvers count, fin depth and fins count of each orientation, window height, sill height, glazing U-value, solar heat gain coefficient, and visual light transmittance were imported into the Octopus to investigate their interactive impact on the Useful Daylight Illuminance (UDI), Thermal Discomfort Percentage (TDP) and Energy Use Intensity (EUI). The optimized trade-off solutions represented a considerable improvement; UDI increased by 24.61%, 21.90%, 14.91% & 26.41%, and TDP reduced by 10.38%, 1.5%, 13.95%, & 17.68%, and EUI decreased by 10.10%, 1.9%, 15.82%, & 19.19% compared to initially generated solutions for Ahmedabad, Delhi, Mumbai and Bangalore cities respectively. Finally, the Bayesian machine learning technique has been used for sensitivity analysis to identify the correlation between the design variables and performance objectives. The outcomes of this research can help architects in selecting optimal glazing and shading features for both new building design and retrofitting of existing buildings in the tropical climatic conditions of India.
Chaturvedi et al. (Fri,) studied this question.