Façade design of side-lit spaces for different climates and surroundings by machine learning and NSGAIII

Abstract

This work aims to propose a simplified decision tool for the design of side-lit spaces that accounts for the impacts of climate and surroundings. The framework was developed using a smart optimization algorithm NSGA-III in combination with CatBoost ensemble machine learning technique and simulation. WWRs and overhang depths were optimized to maximize daylight penetration, minimize glare risk and reduce energy demand in different climates, orientations, space proportions, and surrounding obstruction angles. Besides, optimal solutions were used to determine the range of attainable targets for daylight, glare, and energy metrics in each climate, which can be used as requirements of national codes and standards. For example, for WDR = 3:2 and an OA of 20, energy demand varied between 260 and 266 kWh/m2, ASE remained at 40–50%, and sDA was reported 100% for south-oriented cases in Tehran. A sensitivity analysis was also performed to provide insights into how various design parameters affected daylight and energy performance in architectural spaces. The results revealed that daylight availability metrics were highly sensitive to the Window-to-Wall Ratio (WWR), while glare metrics were primarily affected by obstruction angle. Energy consumption was mainly influenced by room depth, WWR, window orientation, and obstruction angle. Notably, these parameters ranked similarly across all considered climates, albeit with varying degrees of significance. Results were presented in the form of guide charts that offer a practical tool for designing buildings in highly obstructed contexts and enable non-programmer architects and designers to make informed decisions.