Optimizing the View Percentage, Daylight Autonomy, Sunlight Exposure, and Energy Use: Data-Driven-Based Approach for Maximum Space Utilization in Residential Building Stock in Hot Climates

Abstract

This paper introduces a comprehensive methodology for creating diverse layout generation configurations, aiming to address limitations in existing building optimization studies that rely on simplistic hypothetical buildings. This study’s objective was to achieve an optimal balance between minimizing the energy use intensity (EUI) in kWh/m2, maximizing the views percentages to the outdoor (VPO), achieving spatial daylight autonomy (sDA), and minimizing annual sunlight exposure (ASE). To ensure the accuracy and reliability of the simulation, the research included calibration and validation processes using the Ladybug and Honeybee plugins, integrated into the Grasshopper platform. These processes involved comparing the model’s performance against an existing real-world case. Through more than 1500 iterations, the study extracted three multi-regression equations that enabled the calculation of EUI in kWh/m2. These equations demonstrated the significant influence of the window-to-wall ratio (WWR) and space proportions (SP) on the EUI. By utilizing these multi-regression equations, we were able to fine-tune the design process, pinpoint the optimal configurations, and make informed decisions to minimize energy consumption and enhance the sustainability of residential buildings in hot arid climates. The findings indicated that 61% of the variability in energy consumption can be attributed to changes in the WWR, as highlighted in the first equation. Meanwhile, the second equation suggested that around 27% of the variability in energy consumption can be explained by alterations in space proportions, indicating a moderate correlation. Lastly, the third equation indicated that approximately 89% of the variability in energy consumption was associated with changes in the SP and WWR, pointing to a strong correlation between SP, WWR, and energy consumption. The proposed method is flexible to include new objectives and variables in future applications.