Optimization of Horizontal and Vertical Shading Devices for Daylight Performance in Residential Buildings in Isfahan, A Simulation and Machine Learning Approach
Ahmad Najafi
1
(
Department of Architecture and Urban Planning, National University of Skills (NUS), Tehran, Iran.
)
Mahdi Torkan
2
(
Department of Architecture and Urban Planning, National University of Skills (NUS), Tehran, Iran
)
Mohammad saeid Sobhani
3
(
Department of Architecture and Urban Planning, National University of Skills (NUS), Tehran, Iran
)
Keywords: Optimization of Shading Devices, Daylighting, Machine Learning Algorithms, Lighting Simulation, Visual Comfort,
Abstract :
This study investigates the optimization of horizontal and vertical shading devices to improve daylighting performance in residential buildings in Isfahan, using a combination of simulation methods and machine learning. The primary objective is to balance enhancing visual comfort by maximizing natural light penetration and reducing solar heat gain. Despite the importance of shading devices in reducing cooling loads, previous research has often overlooked their impact on daylight distribution and visual comfort in hot and dry climates. This study addresses this gap by proposing a comprehensive framework. The research methodology consists of three main parts: (1) setting up a reference living room in Isfahan with precise geometric and material specifications, (2) conducting daylighting simulations using Ecotect software (version 2011) to evaluate the performance of shading devices under different seasonal conditions, and (3) employing machine learning models, including multivariate regression, decision trees, random forests, and artificial neural networks, to analyze and optimize the design of shading devices. Genetic algorithms were used to identify optimal configurations. Key findings indicate that the distance between shading elements has the most significant impact on daylight penetration, while the depth and number of shading elements negatively affect daylighting performance. The random forest model, with high predictive accuracy (R² = 0.9679 for vertical shading devices and 0.9877 for horizontal ones), was identified as the best-performing model. Sensitivity analysis also confirmed the importance of distance in optimizing shading devices. The study concludes that optimizing shading devices can significantly improve daylighting performance in residential buildings. It is recommended that designers maximize the distance between shading elements, reduce their depth, and carefully adjust their number. This research demonstrates that integrating machine learning with simulation tools provides a powerful framework for designing sustainable and energy-efficient buildings.
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