Analyzing the Impact of Different Shading Devices on Energy Consumption and Thermal Comfort in Office Buildings Using Machine Learning and Energy Simulation in Three Different Climates of Iran
Abstract
Mohammad Hassan Abedini
This study investigates the impact of three types of shading devices, including overhangs, louvers, and side fins, on energy consumption and thermal comfort in office buildings. The analysis utilizes climatic data from three cities—Yazd, Tehran, and Bandar Abbas—extracted from EPW files and simulated using the Honeybee and Ladybug tools. Additionally, the XGBoost machine learning model is employed to provide more accurate predictions of shading devices' effects on indoor temperature and energy consumption. Key climatic factors such as temperature, solar radiation, humidity, and wind speed are used as input variables for the model. The results indicate that overhangs in the hot and dry climate of Yazd significantly reduce energy consumption, while louvers demonstrate the highest efficiency in the hot and humid conditions of Bandar Abbas. This study highlights the importance of combining energy simulations with machine learning algorithms to optimize shading device design and improve thermal and lighting comfort.