Digital twin based deep learning framework for personalized thermal comfort prediction and energy efficient operation in smart buildings

Digital twin based deep learning framework for personalized thermal comfort prediction and energy efficient operation in smart buildings

Abstract The regulation of indoor thermal comfort is a critical aspect of smart building design, significantly influencing energy efficiency and occupant well-being. Traditional comfort models, such as Fanger’s equation and adaptive approaches, often fall short in capturing individual occupant prefe...

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Main Authors: Ahmad Almadhor, Nejib Ghazouani, Belgacem Bouallegue, Natalia Kryvinska, Shtwai Alsubai, Moez Krichen, Abdullah Al Hejaili, Gabriel Avelino Sampedro
Format: Article
Language:English
Published: Nature Portfolio 2025-07-01
Series:Scientific Reports
Subjects:
Thermal Comfort Prediction
Personalized Thermal Comfort Prediction
Digital Twin Technology
IoT
Smart Buildings
Smart HVAC Systems
Online Access:https://doi.org/10.1038/s41598-025-10086-y
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https://doi.org/10.1038/s41598-025-10086-y

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