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: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-07-01
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| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-025-10086-y |
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| Summary: | 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 preferences and the dynamic nature of indoor environmental conditions. To overcome these limitations, we introduce a Digital Twin-driven framework integrated with an advanced attention-based Long Short-Term Memory (LSTM) model specifically tailored for personalised thermal comfort prediction and intelligent HVAC control. The attention mechanism effectively focuses on critical temporal features, enhancing both predictive performance and interpretability. Next, the Digital Twin enables the real-time simulation of indoor environments and occupant responses, facilitating proactive comfort management. We utilise a subset of the ASHRAE Global Thermal Comfort Database II, and extensive pre-processing, including median-based data imputation and feature normalisation, is conducted. The proposed model categorises Thermal Sensation Votes (TSVs) recorded on a 7-point ASHRAE scale into three classes: Uncomfortably Cold (UC) for TSV $$\le$$ -1, Neutral (N) for TSV = 0, and Uncomfortably Warm (UW) for TSV $$\ge$$ +1. The model achieves a test accuracy of 83.8%, surpassing previous state-of-the-art methods. Furthermore, Explainable AI (XAI) techniques, such as SHAP and LIME, are integrated to enhance transparency and interpretability, complemented by scenario-based energy efficiency analyses to evaluate energy-comfort trade-offs. This comprehensive approach provides a robust, interpretable, and energy-efficient solution for occupant-centric HVAC management in smart building systems. |
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| ISSN: | 2045-2322 |