Fig. 6 | Scientific Reports

Fig. 6

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

Fig. 6

Digital Twin-based thermal comfort prediction analysis: (a) frequency of predicted comfort states, (b) optimal comfort zones identified across temperature-humidity combinations, (c) individualized comfort profiles capturing occupant-specific thermal preferences, and (d) time-series analysis of comfort state transitions highlighting temporal variability.

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