Adaptive-rational thermal comfort model: Adaptive predicted mean vote with variable adaptive coefficient

Area of Science:

• Building science
• Environmental engineering
• Human thermal comfort

Background:

• Traditional thermal comfort models like the rational approach (e.g., PMV) focus on heat balance but struggle to incorporate occupant thermal adaptations.
• Adaptive approaches account for thermal adaptations but often neglect the body's physiological heat balance.
• Existing models have limitations in accurately predicting thermal comfort, especially considering occupant behavior and environmental variability.

Purpose of the Study:

• To develop and validate a novel adaptive-rational thermal comfort model (arPMV) that integrates both heat balance and adaptive thermal comfort principles.
• To improve the accuracy and robustness of thermal sensation predictions in buildings.
• To reduce building energy consumption by optimizing thermal comfort zones.

Main Methods:

• Proposed an adaptive-rational thermal comfort model (arPMV) by combining predicted mean vote (PMV) with a variable adaptive coefficient.
• Developed a method to link thermal adaptations' feedback effects to ambient temperature using a linear relationship with the reciprocal of ambient temperature.
• Quantified the model's constants based on predicted mean vote, thermal sensation vote, and ambient temperature, and validated it across naturally ventilated, air-conditioned, and mixed-mode buildings.

Main Results:

• The proposed arPMV model demonstrated significant improvements in thermal sensation prediction.
• Mean absolute error was reduced by 24.8%-83.5% across different building types.
• Robustness of thermal sensation prediction was improved by 49.7%-83.4%.

Conclusions:

• The adaptive-rational thermal comfort model (arPMV) offers a more comprehensive approach to predicting thermal comfort by integrating physiological and behavioral factors.
• arPMV significantly enhances prediction accuracy and robustness compared to traditional methods.
• This improved prediction capability can lead to more effective building energy management strategies by optimizing thermal comfort settings.