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Author Spotlight: Optimization of Airflow Velocities in Battery Cooling Systems for Enhanced Thermal Performance and Reduced Energy Consumption
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Adaptive-rational thermal comfort model: Adaptive predicted mean vote with variable adaptive coefficient.

Sheng Zhang1, Zhang Lin2

  • 1Department of Architecture and Civil Engineering, City University of Hong Kong, Hong Kong, China.

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|March 11, 2020
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Summary

This study introduces an adaptive-rational thermal comfort model (arPMV) to better predict occupant comfort by integrating heat balance and adaptive approaches. The new model enhances thermal sensation prediction accuracy and robustness across various building types.

Keywords:
adaptive approachadaptive predicted mean voterational approachthermal adaptationsthermal comfort modelvariable adaptive coefficient

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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.