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Development of a Bayesian based adaptive optimisation algorithm for the thermostat settings in agile open plan

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  • 1Sustainable Buildings Research Centre (SBRC), University of Wollongong (UOW), 2522, Australia.

Energy and Buildings
|October 14, 2020
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Summary

A new adaptive algorithm optimizes office thermostat settings by considering occupant preferences and environmental conditions. This approach significantly reduces thermal discomfort compared to fixed settings, improving overall comfort in open-plan offices.

Keywords:
Adaptive optimisationAgile open plan officesRecursive Bayesian inferenceThermal preferencesThermostat settings

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Area of Science:

  • Building Science
  • Human-Building Interaction
  • Data Science

Background:

  • Optimizing indoor climate control in large, open-plan offices presents challenges due to varying occupant needs and environmental conditions.
  • Traditional thermostat settings often fail to address temporal and spatial variations in thermal comfort, leading to widespread dissatisfaction.

Purpose of the Study:

  • To develop and evaluate a Bayesian-based adaptive optimization algorithm for indoor thermostat settings in an open-plan office.
  • To quantify the reduction in occupant thermal dissatisfaction using adaptive versus fixed thermostat strategies.

Main Methods:

  • Collected occupant thermal dissatisfaction and environmental data over 19 months using dense sensor networks.
  • Employed logistic regression with Bayesian inference to identify optimal thermostat settings.
  • Designed and implemented optimization scenarios considering temporal and spatial variations.

Main Results:

  • The adaptive algorithm, considering temporal variations, significantly reduced overall thermal dissatisfaction.
  • Optimal adaptive settings achieved up to 1.47% (temperature) and 1.21% (PMV) reduction in whole-office thermal dissatisfaction.
  • Zonal optimization further enhanced dissatisfaction reduction, ranging from 0.88% to 5.19%.

Conclusions:

  • Incorporating temporal variations in occupant thermal preferences is crucial for reducing dissatisfaction in open-plan offices.
  • Adaptive, zone-specific optimization strategies offer substantial improvements in thermal comfort compared to static approaches.
  • The developed Bayesian algorithm provides an effective method for dynamic building climate control.