Does One Size Fit All? A Bayesian Data-Mining Approach to Thermal Comfort in Office Spaces

Abstract

Building Automation and Control Systems (BACS) enhance energy efficiency in office buildings but often leave occupants dissatisfied, especially in shared offices with diverse indoor environmental preferences. Personal Comfort Models (PCM) capture individual preferences, yet these models can be difficult to apply in shared environments. This study examines whether a generalized personalized modeling would be necessary, or instead aggregated thermal preferences adequately represent individual needs in shared spaces. By mining the ASHRAE Global Thermal Comfort Database II, we analyzed 11 naturally ventilated office buildings spanning four climate zones, each providing 187 to 568 data points of subjective thermal preferences and concurrent environmental measurements. To capture uncertainties in occupant feedback and monitoring, we developed a Bayesian multinomial logistic framework. We compare an aggregated model (all responses together) with a hierarchical (partial-pooling) model having occupant-specific intercepts, using the No‑U‑Turn Sampler for Hamiltonian Monte Carlo. The Widely Applicable Bayesian Information Criterion confirms that hierarchical approach outperforms the aggregated model in every case. In four of the eleven studies, aggregated air temperature ranges satisfied occupants’ demands within a 95% posterior credible interval. Moreover, in most buildings, these temperature ranges aligned well with the majority of occupant preferences, suggesting that prior knowledge of building conditions or occupant variability might reduce the need for highly customized thermal environment. Future research should explore these findings in conditioned buildings and across other indoor environmental quality domains, where greater variability could exist.