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

ABSTRACT: The development of a Bayesian based adaptive optimisation algorithm for optimising the indoor thermostat settings in a large agile open plan office is presented. Occupant expressions of thermal dissatisfaction and indoor environmental conditions were collected using densely-placed devices over a period of approximately 19 months. A logistic regression model was employed to identify the optimal settings, using regression coefficients that were estimated using Bayesian inference. A series of optimisation scenarios with and without considering the temporal variations of occupant thermal preferences and the spatial deviation of the indoor conditions was designed and implemented to evaluate their potential benefit in terms of overall occupant thermal dissatisfaction reduction. We developed two

SUBMITTER: Lin W 

PROVIDER: S-EPMC7545340 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature