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

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Energy and Buildings


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 metrics that were tailored to quantify the overall reduction of thermal dissatisfaction when using optimal air temperature and PMV thermostat settings. These two metrics represented the average reduction of overall indoor thermal dissatisfaction each time a thermostat value was updated. The results showed that it was useful to consider the temporal variations of occupant thermal preferences to reduce the overall occupant thermal dissatisfaction in the office, and that using the same approach on individual zones within the open plan office would lead to further improvements. The case study demonstrated that the optimal adaptive temperature and PMV thermostat settings led to an overall thermal dissatisfaction reduction of 1.47% and 1.21% in the whole office, respectively (as opposed to 0.25% and 0.19% when single fixed temperature-based and PMV-based thermostat settings were used). By applying the proposed adaptive optimisation algorithm on individual zones in the office, the occupant thermal dissatisfaction reductions ranged from 0.88% to 5.17% for PMV-based settings, and from 1.20% to 5.19% for temperature-based settings.

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