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

机译：敏捷开放式计划办公室温控器设置的贝叶斯基于自适应优化算法的开发

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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. (C) 2020 Elsevier B.V. All rights reserved.
机译：介绍了贝叶斯基于自适应优化算法的开发，用于优化大型敏捷开放式办公室中的室内温控器设置。在大约19个月的时间内使用密集放置的装置收集热不满和室内环境条件的占用表达。使用逻辑回归模型来识别使用使用贝叶斯推断估计的回归系数的最佳设置。设计并实施了一系列具有和不考虑乘员热偏好的时间变化和室内条件的空间偏差的优化方案，并实施了在总乘员热不满减少方面评估它们的潜在益处。我们开发了两项定量定量的指标，以量化使用最佳空气温度和PMV恒温器设置时的热不满的总体减少。每次更新恒温器值时，这两个度量表示整个室内热不满的平均降低。结果表明，考虑乘员热偏好的时间变化是有用的，以减少办公室的总乘员热不满，并且在开放式办公室内的各个区域上使用相同的方法将导致进一步改善。案例研究证明，最佳自适应温度和PMV恒温器设置分别导致整个办公室的总体热不满1.47％和1.21％（当单固定温度为基础和PMV时，如0.25％和0.19％）使用基于恒温器设置）。通过在办公室的各个区域上应用所提出的自适应优化算法，占用者的热差距减少为PMV的设置的0.88％至5.17％，对于基于温度的设置的1.20％至5.19％。 （c）2020 Elsevier B.v.保留所有权利。

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