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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 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.

SUBMITTER: Lin W

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

REPOSITORIES: biostudies-literature

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

Lin Wenye W Kokogiannakis Georgios G

Energy and buildings 20201009

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 optim ...[more]

PMID: 33052170

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Project description:BackgroundStep counts are increasingly used in public health and clinical research to assess wellbeing, lifestyle, and health status. However, estimating step counts using commercial activity trackers has several limitations, including a lack of reproducibility, generalizability, and scalability. Smartphones are a potentially promising alternative, but their step-counting algorithms require robust validation that accounts for temporal sensor body location, individual gait characteristics, and heterogeneous health states.ObjectiveOur goal was to evaluate an open-source step-counting method for smartphones under various measurement conditions against step counts estimated from data collected simultaneously from different body locations ("internal" validation), manually ascertained ground truth ("manual" validation), and step counts from a commercial activity tracker (Fitbit Charge 2) in patients with advanced cancer ("wearable" validation).MethodsWe used eight independent datasets collected in controlled, semi-controlled, and free-living environments with different devices (primarily Android smartphones and wearable accelerometers) carried at typical body locations. Five datasets (N=103) were used for internal validation, two datasets (N=107) for manual validation, and one dataset (N=45) used for wearable validation. In each scenario, step counts were estimated using a previously published step-counting method for smartphones that uses raw sub-second level accelerometer data. We calculated mean bias and limits of agreement (LoA) between step count estimates and validation criteria using Bland-Altman analysis.ResultsIn the internal validation datasets, participants performed 751.7±581.2 (mean±SD) steps, and the mean bias was -7.2 steps (LoA -47.6, 33.3) or -0.5%. In the manual validation datasets, the ground truth step count was 367.4±359.4 steps while the mean bias was -0.4 steps (LoA -75.2, 74.3) or 0.1 %. In the wearable validation dataset, Fitbit devices indicated mean step counts of 1931.2±2338.4, while the calculated bias was equal to -67.1 steps (LoA -603.8, 469.7) or a difference of 0.3 %.ConclusionsThis study demonstrates that our open-source step counting method for smartphone data provides reliable step counts across sensor locations, measurement scenarios, and populations, including healthy adults and patients with cancer.

Dataset Information

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

Publications

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

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