Probabilistic adaptive thermal comfort for resilient design

Probabilistic adaptive thermal comfort for resilient design

Adaptive thermal comfort theory has become the bedrock of much thinking about how to judge if a free-running environment is suitable for human occupation. In design work, the conditions predicted by a thermal model, when the model is presented with one possible annual weather time series (a reference year), are compared to the limits of human comfort. If the temperatures are within the comfort limits, the building is judged to be suitable. However, the weather in many locations can vary year-on-year by a considerable margin, and this begs the question, how robust are the predictions of adaptive comfort theory likely to be over the many years a building might be in use? We answer this question using weather data recorded for up to 30 years for locations within each of the five major Köppen climate classifications. We find that the variation in the annual time series is so great that the predicted comfort temperature frequently lies outside the acceptable range given by the reference year. Return periods for the excursions of the time series are calculated for each location. The results for one location are then validated using the world's longest temperature record. These results suggest that industry and academia would be best advised to move to a probabilistic methodology, like the proposed one, when using adaptive comfort theory to judge the likely conditions within a building. Extra pertinence is provided by concerns over increases in mortality and morbidity in buildings due to a rapidly warming climate.

Keywords:
Synthetic Weather
Subjects:
Climate and climate change

Cite this dataset as:
Coley, D., Herrera Fernandez, A., Fosas, D., Liu, C., Vellei, M., 2017. Probabilistic adaptive thermal comfort for resilient design. University of Bath. https://doi.org/10.15125/BATH-00369.

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Creators

David Coley
University of Bath

Daniel Fosas
University of Bath

Chunde Liu
University of Bath

Marika Vellei
University of Bath

Documentation

Data collection method:

3,000 years of weather was generated for London, Edinburgh and Manchester and are available to download in this data repository. The weather generator used in this work was that used by the UK Climate Impacts Programme. The probabilistic projection methodology in UKCP09 involves sampling climate modelling uncertainties by combining results from perturbed variants of the UK Met Office global climate model (HadCM3) with projections from an ensemble of four alternative international climate models used by the fourth IPCC assessment report. Running the weather generator involves declaring a time period and a world carbon emission scenario. In this work the time slice was set to the 2020s, as this is the closest to the current date, and the emission scenario to low (to create the minimum perturbation from current weather).

Funders

Engineering and Physical Sciences Research Council (EPSRC)
https://doi.org/10.13039/501100000266

COLBE - The Creation of Localized Current and Future Weather for the Built Environment
EP/M021890/1

Publication details

Publication date: 2017
by: University of Bath

Version: 1

DOI: https://doi.org/10.15125/BATH-00369

URL for this record: https://researchdata.bath.ac.uk/id/eprint/369

Related articles

Coley, D., Herrera, M., Fosas, D., Liu, C. and Vellei, M., 2017. Probabilistic adaptive thermal comfort for resilient design. Building and Environment, 123, pp.109-118. Available from: https://doi.org/10.1016/j.buildenv.2017.06.050.

Contact information

Please contact the Research Data Service in the first instance for all matters concerning this item.

Contact person: David Coley

Departments:

Faculty of Engineering & Design
Architecture & Civil Engineering