369 21 6760 2 17394 document 6760 doi.zip application/zip c031efd70af9620d13b76fd4dbd098df MD5 7258265 2017-08-03 13:52:15 https://researchdata.bath.ac.uk/369/1/doi.zip 369 1 1 application/zip other en public

doi.zip

data archive 5699 disk0/00/00/03/69 2017-08-07 09:49:20 2024-07-15 10:58:32 2017-08-07 09:49:20 data_collection show Coley David D.A.Coley@bath.ac.uk 0000-0001-5744-1809 University of Bath TRUE Herrera Fernandez Antonio Manuel amhf20@bath.ac.uk 0000-0001-9662-0017 University of Bath FALSE Fosas Daniel D.Fosas.De.Pando@bath.ac.uk 0000-0001-8407-3431 University of Bath FALSE Liu Chunde C.Liu2@bath.ac.uk 0000-0002-4814-2046 University of Bath FALSE Vellei Marika M.Vellei@bath.ac.uk 0000-0001-5676-5991 University of Bath FALSE CW0010 dept_civ_eng Synthetic Weather 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. 2017 University of Bath public RightsHolder University of Bath Engineering and Physical Sciences Research Council https://doi.org/10.13039/501100000266 EP/M021890/1 COLBE - The Creation of Localized Current and Future Weather for the Built Environment 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). https://doi.org/10.1016/j.buildenv.2017.06.050 en 1 10.15125/BATH-00369 https://doi.org/10.1016/j.buildenv.2017.06.050 pub open