Future probabilistic hot summer years for overheating risk assessments

Liu, C.; Kershaw, Tristan; Eames, Matthew E.; Coley, David

doi:10.1016/j.buildenv.2016.05.028

Cited by 32 publications

(23 citation statements)

References 28 publications

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“…This is a problem, as summers such as 2003 which resulted in so many deaths across Europe are not ranked highly in the base dataset when considering average summertime temperature. Various attempts have been made to address such concerns, largely by creating new reference years based on warmer periods or on predictions of climate change (see, for example: [18][19][20][21][22][23]). …”

Section: Accepted Manuscriptmentioning

confidence: 99%

“…Weighted cooling degree hours have been suggested as an alternative metric for the selection of a DSY that might solve this [18,25]. Furthermore, as it is known that different weather parameters have a differing influence on the relative risk of overheating for different building types [23], three design reference years were selected in [26] based on the daily mean temperature, relative humidity and solar radiation respectively.…”

Section: Accepted Manuscriptmentioning

confidence: 99%

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Probabilistic adaptive thermal comfort for resilient design

Coley

Herrera

Fosas

et al. 2017

Building and Environment

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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. • A new probabilistic adaptive comfort theory introduced.• New comfort equations for resilient building design presented.

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Section: Accepted Manuscriptmentioning

confidence: 99%

Section: Accepted Manuscriptmentioning

confidence: 99%

Probabilistic adaptive thermal comfort for resilient design

Coley

Herrera

Fosas

et al. 2017

Building and Environment

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“…Liu et al [56] present approaches for development of current and future weather files. Two probabilistic hot summer years were proposed, and there was noticed an important limitation in using different metrics to compare overheating years.…”

Section: Overheatingmentioning

confidence: 99%

Climate Change Adaptation Measures for Buildings—A Scoping Review

et al. 2020

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As the climate changes globally and locally, the built environment will be subject to different climatic exposure than in the past. Adaptation measures are required to ensure the long-term integrity and successful operation of the built environment. This study examines literature on climate adaptation measures for buildings through a scoping literature review. It is centered around the main journals in the field of climate adaptation of the built environment, then expanded to map the extent of scientific publications about climate adaptation in general. Studies that regard future climate scenarios have been of particular interest. The majority of the identified literature concerns climate change impacts on buildings in warm climates, with overheating being seen as the greatest challenge. Additionally, few empirical studies are found; most identified research is based on computer simulations or literature reviews. The volume of research on the consequences of climate change on buildings in cold regions is surprisingly small, considering the pecuniary stakes involved. The predictions of climate scenarios suggest regulatory/policy measures on climate adaptation should be taken as quickly as possible to avoid greater costs in the future. However, further research into future scenarios is also essential.

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“…The UKCP09 WG provides current and future synthetic weather data which have been used for creating building simulation weather files in previous studies. 4,19,[23][24][25][26][27] The UKCP09 WG is a stochastic tool that primarily generates the precipitation sequence based on the Neyman-Scott Rectangular Pulses model. 28 Then, the precipitation sequence is used to produce the time series of the other weather variables based on inter-variable relationships observed from the baseline climate data over the control period (1961 to 1990).…”

Section: Synthetic Weather Datamentioning

confidence: 99%

Current and future test reference years at a 5 km resolution

Liu

Chung

Cecinati

et al. 2019

Building Services Engineering Research and Technology

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Frequently, the computer modelling of the natural and human-made environment requires localised weather files. Traditionally, the weather files are based on the observed weather at a small number of locations (14 for the UK). Unfortunately, both the climate and the weather are known to be highly variable across the landscape, so the small number of locations has the potential to cause large errors. With respect to buildings, this results in incorrect estimates of the annual energy use (sometimes by a factor of 2), or of overheating risk. Here we use a validated weather generator running on a 5 × 5 km grid to create probabilistic test reference years (pTRYs) for the UK at 11,326 locations. We then investigate the spatial variability of these pTRYs and of annual energy estimates and temperatures in buildings generated by them, both now and in 2080. Further pTRYs targeted at understanding the impact of minimum and maximum temperatures are proposed and produced at the same locations. Finally, we place these pTRYs, which represent the first set of reference weather files at this spatial resolution in the world and that include the urban heat island effect, into a publicly accessible database so researchers and industry can access them. Practical applications: Insufficiently localised weather data for building simulations have limited the accuracy of previous estimations of energy use and overheating risk in buildings. This work produces localised probabilistic test reference years (pTRYs) across the whole UK for now and future climates. In addition, a new pTRY method has been proposed in order to overcome an unexpected shortcoming of traditional pTRYs in representing typical maximum and minimum temperatures. These current and future weather data will be of interest to various disciplines including those interested in low carbon design, renewable energy and climate resilience.

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Future probabilistic hot summer years for overheating risk assessments

Cited by 32 publications

References 28 publications

Probabilistic adaptive thermal comfort for resilient design

Probabilistic adaptive thermal comfort for resilient design

Climate Change Adaptation Measures for Buildings—A Scoping Review

Current and future test reference years at a 5 km resolution

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