Statistical techniques to emulate dynamic building simulations for overheating analyses in future probabilistic climates

Research output: Contribution to journalArticle

Abstract

As projections of climate change become more detailed and sophisticated, analysing the effects of these projections on, for example, building performance will become more complex. This study, as part of the Low Carbon Futures project, proposes a method for integrating the latest UK Climate Projections 2009, which are probabilistic in nature, into dynamic building simulation calculations. This methodology offers the possibility that, in an analysis of overheating in buildings, it will be viable for a building designer to assess future thermal comfort of a building in a probabilistic way, with various climate scenarios informing a risk analysis of whether that building will become unsuitable as a working/living environment. To reduce the computational requirements of such an analysis, a series of statistical manipulations and approximations are proposed that serve to reduce substantially the amount of computation that would otherwise be necessary when using such climate projections. The resulting tool, which in essence captures the behaviour of complex simulation models using linear filtering techniques and regression, is successfully validated against results obtained from building simulation software results for a domestic building case-study, including versions of the building with specific adaptation scenarios applied that might offset the predicted overheating.

Original languageEnglish
Pages (from-to)271-284
Number of pages14
JournalJournal of Building Performance Simulation
Volume4
Issue number3
DOIs
Publication statusPublished - 2011

Keywords

  • probabilistic climate projections
  • buildings and simulation
  • adaptation
  • overheating
  • uncertainty

Cite this

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title = "Statistical techniques to emulate dynamic building simulations for overheating analyses in future probabilistic climates",
abstract = "As projections of climate change become more detailed and sophisticated, analysing the effects of these projections on, for example, building performance will become more complex. This study, as part of the Low Carbon Futures project, proposes a method for integrating the latest UK Climate Projections 2009, which are probabilistic in nature, into dynamic building simulation calculations. This methodology offers the possibility that, in an analysis of overheating in buildings, it will be viable for a building designer to assess future thermal comfort of a building in a probabilistic way, with various climate scenarios informing a risk analysis of whether that building will become unsuitable as a working/living environment. To reduce the computational requirements of such an analysis, a series of statistical manipulations and approximations are proposed that serve to reduce substantially the amount of computation that would otherwise be necessary when using such climate projections. The resulting tool, which in essence captures the behaviour of complex simulation models using linear filtering techniques and regression, is successfully validated against results obtained from building simulation software results for a domestic building case-study, including versions of the building with specific adaptation scenarios applied that might offset the predicted overheating.",
keywords = "probabilistic climate projections, buildings and simulation, adaptation, overheating, uncertainty",
author = "S. Patidar and David Jenkins and Gibson, {G. J.} and Banfill, {P. F. G.}",
year = "2011",
doi = "10.1080/19401493.2010.531144",
language = "English",
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pages = "271--284",
journal = "Journal of Building Performance Simulation",
issn = "1940-1493",
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TY - JOUR

T1 - Statistical techniques to emulate dynamic building simulations for overheating analyses in future probabilistic climates

AU - Patidar, S.

AU - Jenkins, David

AU - Gibson, G. J.

AU - Banfill, P. F. G.

PY - 2011

Y1 - 2011

N2 - As projections of climate change become more detailed and sophisticated, analysing the effects of these projections on, for example, building performance will become more complex. This study, as part of the Low Carbon Futures project, proposes a method for integrating the latest UK Climate Projections 2009, which are probabilistic in nature, into dynamic building simulation calculations. This methodology offers the possibility that, in an analysis of overheating in buildings, it will be viable for a building designer to assess future thermal comfort of a building in a probabilistic way, with various climate scenarios informing a risk analysis of whether that building will become unsuitable as a working/living environment. To reduce the computational requirements of such an analysis, a series of statistical manipulations and approximations are proposed that serve to reduce substantially the amount of computation that would otherwise be necessary when using such climate projections. The resulting tool, which in essence captures the behaviour of complex simulation models using linear filtering techniques and regression, is successfully validated against results obtained from building simulation software results for a domestic building case-study, including versions of the building with specific adaptation scenarios applied that might offset the predicted overheating.

AB - As projections of climate change become more detailed and sophisticated, analysing the effects of these projections on, for example, building performance will become more complex. This study, as part of the Low Carbon Futures project, proposes a method for integrating the latest UK Climate Projections 2009, which are probabilistic in nature, into dynamic building simulation calculations. This methodology offers the possibility that, in an analysis of overheating in buildings, it will be viable for a building designer to assess future thermal comfort of a building in a probabilistic way, with various climate scenarios informing a risk analysis of whether that building will become unsuitable as a working/living environment. To reduce the computational requirements of such an analysis, a series of statistical manipulations and approximations are proposed that serve to reduce substantially the amount of computation that would otherwise be necessary when using such climate projections. The resulting tool, which in essence captures the behaviour of complex simulation models using linear filtering techniques and regression, is successfully validated against results obtained from building simulation software results for a domestic building case-study, including versions of the building with specific adaptation scenarios applied that might offset the predicted overheating.

KW - probabilistic climate projections

KW - buildings and simulation

KW - adaptation

KW - overheating

KW - uncertainty

U2 - 10.1080/19401493.2010.531144

DO - 10.1080/19401493.2010.531144

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SP - 271

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