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Probabilistic climate projections with dynamic building simulation: Predicting overheating in dwellings

This study, as part of the Low Carbon Futures project, proposes a methodology to incorporate probabilistic climate projections into dynamic building simulation analyses of overheating in dwellings. Using a large climate projection database, suitable building software and statistical techniques (focussing on principal component analysis), output is presented that demonstrates the future overheating risk of a building in the form of a probability curve. Such output could be used by building engineers and architects to design a building to an acceptable future overheating risk level, i.e. providing evidence that the building, with specific adaptation measures to prevent overheating, should achieve thermal comfort for the majority of future climate projections. This methodology is overviewed and the use of the algorithm proposed in relation to existing building practices. While the methodology is being applied to a range of buildings and scenarios, this study concentrates on night-time overheating in UK dwellings with simple and achievable adaptation measures investigated.
- Heriot-Watt University United Kingdom
- Maxwell Institute for Mathematical Sciences United Kingdom
- Heriot-Watt University United Kingdom
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).57 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Top 10% influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Top 10% impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Top 10%
