Symonds, PH;
Taylor, J;
Chalabi, Z;
Mavrogianni, A;
Davies, M;
Hamilton, I;
Vardoulakis, S;
... Macintyre, H; + view all
(2016)
Development of an England-wide indoor overheating and air pollution model using artificial neural networks.
Journal of Building Performance Simulation
, 9
(6)
pp. 606-619.
10.1080/19401493.2016.1166265.
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Abstract
With the UK climate projected to warm in future decades, there is an increased research focus on the risks of indoor overheating. Energy-efficient building adaptations may modify a buildings risk of overheating and the infiltration of air pollution from outdoor sources. This paper presents the development of a national model of indoor overheating and air pollution, capable of modelling the existing and future building stocks, along with changes to the climate, outdoor air pollution levels, and occupant behaviour. The model presented is based on a large number of EnergyPlus simulations run in parallel. A metamodelling approach is used to create a model that estimates the indoor overheating and air pollution risks for the English housing stock. The performance of neural networks (NNs) is compared to a support vector regression (SVR) algorithm when forming the metamodel. NNs are shown to give almost a 50% better overall performance than SVR.
| Type: | Article |
|---|---|
| Title: | Development of an England-wide indoor overheating and air pollution model using artificial neural networks |
| Location: | UK |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1080/19401493.2016.1166265 |
| Publisher version: | http://dx.doi.org/10.1080/19401493.2016.1166265 |
| Language: | English |
| Additional information: | This is an Accepted Manuscript of an article published by Taylor & Francis in Journal of Building Performance Simulation on 13 April 2016, available online: http://www.tandfonline.com/10.1080/19401493.2016.1166265. |
| Keywords: | Metamodelling, machine learning, neural networks, stock modelling, overheating, indoor air pollution |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of the Built Environment UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of the Built Environment > Bartlett School Env, Energy and Resources |
| URI: | https://discovery.ucl.ac.uk/id/eprint/1482123 |
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