Development of an England-wide indoor overheating and air pollution model using artificial neural networks

Phil Symonds*, Jonathon Taylor, Zaid Chalabi, Anna Mavrogianni, Michael Davies, Ian Hamilton, Sotiris Vardoulakis, Clare Heaviside, Helen Macintyre

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

29 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)606-619
Number of pages14
JournalJournal of Building Performance Simulation
Issue number6
Publication statusPublished - 1 Nov 2016

Bibliographical note

Funding Information:
This research was funded by the National Institute for Health Research Health Protection Research Unit (NIHR HPRU) on the topic of Environmental Change and Health. The project is lead by the London School of Hygiene and Tropical Medicine in partnership with Public Health England (PHE), and in collaboration with the University of Exeter, University College London, and the Met Office. The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR, the Department of Health or PHE.

Publisher Copyright:
© 2016 International Building Performance Simulation Association (IBPSA).


  • indoor air pollution
  • machine learning
  • metamodelling
  • neural networks
  • overheating
  • stock modelling


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