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

Research output: Contribution to journalArticlepeer-review

20 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)606-619
Number of pages14
JournalJournal of Building Performance Simulation
Volume9
Issue number6
DOIs
Publication statusPublished - 1 Nov 2016

Keywords

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

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