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 language | English |
|---|---|
| Pages (from-to) | 606-619 |
| Number of pages | 14 |
| Journal | Journal of Building Performance Simulation |
| Volume | 9 |
| Issue number | 6 |
| DOIs | |
| Publication status | Published - 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).
Keywords
- indoor air pollution
- machine learning
- metamodelling
- neural networks
- overheating
- stock modelling