Development of an England-wide indoor overheating and air pollution model using artificial neural networks. Issue 6 (1st November 2016)

• Record Type: Journal Article
• Authors: Symonds, Phil; Taylor, Jonathon; Chalabi, Zaid; Mavrogianni, Anna; Davies, Michael; Hamilton, Ian; Vardoulakis, Sotiris; Heaviside, Clare; Macintyre, Helen
• Is Part Of: Journal of building performance simulation. Volume 9:Issue 6(2016)
• Page Start: 606
• Page End: 619
• Publication Date: 2016-11-01
• Subjects: metamodelling -- machine learning -- neural networks -- stock modelling -- overheating -- indoor air pollution; 690.0113
• DOI: 10.1080/19401493.2016.1166265
• Languages: English
• ISSNs: 1940-1493

Abstract

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.