Development of a statistical model for the prediction of overheating in UK homes using descriptive time series analysis
conference contributionposted on 13.06.2017 by Argyris Oraiopoulos, Tom Kane, Steven Firth, Kevin Lomas
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Overheating risk in dwellings is often predicted using modelling techniques based on assumptions of heat gains, heat losses and heat storage. However, a simpler method is to use empirical data to predict internal temperatures in dwellings based on external climate data. The aim of this research is to use classical time series descriptive analysis and construct statistical models that allow the prediction of future internal temperatures based external weather data. Initial results from the analysis of a living room in a house show that the proposed method can successfully predict the risk of overheating based on four different overheating criteria.
This research was made possible by the support from the Engineering and Physical Sciences Research Council (EPSRC) for the London-Loughborough Centre for Doctoral Research in Energy Demand (grant EP/H009612/1). The 4M consortium was funded by the EPSRC under their Sustainable Urban Environment programme (grant EP/F007604/1).
- Architecture, Building and Civil Engineering