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Development of a statistical model for the prediction of overheating in UK homes using descriptive time series analysis

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conference contribution
posted on 13.06.2017 by Argyris Oraiopoulos, Tom Kane, Steven Firth, Kevin Lomas
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.

Funding

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).

History

School

  • Architecture, Building and Civil Engineering

Published in

15th International Building Performance Simulation Association (IBPSA)

Citation

ORAIOPOULOS, A. ...et al., 2017. Development of a statistical model for the prediction of overheating in UK homes using descriptive time series analysis. Presented at the Building Simulation 2017: The 15th International Conference of IBPSA, San Francisco, August 7-9th.

Publisher

IBPSA

Version

AM (Accepted Manuscript)

Publisher statement

This work is made available according to the conditions of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) licence. Full details of this licence are available at: https://creativecommons.org/licenses/by-nc-nd/4.0/

Acceptance date

26/05/2017

Publication date

2017

Notes

This is a conference paper.

Language

en

Location

San Francisco

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