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Please use this identifier to cite or link to this item: https://dspace.lboro.ac.uk/2134/25364

Title: Development of a statistical model for the prediction of overheating in UK homes using descriptive time series analysis
Authors: Oraiopoulos, Argyris
Kane, Tom
Firth, Steven K.
Lomas, Kevin J.
Issue Date: 2017
Publisher: 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.
Abstract: 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.
Description: This is a conference paper.
Sponsor: 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).
Version: Accepted for publication
URI: https://dspace.lboro.ac.uk/2134/25364
Publisher Link: http://www.buildingsimulation2017.org/index.html
Appears in Collections:Conference Papers and Presentations (Architecture, Building and Civil Engineering)

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