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A new empirical model incorporating spatial interpolation of meteorological data for the prediction of overheating risks in UK dwellings

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conference contribution
posted on 2017-07-14, 15:15 authored by Matej Gustin, Argyris Oraiopoulos, Rob McLeod, Kevin LomasKevin Lomas
Heat-related morbidity and mortality is anticipated to increase as climatic change induced overheating become increasingly common. The development of building-specific predictive models has the potential to alert occupants and emergency services to the severity of impending risks. This research aims to evaluate the implementation of a newly developed time series model for overheating prediction. Since risk forecasting is contingent upon the accuracy of the model at different future time steps, the sensitivity of model outputs to the uncertainty in the data inputs needs to be understood. Internal and external climatic variables were monitored in an unoccupied domestic dwelling in order to evaluate the empirical model’s predictive accuracy. The uncertainty related to the proximity of external weather stations was evaluated using data taken from four nearby weather stations and further bespoke data sets derived by interpolation. The results confirmed the overall accuracy of the newly developed time series predictive model, whilst highlighting the benefits of climatic data interpolation in reducing predictive uncertainties. The empirically derived modelling approach showed a low variance to the actual temperature evolution over a seven-day predictive period, pointing to its validity as a robust model for the prediction of future overheating risks.

Funding

This research was made possible by EPSRC support for the London-Loughborough CDT in Energy Demand (grants EP/L01517X/1 and EP/H009612/1).

History

School

  • Architecture, Building and Civil Engineering

Published in

PLEA 2017

Citation

GUSTIN, M. ... et al, 2017. A new empirical model incorporating spatial interpolation of meteorological data for the prediction of overheating risks in UK dwellings. IN: Brotas, L., Roaf, S. and Nicol, F. (eds.) Proceedings of PLEA 2017, Edinburgh, 3rd-5th July 2017, Vol.3, pp.3786-3793.

Publisher

© PLEA

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

2017-04-29

Publication date

2017

Notes

This paper was presented at PLEA 2017 and is also available at http://plea-arch.org/plea-proceedings/.

ISBN

9780992895754

Language

  • en

Location

Edinburgh