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Automated dynamic thermal simulation of houses and housing stocks using readily available reduced data

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journal contribution
posted on 13.09.2019, 09:44 by Ali Badiei, David AllinsonDavid Allinson, Kevin LomasKevin Lomas
This paper describes a new method to swiftly model the dynamics of heating energy demand and indoor air temperatures of houses and housing stocks. The Reduced data Energy Model RdDEM) provides a cost-effective alternative to steady-state modelling by enhancing the input dataset from the Reduced data Standard Assessment Procedure (RdSAP) – the method used to calculate Energy Performance Certificates (EPC) in the UK. This eliminates the main drawbacks associated with dynamic thermal simulation (DTS) of housing stocks, namely the large amount of required input data and the significant time required to model each house. The RdDEM algorithms create RdSAP-equivalent geometry, construction, thermal mass and boundary conditions in Energy Plus DTS software. The new inferences and methodological enhancements were first tested and then implemented at scale using a sample of 83 semi detached houses. Most energy results from RdDEM were within 10% of those from RdSAP. The differences are explained by the different ways that indoor air temperature is calculated. The RdDEM method provides a dynamic alternative to RdSAP for understanding the dynamics of energy demand and indoor air temperatures in homes. This could include assessing the peak demand of a community energy scheme or assessing the summertime overheating risk in individual dwellings. Ultimately, it could provide a dynamic housing stock model using the data already collected from millions of houses to generate EPCs.

History

School

  • Architecture, Building and Civil Engineering

Published in

Energy and Buildings

Volume

203

Publisher

Elsevier

Version

VoR (Version of Record)

Rights holder

© The Authors

Publisher statement

This is an Open Access Article. It is published by Elsevier under the Creative Commons Attribution 4.0 International Licence (CC BY 4.0). Full details of this licence are available at: https://creativecommons.org/licenses/by/4.0/

Acceptance date

12/09/2019

Publication date

2019-09-12

Copyright date

2019

ISSN

0378-7788

eISSN

1872-6178

Language

en

Depositor

Dr David Allinson

Article number

109431