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research article

Data for Urban Scale Building Energy Modelling: Assessing Impacts and Overcoming Availability Challenges

Goy, Solène
•
Maréchal, François  
•
Finn, Donal
August 17, 2020
Energies

Data are essential to urban building energy models and yet, obtaining sufficient and accurate building data at a large-scale is challenging. Previous studies have highlighted that the data impact on urban case studies has not been sufficiently discussed. This paper addresses this gap by providing an analysis of the impact of input data on building energy modelling at an urban scale. The paper proposes a joint review of data impact and data accessibility to identify areas where future survey efforts should be concentrated. Moreover, a Morris sensitivity analysis is carried out on a large-scale residential case study, to rank input parameters by impact on space heating demand. This paper shows that accessible data impact the whole modelling process, from approach selection to model replicability. The sensitivity analysis shows that the setpoint and thermal characteristics were the most impactful for the case study considered. Solutions proposed to overcome availability and accessibility issues include organising annual workshops between data users and data owners, or developing online databases that could be populated on a volunteer-basis by data owners. Overall, overcoming data challenges is essential for the transition towards smarter cities, and will require an improved communication between all city stakeholders.

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Type
research article
DOI
10.3390/en13164244
Author(s)
Goy, Solène
Maréchal, François  
Finn, Donal
Date Issued

2020-08-17

Published in
Energies
Volume

13

Issue

16

Article Number

4244

Subjects

Data

•

Building

•

Modelling

•

Energy

•

Urban scale

•

Sensitivity analysis

Note

This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution.

Editorial or Peer reviewed

REVIEWED

Written at

EPFL

EPFL units
SCI-STI-FM  
Available on Infoscience
August 24, 2020
Use this identifier to reference this record
https://infoscience.epfl.ch/handle/20.500.14299/171063
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