Garwood, T.L., Hughes, B.R., O'Connor, D. orcid.org/0000-0003-2861-1842 et al. (3 more authors) (2017) Geometry Extraction for High Resolution Building Energy Modelling Applications from Point Cloud Data: A Case Study of a Factory Facility. In: Energy Procedia. 9th International Conference on Applied Energy, ICAE 2017, 21-24 Aug 2017, Cardiff, UK. Elsevier , pp. 1805-1810.
Abstract
The industrial sector accounts for 17% of end-use energy in the UK, and 54% globally. Therefore, there is substantial scope for simulating and assessing potential energy retrofit options for industrial buildings. Building Energy Modelling (BEM) applied to industrial buildings p oses a complex but important opportunity for reducing global energy demand, due to years of renovation and expansion. Large and complex industrial buildings make modelling existing geometry for BEM difficult and time consuming. This paper presents a potential solution for quickly capturing and processing as-built geometry of a factory to be utilized in BEM. Laser scans were captured from the interior of an industrial facility to produce a Point Cloud. The existing capabilities of a Point Cloud processing software were assessed to identify the potential development opportunities to automate the conversion of Point Clouds to building geometry for BEM applications. In conclusion, scope exists for increasing the speed of 3D geometry creation of an existing industrial building for application in BEM and subsequent thermal simulation.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2017 The Author(s). Published by Elsevier Ltd. Available under a Creative Commons license http://creativecommons.org/licenses/by-nc-nd/4.0/ |
Keywords: | Building Energy Modelling; Point Cloud; Manufacturing; Industrial Energy Demand; Thermal Simulation |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Mechanical Engineering (Sheffield) |
Funding Information: | Funder Grant number UNIVERSITY OF SHEFFIELD UNSPECIFIED BMW UNSPECIFIED |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 27 Feb 2018 15:36 |
Last Modified: | 19 Dec 2022 12:53 |
Published Version: | https://doi.org/10.1016/j.egypro.2017.12.567 |
Status: | Published |
Publisher: | Elsevier |
Refereed: | Yes |
Identification Number: | 10.1016/j.egypro.2017.12.567 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:127737 |