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  5. A generalization approach for reduced order modelling of commercial buildings
 
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A generalization approach for reduced order modelling of commercial buildings

Author(s)
Shamsi, Mohammad Haris  
O'Grady, Walter  
Ali, Usman  
O'Donnell, James  
Uri
http://hdl.handle.net/10197/11005
Date Issued
2017-09
Date Available
2019-08-20T10:32:07Z
Abstract
Grey-box techniques can counter the computational inefficiency and resource-intensive nature of the conventional complex white-box models. However, these approaches might tend to be too specific in their application and scalability is limited by network order. To overcome these challenges, this study proposes a generalized approach for selection of reduced-order RC network models for commercial buildings using the peak power consumption characterization. The devised methodology is used to design the RC networks of buildings connected to district heating network at University College Dublin. The close proximity between measured and simulated demand indicate the influence of power demand on RC network selection.
Sponsorship
Science Foundation Ireland
Type of Material
Conference Publication
Publisher
Elsevier BV
Volume
122
Start Page
901
End Page
906
Copyright (Published Version)
2017 the Authors
Subjects

Grey-box models

RC network models

Energy management

Optimization models

Building simulation

DOI
10.1016/j.egypro.2017.07.401
Language
English
Status of Item
Not peer reviewed
Journal
Scartezzini, J-L. (ed.). CISBAT 2017 International Conference Future Buildings & Districts – Energy Efficiency from Nano to Urban Scale
Conference Details
CISBAT 2017 International Conference – Future Buildings & Districts – Energy Efficiency from Nano to Urban Scale, Lausanne, Switzerland, 6-8 September 2017
ISSN
1876-6102
This item is made available under a Creative Commons License
https://creativecommons.org/licenses/by-nc-nd/3.0/ie/
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2017_Haris_CISBAT_2017.pdf

Size

404.98 KB

Format

Adobe PDF

Checksum (MD5)

5b5466972a9ce9eaae96d327d9ba1686

Owning collection
Mechanical & Materials Engineering Research Collection
Mapped collections
Energy Institute Research Collection

Item descriptive metadata is released under a CC-0 (public domain) license: https://creativecommons.org/public-domain/cc0/.
All other content is subject to copyright.

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