A generalization approach for reduced order modelling of commercial buildings

Mohammad Haris Shamsi; Walter O'Grady; Usman Ali; James O'Donnell

doi:10.1016/j.egypro.2017.07.401

A generalization approach for reduced order modelling of commercial buildings

Mohammad Haris Shamsi
, Walter O'Grady
, Usman Ali
, James O'Donnell

Research output: Contribution to journal › Conference article › peer-review

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.

Original language	English
Pages (from-to)	901-906
Number of pages	6
Journal	Energy Procedia
Volume	122
DOIs	https://doi.org/10.1016/j.egypro.2017.07.401
Publication status	Published - 2017
Externally published	Yes
Event	International Conference on Future Buildings and Districts - Energy Efficiency from Nano to Urban Scale, CISBAT 2017 - Lausanne, Switzerland Duration: 6 Sep 2017 → 8 Sep 2017

Keywords

building simulation
energy management
grey-box models
optimization models
RC network models

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1016/j.egypro.2017.07.401

Cite this

@article{3234587803af4d23bd4049f97d84e0d0,

title = "A generalization approach for reduced order modelling of commercial buildings",

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.",

keywords = "building simulation, energy management, grey-box models, optimization models, RC network models",

author = "Shamsi, \{Mohammad Haris\} and Walter O'Grady and Usman Ali and James O'Donnell",

note = "Publisher Copyright: {\textcopyright} 2017 The Authors. Published by Elsevier Ltd.; International Conference on Future Buildings and Districts - Energy Efficiency from Nano to Urban Scale, CISBAT 2017 ; Conference date: 06-09-2017 Through 08-09-2017",

year = "2017",

doi = "10.1016/j.egypro.2017.07.401",

language = "English",

volume = "122",

pages = "901--906",

journal = "Energy Procedia",

issn = "1876-6102",

}

TY - JOUR

T1 - A generalization approach for reduced order modelling of commercial buildings

AU - Shamsi, Mohammad Haris

AU - O'Grady, Walter

AU - Ali, Usman

AU - O'Donnell, James

PY - 2017

Y1 - 2017

N2 - 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.

AB - 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.

KW - building simulation

KW - energy management

KW - grey-box models

KW - optimization models

KW - RC network models

UR - https://www.scopus.com/pages/publications/85029894538

U2 - 10.1016/j.egypro.2017.07.401

DO - 10.1016/j.egypro.2017.07.401

M3 - Conference article

AN - SCOPUS:85029894538

SN - 1876-6102

VL - 122

SP - 901

EP - 906

JO - Energy Procedia

JF - Energy Procedia

T2 - International Conference on Future Buildings and Districts - Energy Efficiency from Nano to Urban Scale, CISBAT 2017

Y2 - 6 September 2017 through 8 September 2017

ER -

A generalization approach for reduced order modelling of commercial buildings

Abstract

Keywords

UN SDGs

Access to Document

Other files and links

Fingerprint

Cite this