The University of Southampton
University of Southampton Institutional Repository

Occupancy profile variation analyzed through generative modelling to control building energy behavior

Occupancy profile variation analyzed through generative modelling to control building energy behavior
Occupancy profile variation analyzed through generative modelling to control building energy behavior

Nowadays, building energy models use parametric analyses to optimize design strategies considering multiple variables. Integrated dynamic models combining design tool and visual programming language (VPL) and simulation tools to calculate building performance with BIM tool for the whole-building energy simulation have been adopted in the recent studies. Through these tools, it is possible to identify parametric systems, which become a "genome", where a rapid comparison of different alternatives is possible through fitness criteria defined by design goals. The aim of the paper is to use this concept and the suitable parametric tools such as Grasshopper for Rhinoceros to handle variable hypotheses on users' occupancy that influence building energy performance. The paper focuses on occupancy variability applying the methodology to a university building located in northern Italy in the University of Brescia Campus to evaluate how generative modelling can represent an adequate approach to energy simulation of occupant behaviour. Sensors are now monitoring the real occupancy trend of the case study building and different scenarios defined in the parametric model could be compared to the real weekly. Using parametric tool and GA (Genetic Algorithms) can be analysed hundreds of occupancy patterns in order to better understand the influence of the occupancy on the building energy use and at the same time evaluate different strategies to save energy.

energy behaviour, generative modelling, Occupancy profiles, parametric analysis
1495-1505
Zani, Andrea
ca13a6e5-4d53-43dc-bd25-f88a0709c48b
Tagliabue, Lavinia C.
30e84a7d-5ac8-47fc-9a45-10233778402a
Poli, Tiziana
8118fcbf-e043-4925-9468-4c4b6d82247f
Ciribini, Angelo L.C.
c895dc29-b5a2-4db7-ab68-04a3a6c79704
De Angelis, Enrico
ea55c031-024d-4b1e-a1cb-5a6d97bb1d6a
Manfren, Massimiliano
f2b8c02d-cb78-411d-aed1-c4d056365392
Zani, Andrea
ca13a6e5-4d53-43dc-bd25-f88a0709c48b
Tagliabue, Lavinia C.
30e84a7d-5ac8-47fc-9a45-10233778402a
Poli, Tiziana
8118fcbf-e043-4925-9468-4c4b6d82247f
Ciribini, Angelo L.C.
c895dc29-b5a2-4db7-ab68-04a3a6c79704
De Angelis, Enrico
ea55c031-024d-4b1e-a1cb-5a6d97bb1d6a
Manfren, Massimiliano
f2b8c02d-cb78-411d-aed1-c4d056365392

Zani, Andrea, Tagliabue, Lavinia C., Poli, Tiziana, Ciribini, Angelo L.C., De Angelis, Enrico and Manfren, Massimiliano (2017) Occupancy profile variation analyzed through generative modelling to control building energy behavior. Procedia Engineering, 180, 1495-1505. (doi:10.1016/j.proeng.2017.04.312).

Record type: Article

Abstract

Nowadays, building energy models use parametric analyses to optimize design strategies considering multiple variables. Integrated dynamic models combining design tool and visual programming language (VPL) and simulation tools to calculate building performance with BIM tool for the whole-building energy simulation have been adopted in the recent studies. Through these tools, it is possible to identify parametric systems, which become a "genome", where a rapid comparison of different alternatives is possible through fitness criteria defined by design goals. The aim of the paper is to use this concept and the suitable parametric tools such as Grasshopper for Rhinoceros to handle variable hypotheses on users' occupancy that influence building energy performance. The paper focuses on occupancy variability applying the methodology to a university building located in northern Italy in the University of Brescia Campus to evaluate how generative modelling can represent an adequate approach to energy simulation of occupant behaviour. Sensors are now monitoring the real occupancy trend of the case study building and different scenarios defined in the parametric model could be compared to the real weekly. Using parametric tool and GA (Genetic Algorithms) can be analysed hundreds of occupancy patterns in order to better understand the influence of the occupancy on the building energy use and at the same time evaluate different strategies to save energy.

Text
1-s2.0-S1877705817318192-main - Version of Record
Download (393kB)

More information

e-pub ahead of print date: 23 May 2017
Published date: 2017
Keywords: energy behaviour, generative modelling, Occupancy profiles, parametric analysis

Identifiers

Local EPrints ID: 414002
URI: http://eprints.soton.ac.uk/id/eprint/414002
PURE UUID: 53f4ae25-ef23-4d7a-8cdc-308283b1dd7e
ORCID for Massimiliano Manfren: ORCID iD orcid.org/0000-0003-1438-970X

Catalogue record

Date deposited: 12 Sep 2017 16:31
Last modified: 16 Mar 2024 04:29

Export record

Altmetrics

Contributors

Author: Andrea Zani
Author: Lavinia C. Tagliabue
Author: Tiziana Poli
Author: Angelo L.C. Ciribini
Author: Enrico De Angelis

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×