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
2017
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, .
(doi:10.1016/j.proeng.2017.04.312).
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
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e-pub ahead of print date: 23 May 2017
Published date: 2017
Keywords:
energy behaviour, generative modelling, Occupancy profiles, parametric analysis
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Local EPrints ID: 414002
URI: http://eprints.soton.ac.uk/id/eprint/414002
PURE UUID: 53f4ae25-ef23-4d7a-8cdc-308283b1dd7e
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Date deposited: 12 Sep 2017 16:31
Last modified: 06 Jun 2024 01:59
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Contributors
Author:
Andrea Zani
Author:
Lavinia C. Tagliabue
Author:
Tiziana Poli
Author:
Angelo L.C. Ciribini
Author:
Enrico De Angelis
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