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dc.contributor.author
Bre, Facundo
dc.contributor.author
Silva, Arthur Santos
dc.contributor.author
Ghisi, Enedir
dc.contributor.author
Fachinotti, Victor Daniel
dc.date.available
2018-03-08T18:45:41Z
dc.date.issued
2016-12
dc.identifier.citation
Bre, Facundo; Silva, Arthur Santos; Ghisi, Enedir; Fachinotti, Victor Daniel; Residential building design optimisation using sensitivity analysis and genetic algorithm; Elsevier Science Sa; Energy and Buildings; 133; 12-2016; 853-866
dc.identifier.issn
0378-7788
dc.identifier.uri
http://hdl.handle.net/11336/38286
dc.description.abstract
The objective of this paper is to combine sensitivity analysis and simulation-based optimisation in order to optimise the thermal and energy performance of residential buildings in the Argentine Littoral region. An actual house was selected as case study. This is a typical, local, single-family house having some rooms conditioned only by natural ventilation, and other rooms with natural ventilation supplemented by mechanical air-conditioning (hybrid ventilation). Hence, the total degree-hours at the naturally ventilated living room and the total energy consumption by air-conditioning at the bedrooms were chosen as objective functions to be minimised. The global objective function characterising the thermal and energy performance of the house was defined as the weighted sum of these objective functions. This objective function was computed using the EnergyPlus building performance simulation programme. Then, we performed a sensitivity analysis using the Morris screening method to rank the influence of the design variables on the objective function. This showed that the type of external walls, the windows infiltration rate and the solar azimuth were the most influential design variables on the given objective function for the considered house, and also that the azimuth either had a highly nonlinear effect on the objective function or was highly correlated to the others variables, deserving in any case a finer discretisation. Finally, we solved an optimisation problem using genetic algorithms in order to find the optimal set of design variables for the considered house. The results highlighted the efficiency and the effectiveness of the proposed methodology to redesign a typical house in the Argentine Littoral region, improving hugely its thermal and energy performance.
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
Elsevier Science Sa
dc.rights
info:eu-repo/semantics/restrictedAccess
dc.rights.uri
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.subject
Energy Consumption
dc.subject
Energyplus
dc.subject
Genetic Algorithms
dc.subject
Hybrid Ventilation
dc.subject
Multi-Objective Optimisation
dc.subject
Residential Building
dc.subject
Sensitivity Analysis
dc.subject.classification
Otras Ingeniería Civil
dc.subject.classification
Ingeniería Civil
dc.subject.classification
INGENIERÍAS Y TECNOLOGÍAS
dc.subject.classification
Ciencias de la Computación
dc.subject.classification
Ciencias de la Computación e Información
dc.subject.classification
CIENCIAS NATURALES Y EXACTAS
dc.title
Residential building design optimisation using sensitivity analysis and genetic algorithm
dc.type
info:eu-repo/semantics/article
dc.type
info:ar-repo/semantics/artículo
dc.type
info:eu-repo/semantics/publishedVersion
dc.date.updated
2018-03-07T15:54:41Z
dc.journal.volume
133
dc.journal.pagination
853-866
dc.journal.pais
Países Bajos
dc.journal.ciudad
Amsterdam
dc.description.fil
Fil: Bre, Facundo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Centro de Investigaciones en Métodos Computacionales. Universidad Nacional del Litoral. Centro de Investigaciones en Métodos Computacionales; Argentina. Universidad Tecnológica Nacional. Facultad Regional Concepción del Uruguay; Argentina
dc.description.fil
Fil: Silva, Arthur Santos. Universidade Federal de Santa Catarina; Brasil
dc.description.fil
Fil: Ghisi, Enedir. Universidade Federal de Santa Catarina; Brasil
dc.description.fil
Fil: Fachinotti, Victor Daniel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Centro de Investigaciones en Métodos Computacionales. Universidad Nacional del Litoral. Centro de Investigaciones en Métodos Computacionales; Argentina
dc.journal.title
Energy and Buildings
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/http://www.sciencedirect.com/science/article/pii/S0378778816312440
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.1016/j.enbuild.2016.10.025


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