Multi-objective optimization of the solar orientation of two residential multifamily buildings in south Brazil

[Display omitted] •A low-cost energy demand optimization strategy is analyzed.•Optimizes heating demand, without neglecting cooling, for colder climates.•Simplified optimization is used to reduce computational costs.•The optimization method was developed in open-access software.•Impact of the urban...

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Published inEnergy and buildings Vol. 285; p. 112838
Main Authors Benincá, Letiane, Crespo Sánchez, Eva, Passuello, Ana, Karini Leitzke, Rodrigo, Grala da Cunha, Eduardo, Maria González Barroso, José
Format Journal Article
LanguageEnglish
Published Elsevier B.V 15.04.2023
Subjects
Online AccessGet full text
ISSN0378-7788
1872-6178
DOI10.1016/j.enbuild.2023.112838

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Abstract [Display omitted] •A low-cost energy demand optimization strategy is analyzed.•Optimizes heating demand, without neglecting cooling, for colder climates.•Simplified optimization is used to reduce computational costs.•The optimization method was developed in open-access software.•Impact of the urban context inserted into the optimization process.•Demonstrates energy discrimination in the same building. The shape and orientation of a building influence the energy demand, therefore optimal decisions should only be made rigorously supported by energy evaluation programs, which allow for measuring the energy demand of a building more precisely. The main purpose of this research is to evaluate the shape and orientation of massive residential social housing multifamily buildings to find the best solar positioning to minimize cooling and heating demands simultaneously in the bioclimatic zone 2 (Cfa) in the southern region of Brazil. To do this, this study utilizes multi-objective optimization with a genetic algorithm (NSGA-II) simulating the thermal behavior in EnergyPlus and performing the optimization with a Python language programming code, totalizing 80,000 simulations. The main results showed that solar orientation optimization could reduce the total demand by 4% for the “H” shape and 22% for linear buildings in the isolated scenario. For the condominium condition, the reduction is 2% for the “H” typology and 8% for the linear shape. The results presented can help engineers and architects to design more energy-efficient buildings and address the energetic vulnerability in the same building. Moreover, future work can be carried out to improve the constructive pattern replicated all over the country, improving the surroundings.
AbstractList [Display omitted] •A low-cost energy demand optimization strategy is analyzed.•Optimizes heating demand, without neglecting cooling, for colder climates.•Simplified optimization is used to reduce computational costs.•The optimization method was developed in open-access software.•Impact of the urban context inserted into the optimization process.•Demonstrates energy discrimination in the same building. The shape and orientation of a building influence the energy demand, therefore optimal decisions should only be made rigorously supported by energy evaluation programs, which allow for measuring the energy demand of a building more precisely. The main purpose of this research is to evaluate the shape and orientation of massive residential social housing multifamily buildings to find the best solar positioning to minimize cooling and heating demands simultaneously in the bioclimatic zone 2 (Cfa) in the southern region of Brazil. To do this, this study utilizes multi-objective optimization with a genetic algorithm (NSGA-II) simulating the thermal behavior in EnergyPlus and performing the optimization with a Python language programming code, totalizing 80,000 simulations. The main results showed that solar orientation optimization could reduce the total demand by 4% for the “H” shape and 22% for linear buildings in the isolated scenario. For the condominium condition, the reduction is 2% for the “H” typology and 8% for the linear shape. The results presented can help engineers and architects to design more energy-efficient buildings and address the energetic vulnerability in the same building. Moreover, future work can be carried out to improve the constructive pattern replicated all over the country, improving the surroundings.
ArticleNumber 112838
Author Benincá, Letiane
Passuello, Ana
Karini Leitzke, Rodrigo
Maria González Barroso, José
Crespo Sánchez, Eva
Grala da Cunha, Eduardo
Author_xml – sequence: 1
  givenname: Letiane
  orcidid: 0000-0003-2854-757X
  surname: Benincá
  fullname: Benincá, Letiane
  email: benincalf@gmail.com
  organization: Architecture Technology Department, Barcelona School of Architecture (ETSAB), Universitat Politècnica de Catalunya (UPC), Barcelona, Spain
– sequence: 2
  givenname: Eva
  surname: Crespo Sánchez
  fullname: Crespo Sánchez, Eva
  email: eva.crespo@upc.edu
  organization: Architecture Technology Department, Sustainability and Metabolism in Architecture and Technology (SMART), Barcelona School of Architecture (ETSAB), Universitat Politècnica de Catalunya (UPC), Barcelona, Spain
– sequence: 3
  givenname: Ana
  orcidid: 0000-0003-2961-7787
  surname: Passuello
  fullname: Passuello, Ana
  email: ana.passuello@ufrgs.br
  organization: Postgraduate Program in Civil Engineering: Construction and Infrastructure (PPGCI), Federal University of Rio Grande do Sul (UFRGS), Brazil
– sequence: 4
  givenname: Rodrigo
  surname: Karini Leitzke
  fullname: Karini Leitzke, Rodrigo
  organization: Departament of Technology, Federal University of Pelotas (UFPel), Pelotas, Brazil
– sequence: 5
  givenname: Eduardo
  surname: Grala da Cunha
  fullname: Grala da Cunha, Eduardo
  email: eduardo.grala@ufpel.edu.br
  organization: Departament of Technology, Federal University of Pelotas (UFPel), Pelotas, Brazil
– sequence: 6
  givenname: José
  surname: Maria González Barroso
  fullname: Maria González Barroso, José
  organization: Architecture Technology Department, Barcelona School of Architecture (ETSAB), Universitat Politècnica de Catalunya (UPC), Barcelona, Spain
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Keywords EnergyPlus
NSGA-II
Genetic algorithm
Heating demand
Multi-objective optimization
Energy efficiency
Cooling demand
Python
Language English
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Snippet [Display omitted] •A low-cost energy demand optimization strategy is analyzed.•Optimizes heating demand, without neglecting cooling, for colder...
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StartPage 112838
SubjectTerms Cooling demand
Energy efficiency
EnergyPlus
Genetic algorithm
Heating demand
Multi-objective optimization
NSGA-II
Python
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Title Multi-objective optimization of the solar orientation of two residential multifamily buildings in south Brazil
URI https://dx.doi.org/10.1016/j.enbuild.2023.112838
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