CALIBRAÇÃO DE UM MODELO PREDITIVO PARA EFICIÊNCIA ENERGÉTICA EM EDIFÍCIOS DE ESCRITÓRIO
DOI:
https://doi.org/10.46421/entac.v17i1.1460Keywords:
Energy efficiency, Parametric simulation, Office buildings, BESTEST 600Abstract
The seek to supply the world energy demand has stimulated not only the search for alternative sources but also energy efficiency of existing systems. As buildings have a great impact on energy consumption, simulation tools can help select the most suitable strategies towards environmental comfort and energy efficiency. The objective of this research was to calibrate the Rhinoceros-Grasshopper parameterization platform for energy efficiency and thermal comfort study for office buildings. This platform was linked to EnergyPlus for thermalenergetic simulations of the designed model. For calibration, BESTEST Case 600 was simulated, and the model adjusted to validate the test. The results were compared to those accepted from ASHRAE 140-2014. Results showed energy demand for cooling of 6.342 kWh and for heating of 4.089 kWh, this last one close to the test limit but out of the minimum value required. Thus, the calibrated model demonstrated that the Rhinoceros-Grasshopper parametric software can aid in the design decisions regarding issues of thermal comfort and energy saving, in addition to increasing the possible number of satisfactory solutions for architects and engineers.
References
ASHRAE. Standard 90.1-2013: Energy standard for buildings except low-rise residentialbuildings. Atlanta, 2013.
______. Standard 140-2014: Method of Test for the Evaluation of Building Energy AnalysisComputer Programs. Atlanta, 2014.
ATTIA, S. et al. Simulation-based decision support tool for early stages of zero-energybuilding design. Energy and Buildings, v. 49, p. 2 15, jun. 2012.
BRASIL. MINISTÉRIO DE MINAS E ENERGIA. Balanço energético nacional 2017: Ano base2016. Rio de Janeiro: Empresa de Pesquisa Energética, 2017. Disponível em:<https://ben.epe.gov.br/downloads/Relatorio_Final_BEN_2017.pdf>. Acesso em 15 jan.2018.
CELANI, G. Além da avaliação energética e ambiental nas etapas iniciais doprocesso de projeto. PARC Pesquisa em Arquitetura e Construção, v. 3, n. 2, p. 56 64,2012.
ELBELTAGI, E. et al. Visualized strategy for predicting buildings energy consumptionduring early design stage using parametric analysis. Journal of Building Engineering, v.
13, n. August, p. 127 136, set. 2017.
EVINS, R. A review of computational optimisation methods applied to sustainablebuilding design. Renewable and Sustainable Energy Reviews, v. 22, p. 230 245, jun.2013.
KARSSIES, W. W. (WOUTER). Optimization workflow regarding daylighting, energy andglare, for performance assessment of new generation semi-transparent photovoltaicfaçades. 2017. 64 f. Dissertação (Mestrado) Eindhoven University of Technology,Eindhoven, 2017.
KIRIMTAT, A. et al. Review of simulation modeling for shading devices in buildings.
Renewable and Sustainable Energy Reviews, v. 53, p. 23 49, jan. 2016.
MARTINO, J. A. Algoritmos evolutivos como método para desenvolvimento de projetosde arquitetura. 2015. 310 f. Tese (Doutorado em Arquitetura, Tecnologia e Cidade)Faculdade de Engenharia Civil, Arquitetura e Urbanismo, Universidade Estadual deCampinas, Campinas, 2015.
MCNEEL EUROPE. Rhinoceros 5.0, 2017. Disponível em: <https://www.rhino3d.com/>NABONI, E. Integration of Outdoor Thermal and Visual Comfort in Parametric Design.
In: International PLEA Conference, 30, 2014, Ahmedabad. Ahmedabad: PLEA,2014.
NEGENDAHL, K.; NIELSEN, T. R. Building energy optimization in the early design stages: Asimplified method. Energy and Buildings, v. 105, p. 88 99, 2015.
NGUYEN, A. T.; REITER, S.; RIGO, P. A review on simulation-based optimization methodsapplied to building performance analysis. Applied Energy, v. 113, p. 1043 1058, 2014.
ØSTERGÅRD, T.; JENSEN, R. L.; MAAGAARD, S. E. Building simulations supporting decisionmaking in early design A review. Renewable and Sustainable Energy Reviews, v. 61,p. 187 201, ago. 2016.
PÉREZ-LOMBARD, L. et al. A review of benchmarking, rating and labelling conceptswithin the framework of building energy certification schemes. Energy and Buildings, v.
41, n. 3, p. 272 278, mar. 2009.
PICCO, M.; LOLLINI, R.; MARENGO, M. Towards energy performance evaluation in earlystage building design: A simplification methodology for commercial building models.
Energy and Buildings, v. 76, p. 497 505, jun. 2014.
ROUDSARI, M. S.; PAK, M. Ladybug: A Parametric Environmental Plugin for Grasshopperto Help Designers Create an Environmentally-Conscious Design. In: Conference ofInternational Building Performance Simulation Association, 13, 2013, Chambéry. Anais...France: IBPSA, 2013.
TOULOUPAKI, E.; THEODOSIOU, T. Energy performance optimization as a generativedesign tool for nearly Zero Energy Buildings. In: International High-Performance BuiltEnvironment Conference, 2016, Sydney. Anais... Sydney: iHBE, 2016.
YU, W. et al. Application of multi-objective genetic algorithm to optimize energyefficiency and thermal comfort in building design. Energy and Buildings, v. 88, p. 135143, 1 fev. 2015.
ZANI, A. et al. Computational Design and Parametric Optimization Approach withGenetic Algorithms of an Innovative Concrete Shading Device System. ProcediaEngineering, v. 180, p. 1473 1483, 2017.
ZHANG, L.; ZHANG, L.; WANG, Y. Shape optimization of free-form buildings based onsolar radiation gain and space efficiency using a multi-objective genetic algorithm inthe severe cold zones of China. Solar Energy, v. 132, p. 38 50, jul. 2016.
