MULTI-VARIABLE OPTIMIZATION MODELS FOR BUILDING ENVELOPE DESIGN USING ENERGYPLUS SIMULATION AND METAHEURISTIC ALGORITHMS

GRYGIEREK, Krzysztof; FERDYN-GRYGIEREK, Joanna

doi:10.21307/acee-2019-025

Abstract

The paper presents the method of optimal design of the building envelope. The influence of four types of windows, their size, building orientation, insulation of external walls, ceiling to unheated attic and ground floor on the life cycle costs in a single-family building in Polish climate conditions is analyzed. The optimization procedure is developed by means of the coupling between MATLAB and EnergyPlus. The results using three metaheuristic methods: genetic algorithms, particle swarm optimization, and algorithm based on teaching and learning are compared. The analyses have shown the possibility of reducing the life cycle costs through the optimal selection of the building structure. The high initial investment (above the required standard) pays off in the long run when using a building.

References

Gervásio, H., Santos, P., Martins, R., & Simões da Silva, L. (2014). A macro-component approach for the assessment of building sustainability in early stages of design. Building and Environment, 73, 256–270.
Search in Google Scholar Back to article
Tuhus-Dubrow D., & Krarti M. (2010). Genetic-algorithm based approach to optimize building envelope design for residential buildings. Building and Environment, 45, 1574–1581.
Search in Google Scholar Back to article
Kapsalaki, M., Leal, V., & Santamouris, M. (2012). A methodology for economic efficient design of Net Zero Energy Buildings. Energy and Buildings, 55, 765–778.
Search in Google Scholar Back to article
Garber, R. (2009). Optimisation stories: the impact of building information modelling on contemporary design practice. Architectural Design, 79, 6–13.
Search in Google Scholar Back to article
Nguyen, A.T., Reiter, S., & Rigo, P. (2014). A review on simulation-based optimization methods applied to building performance analysis. Applied Energy, 113, 1043–1058.
Search in Google Scholar Back to article
Prianto, E., & Depecker, P. (2003). Optimization of architectural design elements in tropical humid region with thermal comfort approach. Energy and Buildings, 35, 273–280.
Search in Google Scholar Back to article
Wang, L., Wong Nyuk, H., & Li, S. (2007). Facade design optimization for naturally ventilated residential buildings in Singapore. Energy and Buildings, 39, 954–961.
Search in Google Scholar Back to article
Bambrook, S.M., Sproul, A.B., & Jacob, D. (2011). Design optimisation for a low energy home in Sydney. Energy and Buildings, 43, 1702–1711.
Search in Google Scholar Back to article
Ascione, F., Bianco, N., De Stasio, C., Mauro, G.M., & Vanoli, G.P. (2015). A new methodology for cost-optimal analysis by means of the multi-objective optimization of building energy performance. Energy and Buildings, 88, 78–90.
Search in Google Scholar Back to article
Ferrara, M., Fabrizio, E., Virgone, J., & Filippi, M. (2014). A simulation-based optimization method for cost-optimal analysis of nearly zero energy buildings. Energy and Buildings, 84, 442–457.
Search in Google Scholar Back to article
Wang, B., Xia, X., & Zhang, J. (2014). A multi-objective optimization model for the life-cycle cost analysis and retrofitting planning of buildings. Energy and Buildings, 77, 227–235.
Search in Google Scholar Back to article
Storn, R., & Price, K. (1997). Differential evolution: a simple and efficient heuristic for global optimization over continuous spaces. Journal of Global Optimization, 11, 341–359.
Search in Google Scholar Back to article
Ascione, F., Bianco, N., De Stasio, C., Mauro, G.M., & Vanoli, G.P. (2016). Multi-stage and multi-objective optimization for energy retrofitting a developed hospital reference building: a new approach to assess cost-optimality. Applied Energy, 174, 37–68.
Search in Google Scholar Back to article
Hamdy, M., Hasan, A., & Siren, K. (2013). A multistage optimization method for cost-optimal and nearly-zero-energy building solutions in line with the EPBD-Recast 2010. Energy and Buildings, 56, 189–203.
Search in Google Scholar Back to article
Malatji, E.M., Zhang, J., & Xia, X. (2013). A multiple objective optimisation model for building energy efficiency investment decision. Energy and Buildings, 61, 81–87.
Search in Google Scholar Back to article
Yi, Y.K., & Malkawi, A. (2009). Optimizing building form for energy performance based on hierarchical geometry relation. Automation in Construction, 18, 825–833.
Search in Google Scholar Back to article
Wang, W., Rivard, H., & Zmeureanu, R. (2006). Floor shape optimization for green building design. Advanced Engineering Informatics, 20, 363–378.
Search in Google Scholar Back to article
Wang, W., Zmeureanu, R., & Rivard, H. (2005). Applying multi-objective genetic algorithms in green building design optimization. Building and Environment, 40, 1512–1525.
Search in Google Scholar Back to article
Wright, J. (2002). Optimization of building thermal design and control by multi-criterion genetic algorithm. Energy and Buildings, 34, 959–972.
Search in Google Scholar Back to article
Caldas, L.G., & Norford, L.K. (2003). Genetic algorithms for optimization of building envelopes and the design and control of HVAC systems. Journal of Solar Energy Engineering, 125, 343–351.
Search in Google Scholar Back to article
Harmathy, N., Magyar, Z., & Folic, R. (2016). Multi-criterion optimization of building envelope in the function of indoor illumination quality towards overall energy performance improvement. Energy, 114, 302–317.
Search in Google Scholar Back to article
Gasparella, A., Pernigotto, G., Cappelletti, F., Romagnoni, P., & Baggio, P. (2011). Analysis and modelling of window and glazing systems energy performance for a well insulated residential building. Energy and Buildings, 43, 1030–1037.
Search in Google Scholar Back to article
Tuhus-Dubrow, D., & Krarti, M. (2010). Genetic-algorithm based approach to optimize building envelope design for residential buildings. Building and Environment, 45, 1574–1581.
Search in Google Scholar Back to article
Magnier. L., & Haghighat. F. (2010). Multiobjective optimization of building design using TRNSYS simulations, genetic algorithm and Artificial Neural Network. Building and Environment, 45, 739–746.
Search in Google Scholar Back to article
Znouda. E., Ghrab-Morcos. N., & Hadj-Alouane. A. (2007). Optimization of Mediterranean building design using genetic algorithms. Energy and Buildings, 39, 148–153.
Search in Google Scholar Back to article
Ferdyn-Grygierek, J., & Grygierek, K. (2017). Multi-variable optimization of building thermal design using genetic algorithms. Energies, 10, 1570.
Search in Google Scholar Back to article
Grygierek, K., & Ferdyn-Grygierek, J. (2018). Multi-objective optimization of the envelope of building with natural ventilation. Energies, 11, 1383.
Search in Google Scholar Back to article
Bichiou, Y., & Krarti, M. (2011). Optimization of envelope and HVAC systems selection for residential buildings. Energy and Buildings, 43, 3373–3382.
Search in Google Scholar Back to article
Ferdyn-Grygierek, J., & Grygierek, K. (2017). Optimization of window size design for detached house using TRNSYS simulations and genetic algorithm. Architecture Civil Engineering Environment, 10(4), 133–140.
Search in Google Scholar Back to article
Ihm, P., & Krarti, M. (2012). Design optimization of energy efficient residential buildings in Tunisia. Building and Environment, 58, 81–90.
Search in Google Scholar Back to article
Ferrara, M., Fabrizio, E., Virgone, J., & Filippi, M. (2014). A simulation-based optimization method for cost-optimal analysis of nearly Zero Energy Buildings. Energy and Buildings, 84, 442–457.
Search in Google Scholar Back to article
Ascione, F., Bianco, N., De Stasio, C., Mauro, G.M., & Vanoli, G.P. (2016). Simulation-based model predictive control by the multi-objective optimization of building energy performance and thermal comfort. Energy and Buildings, 111, 131–44.
Search in Google Scholar Back to article
Azari, R., Garshasbi, S., Amini, P., Rashed-Ali, H., & Mohammadi, Y. (2016). Multi-objective optimization of building envelope design for life cycle environmental performance. Energy and Buildings, 126, 524–534.
Search in Google Scholar Back to article
Grygierek, K., & Ferdyn-Grygierek, J. (2018). Multi-objectives optimization of ventilation controllers for passive cooling in residential buildings. Sensors, 18, 1144.
Search in Google Scholar Back to article
Carreras, J., Pozo, C., Boer, D., Guillén-Gosálbez, G., Caballerod, J.A., Ruiz-Femeniad, R., & Jiménez, L. (2016). Systematic approach for the life cycle multi-objective optimization of buildings combining objective reduction and surrogate modeling. Energy and Buildings, 130, 506–518.
Search in Google Scholar Back to article
Ascione, F., Bianco, N., De Stasio, C., Mauro, G.M., & Vanoli, G.P. (2017). CASA, cost-optimal analysis by multi-objective optimisation and artificial neural networks: A new framework for the robust assessment of cost-optimal energy retrofit, feasible for any building. Energy and Buildings, 146, 200–219.
Search in Google Scholar Back to article
Development of a new methodology to optimize building life cycle cost, environmental impacts, and occupant satisfaction. Energy, 121, 606–615.
Search in Google Scholar Back to article
EnergyPlus™ Version 8.7 Documentation; US Department of Energy: Washington, DC, USA, 2016. Available online: https://energyplus.net/sites/all/modules/custom/nrel_custom/pdfs/pdfs_v8.7.0/EngineeringReference.pdf (accessed on 12 February 2018)
Search in Google Scholar Back to article
Polish Standard PN-83/B-03430/Az3: 2000 Ventilation in dwellings and public utility buildings (in Polish).
Search in Google Scholar Back to article
THERM 6.3 / WINDOW 6.3 NFRC Simulation Manual, Lawrence Berkeley National Laboratory, July 2013. Available online: https://windows.lbl.gov/software/NFRC/SimMan/NFRCSim6.3-2013-07-Manual.pdf.
Search in Google Scholar Back to article
Regulation of the Minister of Infrastructure of 12 April 2002 on the Technical Conditions That Should Be Met by Buildings and Their Location (Journal of Laws 2002, No 75, item. 690, with recast). Polish Ministry of Infrastructure: Warsaw, Poland, 2002 (In Polish).
Search in Google Scholar Back to article
Hasan, A., Vuolle, M., & Siren, K. (2008). Minimisation of life cycle cost of a detached house using combined simulation and optimization. Building and Environment, 43, 2022–2034.
Search in Google Scholar Back to article
Regulation of the Minister of Infrastructure of 27 February 2015 on the methodology for calculating the energy performance of a building or part of a building and energy performance certificates (Journal of Laws, 2015, item. 376). Polish Ministry of Infrastructure: Warsaw, Poland, 2015 (in Polish).
Search in Google Scholar Back to article
Directive 2010/31/EU of the European Parliament and the Council of the European Union of the 19 May 2010 on the Energy Performance of Buildings. Official Journal of the European Communities. Brussels: European Parliament and the Council of the European Union. 2010, 13–35.
Search in Google Scholar Back to article
Commission Delegated Regulation (EU) No 244/2012 of 16 January 2012 supplementing Directive 2010/31/EU of the European Parliament and of the Council on the energy performance of buildings by establishing a comparative methodology framework for calculating cost-optimal levels of minimum energy performance requirements for buildings. Official Journal of the European Union 55, 2012, 18–36.
Search in Google Scholar Back to article
Grygierek K. (2016). Optimization of trusses with self-adaptive approach in genetic algorithms. Architecture Civil Engineering Environment, 9(4), 67–78.
Search in Google Scholar Back to article
Goldberg, D.E. (1989). Genetic algorithms in search, optimization and machine learning. Addison-Wesley Publishing Company, Inc., ISBN 978-0201157673.
Search in Google Scholar Back to article
Deep, K., Singh, K.P., Kansal, M.L., & Mohan, C.A. (2009). Real coded genetic algorithm for solving integer and mixed integer optimization problems. Applied Mathematics and Computation, 212, 505–518.
Search in Google Scholar Back to article
Kennedy, J., & Eberhart, R. (1995). Particle swarm optimization. In: IEEE international conference on neural networks, Vol.4. IEEE Press, 1942–1948.
Search in Google Scholar Back to article
Hasançebi, O., Çarbas, S., Dogan, E., Erdal, F., & Saka, M.P. (2009). Performance evaluation of meta-heuristic search techniques in the optimum design of real size pin jointed structures. Computers and Structures, 87, 284–302.
Search in Google Scholar Back to article
Rao, R.V., Savsani, V.J., & Vakharia, D.P. (2011). Teaching-learning-based optimization: A novel method for constrained mechanical design optimization problems. Computer-Aided Design, 43, 303–315.
Search in Google Scholar Back to article

MULTI-VARIABLE OPTIMIZATION MODELS FOR BUILDING ENVELOPE DESIGN USING ENERGYPLUS SIMULATION AND METAHEURISTIC ALGORITHMS

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