Document Type : Original Article


1 Department of Architecture, Birjand Branch, Islamic Azad University, Birjand, Iran

2 Department of Architecture, University of Science and Industry, Tehran, Iran


All important decisions that affect the thermal performance of the building are made in the early stages of design. Accordingly, in this research, the initial stage of architectural design which is related to space plan was targeted. The aim of this research is the perfect approach to evaluate, and optimize the energy a set of alternative spatial layout solutions through the functional computational design model. The method of this research includes the production of coherent design solutions and the evaluation and optimization of the energy performance of the selected solutions. In the first part, space allocation at a level produces the plan through an evolutionary technique. In the next step, certain plans were evaluated for energy performance, performance rank, and optimization. The energy simulation tool is Honeybee and Ladybug plugins,. The optimization tool is Pareto Evolutionary Algorithm in the Octopus plugin. The reproduction rate, the mutation rate and the possibility of mutation were 0.9, 0.8 and 0.2, respectively. The results showed that each algorithm is a suitable tool for design solutions, thermal performance of floor plans, helping architects’ perspective in the decision-making process, and speeding up the design process. Finally, based on the optimization, the final result of the research algorithm is 70 elite answers in the Pareto front. Only during the Pareto front optimal responses, energy consumption can be reduced by more than 30%; in daylight time and more than 39% improvement was achieved.


Main Subjects

  1. Janda, K., 2009. Worldwide status of energy standards for buildings: a 2009 update, Proceedings of the European Council for an Energy Efficient Economy (ECEEE) Summer Study, pp. 1-6.
  2. IEA – International Energy Agency, 2012. World Energy Outlook 2012. Accessed from ⟨⟩ in May 2013.
  3. Kanters, J., Horvat, M. and Dubois, M.-C., 2014. Tools and methods used by architects for solar design, Energy and Buildings, 68, pp. 721-731. Doi:10.1016/j.enbuild.2012.05.031
  4. Attia, S., Gratia, E., De Herde, A. and Hensen, J. L., 2012. Simulation-based decision support tool for early stages of zero-energy building design, Energy and Buildings, 49, pp. 2-15. Doi:10.1016/j.enbuild.2012.01.028
  5. Michalek, J., Choudhary, R. and Papalambros, P., 2002. Architectural layout design optimization, Engineering Optimization, 34(5), pp. 461-484. Doi:10.1080/03052150214016
  6. Du, T., Jansen, S., Turrin, M. and van den Dobbelsteen, A., 2020. Effects of architectural space layouts on energy performance: A review, Sustainability, 12(5), pp. 1829. Doi:10.3390/su12051829
  7. Ekici, B., Cubukcuoglu, C., Turrin, M. and Sariyildiz, I. S., 2019. Performative computational architecture using swarm and evolutionary optimisation: A review, Building and Environment, 147, pp. 356-371. Doi:10.1016/j.buildenv.2018.10.023
  8. Rodrigues, E., Gaspar, A. R. and Gomes, Á., 2014. Automated approach for design generation and thermal assessment of alternative floor plans, Energy and Buildings, 81, pp. 170-181. Doi:10.1016/j.enbuild.2014.06.016
  9. Wang, X.-Y., Yang, Y. and Zhang, K., 2018. Customization and generation of floor plans based on graph transformations, Automation in Construction, 94, pp. 405-416. Doi:10.1016/j.autcon.2018.07.017
  10. Yi, H., 2016. User-driven automation for optimal thermal-zone layout during space programming phases, Architectural Science Review, 59(4), pp. 279-306. Doi:10.1080/00038628.2015.1021747
  11. Sharpe, R., Marksjö, B., Mitchell, J. and Crawford, J., 1985. An interactive model for the layout of buildings, Applied Mathematical Modelling, 9(3), pp. 207-214. Doi:10.1016/0307-904X(85)90009-5
  12. Rodrigues, E., Gaspar, A. R. and Gomes, Á., 2013. An evolutionary strategy enhanced with a local search technique for the space allocation problem in architecture, Part 1: Methodology, Computer-Aided Design, 45(5), pp. 887-897. Doi:10.1016/j.cad.2013.01.001
  13. Dino, I. G. and Üçoluk, G., 2017. Multiobjective design optimization of building space layout, energy, and daylighting performance, Journal of Computing in Civil Engineering, 31(5), pp. 04017025. Doi:10.1061/(ASCE)CP.1943-5487.0000669
  14. Pacheco, R., Ordóñez, J. and Martínez, G., 2012. Energy efficient design of building: A review, Renewable and Sustainable Energy Reviews, 16(6), pp. 3559-3573. Doi:10.1016/j.rser.2012.03.045
  15. Gupta, R. and Ralegaonkar, R., 2004. Estimation of beam radiation for optimal orientation and shape decision of buildings in India, Architectural Journal of Institution of Engineers India, 85, pp. 27-32.
  16. Plörer, D., Hammes, S., Hauer, M., van Karsbergen, V. and Pfluger, R., 2021. Control strategies for daylight and artificial lighting in office buildings—A bibliometrically assisted review, Energies, 14(13), pp. 3852. Doi:10.3390/en14133852
  17. Amasyali, K. and El-Gohary, N. M., 2018. A review of data-driven building energy consumption prediction studies, Renewable and Sustainable Energy Reviews, 81, pp. 1192-1205. Doi:10.1016/j.rser.2017.04.095
  18. Wang, L., Greenberg, S., Fiegel, J., Rubalcava, A., Earni, S., Pang, X., Yin, R., Woodworth, S. and Hernandez-Maldonado, J., 2013. Monitoring-based HVAC commissioning of an existing office building for energy efficiency, Applied Energy, 102, pp. 1382-1390. Doi:10.1016/j.apenergy.2012.09.005
  19. Fernandez Bandera, C. and Ramos Ruiz, G., 2017. Towards a new generation of building envelope calibration, Energies, 10(12), pp. 2102. Doi:10.3390/en10122102
  20. Zuhaib, S., Hajdukiewicz, M. and Goggins, J., 2019. Application of a staged automated calibration methodology to a partially-retrofitted university building energy model, Journal of Building Engineering, 26, pp. 100866. Doi:10.1016/j.jobe.2019.100866
  21. Andrade-Cabrera, C., Burke, D., Turner, W. J. and Finn, D. P., 2017. Ensemble Calibration of lumped parameter retrofit building models using Particle Swarm Optimization, Energy and Buildings, 155, pp. 513-532. Doi:10.1016/j.enbuild.2017.09.035
  22. Ferrara, M., Lisciandrello, C., Messina, A., Berta, M., Zhang, Y. and Fabrizio, E., 2020. Optimizing the transition between design and operation of ZEBs: Lessons learnt from the Solar Decathlon China 2018 SCUTxPoliTo prototype, Energy and Buildings, 213, pp. 109824. Doi:10.1016/j.enbuild.2020.109824
  23. Carlon, E., Schwarz, M., Prada, A., Golicza, L., Verma, V. K., Baratieri, M., Gasparella, A., Haslinger, W. and Schmidl, C., 2016. On-site monitoring and dynamic simulation of a low energy house heated by a pellet boiler, Energy and Buildings, 116, pp. 296-306. Doi:10.1016/j.enbuild.2016.01.001
  24. Li, W., Tian, Z., Lu, Y. and Fu, F., 2018. Stepwise calibration for residential building thermal performance model using hourly heat consumption data, Energy and Buildings, 181, pp. 10-25. Doi:10.1016/j.enbuild.2018.10.001
  25. Trompoukis, X., Asouti, V., Kampolis, I. and Giannakoglou, K., 2012. CUDA implementation of Vertex-Centered, Finite Volume CFD methods on Unstructured Grids with Flow Control Applications, GPU Computing Gems Jade Edition: Elsevier, pp. 207-223. Doi:10.1016/B978-0-12-385963-1.00017-4
  26. Zitzler, E., Laumanns, M. and Thiele, L., 2001. SPEA2: Improving the strength Pareto evolutionary algorithm, TIK-report, 103. Doi:10.3929/ethz-a-004284029
  27. Salgueiro Sicilia, Y., Toro Pozo, J. L. and Bello Pérez, R., 2016. Evaluación del desempeño de la metaheurística MOVMO en funciones de prueba con restricciones, Revista Cubana de Ciencias Informáticas, 10(1), pp. 182-193. Available at:
  28. Ghasemzadeh, M., 2012. Dimensional criteria and design considerations of urban residential unit spaces. Tehran, Iran: Road, Housing and Urban Development Research Center.
  29. Amrhein, V., Greenland, S. and McShane, B., 2019. Scientists rise up against statistical significance, Nature, 567(7748), pp. 305-307.