A Novel Design-based Optimization Method for Building by Sensitivity Analysis

Document Type : Original Article

Authors

1 Faculty of Architecture and Urbanism, Imam Khomeini International University, Qazvin, Iran

2 Faculty of Architecture and Urban Development, Imam Khomeini International University, Qazvin, Iran

3 Department of architecture; Tarbiat Modares University; Tehran, Iran

4 Department of Architecture, Tarbiat Modares University, Tehran, Iran

10.22059/jser.2023.352184.1269

Abstract

The important objective of a building must be to provide a comfortable environment for people. Heating ventilation and air conditioning systems provide a comfortable environment but they have high energy consumption. Therefore, designing an energy-efficient building that balances energy performance and thermal comfort is necessary. Choosing effective parameters for energy performance is an important factor in achieving this goal. This research aims to produce a methodology for multi-objective optimization of daylight and thermal comfort in order to study the effect of wall material and shading of an office building (Tehran a basic-location). The building simulation was developed and validated by comparing predicted daylight and thermal comfort hours based on tests and training in Jupiter Notebook. The sensitivity analysis uses a multiple linear regression method. Secondly, optimization is based on a genetic algorithm with effective parameters to optimize daylight and thermal comfort performance. For this, we developed a parametric model using the Grasshopper plugin for Rhino and then used Honeybee and Ladybug plugins to simulate thermal comfort and daylight, and finally used Octopus engine to find an optimization solution. The result of this paper is essential as a preliminary analysis for building optimization in the open-plan office.

Keywords

  1. Raturi, A.K., Renewables 2019 global status report. 2019.
  2. Chiazor, M., The effects of energy efficient design and indoor envirnomental quality in new office buildings. 2009.
  3. Amleh, D., A. Halawani, and M.H. Hussein, Simulation-Based Study for Healing environment in intensive care units: enhancing daylight and access to view, optimizing an ICU room in temperate climate, the case study of Palestine. Ain Shams Engineering Journal, 2023. 14(2): p. 101868.
  4. EIA, U., Annual energy review 2011. DOE/EIA, 2011. 384.
  5. Moon, J.W. and S.K. Jung, Development of a thermal control algorithm using artificial neural network models for improved thermal comfort and energy efficiency in accommodation buildings. Applied Thermal Engineering, 2016. 103: p. 1135-1144.
  6. Lodi, C., et al., Improvement of thermal comfort and energy efficiency in historical and monumental buildings by means of localized heating based on non-invasive electric radiant panels. Applied Thermal Engineering, 2017. 126: p. 276-289.
  7. Hawila, A.A.W. and A. Merabtine, A statistical-based optimization method to integrate thermal comfort in the design of low energy consumption building. Journal of Building Engineering, 2021. 33: p. 101661.
  8. Enescu, D., A review of thermal comfort models and indicators for indoor environments. Renewable and Sustainable Energy Reviews, 2017. 79: p. 1353-1379.
  9. ISO, I., 7730: Ergonomics of the thermal environment Analytical determination and interpretation of thermal comfort using calculation of the PMV and PPD indices and local thermal comfort criteria. Management, 2005. 3(605): p. e615.
  10. Nasrollahzadeh, N., Comprehensive building envelope optimization: Improving energy, daylight, and thermal comfort performance of the dwelling unit. Journal of Building Engineering, 2021. 44: p. 103418.
  11. Ziaee, N. and R. Vakilinezhad, Multi-objective optimization of daylight performance and thermal comfort in classrooms with light-shelves: Case studies in Tehran and Sari, Iran. Energy and Buildings, 2022. 254: p. 111590.
  12. Zhao, J. and Y. Du, Multi-objective optimization design for windows and shading configuration considering energy consumption and thermal comfort: A case study for office building in different climatic regions of China. Solar Energy, 2020. 206: p. 997-1017.
  13. Pagliolico, S.L., et al., Preliminary results on a novel photo-bio-screen as a shading system in a kindergarten: Visible transmittance, visual comfort and energy demand for lighting. Solar Energy, 2019. 185: p. 41-58.
  14. Marzouk, M., M. ElSharkawy, and A. Mahmoud, Optimizing daylight utilization of flat skylights in heritage buildings. Journal of Advanced Research, 2022. 37: p. 133-145.
  15. De Luca, F., A. Sepúlveda, and T. Varjas, Multi-performance optimization of static shading devices for glare, daylight, view and energy consideration. Building and Environment, 2022. 217: p. 109110.
  16. Hensen, J.L. and R. Lamberts, Building performance simulation for sustainable building design and operation. Proceedings of the 60th Anniversary Environmental Engineering Department, Czech Technical University, Prague, 2011: p. 1-8.
  17. Mangkuto, R.A., M. Rohmah, and A.D. Asri, Design optimisation for window size, orientation, and wall reflectance with regard to various daylight metrics and lighting energy demand: A case study of buildings in the tropics. Applied energy, 2016. 164: p. 211-219.
  18. Frey, H.C., A. Mokhtari, and T. Danish, Evaluation of selected sensitivity analysis methods based upon applications to two food safety process risk models. Prepared by North Carolina State University for Office of Risk Assessment and Cost-Benefit Analysis, US Department of Agriculture, Washington, DC, 2003.
  19. Gagnon, R., L. Gosselin, and S. Decker, Sensitivity analysis of energy performance and thermal comfort throughout building design process. Energy and Buildings, 2018. 164: p. 278-294.
  20. Sanchez, D.G., et al., Application of sensitivity analysis in building energy simulations: Combining first-and second-order elementary effects methods. Energy and Buildings, 2014. 68: p. 741-750.
  21. Kheiri, F., A review on optimization methods applied in energy-efficient building geometry and envelope design. Renewable and Sustainable Energy Reviews, 2018. 92: p. 897-920.
  22. Nguyen, A.T., Sustainable housing in Vietnam: Climate responsive design strategies to optimize thermal comfort. 2013.
  23. Roudsari, M.S., M. Pak, and A. Smith. Ladybug: a parametric environmental plugin for grasshopper to help designers create an environmentally-conscious design. in Proceedings of the 13th international IBPSA conference held in Lyon, France Aug. 2013.
  24. Reinhart, C.F., J.A. Jakubiec, and D. Ibarra. Definition of a reference office for standardized evaluations of dynamic façade and lighting technologies. in Proceedings of BS2013: 13th Conference of International Building Performance Simulation Association, Chambéry, France, August 26. 2013.
  25. Iesna, I., LM-83-12 IES Spatial Daylight Autonomy (sDA) and Annual Sunlight Exposure (ASE). New York, NY, USA: IESNA Lighting Measurement, 2012.
  26. Shahsavari, F., W. Yan, and R. Koosha. A case study for sensitivity-based building energy optimization. in ARCC Conference Repository. 2019.
  27. Saltelli, A., et al., Variance based sensitivity analysis of model output. Design and estimator for the total sensitivity index. Computer physics communications, 2010. 181(2): p. 259-270.
  28. Nabil, A. and J. Mardaljevic, Useful daylight illuminance: a new paradigm for assessing daylight in buildings. Lighting Research & Technology, 2005. 37(1): p. 41-57.
  29. Reinhart, M., Rogers.(September 2006). Dynamic Daylight Performance Metrics for Sustainable Building Design. LEUKOS The Journal of the Illuminating Engineering Society of North America. 3(1).
  30. Fanger, P.O., Thermal comfort. Analysis and applications in environmental engineering. Thermal comfort. Analysis and applications in environmental engineering., 1970.
  31. Matzarakis, A., Climate, thermal comfort and tourism. Climate change and tourism-assessment and coping strategies, 2007: p. 139-154.
  32. Menberg, K., Y. Heo, and R. Choudhary, Sensitivity analysis methods for building energy models: Comparing computational costs and extractable information. Energy and Buildings, 2016. 133: p. 433-445.
  33. Allam, A.S., et al., Estimating the standardized regression coefficients of design variables in daylighting and energy performance of buildings in the face of multicollinearity. Solar Energy, 2020. 211: p. 1184-1193.
  34. Fang, Y. and S. Cho, Design optimization of building geometry and fenestration for daylighting and energy performance. Solar Energy, 2019. 191: p. 7-18.
  35. Magnier, L. and F. Haghighat, Multiobjective optimization of building design using TRNSYS simulations, genetic algorithm, and Artificial Neural Network. Building and Environment, 2010. 45(3): p. 739-746.
  36. Rutten, D. Evolutionary principles applied to problem solving. in AAG10 conference, Vienna. 2010.
  37. De Angelis, E., et al., A Tool for the Optimization of Building Envelope Technologies–Basic Performances against Construction Costs of Exterior Walls. Proceedings of CISBAT 2013, 2013.
  38. Deb, K. and M.-o.O.U.E. Algorithms, a n Introduction, Multi-objective evolutionary Optimisation for Product Design an d Manufacturing. 2011, Springer London.