Advertisement

Building Simulation

, Volume 11, Issue 4, pp 647–661 | Cite as

Fast bidirectional building performance optimization at the early design stage

Research Article Building Thermal, Lighting, and Acoustics Modeling
  • 36 Downloads

Abstract

Decisions made at the early design stage have tremendous impacts on building performance (energy consumption, daylight, life cycle cost, natural ventilation, sunshine hours, etc.). Owing to progress in the design process, the opportunity to improve building performance is constantly reducing, while the cost of optimization is constantly increasing. The literature review shows that the commonly used building performance optimization workflow is divided into two categories: the forward optimization workflow and the inverse optimization workflow. In the forward workflow, designers are allowed to optimize building schemes according to feedback gleaned from the performance metrics; in the inverse workflow, however, designers are allowed to utilize software to search for optimal design solutions. Both workflows have their advantages, and their collective advantages can result in a highly efficient building design; however, in practice, the two processes are often separated. Furthermore, the simulation engines used in these two workflows are simulation software quite widely used. Using these software often requires a large amount of information, which are not suitable for an early design. In this paper, a bidirectional workflow for building performance optimization at the early design stage is proposed. The building energy consumption prediction model is then improved to make the workflow provide real-time performance feedback, and the optimization workflow is realized in SketchUp. This approach can provide quick feedback from building performance metrics, and allows designers to search for optimal solutions, using a genetic algorithm to support early design decisions. Because of the different structures of the simplified model and the standard model in BESTEST, we chose to use the results of DesignBuilder as the baseline to calibrate the simplified model. The model verification results show that the relative deviation of the total energy consumption of working condition 1 and 2 is between 20% and 27% due to the relatively large heating deviation in Beijing. The relative deviation of the total energy consumption of other cities is within 10%. In future work, we plan to rebuild the codes of the simplified model, and perform energy calibration under the standard procedure in BESTEST. Finally, the workflow is illustrated through a case study. Compared to previous studies, through the inverse-forward workflow and the simplified energy prediction model, the proposed workflow is demonstrated to better provide fast performance optimization at the early design stage.

Keywords

early design stage performance optimization simplified energy prediction model bidirectional optimization workflow 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Notes

Acknowledgements

This research is supported by: the Innovative Research Groups of the National Natural Science Foundation of China (No. 51521005), the Key Project of the National Natural Science Foundation of China (No. 51638003), National Key R&D Program of China (No. 2016YFC0700209).

References

  1. Attia S (2012). Computational optimization zero energy building design: Interviews with 28 international experts. International energy agency (IEA) task 40: Towards net zero energy buildings subtask B.Google Scholar
  2. Attia S, Beltrán L, De Herde A, Hensen JLM (2009). “Architect friendly”: A comparison of ten different building performance simulation tools. In: Proceedings of the 11th International IBPSA Building Simulation Conference, Glasgow, UK.Google Scholar
  3. Attia S, Gratia E, De Herde A, Hensen JLM (2012). Simulation-based decision support tool for early stages of zero-energy building design. Energy and Buildings, 49: 2–15.CrossRefGoogle Scholar
  4. Augenbroe G (2001). Building simulation trends going into the new millenium. In: Proceedings of the 7th International IBPSA Building Simulation Conference, Rio de Janeiro, BrazilGoogle Scholar
  5. Baker N, Steemers K (2000). Energy and Environment in Architecture: A Technical Design Guide. New York: E&FN Spon.CrossRefGoogle Scholar
  6. Bichiou Y, Krarti M (2011). Optimization of envelope and HVAC systems selection for residential buildings. Energy and Buildings, 43: 3373–3382.CrossRefGoogle Scholar
  7. Bogenstt äter U (2000). Prediction and optimization of life-cycle costs in early design. Building Research & Information, 28: 376–386.CrossRefGoogle Scholar
  8. Bre F, Silva AS, Ghisi E, Fachinotti V (2016). Residential building design optimisation using sensitivity analysis and genetic algorithm. Energy and Buildings, 133: 853–866.CrossRefGoogle Scholar
  9. Busch RD (1996). Method of Energy Analysis. In: Hunn BD (Ed.), Fundamentals of Building Energy Dynamics. Cambridge, MA, USA: MIT Press, pp. 220–237.Google Scholar
  10. Caldas L, Santos L (2016). Painting with light: An interactive evolutionary system for daylighting design. Building and Environment, 109: 154–174.CrossRefGoogle Scholar
  11. Cellura M, Guarino F, Longo S, Mistretta M (2017). Modeling the energy and environmental life cycle of buildings: A co-simulation approach. Renewable and Sustainable Energy Reviews, 80: 733–742.CrossRefGoogle Scholar
  12. Cofaigh EO, Fitzgerald E, Alcock R, McNicholl A, Peltonen V, Marucco A (1999). A green Vitruvius—Principles and Practice of Sustainable Architecture Design. London: James & James (Science Publishers) Ltd.Google Scholar
  13. Deiman EP, Plat HT (1993). Cost information in succeeding stages of the design process, In: Behesti MR, Zreik K (Eds.), Advanced Technologies: Architecture, Planning, Civil Engineering. Amsterdam: Elsevier.Google Scholar
  14. De Wilde P, Van der Voorden M (2004). Providing computational support for the selection of energy saving building components. Energy and Buildings, 36: 749–758.CrossRefGoogle Scholar
  15. Echenagucia TM, Capozzoli A, Cascone Y, Sassone M (2015). The early design stage of a building envelope: Multi-objective search through heating, cooling and lighting energy performance analysis. Applied Energy, 154: 577–591.CrossRefGoogle Scholar
  16. Fischer RD, Flanigan LJ, Talbert SG (1982). Degree day method for simplified energy analysis. ASHRAE Transactoins, 88(2): 522–571Google Scholar
  17. Gao Y, Hou H, Wu W (2016). Discussion on application of building performance simulation software based on SketchUp. Design & Research, 2016(7), 96–98. (in Chinese)Google Scholar
  18. Hong T, Chou SK, Bong TY (2000). Building simulation: An overview of developments and information sources. Building and Environment, 35: 347–361.CrossRefGoogle Scholar
  19. Jin JT, Jeong JW (2014). Optimization of a free-form building shape to minimize external thermal load using genetic algorithm. Energy and Buildings, 85: 473–482.CrossRefGoogle Scholar
  20. Kensek K (2014). Building Information Modeling. London: Routledge.Google Scholar
  21. Konis K, Gamas A, Kensek K (2016). Passive performance and building form: An optimization framework for early-stage design support. Solar Energy, 125: 161–179.CrossRefGoogle Scholar
  22. Kovacic I, Zoller V (2015). Building life cycle optimization tools for early design phases. Energy, 92: 409–419.CrossRefGoogle Scholar
  23. Liu D, Liu J, Yang L (2013). Review of building energy consumption calculation. Heating Ventilating & Air Conditioning, 43(1): 95–99. (in Chinese)MathSciNetGoogle Scholar
  24. Manzan M, Padovan R (2015). Multi-criteria energy and daylighting optimization for an office with fixed and moveable shading devices. Advances in Building Energy Research, 9: 238–252.CrossRefGoogle Scholar
  25. Miles JC, Sisk GM, Moore CJ (2001). The conceptual design of commercial buildings using a genetic algorithm. Computers and Structures, 79: 1583–1592.CrossRefGoogle Scholar
  26. Mitchell M (1996). An Introduction to Genetic Algorithms. Cambridge, MA, USA: MIT Press.MATHGoogle Scholar
  27. Nall DM, Arens EA (1979). Influence of degree day base temperature on residential building energy prediction. ASHRAE Transactions, 85: 727–735.Google Scholar
  28. Negendahl K, Nielsen TR (2015). Building energy optimization in the early design stages: A simplified method. Energy and Buildings, 105: 88–99.CrossRefGoogle Scholar
  29. Ochoa CE, Capeluto IG (2009). Advice tool for early design stages of intelligent facades based on energy and visual comfort approach. Energy and Buildings, 41: 480–488.CrossRefGoogle Scholar
  30. Østergård T, Jensen RL, Maagaard SE (2016). Building simulations supporting decision making in early design—A review. Renewable and Sustainable Energy Reviews, 61: 187–201.CrossRefGoogle Scholar
  31. Palonen M, Hamdy M, Hasan A (2013). MOBO: A new software for multi-objective building performance optimization. In: Proceedings of the 13th International IBPSA Building Simulation Conference, Chambéry, France.Google Scholar
  32. Picco M, Lollini R, Marengo M (2014). Towards energy performance evaluation in early stage building design: A simplification methodology for commercial building models. Energy and Buildings, 76: 497–505.CrossRefGoogle Scholar
  33. Rakha T, Nassar K (2011). Genetic algorithms for ceiling form optimization in response to daylight levels. Renewable Energy, 36: 2348–2356.CrossRefGoogle Scholar
  34. Ramallo-González AP, Coley DA (2014). Using self-adaptive optimisation methods to perform sequential optimisation for low-energy building design. Energy and Buildings, 81: 18–29.CrossRefGoogle Scholar
  35. Sargent JA, Niemasz J, Reinhart CF (2011). SHADERADE: Combing Rhinoceros and EnergyPlus for the design of static exterior shading devices. In: Proceedings of the 12th International IBPSA Building Simulation Conference, Sydney, Australia.Google Scholar
  36. Shen J (2012). Research on methods of using technical analysis in grasshopper for green building design. Master Thesis, South China University of Technology, China.Google Scholar
  37. Shi X (2011). Design optimization of insulation usage and space conditioning load using energy simulation and genetic algorithm. Energy, 36: 1659–1667.CrossRefGoogle Scholar
  38. Stamper MB (1979). Annual cycle moisture analysis. In: Proceedings of the Conference of the Thermal Performance of the Exterior Envelopes of Buildings, Kissimmee, FL, USAGoogle Scholar
  39. Tian W, Yang S, Zuo J, Li Z, Liu Y (2016). Relationship between built form and energy performance of office buildings in a severe cold Chinese region. Building Simulation, 10: 11–24.CrossRefGoogle Scholar
  40. Tuhus-Dubrow D, Krarti M (2010). Genetic-algorithm based approach to optimize building envelope design for residential buildings. Building and Environment, 45: 1574–1581.CrossRefGoogle Scholar
  41. Turrin M, von Buelow P, Stouffs R (2011). Design explorations of performance driven geometry in architectural design using parametric modeling and genetic algorithms. Advanced Engineering Informatics, 25: 656–675.CrossRefGoogle Scholar
  42. Van der Meer WJ (1978). Energy conservative housing for New Mexico. Albuquerque, NM: University of New Mexico.Google Scholar
  43. Wang S, Xu X (2006). Simplified building model for transient thermal performance estimation using GA-based parameter identification. International Journal of Thermal Sciences, 45: 419–432.CrossRefGoogle Scholar
  44. Wang W, Rivard H, Zmeureanu R (2006). Floor shape optimization for green building design. Advanced Engineering Informatics, 20: 363–378.CrossRefGoogle Scholar
  45. Westphal FS, Lamberts R (2004). The use of simplified weather data to estimate thermal loads of non-residential buildings. Energy and Buildings, 36: 847–54.CrossRefGoogle Scholar
  46. White JA, Reichmuth R (1996). Simplified method for predicting building energy consumption using average monthly temperatures. In: Proceedings of the 31st Intersociety Energy Conversion Engineering Conference, Washington DC, USA, pp. 1834–1839Google Scholar
  47. Xia C (2008). Research on energy conservation design methodology oriented to building’s conceptual design stage. PhD Thesis, Tsinghua University, China.Google Scholar
  48. Youssef AMA, Zhai ZJ, Reffat RM (2016). Genetic algorithm based optimization for photovoltaics integrated building envelope. Energy and Buildings, 127: 627–636.CrossRefGoogle Scholar
  49. Yi YK, Kim H (2015). Agent-based geometry optimization with Genetic Algorithm (GA) for tall apartment’s solar right. Solar Energy, 113: 236–250.CrossRefGoogle Scholar
  50. Yu Q (2011). Study on building energy-saving parametric design method in schematic stage. Master Thesis, Tsinghua University, China.Google Scholar
  51. Zeng J (2006). Performance-adjustable building envelope research. PhD Thesis, Tsinghua University, China.Google Scholar
  52. Zhai ZJ, McNeill JS (2014). Roles of building simulation tools in sustainable building design. Building Simulation, 7: 107–109.CrossRefGoogle Scholar
  53. Zhang L, Zhang L, Wang Y (2016). Shape optimization of free-form buildings based on solar radiation gain and space efficiency using a multi-objective genetic algorithm in the severe cold zones of China. Solar Energy, 132: 38–50.CrossRefGoogle Scholar
  54. Zhou X (2009). Study on building energy-saving design method based on total energy demand in scheme stage. Master Thesis, Tsinghua University, China.Google Scholar

Copyright information

© Tsinghua University Press and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Ziwei Li
    • 1
    • 2
  • Hongzhong Chen
    • 1
    • 2
  • Borong Lin
    • 1
    • 2
  • Yingxin Zhu
    • 1
    • 2
  1. 1.Department of Building Science, School of ArchitectureTsinghua UniversityBeijingChina
  2. 2.Key Laboratory of Eco Planning & Green Building, Ministry of EducationTsinghua UniversityBeijingChina

Personalised recommendations