Sustainable Built Environments

2013 Edition
| Editors: Vivian Loftness, Dagmar Haase

Sustainability Performance Simulation Tools for Building Design

Reference work entry
DOI: https://doi.org/10.1007/978-1-4614-5828-9_349

Introduction

Sustainable building , also commonly known as high-performance or green building , is the practice of creating structures and using processes that are environmentally responsible and resource-efficient throughout a building’s life cycle from siting to design, construction, operation, maintenance, renovation, and deconstruction [1]. This practice expands and complements the classical building design concerns of economy, utility, durability, and comfort. Historically, there have been many well-established theoretical frameworks that relate building design with its environmental as well as human occupant performance within those buildings [2, 3, 4, 5]. Design decision support tools, both physical and computational, have invariably been developed in accordance with the needs of these respective design processes and, in some instances, directly linked to meeting various building code and standard requirements. Therefore, such tools must be understood and appreciated in the...

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Bibliography

  1. 1.
  2. 2.
    Ruck NC (1989) Building design and human performance. Van Nostrand Reinhold, New YorkGoogle Scholar
  3. 3.
    Hartkopf V, Loftness VE, Mill PAD (1986) The concept of total building performance and building diagnostics. In: Building performance: function, preservation, and rehabilitation. ASTM special technical publication. STP901 American Society for Testing and. Materials, Philadelphia, pp 5–22Google Scholar
  4. 4.
    Thomas R (ed) (2002) Environmental design: an introduction for architects and engineers, 2nd edn. Spon Press, LondonGoogle Scholar
  5. 5.
    Yudelson J (2009) Green building through integrated design. McGraw-Hill, New YorkGoogle Scholar
  6. 6.
    Mahdavi A, Lam KP (1993) A dialectic of process and tool: knowledge transfer and decision-making strategies in the building delivery process. In: Krishan M, Martin B, Kwok WT (eds) Management of information technology for construction. World Scientific, Singapore, pp 345–356Google Scholar
  7. 7.
    Augenbroe G (2002) Trends in building simulation. Build Environ 37:891–902Google Scholar
  8. 8.
    Kusuda T(1999) Early history and future prospects of buildings system simulation. In: IBPSA, JapanGoogle Scholar
  9. 9.
    Clarke JA (2001) Energy simulation in building design. Butterworth-Heinemann, OxfordGoogle Scholar
  10. 10.
    International building performance simulation association. http://www.ibpsa.org/m_papers.asp.
  11. 11.
    Journal of Building Performance Simulation (2008) Taylor and Francis, UKGoogle Scholar
  12. 12.
    Building simulation: an international journal (2008) Springer and Tsinghua University PressGoogle Scholar
  13. 13.
    Malkawi A, Choudhary R (1999) Visualizing the sensed environment in the real world. J Hum Environ Syst 1:61–69Google Scholar
  14. 14.
  15. 15.
  16. 16.
    Ruck NC (ed) (1989) Building design and human performance. Van Nostrand Reinhold, New YorkGoogle Scholar
  17. 17.
    Thomas R (ed) (2002) Environmental design: an introduction for architects and engineers, 2nd edn. Spon Press, LondonGoogle Scholar
  18. 18.
    Loftness V, Hartkopf V, Mill P (1989) Chapter 9. A critical framework for building evaluation: total building performance, systems integration, and levels of measurement and assessment. In: Preiser WFE (ed) Building evaluation. Plenum, New YorkGoogle Scholar
  19. 19.
    Gallaher MP, O’Connor AC, Dettbarn JL Jr, Gilday LT (2004) Cost analysis of inadequate interoperability in the U.S. Capital Facilities Industry. Research report sponsored by National Institute of Standards and Technology, Advanced technology program, Aug 2004Google Scholar
  20. 20.
    Mattar SG (1983) Buildability and building envelope design. In: Proceedings of the second Canadian conference on building science and technology, Waterloo, Nov 1983Google Scholar
  21. 21.
    Smith RD (1998) Simulation http://www.modelbenders.com/encyclopedia/encyclopedia.html. Accessed 15 Nov 2010
  22. 22.
    Sokolowski JA, Banks CM (2009) Principles of modeling and simulation. Wiley, HobokenMATHGoogle Scholar
  23. 23.
    www.buildingsmart.com. Accessed 8 Feb 2012
  24. 24.
    US National Institute of Building Sciences (2007) National building information model, version 1, part 1, overview, principles and methodologies. http://www.buildingsmartalliance.org/nbims/. Accessed 15 Nov 2010
  25. 25.
    Lam KP and Zhang R (2010) Plot 12a Tianjin Eco-City Project - Sunlight and Daylight Simulation Report, JulyGoogle Scholar
  26. 26.
    Lam KP and Zhang R (2010) Plot 12a Tianjin Eco-City Project - Sunlight and Daylight Simulation Report, MayGoogle Scholar
  27. 27.
    Lam KP (2002) Performance-based design. In: Batimat Asia specifiers’ forum, Singapore, 6–8 Nov 2002Google Scholar
  28. 28.
    Chang W, Lim B, Williamson J (1998) Fire legislation reform and the architect. Royal Australian Institute of Architects (Queensland Chapter) Seminar, Oct 1998Google Scholar
  29. 29.
    Hatton T (1996) Problems of the prescriptive. In: Fire code reform national seminar series: performance-based approach to building fire safety design, Adelaide, Aug 1996Google Scholar
  30. 30.
    Beck V (1997) Performance-based fire engineering design and its application in Australia. In: Fire safety science-proceedings of the fifth international symposium of the international association of fire safety science, Fire Protection Research Foundation, Quincy MA, pp 23–40Google Scholar
  31. 31.
    Department of energy building energy software tools directory. http://apps1.eere.energy.gov/buildings/tools_directory/subjects_sub.cfm. Accessed 15 Nov 2010
  32. 32.
    Augenbroe G (1992) Integrated building performance evaluation in the early design stages. Build Environ 27(2):149–161Google Scholar
  33. 33.
    Mahdavi A, Lam KP (1991) Performance simulation as a front-end tool for integrative conceptual design evaluation. In: Proceedings of the 2nd international conference of the international building performance simulation association (IBPSA), France, pp 185–192Google Scholar
  34. 34.
    Wong NH, Lam KP, Feriadi H (2000) The use of performance-based simulation tools for building design and evaluation – a Singapore perspective. Build Environ 35:709–736Google Scholar
  35. 35.
    Ma C (2001) Analysis of building energy simulation tools. Public Works and Government Services Canada, Summer 2001Google Scholar
  36. 36.
  37. 37.
    http://www.resnet.us/. Accessed on 15 Nov 2010
  38. 38.
    COMNET: Commercial buildings energy modeling guidelines and procedures. August 2010. http://www.comnet.org/sites/default/files/images/COMNET-MGP-2.pdf. Accessed 15 Nov 2010
  39. 39.
    Lam KP, Mahdavi A (1995) Representation and performance: Interface design for building performance modeling. In: Tan M, Teh R (eds) CAAD Futures 1995: International conference on computer aided architectural design, 24–26 September 1995, Centre for advanced studies in architecture, Singapore, December 1995, pp 141–152Google Scholar
  40. 40.
    Energy modeling tools assessment for early design phase. Final research report prepared for the Northwest energy efficiency alliance, Portland, OR 97204. December 31, 2004Google Scholar
  41. 41.
    Lam KP, Mahdavi A, Gupta S, Wong NH, Brahme R, Kang Z (2002) Integrated and distributed computational support for building performance evaluation. Adv Eng Software 33:199–206MATHGoogle Scholar
  42. 42.
    World Business Council for Sustainable Development (2007) Energy efficiency in buildings: facts and trends. http://www.wbcsd.org/DocRoot/H94WhkJoIYq5uDtsLfxR/WBCSD_EEB_final.pdf
  43. 43.
    EIA (2009) Commercial buildings energy consumption survey data. http://www.eia.doe.gov/emeu/cbecs/. Accessed 15 Nov 2010
  44. 44.
    Brambley M, Hasen D, Haves P, Holmberg D, McDonald S, Roth K, Torcellini P (2005) Advanced sensors and controls for building applications: market assessment and potential R&D pathways. Pacific Northwest National Lab. Report No. 15149Google Scholar
  45. 45.
    World Business Council for Sustainable Development (2009) Transforming the market: energy efficiency in buildings. WBCSD, Washington DCGoogle Scholar
  46. 46.
  47. 47.
    Moore BJ, Fisher DS (2003) Pump differential pressure set-point reset based on chilled water valve position. ASHRAE Transactions 109(1):373–379Google Scholar
  48. 48.
    Nassif N, Kajl S, Sabourin R (2005) Optimization of HVAC control system strategy using two-objective genetic algorithm. HVAC & R Research 11(3):459–486Google Scholar
  49. 49.
    Zhang Y, Hanby VI (2006) HVAC & R research. Model-based control of renewable energy systems in buildings. HVAC & R Research 12(3a):577–598Google Scholar
  50. 50.
    Wang SW, Ma ZJ (2008) Supervisory and optimal control of building HVAC systems: a review. HVAC & R Research 14(1):3–32Google Scholar
  51. 51.
    Dong B (2010) Integrated building heating, cooling and ventilation control. Doctor of Philosophy Dissertation, Carnegie Mellon University, Pittsburgh, PA, USAGoogle Scholar
  52. 52.
    Zaheer-uddin M, Zheng GR (2001) Multistage optimal operating strategies for HVAC systems. ASHRAE Trans 107(2):346–352Google Scholar
  53. 53.
    Xu P, Haves P (2004) A simulation-based testing and training environment for building controls. In: Proceedings of SimBuild, Boulder, CO, 2004Google Scholar
  54. 54.
    House JM, Smith TF (1995) A system approach to optimal control for HVAC and building system. ASHRAE Trans 101(2):647–640Google Scholar
  55. 55.
    Kota NN, House JM, Arora JS, Smith TF (1996) Optimal control of HVAC systems using DDP and NLP techniques. Opt Control Appl Meth 17(1):71–78MATHGoogle Scholar
  56. 56.
    Sun J, Reddy A (2005) Optimal control of building HVAC&R systems using complete simulation-based sequential quadratic programming (CSB-SQP). Building Environ 40(5):657–669Google Scholar
  57. 57.
    Chang YC (2004) A novel energy conservation method- optimal chiller loading. Electric Power Syst Res 69(2):221–226Google Scholar
  58. 58.
    Sane H, Guay M (2008) Minmax dynamic optimization over a finite-time horizon for building demand control. In: Proceedings of American control conference. June 11–13, Seattle, Washington, USAGoogle Scholar
  59. 59.
    Flake BA (1998) Parameter estimation and optimal supervisory control of chilled water plants. PhD Thesis, University of Wisconsin-Madison, USAGoogle Scholar
  60. 60.
    Chang YC, Chen WH, Lee CY, Huang CN (2006) Simulated annealing based optimal chiller loading for saving energy. Energ Convers Manage 47(15–16):2044–2058Google Scholar
  61. 61.
    Henze GP, Dodier RH, Krarti M (1997) Development of a predictive optimal controller for thermal energy-storage systems. HVAC & R Research 3(3):233–264Google Scholar
  62. 62.
    Wang SW, Jin XQ (2000) Model-based optimal control of VAV air-conditioning system using genertic algorithm. Building Environ 35(6):471–487MathSciNetGoogle Scholar
  63. 63.
    Xing HY (2004) Building load control and optimization. Doctor of Philosophy Dissertation, MIT, Cambridge, MAGoogle Scholar
  64. 64.
    Kummert K, Andre P (2005) Simulation of a model-based optimal controller for heating systems under realistic hypotheses. In: Proceedings of 9th IBPSA 2005. Montreal, Canada.Google Scholar
  65. 65.
    Coffey B (2008) A development and testing framework for simulation-based supervisory control with application to optimal zone temperature ramping demand response using a modified genetic algorithm. Master Thesis, Concordia University, Quebec, CanadaGoogle Scholar
  66. 66.
    Page J, Robinson D, Page, Morel N, Scartezzini JL (2007) A generalised stochastic model for the simulation of occupant presence. Energ Buildings 40(2):83–98Google Scholar
  67. 67.
    Fritsch R, Kohler A, Nygard-Ferguson M, Scartezzini J-L (1990) A stochastic model of user behaviour regarding ventilation. Build Environ 25(2):173–181Google Scholar
  68. 68.
    Degelman LO (1999) A model for simulation of daylighting and occupancy sensors as an energy control strategy for office buildings. In: Proceedings of building simulation’99, an IBPSA conference, Kyoto, pp 571–578Google Scholar
  69. 69.
    Reinhart C (2004) Lightswitch-2002: a model for manual and automated control of electric lighting and blinds. Solar Energy 77:15–28Google Scholar
  70. 70.
    Wang D, Federspiel C, Rubinstein F (2005) Modelling occupancy in single person offices. Energ Buildings 37:121–126Google Scholar
  71. 71.
    Bourgeois D, Reinhart C, Macdonald I (2006) Adding advanced behavioural models in whole building energy simulation: a study on the total energy impact of manual and automated lighting control. Energ Buildings 38:814–823Google Scholar
  72. 72.
    Page J, Robinson D, Morel N, Scartezzini JL (2007) A generalised stochastic model for the simulation of occupant presence. Energ Buildings 40(2):83–98Google Scholar
  73. 73.
    Emmerich SJ, Persily AK (2001) State-of-the-art review of CO2 demand controlled ventilation technology and application, NISTIR 6729. NIST, US Department of CommerceGoogle Scholar
  74. 74.
    Trivedi M, Huang K, Mikic I (2000) Intelligent environment and active networks. In: Proceedings of IEEE international conference on systems, man, and cybernetics, Nashville, TennesseeGoogle Scholar
  75. 75.
    Lymberopoulos DA, Bamis T, Teixeira and A. Savvides. 2008. BehaviorScope: Real-time remote human monitoring using sensor networks. In: Proceedings of international conference on information processing in sensor networks, IPSN 2008, St. Louis, MissouriGoogle Scholar
  76. 76.
    Federspiel CC (1997) Estimating the inputs of gas transport processes in buildings. IEEE Trans Control Syst Technol 5:480–489Google Scholar
  77. 77.
    Wang SW, Jin XQ (1998) CO2-based occupancy detection for on-lin outdoor air flow control. Indoor Built Environ 7:165–181Google Scholar
  78. 78.
    Torrance MC (1995) Advances in human-computer interaction: the intelligent room. In: Working notes of the CHI 95 research symposium, Denver, COGoogle Scholar
  79. 79.
    Mozer MC (1998) The neural network house: an environment that adapts to its inhabitants. In: Coen M (ed) Proceedings of the American association for artificial intelligence spring symposium on intelligent environments. AAAI Press, Menlo Park, CA, pp 110–114Google Scholar
  80. 80.
    Lesser V, Atighetchi M, Benyo B, Horling B, Raja A, Vincent R, Wagner T, Ping X, Zhang SX (1999) The intelligent home testbed. In: Proceedings of the autonomy control software workshop, Seattle, WAGoogle Scholar
  81. 81.
    Cook D, Das S (2004) Smart environments: technology, protocols and applications. Wiley, Hoboken, NJGoogle Scholar
  82. 82.
    Youngblood GM, Cook D (2007) Data mining for hierarchical model creation. IEEE Trans Syst Man Cybernet 37(4):561–572Google Scholar
  83. 83.
    Duong TV, Phung DQ, Bui HH, Venkatesh S (2006) Human behavior recognition with generic exponential family duration modeling in the hidden Semi-Markov model. In: Proceedings of the 18th international Conference on Pattern Recognition, IEEE, vol 3, pp 202–207Google Scholar
  84. 84.
    Roy A, Das SK, Basu K (2007) A predictive framework for location-aware resource management in smart homes. IEEE Trans Mobile Comput 6(11):1270–1283Google Scholar
  85. 85.
    Henze GP, Clemens F, Gottfried K (2004) Evaluation of optimal control for active and passive building thermal storage. Int J Thermal Sciences 43:173–183Google Scholar
  86. 86.
    Henze GP, Kalz DE, Liu S, Felsmann C (2005) Experimental analysis of model-based predictive optimal control for active and passive building thermal storage inventory. HVAC & R Research 11(2):189–214Google Scholar

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  1. 1.Center for Building Performance and DiagnosticsSchool of Architecture, Carnegie Mellon UniversityPittsburghUSA