High Performance Buildings: Measures, Complexity, and Current Trends

Chapter
Part of the Energy Systems book series (ENERGY)

Abstract

Most people are intimately involved with the built environment, while unfamiliar with detailed aspects of its design and operation. Buildings are everywhere and are designed and equipped using an agglomeration of many different design elements or puzzle pieces. With the recent trends towards a more energy efficient world, there has been an attempt to make buildings more efficient by using highly efficient pieces of the design puzzle. Not always does the integration of these subsystems result in an efficient building as a whole. The goal of this chapter is to highlight some of these boundaries and current engineering trends to surpass these obstacles. The discussion will be focused on large commercial buildings in the United States, while similar concerns are prevalent in other building types and in other global locations. We start by highlighting different ways that performance is measured and review the different design elements and equipment choices that are available to construct a building. The large number of interacting components creates complexity and a challenge to obtain a high performance structure. Specifically, technology barriers to realizing high performance buildings through this integration process lie in the ability to create useful models, data analysis tools, and effective control strategies. The chapter concludes with some current applied research in building systems that address the complexities in building systems and methods being developed to overcome the barriers that lie in the way.

Keywords

Thermal Comfort Design Element Natural Ventilation Building Design Mechanical Equipment 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

The author would like to thank Professor Igor Mezić (University of California, Santa Barbara) and Professor Zheng O’Neill (University of Alabama) for their comments on this manuscript.

References

  1. Anderson M, Buehner M, Young P, Hittle D, Anderson C, Tu J, Hodgson D (2008) MIMO robust control for HVAC systems. IEEE Trans Control Syst Technol 16(3):475–483CrossRefGoogle Scholar
  2. Arhitecture 2030 (2012) http://architecture2030.org/. Accessed Aug 2012
  3. ASHRAE (2010) ANSI/ASHRAE 62.2-2010,ventilation and acceptable indoor air quality in low-rise residential buildings (ANSI/ASHRAE approved). American Society of Heating, Refrigerating and Air Conditioning Engineers, AtlantaGoogle Scholar
  4. ASHRAE (2012) High performance buildings. American Society of Heating, Refrigerating and Air Conditioning Engineers, AtlantaGoogle Scholar
  5. Baker N, Steemers K (2000) Energy and environment in architecture. Taylor and Francis, London/New YorkGoogle Scholar
  6. Braun J, Montgomery K, Chaturvedi N (2001) Evaluating the performance of building thermal mass control strategies. HVAC&R Res 7(4):403–428CrossRefGoogle Scholar
  7. Corbin CD, Henze GP, May-Ostendorp P (2012) A model predictive control optimization environment for real-time commercial building application. J Build Perform Simul 6(3):159–174CrossRefGoogle Scholar
  8. Du Z, Jin X, Wu L (2007) PCA-FDA-Based fault diagnosis for sensors in VAV systems. HVAC&R Res 13(2):349CrossRefGoogle Scholar
  9. Eisenhower B, Maile T, Fisher M, Mezić I (2010) Decomposing building system data for model validation and analysis using the koopman operator ibpsa national conference. In: Proceedings of the third national conference of IBPSA-USA, New YorkGoogle Scholar
  10. Eisenhower B, O’Neill Z, Fonoberov V, Mezic I (2012a) Uncertainty and sensitivity decomposition of building energy models. J Build Perform Simul 5(3):171–184CrossRefGoogle Scholar
  11. Eisenhower B, O’Neill Z, Narayanan S, Fonoberov V, Mezic I (2012b) Methodology for meta-model based optimization in building energy models. Energy Build 47:292–301CrossRefGoogle Scholar
  12. Fanger PO (1970) Thermal comfort. Danish Technical Press. CopenhagenGoogle Scholar
  13. Friedman (2012) A fundamentals of sustainable dwellings. Island Press, Washington, DCGoogle Scholar
  14. H.R. 1–111th Congress: American Recovery and Reinvestment Act of 2009: In GovTrack.us (database of federal legislation) (2009). Retrieved 30 Aug 2012 from http://www.govtrack.us/congress/bills/111/
  15. Jain N, Li B, Keir M, Hencey B, Alleyne A (2010) Decentralized feedback structures of a vapor compression cycle system. IEEE Trans Control Syst Technol 18(1):185–193CrossRefGoogle Scholar
  16. Kämpf J, Wetter M, Robinson D (2010) A comparison of global optimization algorithms with standard benchmark functions and real-world applications using energyplus. J Build Perform Simul 3(2):103–120CrossRefGoogle Scholar
  17. Katipamula S, Brambley MR (2005) Methods for fault detection, diagnostics, and prognostics for building systems a review, part I and II. HVAC&R Res 11(1):3–25CrossRefGoogle Scholar
  18. Ma Y, Kelman A, Daly A, Borrelli F (2012) Predictive control for energy efficient buildings with thermal storage. IEEE Control Syst Mag 32(1):44–64CrossRefGoogle Scholar
  19. Oldewurtel F, Parisio, A, Jones C, Gyalistras D, Gwerder M, Stauch V, Lehmann B, Morari M (2012) Use of model predictive control and weather forecasts for energy efficient building climate control. Energy Build 45:15–27CrossRefGoogle Scholar
  20. Ruch D, Chen L, Haberl J, Claridge D (1993) A change-point principle analysis (CP/PCA) method for predicting energy usage in commercial buildings: the PCA model. J Sol Energy Eng 115:77–84CrossRefGoogle Scholar
  21. Smith PF (2007) Sustainability at the cutting edge. Elsevier, AmsterdamGoogle Scholar
  22. Trcka M, Hensen JL (2010) Overview of HVAC system simulation. Autom Constr 19(2):93–99CrossRefGoogle Scholar
  23. Turner C, Frankel M (2008) Energy performance of leed for new construction buildings. Tech. rep., U.S. Green Building CouncilGoogle Scholar
  24. U.S. Department of Energy (2012) 2011 buildings energy data book. Energy efficiency and renewable energy. http://buildingsdatabook.eren.doe.gov/
  25. U.S. Energy Information Administration (2003) Commercial buildings energy consumption survey (CBECS). http://www.eia.gov/consumption/commercial/
  26. Wetter M (2009) Modelica-based modeling and simulation to support research and development in building energy and control systems. J Build Perform Simul 2(2):143–161CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  1. 1.Department of Mechanical Engineering, Center for Energy Efficient DesignUniversity of CaliforniaSanta BarbaraUSA

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