Mathematical Models and Data Analysis

Chapter

Abstract

This chapter starts by introducing the benefits of applied data analysis and modeling methods through a case study example pertinent to energy use in buildings. Next, it reviews fundamental notions of mathematical models illustrates them in terms of sensor response, and differentiates between forward or simulation models and inverse models. Subsequently, various issues pertinent to data analysis and associated uncertainty are described, and the different analysis tools which fall within its purview are discussed. Basic concepts relating to white-box, black-box and grey-box models are then presented. An attempt is made to identify the different types of problems one faces with forward modeling as distinct from inverse modeling and analysis. Notions germane to the disciplines of decision analysis, data mining and intelligent data analysis are also covered. Finally, the various topics covered in each chapter of this book are described.

Keywords

Burning Convection Radar Posit Settling 

References

  1. Berthold, M. and D.J. Hand (eds.) 2003. Intelligent Data Analysis, 2nd Edition, Springer, Berlin.CrossRefMATHGoogle Scholar
  2. Cha, P.D., J.J. Rosenberg and C.L. Dym, 2000. Fundamentals of Modeling and Analyzing Engineering Systems, 2nd Ed., Cambridge University Press, Cambridge, UK.Google Scholar
  3. Claridge, D.E. and M. Liu, 2001. HVAC System Commissioning, Chap. 7.1 Handbook of Heating, Ventilation and Air Conditioning, J.F. Kreider (editor), CRC Press, Boca Raton, FL.Google Scholar
  4. Clemen, R.T. and T. Reilly, 2001. Making Hard Decisions with Decision Tools, Brooks Cole, Duxbury, Pacific Grove, CAGoogle Scholar
  5. Energy Plus, 2009. Energy Plus Building Energy Simulation software, developed by the National Renewable Energy Laboratory (NREL) for the U.S. Department of Energy, under the Building Technologies program, Washington DC, USA. http://www.nrel.gov/buildings/energy_analysis.html#energyplus.
  6. Edwards, C.H. and D.E. Penney, 1996. Differential Equations and Boundary Value Problems, Prentice Hall, Englewood Cliffs, NJMATHGoogle Scholar
  7. Eisen, M., 1988. Mathematical Methods and Models in the Biological Sciences, Prentice Hall, Englewood Cliffs, NJ.MATHGoogle Scholar
  8. Doebelin, E.O., 1995. Engineering Experimentation: Planning, Execution and Reporting, McGraw-Hill, New YorkGoogle Scholar
  9. Dunham, M., 2003. Data Mining: Introductory and Advanced Topics, Pearson Education Inc.Google Scholar
  10. Haimes, Y.Y., 1998. Risk Modeling, Assessment and Management, John Wiley and Sons, New York.MATHGoogle Scholar
  11. Heinsohn, R.J. and J.M.Cimbala, 2003, Indoor Air Quality Engineering, Marcel Dekker, New York, NYCrossRefGoogle Scholar
  12. Hoagin, D.C., F. Moesteller and J.W. Tukey, 1983. Understanding Robust and Exploratory Analysis, John Wiley and Sons, New York.Google Scholar
  13. Hodges, J.L. and E.L. Lehman, 1970. Basic Concepts of Probability and Statistics, 2nd Edition Holden DayGoogle Scholar
  14. Jochem, E. 2000. In Energy End-Use Efficiency in World Energy Assessment, J. Goldberg, ed., pp. 73–217, United Nations Development Project, New York.Google Scholar
  15. Masters, G.M. and W.P. Ela, 2008. Introduction to Environmental Engineering and Science,3rd Ed. Prentice Hall, Englewood Cliffs, NJGoogle Scholar
  16. McNeil, D.R. 1977. Interactive Data Analysis, John Wiley and Sons, New York.Google Scholar
  17. PECI, 1997. Model Commissioning Plan and Guide Commissioning Specifications, version 2.05, U.S.DOE/PECI, Portland, OR, February.Google Scholar
  18. Reddy, T.A., 2006. Literature review on calibration of building energy simulation programs: Uses, problems, procedures, uncertainty and tools, ASHRAE Transactions, 112(1), JanuaryGoogle Scholar
  19. Sprent, P., 1998. Data Driven Statistical Methods, Chapman and Hall, London.MATHGoogle Scholar
  20. Stoecker, W.F., 1989. Design of Thermal Systems, 3rd Edition, McGraw-Hill, New York.Google Scholar
  21. Streed, E.R., J.E. Hill, W.C. Thomas, A.G. Dawson and B.D. Wood, 1979. Results and Analysis of a Round Robin Test Program for Liquid-Heating Flat-Plate Solar Collectors, Solar Energy, 22, p.235.CrossRefGoogle Scholar
  22. Stubberud,A., I. Williams, and J. DiStefano, 1994. Outline of Feedback and Control Systems, Schaum Series, McGraw-Hill.Google Scholar
  23. Tukey, J.W., 1988. The Collected Works of John W. Tukey, W. Cleveland (Editor), Wadsworth and Brookes/Cole Advanced Books and Software, Pacific Grove, CAGoogle Scholar
  24. Weiss, N. and M. Hassett, 1982. Introductory Statistics, Addison-Wesley. NJ.Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  1. 1.The Design School and School of SustainabilityArizona State UniversityTempeUSA

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