Uncertainty and sensitivity decomposition of building energy models

Journal of Building Performance Simulation

Volumn 5, Issue 3, 2012, Pages 171-184

  Eisenhower, Bryan ^a   O'Neill, Zheng ^b   Fonoberov, Vladimir A ^c   Mezić, Igor ^a,d  

^a UNIVERSITY OF CALIFORNIA   (United States)

^b UNITED TECHNOLOGIES RESEARCH CENTER   (United States)

^c Aimdyn Inc   (United States)

^d University of California   (United States)

Author keywords

Decomposition methods; EnergyPlus; Sensitivity analysis; Uncertainty analysis

Indexed keywords

BUILDING ENERGY MODEL; BUILDING ENERGY MODELLING; DECOMPOSITION METHODS; ENERGYPLUS; MODELLING PROCESS; ORDERS OF MAGNITUDE; SENSITIVITY AND UNCERTAINTY ANALYSIS; SOURCES OF UNCERTAINTY;

SENSITIVITY ANALYSIS;

UNCERTAINTY ANALYSIS;

EID: 84883307405     PISSN: 19401493     EISSN: 19401507     Source Type: Journal    
DOI: 10.1080/19401493.2010.549964     Document Type: Article

References (35)

1. Aimdyn GoSUM Software, 2010. Global optimization, sensitivity and uncertainty in models (GoSUM) [online]. Santa Barbara, CA: Aimdyn Inc. Available from: http://aimdyn.com [Accessed 11 August 2010].
2. Brohus, H., et al., 2009a. Application of partial safety factors in building energy performance assessment. In: Building Performance Simulation Association (IBPSA) conference, 27-30 July 2009, Glasgow, UK, 1014-1021.
3. Brohus, H., et al., 2009b. Uncertainty of energy consumption assessment of domestic buildings. In: Building Performance Simulation Association (IBPSA) conference, 27- 30 July 2009, Glasgow, UK, 1022-1029.
4. Campolongo, F., Cariboni, J., and Saltelli, A., 2007. An effective screening design for sensitivity analysis of large models. Environmental Modelling and Software, 22, 1509- 1518.
5. Capozzoli, A., Mechri, H., and Corrado, V., 2009. Impacts of architectural design choices on building energy performance, applications of uncertainty and sensitivity analysis. In: Building Performance Simulation Association (IBPSA) conference, 27-30 July 2009, Glasgow, UK, 1000-1007.
6. Clarke, J., Yeneske, P., and Pinney, A., 1990. The harmonization of thermal properties of building materials, 1990. Technical report, Building Environmental performance analysis club. Watford, UK: Building research Establishment.
7. Corrado, V. and Mechri, H., 2009. Uncertainty and sensitivity analysis for building energy rating. Journal of building physics, 33 (2), 125-156.
8. Costola, D., et al., 2010. Uncertainty in airflow rate calculations due to the use of surface-averaged pressure coefficients. Energy and Buildings, 42, 881-888.
9. Crawley, D.B., et al., 2000. EnergyPlus: energy simulation program. ASHRAE Journal, 42, 49-56.
10. de Wit, S. and Augenbroe, G., 2002. Analysis of uncertainty in building design evaluations and its implications. Energy and Buildings, 34, 951-958.
11. DesignBuilder, 2010. Design builder building simulation [online]. Gloucestershire, UK: DesignBuilder Software Ltd. Available from: http://www.designbuilder.co.uk/ [Accessed 19 July 2010].
12. Dominguez-Munoz, F., et al., 2009. Uncertainty in the thermal conductivity of insulation materials. In: Building Performance Simulation Association (IBPSA) conference, 27-30 July 2009, Glasgow, UK, 1008-1013.
13. ESTCP, 2010. Automated continuous commissoning of commercial buildings. Department of Defense Environmental Security Technology Certification Program Project SI-0929.
14. Firth, S., Lomas, K., and Wright, A., 2010. Targeted household energy-efficiency measures using sensitivity analysis. Building Research and Information, 38 (1), 25-41.
15. Heiselberg, P., et al., 2009. Application of sensitivity analysis in design of sustainable buildings. Renewable Energy, 34, 2030-2036.
16. Lam, J., Wan, K., and Yang, L., 2008. Sensitivity analysis and energy conservation measures implications. Energy Conversion and Management, 49, 3170-3177.
17. Li, G. and Rabitz, H., 2006. Ratio control variate method for efficiently determining high-dimensional model representations. Journal of Computational Chemistry, 27, 1112- 1118.
18. Li, G., et al., 2002. Global uncertainty assessments by high dimensional model representations (HDMR). Chemical Engineering Science, 57, 4445-4460.
19. Lomas, K.J. and Eppel, H., 1992. Sensitivity analysis techniques for building thermal simulation programs. Energy and Buildings, 19, 21-44.
20. Macdonald, I., 2002. Quantifying the effects of uncertainty in building simulation. Thesis (PhD). University of Strath-clyde, Department of Mechanical Engineering.
21. Macdonald, I., 2009. Comparison of sampling techniques on the performance of Monte Carlo based sensitivity analysis. In: Building Performance Simulation Association (IBPSA) conference, 27-30 July 2009, Glasgow, UK, 992-999.
22. Mara, T. and Tarantola, S., 2008. Application of global sensitivity analysis of model output to building thermal simulations. Building Simulation, 1, 290-302.
23. Moon, H., 2005. Assessing mold risks in buildings under uncertainty. Thesis (PhD). Georgia Institute of Technology.
24. Rahni, N., et al., 1997. Application of group screening to dynamic building energy simulation models. Journal of Statistical Computation and Simulation, 57 (1), 285-304.
25. Saltelli, A., Chan, K., and Scott, E.M., 2000. Sensitivity analysis. New York: Wiley.
26. Saltelli, A., et al., 2010. Global sensitivity analysis-the primer. Wiley.
27. Simlab, 2010. Simlab: a professional tool for model developers, scientists and professionals, to learn, use and exploit uncertainty and sensitivity analysis techniques [online]. European Commission: Unit of Econometrics and Applied Statistics of the Joint Research Centre. Available from: http://simlab.jrc.ec.europa.eu/ [Accessed 4 May 2010].
28. Smola, A. and Scholkopf, B., 2004. A tutorial on support vector regression. Statistics and Computing, 14, 199-222.
29. Sobol', I., 2001. Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates. Mathematics and Computers in Simulation, 55, 271-280.
30. Sobol, I. and Kucherenko, S., 2009. Derivative based global sensitivity measures and their link with global sensitivity indices. Mathematics and Computers in Simulation, 79, 3009-3017.
31. Soratana, K. and Marriott, J., 2010. Increasing innovation in homeenergy efficiency: MonteCarlosimulationofpotential improvements. Energy and Buildings, 42 (6), 828-833.
32. Spitler, J., Fisher, D., and Zietlow, D., 1989. A primer on the use of influence coefficients in building simulation. In: Building Performance Simulation Association (IBPSA) conference, 23-24 June 1989, Vancouver, British Columbia, Canada, 299-304.
33. Struck, C., Hensen, J., and Kotek, P., 2009. On the application of uncertainty and sensitivity analysis with abstract building performance simulation tools. Journal of Building Physics, 33 (1), 5-27.
34. Zhang, Y., 2009. "Parallel" EnergyPlus and the development of a parametric analysis tool. In: Building Performance Simulation Association (IBPSA) conference, 27-30 July 2009, Glasgow, UK, 1383-1388.
35. Ziehn, T. and Tomlin, A.S., 2009. GUI-HDMR-a software tool for global sensitivity analysis of complex models. Environmental Modelling and Software, 24, 775-785.