Quantitative risk management for energy retrofit projects

Journal of Building Performance Simulation

Volumn 6, Issue 4, 2013, Pages 257-268

  Heo, Yeonsook ^a   Augenbroe, Godfried ^b   Choudhary, Ruchi ^c  

^a ARGONNE NATIONAL LABORATORY   (United States)

^b GEORGIA INSTITUTE OF TECHNOLOGY   (United States)

^c UNIVERSITY OF CAMBRIDGE   (United Kingdom)

Author keywords

Bayesian calibration; Building energy models; Energy efficiency projects; Energy service companies; Risk analysis

Indexed keywords

BAYESIAN APPROACHES; BAYESIAN CALIBRATION; BUILDING ENERGY MODEL; ENERGY CONSERVATION MEASURES; ENERGY SERVICE COMPANY; PERFORMANCE MEASUREMENTS; PROBABILISTIC OUTPUT; RISK ANALYSIS METHODS;

BAYESIAN NETWORKS; CALIBRATION; ENERGY EFFICIENCY; RISK ANALYSIS; RISK MANAGEMENT;

RISK ASSESSMENT;

EID: 84883306980     PISSN: 19401493     EISSN: 19401507     Source Type: Journal    
DOI: 10.1080/19401493.2012.706388     Document Type: Article

References (30)

1. ASHRAE, 2002. ASHRAE Guideline 14: measurement of energy and demand savings. Technical report. Atlanta, GA: American Society of Heating, Refrigerating, and Air-Conditioning Engineers.
2. BCIS, 2010. BCIS Wessex alterations & refurbishment price book. London, UK: Building Cost Information Service of Royal Institution of Chartered Surveyors.
3. CEN prEN 15203/15315, 2006. Energy performance of buildings-assessment of energy use and definition of energy ratings. Brussels: CEN.
4. de Wit, S., 2001. Uncertainty in predictions of thermal comfort in buildings. Thesis (PhD). Delft University of Technology.
5. DECC, 2010. Electricity market assessment. Technical report. London, UK: Department of Energy and Climate Change.
6. Gelman, A., et al., 2004. Bayesian data analysis. Boca Raton, FL: Chapman & Hall/CRC.
7. Goldman, C.A., et al., 2002. Market trends in the U.S. ESCO industry: results from the NAESCO database project. Technical report. Berkeley, CA: Lawrence Berkeley National Laboratory.
8. Graziano, D.J., et al., 2011. National energy performance database warehouse finance tool specifications - beta version release. Technical report. Argonne, IL: Argonne National Laboratory.
9. Hansen, S.J. and Brown, J.W., 2004. lnvestment grade energy audit: making smart energy choices. Lilburn, GA: Fairmont Press, Inc.
10. Heo, Y., 2011. Bayesian calibration of building energy models for energy retrofit decision-making under uncertainty. Thesis (PhD). Georgia Institute of Technology
11. IPMVP, 2010. International Performance Measurement and Verification Protocol: concepts and options for determining energy and water savings. Volume 1. Technical report. Washington, D.C.: Efficiency Valuation Organization.
12. ISO 13790, 2007. Thermal performance of buildings-transmission and ventilation heat transfer coefficients. Geneva, Switzerland: ISO.
13. Jackson, J., 2010. Promoting energy efficiency investments with risk management decision tools. Energy Policy, 38, 3865-3873.
14. Jacobson, D.I., Dutt, G.S., and Socolow, R.H., 1986. Pressurization testing, infiltration reduction, and energy
15. savings. In: H.R. Trechsel and P.L. Lagus, eds. Measured air leakage of buildings. West Conshohocken, PA: American Society for Testing and Materials.
16. Kennedy, M.C. and O'Hagan, A., 2001. Bayesian calibration of computer models. Journal of the Royal Statistical Society, Series B, 63 (3), 425-464.
17. Krause, P., Boyle, D.P., and Base, F., 2005. Comparison of different efficiency criteria for hydrological model assessment. Advances in Geosciences, 5, 89-97.
18. Lee, S.H., Zhao, F., and Augenbroe, G., 2011. The use of normative energy calculation beyond building performance rating systems. In: Building simulation 2011, proceedings, 14-16 November, Sydney, Australia. Sydney, Australia: International Building Performance Simulation Association, 2753-2760.
19. Legates, D.R. and McCabe, G.J., 1999. Evaluating the use of ''goodness-of-fit'' measures in hydrologic and hydroclimatic model validation. Water Resources Research, 35 (1), 233-2 41.
20. Macdonald, I.A., 2002. Quantifying the effects of uncertainty in building simulation. Thesis (PhD). University of Strathclyde.
21. Mathew, P., et al., 2005. Actuarial pricing of energy efficiency projects: lessons foul and fair. Energy Policy, 33 (10), 1319-1328.
22. Mills, E., et al., 2006. From volatility to value: analysing and managing financial and performance risk in energy savings projects. Energy Policy, 34 (2), 188-199.
23. Morris, M.D., 1991. Factorial sampling plans for prelimina ry computational experiments. Technometrics, 33 (2), 161-174.
24. Reddy, T.A. and Claridge, D.E., 2006. Uncertainty of ''measured'' energy savings from statistical baseline models. HVAC&R Research, 6 (1), 3-20.
25. Reddy, T.A., Maor, I., and Jian, S., 2006. Procedures for reconciling computer-calculated results with measured energy data. Technical report. Atlanta, GA: American Society of Heating, Refrigerating, and Air-Conditioning Engineers.
26. Rijal, H., et al., 2007. Using results from field surveys to predict the effect of open windows on thermal comfort and energy use in buildings. Energy and Buildings, 39 (7), 823-836.
27. Satchwell, A., et al., 2010. A survey of the U.S. ESCO industry: market growth and development from 2008 to 2011. Technical report. Berkeley, CA: Lawrence Berkeley National Laboratory. home energy efficiency: Monte Carlo simulation of potential improvements. Energy and Buildings, 42, 828-833.
28. Sun, Y., et al., 2011. Uncertainty quantification of microclimate variables in building energy simulation. In: Building simulation 2011, proceedings, 14-16 November, Sydney, Australia. Sydney, Australia: International Building Performance Simulation Association, 2423-2430.
29. Wilkinson, D., 2006. Parallel Bayesian computation. In: E.J. Kontoghiorghes, ed. Handbook of parallel computing and statistics. Boca Raton, FL: Chapman & Hall/CRC, 477-508.
30. Wyss, G.D. and Jorgensen, K.H., 1998. A user's guideline to LHS: Sandia's Latin Hypercube Sampling software. Technical report. Albuquerque, NM: Sandia National Laboratories.