Black-box models for fault detection and performance monitoring of buildings

Journal of Building Performance Simulation

Volumn 3, Issue 1, 2010, Pages 53-62

  Jacob, Dirk ^a   Dietz, Sebastian ^a   Komhard, Susanne ^a   Neumann, Christian ^a   Herkel, Sebastian ^a  

^a FRAUNHOFER INSTITUTE FOR SOLAR ENERGY SYSTEMS ISE   (Germany)

Author keywords

Black box models; Day type clustering; Multiple linear regression with change point; Outlier detection; Performance monitoring; Rate of change of the indoor air temperature ( Tind)

Indexed keywords

BLACK-BOX MODEL; CHANGE-POINTS; INDOOR AIR TEMPERATURE; MULTIPLE LINEAR REGRESSIONS; OUTLIER DETECTION; RATE OF CHANGE;

ATMOSPHERIC TEMPERATURE; BUILDINGS; CLUSTERING ALGORITHMS; LINEAR REGRESSION; THERMAL COMFORT;

FAULT DETECTION;

EID: 77951523599     PISSN: 19401493     EISSN: 19401507     Source Type: Journal    
DOI: 10.1080/19401490903414454     Document Type: Article

References (36)

1. Abushakra, B., Haberl, J.S., and Claridge, D.E., 1999. Compilation of diversity factors and schedules for energy and cooling load calculations Phase II Report. ASHRAE 1093-RP, December.
2. Adam, C., et al., 2006. A contribution to the audit of an air-conditioning system: modeling, simulation and benchmarking. In: Proceedings of the 7th international conference on system simulation in buildings. Liege, Belgium.
3. Bundesminesterium fur Verkehr Bau und Stadtentwicklung (BMVBS), 2007. Energieeinsparung contra Behaglich-keit? Forschungen Heft 121. Selbstverlag des Bundesamtes fur Bauwesen und Raumordnung, Bonn. Available from: http://www.bbr.bund.de/cln_005/nn_21272/BBSR/DE/Veroeffentlichungen/Forschungen/2007/Heft121__DL,templateId=raw,property=publicationFile.pdf/Heft121_DL.pdf [Accessed 9 November 2009].
4. Claridge, D.E., 2004. Using simulation models for building commissioning. In: International conference on enhanced building operation. Texas A&M University: Energy Systems Laboratory.
5. Claridge, D.E. and Chen, H., 2006. Missing data estimation for 1-6 h gaps in energy use and weather data using different statistical methods. International Journal of Energy Research, 30, 1075-1091.
6. Crawley, D.B., et al., 2001. EnergyPlus: creating a new-generation building energy simulation program. Energy and Buildings, 33, 319-331.
7. Crawley, M.J., 2007. The R Book. West Sussex, England: John Wiley & Sons Ltd.
8. Dodier, R.H., 1999. RISO: an implementation of distributed belief networks. AAAI Technical Report SS-99-04.
9. Fels, M., 1986. PRISM: an introduction. Energy and Buildings, 9, 5-18.
10. Goldberg, M., 1982. A geometrical approach to nondifferentiable regression models as related to methods for assessing residential energy conservation. Thesis (PhD). Dissertation, Department of Statistics, Princeton University, Princeton, NJ.
11. Haberl, J.S., 1988. Metered data analysis and knowledge based methods that reduce HVAC operation and maintenance problems. In: Proceedings of the 5th symposium on improving building systems in hot and humid climates, Houston, TX, September 12-14.
12. Haberl, J.S. and Cho, S., 2004. Literature review of uncertainty of analysis methods (Inverse Model Toolkit). Report to the Texas Commission on Environmental Quality, ESL-TR-04/10-03, October.
13. Haberl, J.S. and Claridge, D.E., 1987. An expert system for building energy consumption analysis: prototype results. ASHRAE Transactions-Research, 93 (1), 979-998.
14. Haberl, J.S. and Thamilseran, S., 1998. The great building energy predictor shootout II: measuring retrofit savings. ASHRAE Journal, 40 (1), 49-56.
15. Herkel, S., et al., 2008. Energieeffiziente Büros und Produk-tionsstätten - Ergebnisse von Monitoringprogrammen. FVS Jahrestagung 2008. Berlin: Energieeffizientes und solares Bauen.
16. IPMVPC, 2002. International performance measurement & verification protocol: concepts and options for determining energy and water savings. International Performance Measurement & Verification Protocol Committee. Available from: http://www.nrel.gov/docs/fy02osti/31505.pdf.
17. Katipamula, S. and Brambley, M.R., 2005a. Methods for fault detection, diagnostics, and prognostics for building systems - a review, Part I. HVAC&R Research, 11, 3-25.
18. Katipamula, S. and Brambley, M.R., 2005b. Methods for fault detection, diagnostics, and prognostics for building systems - a review, Part II. HVAC&R Research, 11, 169-187.
19. Kissock, J.K., 1996. Development of analysis tools in support of the Texas LoanSTAR program. Dayton, OH: University of Dayton, Department of Mechanical and Aerospace Engineering, August.
20. Kissock, J.K., et al., 1994. EModel Version 1.4de. Department of Mechanical Engineering, Texas A&M Univer-sity, College Station, TX: Copyright Texas A&M University, Energy Systems Laboratory, December.
21. Kissock, J.K., Haberl, J.S., and Claridge, D.E., 2002. Development of a toolkit fol- calculating linear change-point linear and multiple-linear inverse building energy analysis models. Final Report, ASHRAE 1050-RP, November.
22. Lebrun, J., 2001. Simulation of a HVAC system with the help of an engineering equation solver. In: Building simulation, 7th International IBPSA Conference, Rio de Janeiro, Brazil.
23. Liu, M., Song, L., and Claridge, D.E., 2002. Development of whole-building fault detection methods. High performance commercial building systems. California Energy Commission. Public Interest Energy Research Program.
24. Milligan, G.W. and Cooper, M.C., 1985. An examination of procedures for determining the number of clusters in a data set. Psychometrika, 50, 159-179.
25. Rabl, A., 1988. Parameter estimation in buildings: methods for dynamic analysis of measured energy use. ASME Journal of Solar Energy Engineering, 110, 52-66.
26. R Development Core Team, 2008. R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0, Available from: URL http://www.R-project.org [Accessed 9 November 2009].
27. Reddy, T.A., 1989. Application of dynamic building inverse models to three occupied residences monitored non-intrusively. Thermal Performance of the Exterior Envelopes of Buildings IV. In: Proceedings of the ASHRAE/ DOE/CIBSE conference, Orlando, FL, December 654-671.
28. Reddy, T.A. and Claridge, D.E., 1994. Using synthetic data to evaluate multiple regression and principal component analyses for statistical modeling of daily building energy consumption. Energy and Buildings, 21, 35-44.
29. Reddy, T.A., Kissock, J.K., and Ruch, D., 1998. Uncertainty in baseline regression modeling and in determination of retrofit savings. ASME Journal of Solar Energy Engineering, 120, 185-192.
30. Ruch, D. and Claridge, D.E., 1992. A four-parameter change-point model for predicting energy consumption in commercial buildings. ASME Journal of Solar Energy Engineering, 114 (2), 77-83.
31. Ruch, D. and Claridge, D.E., 1993a. A development and comparison of NAC estimates for linear and change-point energy models for commercial buildings. Energy and Buildings, 20 (1), 87-95.
32. Ruch, D., et al., 1993b. A change-point principal component analysis (CP/PCA) method for predicting energy usage in commercial buildings: the PCA model. ASME Journal of Solar Energy Engineering, 115 (2), 77-84.
33. Schrock, D. and Claridge, D.E., 1989. Predicting energy usage in a supermarket. In: Proceedings of the 6th symposium on improving building systems in hot and humid climates, Dallas, TX, Mechanical Engineering Department, Texas A&M University, October 44-54.
34. Seem, J.E., 2005. Pattern recognition algorithm for determin-ing days of the week with similar energy consumption profiles. Energy and Buildings, 37, 127-139.
35. Seem, J.E., 2007. Using intelligent data analysis to detect abnormal energy consumption in buildings. Energy and Buildings, 39, 52-58.
36. Voss, K., et al., 2008. Energy-optimised building - experience and future perspectives from a demonstration programme in Germany. In: Proceedings of ICEBO′08 8th international conference for enhanced building operations, Berlin.