Open Access
Int. J. Metrol. Qual. Eng.
Volume 6, Number 3, 2015
Article Number 308
Number of page(s) 10
Published online 23 October 2015
  1. IEA, “Key World Energy Statistics 2014 – Key World 2014.pdf,” 2014. [Online]. Available: [Accessed: 10-Dec-2014].
  2. L. Pérez-Lombard, J. Ortiz, C. Pout, A review on buildings energy consumption information, Energy Build. 40, 394–398 (2008) [CrossRef]
  3. ASHRAE, ASHRAE Guideline 14, Measurement of energy and demand savings (2002)
  4. P. de Wilde, The gap between predicted and measured energy performance of buildings: A framework for investigation, Autom. Constr. 41, 40–49 (2014) [CrossRef]
  5. A.C. Menezes, A. Cripps, D. Bouchlaghem, R. Buswell, Predicted vs. actual energy performance of non-domestic buildings: Using post-occupancy evaluation data to reduce the performance gap, Appl. Energy 97, 355–364 (2012) [CrossRef]
  6. S. De Wit, G. Augenbroe, Analysis of uncertainty in building design evaluations and its implications, Energy Build. 34, 951–958 (2002) [CrossRef]
  7. S. Attia, M. Hamdy, W. O’Brien, S. Carlucci, Assessing gaps and needs for integrating building performance optimization tools in net zero energy buildings design, Energy Build. 60, 110–124 (2013) [CrossRef]
  8. GUM, Guide to the Expression of Uncertainty in Measurement (1995)
  9. O. Bodnar, G. Wübbeler, C. Elster, On the application of Supplement 1 to the GUM to non-linear problems, Metrologia 48, 333 (2011) [CrossRef]
  10. K. Weise, W. Woger, A Bayesian theory of measurement uncertainty, Meas. Sci. Technol. 4, 1 (1993) [CrossRef]
  11. I. Lira, G. Kyriazis, Bayesian inference from measurement information, Metrologia 36, 163 (1999) [CrossRef]
  12. A. Caucheteux, A Building Energy Efficiency Characterisation Method (BEECHAM) to Assess Existing Buildings Performance (2012)
  13. C. Elster, W. Wöger, M.G. Cox, Draft GUM Supplement 1 and bayesian analysis, Metrologia 44, L31 (2007) [CrossRef]
  14. M. Ángeles Herrador, A.G. González, Evaluation of measurement uncertainty in analytical assays by means of Monte-Carlo simulation, Talanta 64, 415–422 (2004) [CrossRef] [PubMed]
  15. M.G. Cox, B.R. Siebert, The use of a Monte Carlo method for evaluating uncertainty and expanded uncertainty, Metrologia 43, S178 (2006) [CrossRef]
  16. C. Elster, B. Toman, Bayesian uncertainty analysis under prior ignorance of the measurand versus analysis using the Supplement 1 to the Guide: a comparison, Metrologia 46, 261 (2009) [CrossRef]
  17. I. Lira, D. Grientschnig, Bayesian assessment of uncertainty in metrology: a tutorial, Metrologia 47, R1 (2010) [CrossRef]
  18. I. Lira, W. Wöger, Bayesian evaluation of the standard uncertainty and coverage probability in a simple measurement model, Meas. Sci. Technol. 12, 1172 (2001) [CrossRef]
  19. R. Kacker, A. Jones, On use of Bayesian statistics to make the Guide to the Expression of Uncertainty in Measurement consistent, Metrologia 40, 235 (2003) [CrossRef]
  20. S.A. Kalogirou, C.C. Neocleous, C.N. Schizas, Building heating load estimation using artificial neural networks, in Proceedings of the 17th international conference on Parallel architectures and compilation techniques, (1997), Vol. 8, p. 14
  21. T. Olofsson, S. Andersson, Long-term energy demand predictions based on short-term measured data, Energy Build. 33, 85–91 (2001) [CrossRef]
  22. Q. Li, Q. Meng, J. Cai, H. Yoshino, A. Mochida, Applying support vector machine to predict hourly cooling load in the building, Appl. Energy 86, 2249–2256 (2009) [CrossRef]
  23. D. Coakley, P. Raftery, M. Keane, A review of methods to match building energy simulation models to measured data, Renew. Sustain. Energy Rev. 37, 123–141 (2014) [CrossRef]
  24. AFNOR, Norme Européenne, Norme Française, Performance Energetique des batiments, Calcul des besoins d’énergie pour le chauffage, Batiments résidentiels. Norme AFNOR, Aout-1999.
  25. A. Trnsys, Transient System Simulation Program, University. Wis. (2000)
  26. C. Elster, B. Toman, Bayesian uncertainty analysis for a regression model versus application of GUM Supplement 1 to the least-squares estimate, Metrologia 48, 233 (2011) [CrossRef]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.