Identification of the hygrothermal properties of a building envelope material by the covariance matrix adaptation evolution strategy

References

• Beck, J. V., B. Blackwell, and C. R. St. Clair. 1985. Inverse Heat Conduction: Ill-posed Problems. John Wiley & Sons.
   Google Scholar
• Beyer, H.-G., and H.-P. Schwefel. 2002. “Evolution Strategies – A Comprehensive Introduction.” Natural Computing 1 (1): 3–52. doi: 10.1023/A:1015059928466
   Google Scholar
• Biddulph, P., V. Gori, C. A. Elwell, C. Scott, C. Rye, R. Lowe, and T. Oreszczyn. 2014. “Inferring the Thermal Resistance and Effective Thermal Mass of a Wall Using Frequent Temperature and Heat Flux Measurements.” Energy and Buildings 78: 10–16. doi: 10.1016/j.enbuild.2014.04.004
   Web of Science ®Google Scholar
• Box, G. E. P., and G. C. Tiao. 2011. Bayesian Inference in Statistical Analysis. John Wiley & Sons. 10.1002/9781118033197.
   Google Scholar
• Brouns, J., A. Nassiopoulos, F. Bourquin, and K. Limam. 2013. “State-parameter Identification for Accurate Building Energy Audits.” Building Simulation 2013 Conference, France, August.
   Google Scholar
• Chavent, G. 2010. Nonlinear Least Squares for Inverse Problems: Theoretical Foundations and Step-by-step Guide for Applications. New York: Springer.
   Google Scholar
• Evins, R. 2013. “A Review of Computational Optimisation Methods Applied to Sustainable Building Design.” Renewable and Sustainable Energy Reviews 22: 230–245. doi: 10.1016/j.rser.2013.02.004
   Web of Science ®Google Scholar
• Fortin, F. A., D. Rainville, M. A. G. Gardner, M. Parizeau, and C. Gagné. 2012. “DEAP: Evolutionary Algorithms Made Easy.” Journal of Machine Learning Research 13 (1): 2171–2175.
   Google Scholar
• Gossard, D., B. Lartigue, and F. Thellier. 2013. “Multi-objective Optimization of a Building Envelope for Thermal Performance Using Genetic Algorithms and Artificial Neural Network.” Energy and Buildings 67: 253–260. doi: 10.1016/j.enbuild.2013.08.026
   Web of Science ®Google Scholar
• Gosselin, L., M. Tye-Gingras, and F. Mathieu-Potvin. 2009. “Review of Utilization of Genetic Algorithms in Heat Transfer Problems.” International Journal of Heat and Mass Transfer 52 (9): 2169–2188. doi: 10.1016/j.ijheatmasstransfer.2008.11.015
   Web of Science ®Google Scholar
• Hagentoft, C. E., et al., 2004. “Assessment Method of Numerical Prediction Models for Combined Heat, Air and Moisture Transfer in Building Components: Benchmarks for One-dimensional Cases.” Journal of Thermal Envelope and Building Science 27 (4): 327–352.
   Google Scholar
• Hansen, P. 1990. “Truncated Singular Value Decomposition Solutions to Discrete Ill-Posed Problems with Ill-Determined Numerical Rank.” SIAM Journal on Scientific and Statistical Computing 11 (3): 503–518. doi: 10.1137/0911028
   Web of Science ®Google Scholar
• Hansen, P. 1992. “Analysis of Discrete Ill-Posed Problems by Means of the L-Curve.” SIAM Review 34 (4): 561–580. doi: 10.1137/1034115
   Web of Science ®Google Scholar
• Hansen, N. 2006. The CMA Evolution Strategy: A Comparing Review. Towards A New Evolutionary Computation. Berlin: Springer.
   Google Scholar
• Hansen, N., and A. Ostermeier. 2001. “Completely Derandomized Self-Adaptation in Evolution Strategies.” Evolutionary Computation 9 (2): 159–195. doi: 10.1162/106365601750190398
   PubMed Web of Science ®Google Scholar
• Heo, Y., R. Choudhary, and G. A. Augenbroe. 2012. “Calibration of Building Energy Models for Retrofit Analysis under Uncertainty.” Energy and Buildings 47: 550–560. doi: 10.1016/j.enbuild.2011.12.029
   Web of Science ®Google Scholar
• Janssen, H., B. Blocken, and J. Carmeliet. 2007. “Conservative Modelling of the Moisture and Heat Transfer in Building Components under Atmospheric Excitation.” International Journal of Heat and Mass Transfer 50 (5): 1128–1140. doi: 10.1016/j.ijheatmasstransfer.2006.06.048
   Web of Science ®Google Scholar
• Kaipio, J. P., and C. Fox. 2011. “The Bayesian Framework for Inverse Problems in Heat Transfer.” Heat Transfer Engineering 32 (9): 718–753. doi: 10.1080/01457632.2011.525137
   Web of Science ®Google Scholar
• Kaipio, J. P., and E. Somersalo (2005). Statistical and Computational Inverse Problems, Applied Mathematical Science, New York, Springer.
   Google Scholar
• Lauret, P., H. Boyer, C. Riviere, and A. Bastide. 2005. “A Genetic Algorithm Applied to the Validation of Building Thermal Models.” Energy and Buildings 37 (8): 858–866. doi: 10.1016/j.enbuild.2004.11.006
   Web of Science ®Google Scholar
• Machairas, V., A. Tsangrassoulis, and K. Axarli. 2014. “Algorithms for Optimization of Building Design: A Review.” Renewable and Sustainable Energy Reviews 31: 101–112. doi: 10.1016/j.rser.2013.11.036
   Web of Science ®Google Scholar
• Magnier, L., and F. Haghighat. 2010. “Multiobjective Optimization of Building Design Using TRNSYS Simulations, Genetic Algorithm, and Artificial Neural Network.” Building and Environment 45 (3): 739–746. doi: 10.1016/j.buildenv.2009.08.016
   Web of Science ®Google Scholar
• Maillet, D., Y. Jarny, and D. Petit. 2014a. Problèmes inverses en diffusion thermique : Formulation et résolution du problème des moindres carrés. Techniques de l'ingénieur – Transferts thermiques, base documentaire : TIB214DUO (ref. article : be8266).
   Google Scholar
• Maillet, D., Y. Jarny, and D. Petit. 2014b. Problèmes inverses en diffusion thermique : Outils spécifiques de conduction inverse et de régularisation. Techniques de l'ingénieur – Transferts thermiques, base documentaire : TIB214DUO (ref. article : be8267).
   Google Scholar
• Nassiopoulos, A., and F. Bourquin. 2013. “On-Site Building Walls Characterization.” Numerical Heat Transfer, Part A: Applications 63 (3): 179–200. doi: 10.1080/10407782.2013.730422
   Web of Science ®Google Scholar
• Nguyen, A.-T., S. Reiter, and P. Rigo. 2014. “A Review on Simulation-based Optimization Methods Applied to Building Performance Analysis.” Applied Energy 113: 1043–1058. doi: 10.1016/j.apenergy.2013.08.061
   Web of Science ®Google Scholar
• Raudenský, M., K. A. Woodbury, J. Kral, and T. Brezina. 1995. “Genetic Algorithm in Solution of Inverse Heat Conduction Problems.” Numerical Heat Transfer, Part B: Fundamentals 28 (3): 293–306. doi: 10.1080/10407799508928835
   Web of Science ®Google Scholar
• Rouchier, S. 2012. Hygrothermal performance assessment of damaged building materials. PhD thesis., Universite Lyon 1.
   Google Scholar
• Rouchier, S., and M. Woloszyn. 2014. Identification des propriétés hygrothermiques des matériaux par stratégie d'évolution CMA. In IBPSA France 2014 conference, Arras.
   Google Scholar
• Rouchier, S., M. Woloszyn, G. Foray, and J.-J. Roux. 2013. “Influence of Concrete Fracture on the Rain Infiltration and Thermal Performance of Building Facades.” International Journal of Heat and Mass Transfer 61: 340–352. doi: 10.1016/j.ijheatmasstransfer.2013.02.013
   Web of Science ®Google Scholar
• Stuart, A. M. 2013. The Bayesian approach to inverse problems. arXiv:1302.6989 [math].
   Google Scholar
• Tikhonov, A. N., and V. Y. Arsenin. 1977. Solutions of Ill-posed Problems. Washington, DC: Winston.
   Google Scholar
• Vakili, S., and M. S. Gadala. 2013. “Low cost Surrogate model based evolutionary optimization solvers for inverse heat conduction problem.” International Journal of Heat and Mass Transfer 56 (1): 263–273. doi: 10.1016/j.ijheatmasstransfer.2012.09.009
   Web of Science ®Google Scholar
• Vololonirina, O., M. Coutand, and B. Perrin. 2012. Vers une méthode de conception HYGRO-thermique des BATiments performants : Propriétés de transferts des matériaux bois. In Colloque International Francophone NoMaD 2012, LMDC-AUGC.
   Google Scholar
• Wang, S., and X. Xu. 2006. “Parameter Estimation of Internal Thermal Mass of Building Dynamic Models Using Genetic Algorithm.” Energy Conversion and Management 47 (13): 1927–1941. doi: 10.1016/j.enconman.2005.09.011
   Web of Science ®Google Scholar
• Wang, J., and N. Zabaras. 2004. “A Bayesian Inference Approach to the Inverse Heat Conduction Problem.” International Journal of Heat and Mass Transfer 47 (17): 3927–3941. doi: 10.1016/j.ijheatmasstransfer.2004.02.028
   Web of Science ®Google Scholar
• Woloszyn, M., N. Le Pierrés, Y. Kedowidé, J. Virgone, A. Trabelsi, Z. Slimani, E. Mougel, et al., 2014. Vers une méthode de conception HYGRO-thermique des BATiments performants : démarche du projet HYGRO-BAT. IBPSA France 2014 conference, Arras.
   Google Scholar
• Xu, X., and S. Wang. 2007. “Optimal Simplified Thermal Models of Building Envelope Based on Frequency Domain Regression Using Genetic Algorithm.” Energy and Buildings 39 (5): 525–536. doi: 10.1016/j.enbuild.2006.06.010
   Web of Science ®Google Scholar
• Zayane, C. 2011. Identification d'un modèle de comportement thermique de bâtiment à partir de sa courbe de charge. PhD diss., Ecole Nationale Supérieure des Mines de Paris.
   Google Scholar