M. Abadie, M. De-camargo, K. Mendona, and P. Blondeau, Improving the prediction of zonal modeling for forced convection airflows in rooms, Building and Environment, vol.48, pp.173-182, 2012.
URL : https://hal.archives-ouvertes.fr/hal-01009518

A. Aranda, G. Ferreira, M. Mainar-toledo, S. Scarpellini, and E. L. Sastresa, Multiple regression models to predict the annual energy consumption in the spanish banking sector, Energy and Buildings, vol.49, pp.380-387, 2012.

A. Aswani, N. Master, J. Taneja, D. Culler, and C. Tomlin, Reducing transient and steady state electricity consumption in hvac using learningbased model-predictive control, Proceedings of the IEEE, vol.100, 2012.

J. Axley, Multizone Airflow Modeling in Buildings: History and Theory, vol.13, 2007.

M. Aydinalp, V. I. Ugursal, and A. S. Fung, Modeling of the appliance, lighting, and space-cooling energy consumptions in the residential sector using neural networks, Applied Energy, vol.71, pp.87-110, 2002.

M. Aydinalp, V. I. Ugursal, and A. S. Fung, Modeling of the space and domestic hot-water heating energy-consumption in the residential sector using neural networks, Applied Energy, vol.79, pp.159-178, 2004.

M. Aydinalp-koksal and V. I. , Comparison of neural network, conditional demand analysis, and engineering approaches for modeling enduse energy consumption in the residential sector, Applied Energy, vol.85, pp.271-296, 2008.

V. Badescu, Simple and accurate model for the ground heat exchanger of a passive house, Renewable Energy, vol.32, pp.845-855, 2007.

V. Badescu and B. Sicre, Renewable energy for passive house heating II, Model, Energy and Buildings, vol.35, pp.1085-1096, 2003.

V. Badescu and B. Sicre, Renewable energy for passive house heating part I. Building description, Energy and Buildings, vol.35, pp.1077-1084, 2003.

C. A. Balaras, A. G. Gaglia, E. Georgopoulou, S. Mirasgedis, Y. Sarfidis et al., European residential buildings and empirical assessment of the hellenic building stock, energy consumption, emissions and potential energy savings, Building and Environnement, vol.42, pp.1298-1314, 2007.

S. Barlow and D. Fiala, Occupant comfort in UK offices-how adaptive comfort theories might influence future low energy office refurbishment strategies, Energy and Buildings, vol.39, pp.837-846, 2007.

J. L. Bertez, Strategic overview on a global, structured and sustainable way for "efficient building, 2009.

B. Bohm and P. Danig, Monitoring the energy consumption in a district heated apartment building in Copenhagen, with specic interest in the thermodynamic performance, Energy and Buildings, vol.36, pp.229-236, 2004.

D. Bonneau, F. Rongere, D. Covalet, and B. Gauthier, Clim2000: modular software for energy simulation in buildings, Proceedings of IBPSA 93

A. Adelaide, , 1993.

H. Bouia and P. Dalicieux, IBPSA (The International Building Performance Simulation Association), Proceedings of Building Simulation 91 Conference, p.106110, 1991.

H. Boyer, F. Garde, J. Gatina, and J. Brau, A multimodel approach to building thermal simulation for design and research purposes, Energy and Buildings, vol.28, pp.71-78, 1998.
URL : https://hal.archives-ouvertes.fr/hal-00767536

A. Brun, C. Spitz, E. Wurtz, and L. Mora, Behavioural comparison of some predictive tools used in a low-energy building, Building Simulation Eleventh International IBPSA Conference, pp.1185-1190, 2009.

A. Brun, E. Wurtz, and D. Quenard, Experimental and numerical comparison of heat transfer in a naturally ventilated roof cavity, Building Simulation Fourth National Conference of IBPSA-USA, pp.160-169, 2010.

B. Bueno, L. Norford, G. Pigeon, and R. Britter, A resistance-capacitance network model for the analysis of the interactions between the energy performance of buildings and the urban climate, Building and Environment, vol.54, pp.116-125, 2012.

-. Buildopt and . Vie, , 2007.

L. G. Caldas and L. K. Norford, Genetic algorithms for optimization of building envelopes and the design and control of hvac systems, Journal of Solar Energy Engineering, vol.125, pp.343-351, 2003.

E. Carroll, E. Hatton, and M. Brown, Residential energy use behavior change pilot, 2009.

, Phoenics user manual for program version 3.6, CHAM Ltd, 2005.

J. Che, J. Wang, and G. Wang, An adaptive fuzzy combination model based on self-organizing map and support vector regression for electric load forecasting, Energy, vol.37, pp.657-664, 2012.

J. L. Chen, H. B. Liu, W. Wu, and D. T. Xie, Estimation of monthly solar radiation from measured temperatures using support vector machines e a case study, Renewable Energy, vol.36, pp.413-420, 2011.

Q. Chen, Ventilation performance prediction for buildings: A method overview and recent applications, Building and Environnement, vol.44, pp.848-858, 2009.

T. Chow, J. Hand, and P. Strachan, Building-integrated photovoltaic and thermal applications in a subtropical hotel building, Applied Thermal Engineering, vol.23, pp.2035-2049, 2003.

J. Clarke, J. Cockroft, S. Conner, J. Hand, N. Kelly et al., Simulation-assisted control in building energy management systems, Energy and Buildings, vol.34, pp.933-940, 2002.

J. Clarke, C. Johnstone, N. Kelly, and P. Strachan, The simulation od photovoltaic-integrated building facades, Building Simulation, pp.189-195, 1997.

J. Clarke and D. Mclean, Esp-a building and plant energy simulation system, 1988.

C. Cortes and V. Vapnik, Support-vector networks, Machine Learning, vol.20, pp.273-297, 1995.

D. B. Crawley, J. W. Hand, M. Kummert, and B. T. Griffith, Contrasting the capabilities of building energy performance simulation programs, Building and Environnement, vol.43, pp.661-673, 2008.

F. Cron, C. Inard, and R. Belarbi, Numerical analysis of hybrid ventilation performance depending on climate characteristics, HybVent-Hybrid Ventilation, vol.1, 2003.
URL : https://hal.archives-ouvertes.fr/hal-00312190

D. Datta, S. A. Tassou, and D. Marriott, Application of neural networks for the prediction of the energy consumption in a supermarket, Proceedings of the Clima 2000 Conference Brussels, 1997.

K. Deng, P. Barooah, P. G. Mehta, and S. P. Meyn, Building thermal model reduction via aggregation of states, American Control Conference (ACC), Marriott Waterfront, pp.5118-5123, 2010.

M. Dewit, HAMBase: Heat, Air and Moisture Model for Building and Systems Evaluation, 2006.

W. Dols and G. Walton, CONTAMW2.0 user manual, National Institute of Standards and Technology (NIST), 2002.

B. Dong, C. Cao, and S. E. Lee, Applying support vector machines to predict building energy consumption in tropical region, Energy and Buildings, vol.37, pp.545-553, 2005.

. Energyplus, Department of Energy (DOE) (????)

A. Entrop, H. Brouwers, and A. Reinders, Evaluation of energy performance indicators and financial aspects of energy saving techniques in residential real estate, Energy and Buildings, vol.42, pp.618-629, 2010.

. Epbd, On the energy performance of buildings, 2010.

G. Escriva-escriva, C. Alvarez-bel, C. Roldan-blay, and M. Alcazar-ortega, New articial neural network prediction method for electrical consumption forecasting based on building end-uses, Energy and Buildings, vol.43, pp.3112-3119, 2011.

G. Fraisse, C. Viardot, O. Lafabrie, and G. Achard, Development of a simplified and accurate building model based on electrical analogy, Energy and Buildings, vol.34, pp.1017-1031, 2002.

R. Z. Freire, G. H. Oliveira, and N. Mendes, Development of regression equations for predicting energy and hygrothermal performance of buildings, Energy and Buildings, vol.40, pp.810-820, 2008.

A. G. Gaglia, C. A. Balaras, S. Mirasgedis, E. Georgopoulou, Y. Sarafidis et al., Empirical assessment of the hellenic nonresidential building stock, energy consumption, emissions and potential energy savings, Energy Conversion and Management, vol.48, pp.1160-1175, 2007.

N. Ghrab-morcos, CHEOPS: a simplified tool for thermal assessment of Mediterranean residential buildings in hot and cold seasons, Energy and Buildings, vol.37, pp.651-662, 2005.

B. Givoni and F. Vecchia, Predicting thermal performance of occupied houses, Proceedings of the PLEA, p.701706, 2001.

M. Gouda, S. Danahera, and C. Underwood, Building thermal model reduction using nonlinear constrained optimization, Building and Environment, vol.37, pp.1255-1265, 2002.

S. Goyal and P. Barooah, A method for model-reduction of non-linear thermal dynamics of multi-zone buildings, Energy and Buildings, vol.47, pp.332-340, 2012.

F. Gugliermetti, G. Passerini, and F. Bisegna, Climate models for the assessment of office buildings energy performance, Building and Environnement, vol.39, pp.39-50, 2004.

F. Haghighat, Y. Li, and A. C. Megri, Development and validation of a zonal model POMA, Building and Environnement, vol.36, pp.1039-1047, 2001.

I. Hazyuk, C. Ghiaus, and D. Penhouet, Optimal temperature control of intermittently heated buildings using Model Predictive Control: Part Ibuilding modeling, Building and Environment, vol.51, pp.379-387, 2012.

J. Holland, Adaptation in natural and artificial systems, 1975.

M. Ibanez, A. Lazaro, B. Zalba, and L. F. Cabeza, An approach to the simulation of PCMs in building applications using TRNSYS, Applied Thermal Engineering, vol.25, pp.1796-1807, 2005.

P. Ihm, A. Nemri, and M. Krarti, Estimation of lighting energy savings from daylighting, Building and environment, vol.44, pp.509-514, 2009.

C. Inard, ContributionàContribution`Contributionà létude du couplage thermique entre unémetteurun´unémetteur de chauffage et un local. Etudes expérimentales en chambres climatiques, 1988.

C. Inard, H. Bouia, and P. Dalicieux, Prediction of air temperature distribution in buildings with a zonal model, Energy and Buildings, vol.24, pp.125-132, 1996.
URL : https://hal.archives-ouvertes.fr/hal-00312231

R. Jin, X. Du, and W. Chen, The use of metamodeling techniques for optimization under uncertainty, Structural and Multidisciplinary Optimization, vol.25, pp.99-116, 2003.

T. E. Jiru and F. Haghighat, Modeling ventilated double skin facadea zonal approach, Energy and Buildings, vol.40, pp.1567-1576, 2008.

S. A. Kalogirou, Applications of artificial neural-networks for energy systems, Applied Energy, vol.67, pp.17-35, 2000.

S. A. Kalogirou, Use of TRNSYS for modelling and simulation of a hybrid pvthermal solar system for cyprus, Renewable Energy, vol.23, pp.247-260, 2001.

S. A. Kalogirou and M. Bojic, Artificial neural networks for the prediction of the energy consumption of a passive solar building, Energy, vol.25, pp.479-491, 2000.

S. A. Kalogirou, C. C. Neocleous, and C. N. Schizas, Building heating load estimation using artificial neural networks, Proceedings of Clima, 2000.

. Conference, , 1997.

K. Kavaklioglu, Modeling and prediction of turkeys electricity consumption using support vector regression, Applied Energy, vol.88, pp.368-375, 2011.

K. Klein, A transient simulation and program, 1996.

E. Krüger and B. Givoni, Predicting thermal performance in occupied dwellings, Energy and Buildings, vol.36, pp.301-307, 2004.

S. S. Kwok and E. W. Lee, A study of the importance of occupancy to building cooling load in prediction by intelligent approach, Energy Conversion and Management, vol.52, pp.2555-2564, 2011.

G. Lafrance and D. Perron, Evolution of residential electricity demand by end-use in quebec 1979-1989: A conditional demand analysis, Energy Studies Review, vol.6, 1994.

F. Lai, F. Magouì-es, and F. Lherminier, Vapniks learning theory applied to energy consumption forecasts in residential buildings, International Journal of Computer Mathematics, vol.85, pp.1563-1588, 2008.

J. C. Lam, S. C. Hui, and A. L. Chan, Regression analysis of high-rise fully air-conditioned office buildings, Energy and Buildings, vol.26, pp.189-197, 1997.

J. C. Lam and S. C. Hui, Sensitivity analysis of energy performance of office buildings, Building and Environment, vol.31, pp.27-39, 1996.

P. Lauret, H. Boyer, C. Riviere, and A. Bastide, A genetic algorithm applied to the validation of building thermal models, Energy and Buildings, vol.37, pp.858-866, 2005.
URL : https://hal.archives-ouvertes.fr/hal-00766241

L. , 0 reference manual, Lawrence Berkeley National Laboratory and, 2003.

J. Lee, H. Jung, J. Park, J. Lee, and Y. Yoon, Optimization of building window system in asian regions by analyzing solar heat gain and daylighting elements, Renewable Energy, vol.50, pp.522-531, 2013.

J. Lettvin, H. R. Maturana, W. S. Mcculloch, and W. H. Pitts, What the frog's eye tells the frog's brain, Proc. Inst. Radio Engr, vol.47, pp.1940-1951, 1959.

M. Leung, N. C. Tse, L. Lai, and T. Chow, The use of occupancy space electrical power demand in building cooling load prediction, Energy and Buildings, vol.55, pp.151-163, 2012.

Q. Li, Q. Meng, J. Cai, H. Yoshino, and A. Mochida, Applying support vector machine to predict hourly cooling load in the building, Applied Energy, vol.86, pp.2249-2256, 2009.

Q. Li, Q. Meng, J. Cai, H. Yoshino, and A. Mochida, Predicting hourly cooling load in the building: A comparison of support vector machine and different artificial neural networks, Energy Conversion and Management, vol.50, pp.90-96, 2009.

Y. Li, A. Delsante, and J. Symons, Prediction of natural ventilation in buildings with large openings, Building and Environnement, vol.35, pp.191-206, 2000.

Z. Liao and A. Dexter, A simplied physical model for estimating the average air temperature in multi-zone heating systems, Building and Environnement, vol.39, pp.1013-1022, 2004.

I. A. Macdonald, Quantifying the Effects of Uncertainty in Building Simulation, 2002.

C. N. Madu, Simulation in manufacturing: A regression metamodel approach, Computers & Industrial Engineering, vol.18, pp.381-389, 1990.

L. Magnier and F. Haghighat, Multiobjective optimization of building design using TRNSYS simulations, genetic algorithm, and artificial neural network, Building and Environnement, vol.45, pp.739-746, 2010.

T. A. Mara, F. Garde, H. Boyer, and M. Mamode, Empirical validation of the thermal model of a passive solar cell test, Energy and Buildings, vol.33, pp.589-599, 2001.
URL : https://hal.archives-ouvertes.fr/hal-00765776

T. P. Mcdowell, J. W. Thornton, S. Emmerich, and G. Walton, Integration of airflow and energy simulation using CONTAM and TRNSYS, ASHRAE Transactions, vol.109, 2003.

N. Mendes, R. C. Oliveira, and G. H. Santos, Energy efficiency and thermal comfort analysis using the PowerDomus hygrothermal simulation tool, Building Simulation IBPSA Conference, 2005.

W. Michael, Genopt generic optimisation program, 2008.

F. W. Miranville, H. Boyer, T. Mara, and F. Garde, On the thermal behavior of roof-mounted radiant barriers under tropical and humid climatic conditions: modelling and empirical validation, Energy and Buildings, vol.35, pp.997-1008, 2003.

L. Mora, Prédiction des performances thermo-aérauliques des bâtiments par association de modèles de différents niveaux de nesse au sein dun environnement orienté objet, 2003.

L. Mora, A. J. Gadgil, and E. Wurtz, Comparing zonal and cfd model predictions of isothermal indoor airflows to experimental data, Indoor Air, vol.13, pp.77-85, 2003.

M. Mossolly, K. Ghali, and N. Ghaddar, Optimal control strategy for a multi-zone air conditioning system using a genetic algorithm, Energy, vol.34, pp.58-66, 2009.

M. Musy, F. Winkelmann, E. Wurtz, and A. Sergent, Automatically generated zonal models for building air ow simulation: principles and applications, Building and Environnement, vol.37, pp.873-881, 2002.

N. Nassif, S. Kajl, and R. Sabourin, Optimization of hvac control system strategy using two-objective genetic algorithm, HVAC & R RESEARCH, vol.11, pp.459-486, 2005.

A. H. Neto and F. A. Fiorelli, Comparison between detailed model simulation and artificial neural network for forecasting building energy consumption, Energy and Buildings, vol.40, pp.2169-2176, 2008.

R. Ooka and K. Komamura, Optimal design method for building energy systems using genetic algorithms, Building and Environnement, vol.44, pp.1538-1544, 2009.

M. Ordenes, R. Lamberts, and S. Guths, Estimation of thermophysical properties using natural signal analysis with heat and moisture transfer model, Energy and Buildings, vol.41, pp.1360-1367, 2009.

H. K. Ozturk, H. Ceylan, O. E. Canyurt, and A. Hepbasli, Electricity estimation using genetic algorithm approach: a case study of Turkey, Energy, vol.30, pp.1003-1012, 2005.

A. Paniagua-tineo, S. Salcedo-sanz, C. Casanova-mateo, E. Ortizgarcia, M. Cony et al., Prediction of daily maximum temperature using a support vector regression algorithm, Renewable Energy, vol.36, pp.3054-3060, 2011.

B. Paris, J. Eynard, S. Grieu, and M. Polit, Hybrid pid-fuzzy control scheme for managing energy resources in buildings, Applied Soft Computing, vol.8, pp.5068-5080, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00628879

B. Paris, J. Eynard, S. Grieu, T. Talbert, and M. Polit, Heating control schemes for energy management in buildings, Energy and Buildings, vol.42, pp.1908-1917, 2010.
URL : https://hal.archives-ouvertes.fr/hal-00543272

M. Parti and C. Parti, The total and appliance-specific conditional demand for electricty in teh houshold sector, The Bell Journal of Economics, vol.11, pp.309-321, 1980.

S. Petersen and S. Svendsen, Method for simulating predictive control of building systems operation in the early stages of building design, Applied Energy, vol.88, pp.4597-4606, 2011.

B. Poel, G. Van-cruchten, and C. A. Balaras, Energy performance assessment of existing dwellings, Energy and Buildings, vol.39, pp.393-407, 2007.

-. Proposal, Low energy building in europe: current state of play, definitions and best practice, 2009.

M. Qin, G. Walton, R. Belarbi, and F. Allard, Simulation of whole building coupled hygrothermal airflow transfer in different climates, Energy Conversion and Management, vol.52, pp.1470-1478, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00548182

C. Rode and K. Grau, Whole building hygrothermal simulation model, ASHRAE Transactions, vol.109, pp.572-582, 2003.

C. Rode and K. Grau, Integrated calculation of hygrothermal conditions of buildings, Presentation for IEA Annex 41 meeting, 2004.

D. Y. Rong-qin and Y. J. Zhou, Research on a dynamic simulation method of atrium thermal environment based on neural network, Building and Environment, vol.50, pp.214-220, 2012.

P. Rumaniovski, J. Brau, and J. J. Roux, An adapted model for simulation of the interaction between a wall and the building heating system, Proceedings of the thermal performance of the exterior envelopes of buildings IV conference Orlando, pp.224-233, 1989.

H. Sadeghi, M. Zolfaghari, and M. Heydarizade, Estimation of electricity demand in residential sector using genetic algorithm approach, International Journal of Industrial Engineering and Production Research, vol.22, pp.43-50, 2011.

B. Saha, K. Goebel, S. Poll, and J. Christophersen, An integrated approach to battery health monitoring using bayesian regression and state estimation, Autotestcon, pp.646-653, 2007.
DOI : 10.1109/autest.2007.4374280

M. Sahu, B. Bhattacharjee, and S. Kaushik, Thermal design of airconditioned building for tropical climate using admittance method and genetic algorithm, Energy and Buildings, vol.53, pp.1-6, 2012.

V. Siddharth, P. Ramakrishna, T. Geetha, and A. Sivasubramaniam, Automatic generation of energy conservation measures in buildings using genetic algorithms, Energy and Buildings, vol.43, pp.2718-2726, 2011.

J. Srebric, J. Yuan, and A. Novoselac, On-site experimental validation of a coupled multizone and CFD model for building contaminant transport simulations, 2008.

L. Stephan, A. Bastide, and E. Wurtz, Optimizing opening dimensions for naturally ventilated buildings, Applied Energy, vol.88, pp.2791-2801, 2011.
DOI : 10.1016/j.apenergy.2010.12.039
URL : https://hal.archives-ouvertes.fr/hal-00918244

G. Tan and L. R. Glicksman, Application of integrating multi-zone model with CFD simulation to natural ventilation prediction, Energy and Buildings, vol.37, pp.1049-1057, 2005.

J. Teeter and M. Y. Chow, Application of functional link neural network to HVAC thermal dynamic system identification, IEEE Transactions on Industrial Electronics, vol.45, issue.1, 1998.
DOI : 10.1109/41.661318
URL : http://repository.lib.ncsu.edu/bitstream/1840.2/634/1/chow_1998_ieee_transactions_industrial_electronics_170.pdf

K. Tiedermann, Using conditional demand analysis to estimate residential energy use and energy savings, European Council for an Energy Efficient Economy Summer Study, vol.6, pp.1279-1283, 2007.

P. Tittelein, E. Wurtz, and G. Achard, Simspark platform evolution for lowenergy building simulation, International Scientific Journal for Alternative Energy and Ecology, vol.62, 2008.

M. Trocmé, Aide au choix de conception de bâtimentsbâtiments´bâtimentséconomes enénergie en´enénergie, 2009.

D. Tuhus-dubrow and M. Krarti, Genetic-algorithm based approach to optimize building envelope design for residential buildings, Building and Environment, vol.45, pp.1578-1581, 2010.
DOI : 10.1016/j.buildenv.2010.01.005

D. Villanueva, G. Picard, R. L. Riche, and R. T. Haftka, Optimisation multiagent par partitionnement adaptatif de lespace de conception, p.20
URL : https://hal.archives-ouvertes.fr/emse-00728459

, Journes Francophones sur les Systmes Multi-Agents, 2012.

M. Wall, Distribution of solar radiation in glazed spaces and adjacent buildings. a comparison of simulation programs, Energy and Buildings, vol.26, pp.129-135, 1997.

L. Wang and N. H. Wong, Coupled simulations for naturally ventilated residential buildings, Automation in construction, vol.17, pp.386-398, 2008.
DOI : 10.1016/j.autcon.2007.06.004

S. Wang, Dynamic simulation evaluation of building vav airconditioning system and of emcs on-line control strategies, Building Simulation, vol.34, pp.681-705, 1999.

S. Wang and X. Xu, Parameter estimation of internal thermal mass of building dynamic models using genetic algorithm, Energy Conversion and Management, vol.47, pp.1927-1941, 2006.

S. Wang and X. Xu, Simplified building model for transient thermal performance estimation using GA-based parameter identification, International Journal of Thermal Sciences, vol.45, pp.419-432, 2006.

F. Winkelmann, B. Birsdall, W. Bull, K. Ellington, A. Erdem et al., doe-2 supplement, version 2.1e, technical report lbl-34947, 1993.

M. Woloszyn and C. Rode, Tools for performance simulation of heat, air and moisture conditions of whole buildings, Building Simulation, vol.1, pp.5-24, 2008.
URL : https://hal.archives-ouvertes.fr/hal-00353433

M. Woloszyn, G. Rusaouen, and D. Covalet, Whole building simulation tools: Clim2000., Publication A41-T1-F-04-3. Presentation for IEA Annex 41 meeting, 2004.

E. Wurtz, Three-Dimensional Modeling of Thermal and Airflow Transfers in Building Using an Object-Oriented Simulation Environment, 1995.

E. Wurtz, L. Mora, and C. Inard, An equation-based simulation environment to investigate fast building simulation, Building and Environnement, vol.41, pp.1571-1583, 2006.
URL : https://hal.archives-ouvertes.fr/hal-00372570

E. Wurtz, J. M. Nataf, and F. Winkelmann, Two-and three-dimensional natural and mixed convection simulation using modular zonal models in buildings, International Journal of Heat and Mass Transfer, vol.42, pp.923-940, 1999.

X. Xu, J. E. Taylor, A. L. Pisello, and P. J. Culligan, The impact of place-based affiliation networks on energy conservation: An holistic model that integrates the influence of buildings, residents and the neighborhood context, Energy and Buildings, vol.55, pp.637-646, 2012.

X. Xu and S. Wang, Optimal simplified thermal models of building envelope based on frequency domain regression using genetic algorithm, Energy and Buildings, vol.39, pp.525-536, 2007.

Z. Yang, X. Li, C. P. Bowers, T. Schnier, K. Tang et al., An efficient evolutionary approach to parameter identification in a building thermal model, IEEE TRANSACTIONS ON SYSTEMS, vol.42, pp.957-969, 2012.

Z. Zhai, Q. Chen, P. Haves, and J. H. Klems, On approaches to couple energy simulation and computational fluid dynamics programs, Building and Environnement, vol.37, pp.857-864, 2002.

Z. J. Zhai, M. H. Johnson, and M. Krarti, Assessment of natural and hybrid ventilation models in whole-building energy simulations, Energy and Buildings, vol.43, pp.2251-2261, 2011.

H. Zhao and F. , Magouì es, A review on the prediction of building energy consumption, Renewable and Sustainable Energy Reviews, vol.16, pp.3586-3592, 2012.

E. Znouda, N. Ghrab-morcos, and A. Hadj-alouane, Optimization of mediterranean building design using genetic algorithms, Energy and Buildings, vol.39, pp.148-153, 2007.