J. P. Kleijnen, Statistical tools for simulation practitioners, 1987.

T. W. Simpson, J. D. Poplinski, P. N. Koch, and J. K. Allen, Metamodels for computer-based engineering design: Survey and recommendations, Engineering with computers, vol.17, pp.129-150, 2001.

Y. Li, S. H. Ng, M. Xie, and T. N. Goh, A systematic comparison of metamodeling techniques for simulation optimization in decision support systems, Applied Soft Computing, vol.10, pp.1257-1273, 2010.

D. C. Montgomery, Design and analysis of experiments, 2017.

E. Gratia and A. De-herde, A simple design tool for the thermal study of an office building, Energy and Buildings, vol.34, pp.279-289, 2002.

J. Xu, J. Kim, H. Hong, and J. Koo, A systematic approach for energy efficient building design factors optimization, Energy and Buildings, vol.89, pp.87-96, 2015.

R. Pino-mejías, A. Pérez-fargallo, C. Rubio-bellido, and J. Pulido-arcas, Comparison of linear regression and artificial neural networks models to predict heating and cooling energy demand, energy consumption and co2 emissions, Energy, vol.118, pp.24-36, 2017.

D. Shiming and J. Burnett, Energy use and management in hotels in hong kong, International Journal of Hospitality Management, vol.21, pp.371-380, 2002.

A. Aranda, G. Ferreira, M. D. 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. P. Melo, M. Fossati, R. S. Versage, M. J. Sorgato, V. A. Scalco et al., Development and analysis of a metamodel to represent the thermal behavior of naturally ventilated and artificially air-conditioned residential buildings, Energy and Buildings, vol.112, pp.209-221, 2016.

J. C. Lam, K. K. Wan, T. N. Lam, and S. L. Wong, An analysis of future building energy use in subtropical hong kong, Energy, vol.35, pp.1482-1490, 2010.

A. Mastrucci, O. Baume, F. Stazi, and U. Leopold, Estimating energy savings for the residential building stock of an entire city: A gis-based statistical downscaling approach applied to rotterdam, Energy and Buildings, vol.75, pp.358-367, 2014.

S. Karatasou, M. Santamouris, and V. Geros, Modeling and predicting building's energy use with artificial neural networks: Methods and results, Energy and buildings, vol.38, pp.949-958, 2006.

M. Macas, F. Moretti, A. Fonti, A. Giantomassi, G. Comodi et al., The role of data sample size and dimensionality in neural network based forecasting of building heating related variables, Energy and Buildings, vol.111, pp.299-310, 2016.

F. Ascione, N. Bianco, C. De, G. M. Stasio, G. P. Mauro et al., Artificial neural networks to predict energy performance and retrofit scenarios for any member of a building category: A novel approach, Energy, vol.118, pp.999-1017, 2017.

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.

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. Heo and V. M. Zavala, Gaussian process modeling for measurement and verification of building energy savings, Energy and Buildings, vol.53, pp.7-18, 2012.

X. Chen, H. Yang, and K. Sun, Developing a metamodel for sensitivity analyses and prediction of building performance for passively designed highrise residential buildings, Applied energy, vol.194, pp.422-439, 2017.

L. Van-gelder, H. Janssen, and S. Roels, Metamodelling in robust low-energy dwelling design, 2nd Central European Symposium on Building Physics

X. Gong, Y. Akashi, and D. Sumiyoshi, Optimization of passive design measures for residential buildings in different chinese areas, Building and Environment, vol.58, pp.46-57, 2012.

M. Rasouli, G. Ge, C. J. Simonson, and R. W. Besant, Uncertainties in energy and economic performance of HVAC systems and energy recovery ventilators due to uncertainties in building and HVAC parameters, Applied Thermal Engineering, vol.50, pp.732-742, 2013.

J. Carlo and R. Lamberts, Development of envelope efficiency labels for commercial buildings: Effect of different variables on electricity consumption, Energy and Buildings, vol.40, 2002.

D. G. Sanchez, B. Lacarrière, M. Musy, and B. Bourges, Application of sensitivity analysis in building energy simulations: Combining first-and second-order elementary effects methods, Energy and Buildings, vol.68, pp.741-750, 2014.
URL : https://hal.archives-ouvertes.fr/hal-00947577

J. Maderspacher, P. Geyer, T. Auer, and W. Lang, Comparison of different meta model approches with a detailed buiding model for long-term simulations, Conference Proceedings Building Simulation

J. C. Lam, S. C. Hui, and A. L. Chan, Regression analysis of high-rise fully airconditioned of fice buildings, Energy and Buildings, vol.26, pp.189-198, 1997.

I. Jaffal and C. Inard, A metamodel for building energy performance, Energy and Buildings, vol.151, pp.501-510, 2017.

G. E. Box and D. W. Behnken, Some new three level designs for the study of quantitative variables, Technometrics, vol.2, pp.455-475, 1960.