EDP Sciences logo
Web of Conferences logo
Search
Menu
All issues Volume 111 (2019) E3S Web Conf., 111 (2019) 04039 References
Open Access
Issue
E3S Web Conf.
Volume 111, 2019
CLIMA 2019 Congress
Article Number 04039
Number of page(s) 8
Section High Energy Performance and Sustainable Buildings, Simulation models and predictive tools for the buildings HVAC, IEQ and energy
DOI https://doi.org/10.1051/e3sconf/201911104039
Published online 13 August 2019
  1. J. P. C. Kleijnen, Statistical tools for simulation practitioners, Marcel Dekker, Inc., 1987. [Google Scholar]
  2. 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 17 (2001) 2, 129-150. [CrossRef] [Google Scholar]
  3. Y.-F. 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 10 (2010), 4, 1257-1273. [CrossRef] [Google Scholar]
  4. D. C. Montgomery, Design and analysis of experiments, Wiley, 2017. [Google Scholar]
  5. E. Gratia and A. De Herde, A simple design tool for the thermal study of an office building, Energy and Buildings 34 (2002), 3, 279-289. [CrossRef] [Google Scholar]
  6. J. Xu, J.-H. Kim, H. Hong and J. Koo, A systematic approach for energy efficient building design factors optimization, Energy and Buildings 89 (2015), 87–96. [CrossRef] [Google Scholar]
  7. 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 118 (2017), 24–36. [CrossRef] [Google Scholar]
  8. D. Shiming and J. Burnett, Energy use and management in hotels in hong kong, International Journal of Hospitality Management 21 (2002), 4, 371-380. [CrossRef] [Google Scholar]
  9. A. Aranda, G. Ferreira, M. D. Mainar-Toledo, S. Scarpellini and E. Llera Sastresa, Multiple regression models to predict the annual energy consumption in the spanish banking sector, Energy and Buildings 49 (2012), 380–387. [CrossRef] [Google Scholar]
  10. A. P. Melo, M. Fossati, R. S. Versage, M. J. Sorgato, V. A. Scalco and R. Lamberts, Development and analysis of a metamodel to represent the thermal behavior of naturally ventilated and artificially air-conditioned residential buildings, Energy and Buildings 112 (2016), 209–221. [CrossRef] [Google Scholar]
  11. J. C. Lam, K. K. W. Wan, T. N. T. Lam and S. L. Wong, An analysis of future building energy use in subtropical hong kong, Energy 35 (2010), 3, 1482-1490. [CrossRef] [Google Scholar]
  12. 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 75 (2014), 358–367. [CrossRef] [Google Scholar]
  13. S. Karatasou, M. Santamouris and V. Geros, Modeling and predicting building’s energy use with artificial neural networks: Methods and results, Energy and buildings 38 (2006), 8, 949-958. [CrossRef] [Google Scholar]
  14. M. Macas, F. Moretti, A. Fonti, A. Giantomassi, G. Comodi, M. Annunziato, S. Pizzuti and A. Capra, The role of data sample size and dimensionality in neural network based forecasting of building heating related variables, Energy and Buildings 111 (2016), 299–310. [CrossRef] [Google Scholar]
  15. F. Ascione, N. Bianco, C. De Stasio, G. M. Mauro and G. P. Vanoli, Artificial neural networks to predict energy performance and retrofit scenarios for any member of a building category: A novel approach, Energy 118 (2017), 999–1017. [CrossRef] [Google Scholar]
  16. B. Dong, C. Cao and S. E. Lee, Applying support vector machines to predict building energy consumption in tropical region, Energy and Buildings 37 (2005), 5, 545-553. [CrossRef] [Google Scholar]
  17. 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 50 (2009), 1, 90-96. [CrossRef] [Google Scholar]
  18. Y. Heo and V. M. Zavala, Gaussian process modeling for measurement and verification of building energy savings, Energy and Buildings 53 (2012), 7–18. [CrossRef] [Google Scholar]
  19. X. Chen, H. Yang and K. Sun, Developing a meta-model for sensitivity analyses and prediction of building performance for passively designed high-rise residential buildings, Applied energy 194 (2017), 422–439. [CrossRef] [Google Scholar]
  20. L. Van Gelder, H. Janssen and S. Roels, Metamodelling in robust low-energy dwelling design, 2nd Central European Symposium on Building Physics, Vienna University of Technology. [Google Scholar]
  21. X. Gong, Y. Akashi and D. Sumiyoshi, Optimization of passive design measures for residential buildings in different chinese areas, Building and Environment 58 (2012), 46–57. [CrossRef] [Google Scholar]
  22. 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 50 (2013), 1, 732-742. [CrossRef] [Google Scholar]
  23. J. Carlo and R. Lamberts, Development of envelope efficiency labels for commercial buildings: Effect of different variables on electricity consumption, Energy and Buildings 40 (2008), 11, 2002-2008. [CrossRef] [Google Scholar]
  24. 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 68 (2014), 741–750. [CrossRef] [Google Scholar]
  25. 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. [Google Scholar]
  26. J. C. Lam, S. C. M. Hui and A. L. S. Chan, Regression analysis of high-rise fully air-conditioned of fice buildings, Energy and Buildings 26 (1997), 2, 189-198. [CrossRef] [Google Scholar]
  27. I. Jaffal and C. Inard, A metamodel for building energy performance, Energy and Buildings 151 (2017), 501–510. [CrossRef] [Google Scholar]
  28. G. E. P. Box and D. W. Behnken, Some new three level designs for the study of quantitative variables, Technometrics 2 (1960), 4, 455–475. [CrossRef] [Google Scholar]

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.