EDP Sciences logo
Web of Conferences logo
Search
Menu
All issues Volume 111 (2019) E3S Web Conf., 111 (2019) 01053 References
Open Access
Issue
E3S Web Conf.
Volume 111, 2019
CLIMA 2019 Congress
Article Number 01053
Number of page(s) 8
Section Advanced HVAC&R&S Technology
DOI https://doi.org/10.1051/e3sconf/201911101053
Published online 13 August 2019
  1. EPBD, “The Revised Energy Performance of Buildings Directive (EU) 2018/844,” (2018) [Google Scholar]
  2. EnBau, “Performance of buildings across the year,” 2010. [Online]. Available: http://www.enob.info/en/analysis/analysis/details/performance-of-buildings-across-the-year/ [Google Scholar]
  3. R. De Coninck and L. Helsen, “Practical implementation and evaluation of model predictive control for an office building in Brussels,” Energy Build., 111, 290–298, (2016) [Google Scholar]
  4. E. Žáčeková, Z. Váňa, and J. Cigler, “Towards the real-life implementation of MPC for an office building: Identification issues,” Appl. Energy, 135, 53–62, (2014) [Google Scholar]
  5. G. Huang, S. Wang, and X. Xu, “Robust Model Predictive Control of VAV Air-Handling Units Concerning Uncertainties and Constraints,” HVAC&R Res., vol. 16, no. January, 15–33, (2010) [Google Scholar]
  6. W. Liang, R. Quinte, X. Jia, and J. Q. Sun, “MPC control for improving energy efficiency of a building air handler for multi-zone VAVs,” Build. Environ., vol. 92, 256–268, (2015) [Google Scholar]
  7. S. Prívara, Z. Váňa, E. Žáčeková, and J. Cigler, “Building modeling: Selection of the most appropriate model for predictive control,” Energy Build., vol. 55, 341–350, (2012) [Google Scholar]
  8. P. Bacher and H. Madsen, “Identifying suitable models for the heat dynamics of buildings” Energy Build., vol. 43, 1511–1522 (2011) [Google Scholar]
  9. M. Gruber, A. Trüschel, and J. Dalenbäck, “Model-based controllers for indoor climate control in office buildings – Complexity and performance evaluation,” Energy Build., vol. 68, 213–222, (2014) [Google Scholar]
  10. G. P. Henze, “Model predictive control for buildings : a quantum,” vol. 1493, 9–11, (2013) [Google Scholar]
  11. S. Prívara, J. Cigler, Z. Váňa, F. Oldewurtel, C. Sagerschnig, and E. Žáčeková, “Building modeling as a crucial part for building predictive control,” Energy Build., vol. 56, 8–22, (2013) [Google Scholar]
  12. G. Reynders, J. Diriken, and D. Saelens, “Quality of grey-box models and identified parameters as function of the accuracy of input and observation signals,” Energy Build., vol. 82, 263–274, (2014) [Google Scholar]
  13. R. De Coninck, F. Magnusson, J. Åkesson, and L. Helsen, “Toolbox for development and validation of grey-box building models for forecasting and control,” J. Build. Perform. Simul., vol. 1493, 1–16, (2015) [Google Scholar]
  14. A. Pantazaras, S. E. Lee, M. Santamouris, and J. Yang, “Predicting the CO2 levels in buildings using deterministic and identified models,” Energy Build., vol. 127, 774–785, (2016) [Google Scholar]
  15. M. Macarulla, M. Casals, M. Carnevali, N. Forcada, and M. Gangolells, “Modelling indoor air carbon dioxide concentration using grey-box models,” vol. 117, 146–153, (2017) [Google Scholar]
  16. N. Rode, H. Madsen, and S. Bay, “Parameter estimation in stochastic grey-box models,” vol. 40, 225–237, (2004) [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.