Open Access
| Issue |
E3S Web Conf.
Volume 246, 2021
Cold Climate HVAC & Energy 2021
|
|
|---|---|---|
| Article Number | 11006 | |
| Number of page(s) | 8 | |
| Section | Advanced HVAC Control | |
| DOI | https://doi.org/10.1051/e3sconf/202124611006 | |
| Published online | 29 March 2021 | |
- K. Mařík, J. Rojíček, P. Stluka, and J. Vass, “Advanced HVAC Control : Theory vs. Reality,” in IFAC Proceedings Volumes, 2011, vol. 44, no. 1, pp. 3108–3113. [CrossRef] [Google Scholar]
- M. Killian and M. Kozek, “Ten questions concerning model predictive control for energy efficient buildings,” Build. Environ., vol. 105, pp. 403–412, 2016. [Google Scholar]
- P. Bacher and H. Madsen, “Identifying suitable models for the heat dynamics of buildings,” Energy Build., vol. 43, no. 7, pp. 1511–1522, 2011. [Google Scholar]
- H. Madsen et al., “Report of Subtask 3, part 2: Thermal performance characterisation using time series data - statistical guidelines,” 2015. [Google Scholar]
- B. Merema, H. Breesch, and D. Saelens, “Comparison of model identification techniques for MPC in all-air HVAC systems in an educational building,” E3S Web Conf., vol. 111, 2019. [Google Scholar]
- Q. Carton, “Modelidentificatie voor een gebouw en installatie voor een voorspellende regeling van de verwarming en ventilatie,” KU Leuven, Ghent Technology Campus, 2020. [Google Scholar]
- B. Merema, M. Delwati, M. Sourbron, and H. Breesch, “Demand controlled ventilation (DCV) in school and office buildings: Lessons learnt from case studies,” Energy Build., vol. 172, pp. 349–360, 2018. [Google Scholar]
- KU Leuven, KMI, Leuven 2030, and Leuven, “Leuven. cool.” https://leuven.cool/(accessed Sep. 22, 2020). [Google Scholar]
- CTSM-R Development Team, “Continuous Time Stochastic Modeling in R.” 2018. [Google Scholar]
- G. Mustafaraj, G. Lowry, and J. Chen, “Prediction of room temperature and relative humidity by autoregressive linear and nonlinear neural network models for an open office,” Energy Build., vol. 43, no. 6, pp. 1452–1460, 2011. [Google Scholar]
- M. Macarulla, M. Casals, M. Carnevali, N. Forcada, and M. Gangolells, “Modelling indoor air carbon dioxide concentration using grey-box models,” Build. Environ., vol. 117, pp. 146–153, 2017. [Google Scholar]
- 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, pp. 774–785, 2016. [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.