Recommendations for model identification for MPC of an all-Air HVAC system

Quinten Carton; Bart Merema; Hilde Breesch

doi:10.1051/e3sconf/202124611006

All issues

Volume 246 (2021)

E3S Web Conf., 246 (2021) 11006

References

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

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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]
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[1] 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. [Google Scholar]

[2] 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]

[3] 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]

[4] H. Madsen et al., “Report of Subtask 3, part 2: Thermal performance characterisation using time series data - statistical guidelines,” 2015. [Google Scholar]

[5] 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]

[6] 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]

[7] 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]

[8] KU Leuven, KMI, Leuven 2030, and Leuven, “Leuven. cool.” https://leuven.cool/(accessed Sep. 22, 2020). [Google Scholar]

[9] CTSM-R Development Team, “Continuous Time Stochastic Modeling in R.” 2018. [Google Scholar]

[10] 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]

[11] 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]

[12] 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]