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Implementation of a Scenario-based MPC for HVAC Systems: an Experimental Case Study
KTH, School of Electrical Engineering (EES), Automatic Control.
Technical University of LuleƄ.
KTH, School of Electrical Engineering (EES), Automatic Control.
KTH, School of Electrical Engineering (EES), Automatic Control.
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2014 (English)In: Proceedings of the 19th IFAC World Congress, 2014, 2014, 599-605 p.Conference paper, Published paper (Refereed)
Abstract [en]

Heating, Ventilation and Air Conditioning (HVAC) systems play a fundamental role in maintaining acceptable thermal comfort and air quality levels. Model Predictive Control (MPC) techniques are known to bring significant energy savings potential. Developing effective MPC-based control strategies for HVAC systems is nontrivial since buildings dynamics are nonlinear and influenced by various uncertainties. This complicates the use of MPC techniques in practice. We propose to address this issue by designing a stochastic MPC strategy that dynamically learns the statistics of the building occupancy patterns and weather conditions. The main advantage of this method is the absence of a-priori assumptions on the distributions of the uncertain variables, and that it can be applied to any type of building. We investigate the practical implementation of the proposed MPC controller on a student laboratory, showing its effectiveness and computational tractability.

Place, publisher, year, edition, pages
2014. 599-605 p.
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:kth:diva-165779DOI: 10.3182/20140824-6-ZA-1003.02629ISBN: 978-3-902823-62-5 (print)OAI: oai:DiVA.org:kth-165779DiVA: diva2:808797
Conference
19th IFAC World Congress, 2014-08-24 - 2014-08-29 Cape Town, South Africa
Note

QC 20150430

Available from: 2015-04-29 Created: 2015-04-29 Last updated: 2015-04-30Bibliographically approved

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Johansson, Karl Henrik

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Parisio, AlessandraVaragnolo, DamiannoMolinari, MarcoPattarello, GiorgioFabietti, LucaJohansson, Karl Henrik
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  • Other locale
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Output format
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