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Randomized Model Predictive Control for HVAC Systems
KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
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2013 (English)In: BuildSys'13 Proceedings of the 5th ACM Workshop on Embedded Systems For Energy-Efficient Buildings, 2013Conference paper, Published paper (Refereed)
Abstract [en]

Heating, Ventilation and Air Conditioning (HVAC) systems play a fundamental role in maintaining acceptable thermal comfort and Indoor Air Quality (IAQ) levels, essentials for occupants well-being. Since performing this task implies high energy requirements, there is a need for improving the energetic efficiency of existing buildings. A possible solution is to develop effective control strategies for HVAC systems, but this is complicated by the inherent uncertainty of the to-be-controlled system. To cope with this problem, we design a stochastic Model Predictive Control (MPC) strategy that dynamically learns the statistics of the building occupancy and weather conditions and uses them to build probabilistic constraints on the indoor temperature and CO2 concentration levels. More specifically, we propose a randomization technique that finds suboptimal solutions to the generally non-convex stochastic MPC problem. The main advantage of this method is the absence of apriori assumptions on the distributions of the uncertain variables, and that it can be applied to any type of building. We investigate the proposed approach by means of numerical simulations and real tests on a student laboratory, and show its practical effectiveness and computational tractability.

Place, publisher, year, edition, pages
2013.
Keyword [en]
Randomized Model Predictive Control, Smart Buildings, Sustainable Control Systems, Copulas, Learning
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:kth:diva-136278DOI: 10.1145/2528282.2528299ISBN: 978-1-4503-2431-1 (print)OAI: oai:DiVA.org:kth-136278DiVA: diva2:675735
Conference
BuildSys 2013 - 5th ACM Workshop On Embedded Systems For Energy-Efficient Buildings,Nov 11-14, 2013, Rome, Italy
Funder
Swedish Energy AgencyVinnovaKnut and Alice Wallenberg Foundation
Note

QC 20140131

Available from: 2013-12-04 Created: 2013-12-04 Last updated: 2014-01-31Bibliographically approved

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Johansson, Karl Henrik

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Parisio, AlessandraVaragnolo, DamianoRisberg, DanielPattarello, GiorgioMolinari, MarcoJohansson, Karl Henrik
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