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Control of HVAC Systems via Scenario-based Explicit MPC
School of Electrical Engineering, KTH Royal Institute of Technology.
Kungliga tekniska högskolan, KTH.
Kungliga tekniska högskolan, KTH.
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.ORCID iD: 0000-0002-4310-7938
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2015 (English)In: 2014 IEEE 53rd Annual Conference on Decision and Control : (CDC 2014): Los Angeles, CA., 15-17 Dec. 2014, Piscataway, NJ: IEEE Communications Society, 2015, p. 5201-5207Conference paper, Published paper (Refereed)
Abstract [en]

Improving energy efficiency of Heating, Ventilation and Air Conditioning (HVAC) systems is a primary objective for the society. Model Predictive Control (MPC) techniques for HVAC systems have recently received particular attention, since they can naturally account for several factors, such as weather and occupancy forecasts, comfort ranges and actuation constraints. Developing effective MPC based control strategies for HVAC systems is nontrivial, since buildings dynamics are nonlinear and affected by various uncertainties. Further, the complexity of the MPC problem and the burden of on-line computations can lead to difficulties in integrating this scheme into a building management system.We propose to address this computational issue by designing a scenario-based explicit MPC strategy, i.e., a controller that is simultaneously based on explicit representations of the MPC feedback law and accounts for uncertainties in the occupancy patterns and weather conditions by using the scenarios paradigm. The main advantages of this approach are the absence of a-priori assumptions on the distributions of the uncertain variables, the applicability to any type of building, and the limited on-line computational burden, enabling practical implementations on low-cost hardware platforms.We illustrate the practical implementation of the proposed explicit MPC controller on a room of a university building, showing its effectiveness and computational tractability.

Place, publisher, year, edition, pages
Piscataway, NJ: IEEE Communications Society, 2015. p. 5201-5207
National Category
Control Engineering
Research subject
Control Engineering; Enabling ICT (AERI); Intelligent industrial processes (AERI)
Identifiers
URN: urn:nbn:se:ltu:diva-27297DOI: 10.1109/CDC.2014.7040202Local ID: 0b4cc200-6b2d-4c79-8fd8-9841f3da2445ISBN: 978-1-4799-7746-8 (print)OAI: oai:DiVA.org:ltu-27297DiVA, id: diva2:1000480
Conference
IEEE Conference on Decision and Control : 15/12/2014 - 17/12/2014
Note
Godkänd; 2015; 20150417 (damvar)Available from: 2016-09-30 Created: 2016-09-30 Last updated: 2017-11-25Bibliographically approved

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Varagnolo, Damiano

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