Multi-Objective Model Predictive Control Framework for Buildings

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

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Abstract

The aim of this paper is to present the implementation and performance of an MPC framework based on a multi-objective genetic algorithm. The framework optimizes building control by firstly identifying the Pareto frontier with respect to multiple objectives considered, and then selecting the final strategy based on the user-defined priorities for the respective objectives. Although the approach requires more computing resources than the more traditional constrained convex optimization, it is more flexible in terms of the optimization problem formulation. New objectives can be easily added, and the objective priorities altered during the operation of the system. This flexibility makes the framework attractive for global optimization of multiple systems, including systems based on on/o control. The framework is compatible with the Functional Mock-Up Interface and uses models exported to Functional Mock-Up Units. The framework performance is tested in a virtual experimental testbed using a building modeled in EnergyPlus.
Original languageEnglish
Title of host publicationProcedings of the 16th IBPSA International Conference and Exhibition Building Simulation 2019
Publication statusAccepted/In press - 6. May 2019
Event16th IBPSA International Conference and Exhibition Building Simulation - Rome, Italy
Duration: 2. Sep 20194. Sep 2019
http://buildingsimulation2019.org

Conference

Conference16th IBPSA International Conference and Exhibition Building Simulation
CountryItaly
CityRome
Period02/09/201904/09/2019
Internet address

Fingerprint

Model predictive control
Convex optimization
Constrained optimization
Global optimization
Testbeds
Genetic algorithms

Keywords

  • model predictive control
  • building simulation
  • genetic algorithm
  • energyplus
  • multi-objective

Cite this

Arendt, K., Clausen, A., Mattera, C. G., Jradi, M., Johansen, A., Veje, C., ... Jørgensen, B. N. (Accepted/In press). Multi-Objective Model Predictive Control Framework for Buildings. In Procedings of the 16th IBPSA International Conference and Exhibition Building Simulation 2019
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title = "Multi-Objective Model Predictive Control Framework for Buildings",
abstract = "The aim of this paper is to present the implementation and performance of an MPC framework based on a multi-objective genetic algorithm. The framework optimizes building control by firstly identifying the Pareto frontier with respect to multiple objectives considered, and then selecting the final strategy based on the user-defined priorities for the respective objectives. Although the approach requires more computing resources than the more traditional constrained convex optimization, it is more flexible in terms of the optimization problem formulation. New objectives can be easily added, and the objective priorities altered during the operation of the system. This flexibility makes the framework attractive for global optimization of multiple systems, including systems based on on/o control. The framework is compatible with the Functional Mock-Up Interface and uses models exported to Functional Mock-Up Units. The framework performance is tested in a virtual experimental testbed using a building modeled in EnergyPlus.",
keywords = "model predictive control, building simulation, genetic algorithm, energyplus, multi-objective",
author = "Krzysztof Arendt and Anders Clausen and Mattera, {Claudio Giovanni} and Muhyiddine Jradi and Aslak Johansen and Christian Veje and Kj{\ae}rgaard, {Mikkel Baun} and J{\o}rgensen, {Bo N{\o}rregaard}",
year = "2019",
month = "5",
day = "6",
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booktitle = "Procedings of the 16th IBPSA International Conference and Exhibition Building Simulation 2019",

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Arendt, K, Clausen, A, Mattera, CG, Jradi, M, Johansen, A, Veje, C, Kjærgaard, MB & Jørgensen, BN 2019, Multi-Objective Model Predictive Control Framework for Buildings. in Procedings of the 16th IBPSA International Conference and Exhibition Building Simulation 2019. 16th IBPSA International Conference and Exhibition Building Simulation, Rome, Italy, 02/09/2019.

Multi-Objective Model Predictive Control Framework for Buildings. / Arendt, Krzysztof; Clausen, Anders; Mattera, Claudio Giovanni; Jradi, Muhyiddine; Johansen, Aslak; Veje, Christian; Kjærgaard, Mikkel Baun; Jørgensen, Bo Nørregaard.

Procedings of the 16th IBPSA International Conference and Exhibition Building Simulation 2019. 2019.

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

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T1 - Multi-Objective Model Predictive Control Framework for Buildings

AU - Arendt, Krzysztof

AU - Clausen, Anders

AU - Mattera, Claudio Giovanni

AU - Jradi, Muhyiddine

AU - Johansen, Aslak

AU - Veje, Christian

AU - Kjærgaard, Mikkel Baun

AU - Jørgensen, Bo Nørregaard

PY - 2019/5/6

Y1 - 2019/5/6

N2 - The aim of this paper is to present the implementation and performance of an MPC framework based on a multi-objective genetic algorithm. The framework optimizes building control by firstly identifying the Pareto frontier with respect to multiple objectives considered, and then selecting the final strategy based on the user-defined priorities for the respective objectives. Although the approach requires more computing resources than the more traditional constrained convex optimization, it is more flexible in terms of the optimization problem formulation. New objectives can be easily added, and the objective priorities altered during the operation of the system. This flexibility makes the framework attractive for global optimization of multiple systems, including systems based on on/o control. The framework is compatible with the Functional Mock-Up Interface and uses models exported to Functional Mock-Up Units. The framework performance is tested in a virtual experimental testbed using a building modeled in EnergyPlus.

AB - The aim of this paper is to present the implementation and performance of an MPC framework based on a multi-objective genetic algorithm. The framework optimizes building control by firstly identifying the Pareto frontier with respect to multiple objectives considered, and then selecting the final strategy based on the user-defined priorities for the respective objectives. Although the approach requires more computing resources than the more traditional constrained convex optimization, it is more flexible in terms of the optimization problem formulation. New objectives can be easily added, and the objective priorities altered during the operation of the system. This flexibility makes the framework attractive for global optimization of multiple systems, including systems based on on/o control. The framework is compatible with the Functional Mock-Up Interface and uses models exported to Functional Mock-Up Units. The framework performance is tested in a virtual experimental testbed using a building modeled in EnergyPlus.

KW - model predictive control

KW - building simulation

KW - genetic algorithm

KW - energyplus

KW - multi-objective

M3 - Article in proceedings

BT - Procedings of the 16th IBPSA International Conference and Exhibition Building Simulation 2019

ER -

Arendt K, Clausen A, Mattera CG, Jradi M, Johansen A, Veje C et al. Multi-Objective Model Predictive Control Framework for Buildings. In Procedings of the 16th IBPSA International Conference and Exhibition Building Simulation 2019. 2019