Multi-Objective Model Predictive Control Framework for Buildings

Krzysztof Arendt, Anders Clausen, Claudio Giovanni Mattera, Muhyiddine Jradi, Aslak Johansen, Christian Veje, Mikkel Baun Kjærgaard, Bo Nørregaard Jørgensen

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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 publicationProceedings of building simulation 2019 : 16th IBPSA International conference and exhibition
EditorsV. Corrado, E. Fabrizio, A. Gasparella, F. Patuzzi
PublisherInternational Building Performance Simulation Association
Publication date2020
ISBN (Print)9781775052012
Publication statusPublished - 2020
Event16th IBPSA International Conference and Exhibition Building Simulation - Rome, Italy
Duration: 2. Sept 20194. Sept 2019


Conference16th IBPSA International Conference and Exhibition Building Simulation
Internet address
SeriesProceedings of the International Building Performance Simulation Association


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


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