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Semi-Automated Modular Modeling of Buildings for Model Predictive Control


D. Sturzenegger, D. Gyalistras, M. Morari, R. S. Smith

BuildSys 2012 - Workshop of ACM SenSys Conference, Toronto, Canada

A promising alternative to standard control strategies for heating, ventilation, air conditioning and blinds positioning of buildings is Model Predictive Control (MPC). Key to MPC is having a sufficiently simple (preferably linear) model of the building’s thermal dynamics. In this paper we propose and test a general approach to derive MPC compatible models consisting of the following steps: First, we use standard geometry and construction data to derive in an automated way a physical first-principles based linear model of the building’s thermal dynamics. This describes the evolution of room, wall, floor and ceiling temperatures on a per zone level as a function of external heat fluxes (e.g., solar gains, heating/cooling system heat fluxes etc.). Second, we model the external heat fluxes as linear functions of control inputs and predictable disturbances. Third, we tune a limited number of physically meaningful parameters. Finally, we use model reduction to derive a loworder model that is suitable for MPC. The full-scale and low-order models were tuned with and compared to a validated EnergyPlus building simulation software model. The approach was successfully applied to the modeling of a representative Swiss office building.The proposed modular approach flexibly supports stepwise model refinements and integration of models for the building’s technical subsystems.


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R. S. Smith

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  author = "Sturzenegger, David and Gyalistras, Dimitrios and Morari, Manfred and Smith, Roy S.",
  title = "{S}emi-{A}utomated {M}odular {M}odeling of {B}uildings for {M}odel {P}redictive {C}ontrol",
  journal = "BuildSys' 12 Proceeding of the Fourth ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Buildings",
  year = 2012,
  pages = "99--106",
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