TACO, AN AUTOMATED TOOLCHAIN FOR MODEL PREDICTIVE CONTROL OF BUILDING SYSTEMS: IMPLEMENTATION AND VERIFICATION
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Consortium members, Filip Jorissen, Wim Boydens & Lieve Helsen have recently published a paper on TACO, an automated toolchain for model predictive control of building systems: implementation and verification, in the Journal of Building Performance Simulation
TACO will be further developed and demonstrated during the coming years with the goal to significantly reduce the engineering effort required for developing MPC for building applications.
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Abstract
This paper presents TACO (Toolchain for Automated Control and Optimization), which is a Modelica-based automated toolchain for model predictive control (MPC) of building systems. Its goal is to significantly reduce the engineering expertise and the time investment required for applying MPC to buildings. TACO is based on JModelica. Modifications compared to JModelica are discussed and the implementation of our custom MPC problem formulation is presented. The implementation is verified using two example models and is benchmarked with respect to accuracy and computation time. These results show that the computation time can be reduced significantly using the toolchain options, while only slightly reducing the controller optimality.