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.
|Conference||16th IBPSA International Conference and Exhibition Building Simulation|
|Period||02/09/2019 → 04/09/2019|
|Series||Proceedings of the International Building Performance Simulation Association|
- model predictive control
- building simulation
- genetic algorithm