BuildingLAB: A tool to analyze performance of model predictive controllers for buildings

Abstract Model predictive control (MPC) for buildings has undergone an intensive research in the past years. The key principle of MPC is a trade-off between energy savings and user welfare making use of predictions of disturbances acting on the system (ambient temperature, solar radiation, occupancy, etc.). Several studies and experimental setups have shown the energy savings potential of MPC up to 30 % compared to the conventional control strategies. Besides modeling of the buildings, the bottleneck of MPC wide-spreading is the understanding of the MPC paradigm from the HVAC engineers and managers. Therefore the objective is to develop a tool that would make MPC strategy for buildings more understandable for wide public. The application enable users to explore the controllers behavior, tune controllers with aid of displaying and comparing simulation results, validate mathematical models of the particular building, etc.

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