Model Predictive Control of thermal comfort as a benchmark for controller performance

Abstract Assessing controller performance in normal operation needs reproducible conditions and comparison with the best possible result. Tests in emulation are reproducible. Model Predictive Control (MPC) gives the best possible performance when the future inputs and the model of the process are known. When the benchmark is used for building energy management, the cost function of MPC becomes a linear programming problem with constraints given by the comfort. In emulation, the model of the building used in MPC may be obtained by gray-box parameter identification, using signals which excite all the modes of the complete model. The proposed benchmark was used to test a PID and a scheduled start PID-based energy management system. During the test periods, the MPC benchmark always outperformed the PID controllers. It reduced the occupants' discomfort by up to 97%, the energy consumption by up to 18%, and the number of on–off cycles of heat pump by up to 78%.

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