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The linear model predictive control which is frequently used for building climate control benefits from the fact that the resulting optimization task is convex (thus easily and quickly solvable). On the other hand, the nonlinear model predictive control enables the use of a more detailed nonlinear model and it takes advantage of the fact that it addresses the optimization task more directly, however, it requires a more computationally complex algorithm for solving the non-convex optimization problem. In this paper, the gap between the linear and the nonlinear one is bridged by introducing a predictive controller with linear time-dependent model. Making use of linear time-dependent model of the building, the newly proposed controller obtains predictions which are closer to reality than those of linear time invariant model, however, the computational complexity is still kept low since the optimization task remains convex. The concept of linear time-dependent predictive controller is verified on a set of numerical experiments performed using a high fidelity model created in a building simulation environment and compared to the previously mentioned alternatives. Furthermore, the model for the nonlinear variant is identified using an adaptation of the existing model predictive control relevant identification method and the optimization algorithm for the nonlinear predictive controller is adapted such that it can handle also restrictions on discrete-valued nature of the manipulated variables. The presented comparisons show that the current adaptations lead to more efficient building climate control. [ABSTRACT FROM AUTHOR]

Abstract: Recently, there has been an intensive research in the area of Model Predictive Control (MPC) for buildings. 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.). Usually, according to international standards, the thermal comfort is represented by a static range for the operative temperature. By contrast, this paper is devoted to the optimization of the Predicted Mean Vote (PMV) index which, opposed to the static temperature range, describes user comfort directly. PMV index is, however, a nonlinear function of various quantities, which limits the applicability and scalability of the control problem formulation. At first, PMV-based formulation is stated, the main differences between typical MPC problem formulation and PMV based formulation are outlined, a computationally tractable approximation of the nonlinear optimal control problem is presented and its accuracy is validated. Finally, control performance is compared both to a conventional and predictive control strategies and it turns out that the proposed optimal control problem formulation shifts the savings potential of typical MPC by additional 10–15% while keeping the comfort within levels defined by standards. [Copyright &y& Elsevier]