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Modern control methods such as Model Predictive Control (MPC) are getting popular in recent years in many fields of industry. One of the branches that have witnessed great increase of interest in use of the MPC over the last few years is the building climate control area. According to the studies, the energy used in the building sector counts for 20 – 40 % of the overall energy consumption. Almost half of this amount consists of heating, ventilation and air-conditioning (HVAC) costs which implies that energy consumption decrease in this area is one of the most interesting challenges today. Besides enormous potential in reduction of energy consumed by heating, ventilation and air conditioning (HVAC) systems brought by such controller, it suffers from a bottleneck being the necessity of having a reliable mathematical model of the building at disposal. By finding a mathematical model appropriate for the MPC, it is meant to obtain such a model that is able to predict the behavior of the building sufficiently accurately for several hours ahead, which is an especially delicate task. This task is getting even more complicated in case of a real-life application. In this paper, we are looking for a reliable model of a huge three-storey office building in Hasselt, Belgium. For parameter estimation, an advanced identification approach is used – its advantage is that it attacks the problem of minimization of multi-step prediction error and in this way, it corresponds to MPC requirements for a good multi-step predictor. Moreover, we discuss not only the identification approach itself but we also focus on accompanying problems with real-operation data acquisition, processing and special treatment which is an indispensable step for achieving satisfactory identification results. The chosen model is now used in real operation with MPC at Hollandsch Huys. [ABSTRACT FROM AUTHOR]

Highlights: [•] The paper describes a real application of the MPC on an office building. [•] The complete process including the data collection and model acquisition is discussed. [•] For the model parameters estimation, a special approach called model predictive control relevant identification is used. [•] The ultimate result is a two-level controller which spares about 17% in average of energy compared to the original control strategy. [ABSTRACT FROM AUTHOR]