![]() ![]() In this paper a Python-based toolbox, named Fast Simulations (FastSim), that automates the process of setting up and assessing MPC algorithms for their application in buildings, is presented. Nevertheless, the vast majority of researchers prefer the grey-box option. It is hard to determine which approach is the best to be used based on the literature, and the best choice may even depend on the particular case considered (availability of building plans, Building Information Models (BIM), HVAC technical sheets, measurement data). Different procedures already exist to obtain these controller models: white-, grey-, and black-box modelling methods are used for this end. However, these MPC strategies are still not widely used in practice because a substantial engineering effort is needed to identify a tailored model for each building and Heat Ventilation and Air Conditioning (HVAC) system. To this end, they anticipate the dynamic behaviour based on a mathematical model of the system. These optimal controllers are able to minimize the energy use within building, by taking into account the weather forecast and occupancy profiles, while guaranteeing thermal comfort in the building. ![]() ![]() ![]() The use of Model Predictive Control (MPC) in Building Management Systems (BMS) has proven to out- perform the traditional Rule-Based Controllers (RBC). ![]()
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