Conference Year

July 2018


Python toolbox, FastSim, model predictive control, MPC, building management system, HVAC


A PYTHON-BASED TOOLBOX FOR MODEL PREDICTIVE CONTROL APPLIED TO BUILDINGS Javier Arroyoa,b, Bram van der Heijdea,b,c, Lieve Helsena,b, Alfred Spiessensb,c a University of Leuven (KU Leuven), Department of Mechanical Engineering, Leuven, Belgium b EnergyVille, Thor Park, Waterschei, Belgium c VITO NV, Boeretang 200, Mol, Belgium The use of Model Predictive Control (MPC) in Building Management Systems (BMS) has proven to outperform the traditional Rule-Based Controllers (RBC). These optimal controllers are able to minimize the energy use within buildings by taking into account the weather forecast and occupancy profiles. To this end, they anticipate the dynamic behaviour based on a mathematical model of the system. 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 HVAC system. There already exist different procedures to obtain these controller models: white-, grey-, and black-box modelling methods are all being implemented. 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…). Nevertheless, the vast majority of researchers prefer the grey-box option. 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. It provides a modular, extensible and scalable framework thanks to its block-based architecture. In this layout, each of the blocks represents a feature of the controller, such as state-estimation, weather forecast or optimization. Moreover, the interactions between blocks occur through standardized signals facilitating the inclusion of new add-ons to the framework. The approach is tested and verified by simulations using a grey-box model as the controller model and a detailed Modelica model as the emulator. A time-varying Kalman filter is applied to estimate the unmeasured states of the controller model. FastSim is developed and used in a research environment, however this automated process will also facilitate the implementation of MPC for different building systems, both in virtual and real life. Keywords: Python toolbox, FastSim, model predictive control, MPC, building management system, HVAC Abstract submitted for the IBO Workshop