Energy efficient optimal control of buildings and their energy systems

Thermal systems in buildings, used for heating, cooling and ventilation, represent 15% of the worldwide energy use. Their greenhouse gas (GHG) emissions thus have a negative impact on our climate. Current practice control of buildings can be improved through the use of optimization-based control algorithms such as Model Predictive Control (MPC). Energy savings of MPC compared to existing control techniques are typically in the order of 15 % - 40 % (at the same time reducing energy cost, GHG emissions, and if included in the objective even the share of fossil fuels in hybrid systems) and MPC generally leads to better thermal comfort, thereby improving productivity. A scalable and generic toolchain for implementing MPC has been implemented within past PhD research work, the Toolchain for Automated Control and Optimization (TACO). This C3 project complements the VLAIO mandate HBC 2018.2092. While the VLAIO mandate will further develop the algorithms underlying TACO and explore the market, the C3 project will demonstrate the performance and robustness of TACO in multiple buildings. Furthermore, the C3 project will improve the user friendliness of the toolchain and it will develop additional models for often used building components. The end goal is to found a spin-off that commercializes MPC.