Speaker
Description
Today, a significant portion of total final energy consumption can be attributed to heating systems in buildings. Reducing energy consumption and carbon dioxide emissions in this sector is, therefore, crucial to achieving the goals of climate action initiatives worldwide. A promising approach to addressing this objective involves the use of advanced predictive control systems, which have the potential to dynamically adapt to changing external conditions in real-time. However, its implementation is rather challenging due to the high complexity of current building energy systems. In this work, we consider a mixed-integer nonlinear model predictive control (MINMPC) strategy, which can directly tackle system nonlinearities, switching behavior and intricate restrictions. In this context a central point is the development of a proper model for the building energy system, which must carefully follow the system dynamics and at the same time be rather simple and suitable for derivative-based optimization in model predictive control. We aim to simplify this process by automating the model design and, therefore, reducing the need for expert knowledge at this step. We present a novel relax-and-round strategy for MINMPC and demonstrate its performance on a resistor-capacitor (RC) model of an office building at Hannover University of Applied Sciences and Arts, obtained within an automated framework. We show that previously achieved cost savings potential can be noticeably increased by the utilization of the building envelope as an additional energy storage.
