Predictive Building Energy Management by Means of Mixed‐Integer Optimal Control With Automated Setup

ABSTRACT 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 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. A critical challenge in this domain is the derivation of a control‐oriented model that balances high physical fidelity with the mathematical structure required for derivative‐based optimization. We address this bottleneck by introducing an automated modeling framework that streamlines the design process, significantly reducing the reliance on manual expert intervention. We employ a novel relax‐and‐round strategy for MINMPC and demonstrate its performance on a resistance–capacitance (RC) model of an office building at Hannover University of Applied Sciences and Arts, obtained within an automated framework. We show that the presented predictive control strategy enables effective management of thermal and electrical energy and dynamic adaptation to the ambient conditions and variable electricity prices.