Building Energy SystemsCopyright: © EBC
Our Vision is the Optimization regarding building energy systems (BES) of tomorrow by systematically linking experimental and simulative methods.
We, the team BES, conduct research on the holistic optimization of sustainable energy systems in terms of design and operation. For this purpose, we couple experimental and simulative methods to bring innovative systems into practice. In our team, we deal with both new and existing buildings in terms of single and multi-family houses as well as non-residential buildings. We focus on the integrated optimization of technologies related to heating, cooling and electricity used for generation, distribution, storage and transfer to the occupants and users.
In the area of simulation, we rely on both data-driven models through machine learning algorithms and physics-based models. We integrate gained knowledge into the development of our open-source model library AixLib, enabling us to tailor dynamic, coupled building performance and HVAC simulations. Using digital twin concepts, we obtain calibrated dynamic models of building energy systems. Increasingly, we use Building Information Modeling (BIM) to gain simulation models through automated processes.
In the field of experiments, we use programmable logic controllers (PLC) to allow analog signals from high-resolution measurement equipment and various bus systems (sensors and actuators) to communicate with digital signals. To reduce duration and costs of tests, we design our dynamic experiments primarily with statistical design of experiments.
Using the hardware-in-the-loop concept, we combine experimental investigations of, e.g., heat pump systems with dynamically simulated demands of space heating and cooling as well as domestic hot water. For this purpose, we use state-of-the-art tools from the field of the Internet of Things (IoT). We perform evaluations with high-performance, statistical methods to derive scientifically well-founded statements from experiment and simulation for practical applications.
With this flexible combination, we design innovative (home) energy management systems ((H)EMS) for the society of tomorrow. In particular, we develop and apply approaches related to advanced rule-based, agent-based, adaptive as well as model predictive control. Using methods of mathematical optimization, we ensure an ideal system design as well as a cost-efficient and sustainable operation of building energy systems.