Enhancing Education in Multi-Energy Systems with Data Science Notebooks

The adoption of multiple energy sources is crucial for achieving a sustainable and resilient energy infrastructure. This calls for the education and training of a skilled workforce versed in multi-energy systems. Traditional classroom instruction, particularly for manual problemsolving, struggles to effectively convey the complexities of multi-energy systems, which hinders student learning in lectures. This paper proposes a teaching approach based on integrating data science notebooks into lectures for preparing engaging and interactive lessons. An example Jupyter notebook and lesson is presented, which examines the impact of different price signals on the energy consumption and costs of traditional and smart buildings, equipped with multiple energy sources. The proposed teaching approach can replace manual exercises and enhance student learning and understanding through interactive problem-solving, bridging the gap between theory and practice.