Enhancing Education in Multi-Energy Systems with Data Science Notebooks

Ali Ehsan, Tomislav Baskarad , Brian Azzopardi, Eduardo Alejandro Martinez Cesena, Jovica V. Milanovic

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


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 problem-solving, 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.
Original languageEnglish
Title of host publication2024 IEEE 22nd Mediterranean Electrotechnical Conference - IEEE MELECON 2024
Publication statusAccepted/In press - 21 Mar 2024


  • classroom
  • data science
  • education
  • multi-energy
  • student learning
  • teaching


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