Data science & AI for energy engineers
Data science & AI for energy engineers
Data science & AI for energy engineers

Analyse, forecast and optimise energy flexibility and demand.


This course offers a unique opportunity to analyse, forecast, and optimise energy flexibility and demand using data science and artificial intelligence techniques. The course focuses on practical use cases in the energy sector, providing a comprehensive introduction to data science with a hands-on approach. Participants will learn how to use Python and other tools to analyse and visualise energy demand data, as well as how to share their results with other stakeholders using advanced dashboards.


Throughout the course, students will gain practical knowledge of state-of-the-art tools for monitoring and experimenting with energy datasets. They will also explore the limitations of machine learning models and how they rely on time series and statistical principles to forecast energy demand. Participants will learn how to optimize the behavior of energy flexible resources using arbitrary cost functions, tracking their experiments using cutting-edge tools.


Designed based on industry requirements and feedback from hundreds of learners, this course is the fifth iteration of a successful collaboration between EIT InnoEnergy and several leading European partner universities, including KU Leuven, KTH, UPC, and Grenoble INP.


Course dates: 22 July – 02 August 2024


Effort Level: 60 to 80 hours, spread over two weeks.


Course costs:  3,950 EUR (this course is free of charge for EIT InnoEnergy master’s students)


Delivery: Hybrid course offered online with the possibility of face-to-face sessions at KU Leuven and KTH Royal Institute of Technology (locations to be confirmed based on demand)



  • Proficiency in a programming language, preferably Python (e.g., familiarity with control commands and loops, etc.). Experience with advanced concepts such as object-oriented programming or deployment is not required. Useful resources will be provided for students not meeting this criterion, to be completed before the start of the course.
  • Understanding of core concepts in energy and power engineering (e.g., how the grid is organised etc.)


Who is the course for?

This course on energy data science is designed for EIT InnoEnergy master’s students who wish to learn how to streamline existing workflows and develop new services through data-driven decision making. The course content is applicable to a wide range of energy careers, including working in energy aggregators, system operators, and utilities.


Students of the Master’s in Energy for Smart Cities obtain 3 ECTS from following this course.

Philippe Bergin, Technical Competences Director Global Technology Schneider Electric

As an impact company, Schneider Electric is committed to address one of the biggest challenges of our time: Climate Change.  With Intencity, we put all our expertise to build an environmental-friendly building, which makes the most of data, A.I., solar panels, wind turbines, building management System…


We are very proud to share this project with the EIT InnoEnergy students. They are part of this innovative journey as they are asked to provide new ideas which will contribute to make this building even smarter.

How will you learn?

The course follows an immersive learning approach, combining theory and practice with lectures, in-class discussions, and practical lab sessions. Learners will gain a comprehensive understanding of the many different use cases of data in the energy sector, as well as hands-on knowledge and skills in analysing, forecasting, and optimising energy demand data using Python tools. The course will conclude with a real-life application of data science, i.e. the prediction of future thermal energy demand.

What will you achieve?

  • A broad understanding of the many different use cases of data in the energy domain.
  • Concrete knowledge and skills to analyse, forecast and optimise energy demand data, as well as ways to track the end-to-end data pipeline and share your results with relevant stakeholders.
  • Certificate from EIT InnoEnergy and KU Leuven.

What is included in the course?
Where will the course take place?
How much commitment does the course require?
How much programming / Python background is required for the course?
What do I need to do before the course?
Will lectures be recorded / made available?
How does one pass the course and will I get credit?
What does the project entail? Is it in groups?