Co-funded by the
European Union
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 result of a successful collaboration between EIT InnoEnergy and several leading European partner universities, including KU Leuven, KTH, UPC, and Grenoble INP.
Overview
Course dates: summer 2025.
Effort Level: 60 to 80 hours, spread over two weeks.
Course costs: 3,950 EUR
- This course is free of charge for InnoEnergy Masters+ students
Delivery: Hybrid course offered online and face-to-face at KU Leuven. Additional face-to-face sessions at other EIT InnoEnergy partner locations such as KTH Royal Institute of Technology may become available depending on course demand.
Pre-requisites:
- 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.)
Registration: registrations will be opened in Spring 2025.
The final results of the application process will be communicated to applicants in early June 2025.
Who is the course for?
This course on energy data science is designed for InnoEnergy Masters+ 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.
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?
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.