Apply Now for the Data Science and AI for Energy Engineers – Summer School 2026 offered in hybrid format (online and in person in Leuven)

Ednah Carrick May 4, 2026

0 657 4 minutes read

Closing Date: 7 May 2026

Apply Now for the Data Science and AI for Energy Engineers – Summer School 2026 offered in hybrid format (online and in person in Leuven)

This course offers a unique opportunity to analyse, forecast, and optimise energy flexibility and demand using data science and artificial intelligence techniques. The Data Science and AI for Energy Engineers – Summer School 2026 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 the modern tech stack, including 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. Students will also learn about the opportunities and limitations of foundational models, including LLMs, in the energy sector.

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

Why this course on energy data science?

Energy data science gives you the tools to:

Be able to ask better questions about energy data and answer them.
Understand the industrial context in which these data science algorithms are applied.
Possess practical skills to load, explore, analyse and visualise various energy datasets.
Be able to make energy demand forecasts using machine learning models, while also understanding their limitations and how they build on time series and statistical principles.
Know how to optimise the behaviour of energy flexible resources given arbitrary cost functions, ranging from minimising costs to grid peaks and carbon emissions.
Be able to track your experiments using state-of-the-art tools.
Be able to present the results of your analysis in a manner accessible to both specialists and non-specialists.
Explore opportunities and limitations of foundational models, including LLMs, in the energy sector.

Course overview

Dates	6 July to 17 July 2026.
Location	Regional hubs for face-to-face participation in Leuven Option for online attendance for participants in other locations.
Language	English.
Effort Level	60-80 hours spread over two weeks. See FAQ.
Delivery	Hybrid course offered online and face-to-face at KU Leuven
Audience	This course is open to students in their 1st or 2nd year of InnoEnergy Masters+ School programmes (intakes 2024 and 2025). This course is also open to early-stage PhD researchers from KU Leuven. Limited seats are available for other PhD candidates, academics, industry participants, and InnoEnergy alumni.
Prerequisites	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. Introduction to working with data frames and data science-specific libraries will be provided in week 1 of the course. Understanding of core concepts in energy and power engineering (e.g., how the grid is organised etc.)
Tuition fees	InnoEnergy Masters+ School students: 0€ Limited seats are also available for early-stage PhD researchers, academics and industrial participants. Get in touch with [email protected] for information on customised prices.
Registration	Registration will be open on this webpage from 23 April to 7 May. Places in the course will be limited to ensure the quality and depth of the interactions and discussions. The results of the selection process will be communicated to applicants by mid-May.
Financial Support	InnoEnergy MS can apply for a fee waiver and/or a travel grant to cover travel and accommodation expenses in Leuven. Grants are awarded based on the student’s resume and a motivation statement submitted on the application form. The travel grant is capped at €500, and only 5 grants are available

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.

This course is free for students in their 1st or 2nd year of InnoEnergy Masters+ programmes (intakes 2024 and 2025) and for early-stage PhD researchers from KU Leuven. Other PhD candidates and industrial participants interested in learning more about energy data science from leading academic researchers and industrial practitioners are also welcome. Please contact [email protected] for information on special prices and admission.

How will you learn?

The Data Science and AI for Energy Engineers – Summer School 2026 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.

Preliminary Schedule

(subject to changes)

What will you achieve?

A broad understanding of the many different use cases of data in the energy domain.
A deep appreciation of both algorithmic application to the energy transition and the algorithmic risks this will entail.
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 InnoEnergy and KU Leuven.

The course will take place from 6 to 17 July 2026, with the first week held online and the second week offered in a hybrid format (online and in person in Leuven).