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Data science & AI for energy engineers

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Analyse, forecast and optimise energy flexibility and demand.

Learn to analyse, forecast, and optimise energy demand using AI and data science through hands-on Python sessions and real-world energy use cases.

Week 1 (online) covers core concepts; Week 2 (online or in Leuven) explores advanced tools, models, and algorithms for the energy transition.

Now in its 6th edition, this course is powered by InnoEnergy with KU Leuven, KTH, UPC, and Grenoble INP.

Overview

Course dates

  1. 14 July 2025 – 18 July 2025 (introduction, online only)
  2. 21 July 2025 – 25 July 2025 (advanced topics, Leuven and online) 

Effort Level

60 to 80 hours, spread over two weeks. Sessions take place from Monday to Friday from 09.30 – 17.30 CET.

Course Costs

The course is free for InnoEnergy Masters+ students and PhD researchers from KU Leuven, KTH, UPC, and Grenoble INP.

Limited spots are available for early-stage PhDs, alumni, academics, and industry participants.

Contact: onlinelearning.mastersplus@innoenergy.com for pricing details.

Delivery

Hybrid course offered online and face-to-face at KU Leuven.  

Pre-requisites

Basic programming skills, preferably in Python (e.g., loops and control commands) are expected—no need for advanced topics like OOP or deployment. Preparatory materials will be shared for those needing a refresher. Week 1 will cover data frames and key data science libraries.

A foundational understanding of energy and power systems (e.g., grid structure) is also required.

Registration

Registrations will be opened from 7 April 2025 to 23 May 2025. 

The final results of the application process will be communicated to applicants by early-June 2025.

Financial support

InnoEnergy Masters+ students 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 will be capped at 500 Euros. 

Who is the course for?

Built for the Brightest in Energy

This course 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 open for free to students in their 1st or 2nd year of InnoEnergy Masters+ programmes (intakes 2023 and 2024) 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 welcome as well. Please get in touch with us at onlinelearning.mastersplus@innoenergy.com to get information on special prices and admission.

 

Students of the Master’s in Advanced Energy Systems and AI obtain 3 ECTS from following this course.

How will you learn?

Learn by Doing: Real Cases, Real Skills, Real Impact

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.  

Who you will learn from?

From Academia to Industry: Your Knowledge Network

  • Hussain Kazmi (Assistant Professor, KU Leuven, Belgium)
  • Teaching Assistants 
    • Alexandre Gouveia (KU Leuven) 
    • Ada Canaydin (KU Leuven) 
  • Prof. Frank Gielen (InnoEnergy and University of Ghent) 
  • Prof. Jethro Browell (University of Glasgow) 
  • Hilde Weerts (Technical University of Eindhoven) 
  • Sebastian Haglund (Rebase Energy) 
  • Camille Van Niel and Steven Duivenvoorden (ACM – market authority)

What will you achieve?

Gain Insights, Influence Decisions

  • 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.