Solve real-world energy problems using AI and data science

Data science and AI for energy engineers

14 Jul 2025 - 25 Jul 2025

This course provides an opportunity to analyse, forecast, and optimise energy demand and generation using data science and AI techniques. The first week, fully online, introduces data science concepts and scientific programming through a flipped classroom approach. The second week, available online or in-person in Leuven, covers advanced topics with Python tutorials and lectures from KU Leuven lecturers and invited guest speakers from leading organizations.

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Practical information:

14 Jul 2025 - 25 Jul 2025

40 + 40 hours hours

Online & (ESAT, KU Leuven)

English

Target audience: Energy engineers; data scientists and AI engineers

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Register until: 30 May 2025
Prerequisites: Python programming; basic knowledge of energy engineering challenges
Price: Free for KU Leuven PhDs; paid for industrial participants

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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.

Guest lecturers include:

Prof. Hussain Kazmi on state of energy and AI;
Prof. Jethro Browell (U Glasgow) on probabilistic energy forecasting;
Prof. Frank Gielen (InnoEnergy and U Ghent) on investment and innovation landscape in the energy sector;
Dr. Hilde Weerts (Technical University of Eindhoven) on algorithmic fairness;
Sebastian Haglund (Rebase Energy) on open-source energy data and models;
Dr. Steven Duivenvoorden and Camille Van Niels (ACM, Dutch market regulator) on algorithmic risks in energy markets.

Several Python tutorials on how to implement predictive models using real-world datasets will be organized by KU Leuven researchers as well.