The purpose of this course is to introduce a number of commonly used techniques in the fields of machine learning, data science and optimization, focusing particularly on their application in the energy sector. In addition to covering the theory behind these techniques, the course also provides a hands-on introduction to several concrete use cases in the field of data and AI.
What students learn:
- The many different applications and opportunities of digitization in the energy sector.
- How to analyze energy datasets and where to find them in practice.
- How to forecast and model electricity demand, generation and market conditions.
- How to use forecasts in optimal decision-making frameworks to activate energy flexibility or manage investment decisions.
- How to implement these techniques in Python using various state-of-the-art libraries.
- What to watch out for in a world where "AI" can be (mis)used to solve all kinds of problems.
Nederlands 
English 