Machine Learning in Energy: Forecasting, MLOps, and Business Impact
16.03.2026
•
Jean-Michel Begon & Kris Peeters
Inside Luminus: how machine learning models move from experimentation to production to forecast electricity demand.
Welcome to a new episode of The Data Playbook Podcast by Dataminded.
In this episode, Kris Peeters talks with Jean-Michel Begon, Senior Machine Learning Engineer at Luminus, about what it really takes to build, deploy, and maintain machine learning models in a business-critical environment.
Together, they unpack how ML teams in the energy sector move from ideation to experimentation, industrialisation, and monitoring, and why production-ready machine learning is about much more than model accuracy alone.
In this episode, you’ll learn:
How Luminus uses machine learning for electricity consumption forecasting
What a practical ML lifecycle looks like in a business team
How to balance experimentation, standardisation, and production delivery
Why GenAI and LLMs are useful tools, but not a replacement for engineering discipline
Latest
The Data Challenge behind the Einstein Telescope
Tjonnie Li explains gravitational waves, the Einstein Telescope, and why the future of science depends on data engineering.
Scaling Data in Aviation: Inside Brussels Airlines’ Data Strategy
How Brussels Airlines transformed a broken data setup into scalable data products, enabling self-service and real business impact.
Machine Learning in Energy: Forecasting, MLOps, and Business Impact
Inside Luminus: how machine learning models move from experimentation to production to forecast electricity demand.
