Machine Learning in Energy: Forecasting, MLOps, and Business Impact
Mar 16, 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
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.
Building an Engineering-First Company: Dataminded’s Founder Story with Kris Peeters
Kris Peeters reflects on building Dataminded: engineering culture, autonomy, scaling teams, and lessons from 11 years of growth.
