Pablo Estevez
Pablo Estevez leads Data and Machine Learning in Eneco’s Energy Trading teams. For more than ten years he has worked across tech, taking on both hands-on and leadership roles in Machine Learning and Data Science at companies like Booking.com and Meta
Session
09-26
11:05
35min
Data that Keeps Our Energy in Balance - From churn prediction with deep learning to real-time trading systems
Pablo Estevez, Manolis Manousogiannis
This talk explores how data science helps balance energy systems in the face of demand volatility, generation volatility, and the push for sustainability. We’ll dive into two technical case studies: churn prediction using survival models, and the design of a high-availability real-time trading system on Databricks. These examples illustrate how data can support operational resilience and sustainability efforts in the energy sector.
Orbit