About Green Data Science
Our Mission
At Green Data Science, we are on a mission to accelerate the transition to renewable energy through the power of artificial intelligence and data science. We believe that the challenges of climate change demand innovative, data-driven solutions that can transform how we produce, manage, and optimize clean energy.
Our Approach
We combine deep domain expertise in renewable energy with cutting-edge machine learning techniques. Our consultants hold distinguished academic backgrounds in physics, mathematics, and computer science, ensuring impeccable scientific rigor in every project. We don't just apply algorithms — we understand the underlying physical processes that drive renewable energy systems.
Meet Our Founder
Philippe Breuils
Founder & Lead Data Scientist
Philippe Breuils is a data scientist and machine learning engineer specializing in complex systems optimization. With extensive experience in systems analysis, predictive maintenance, and production optimization, notably for biogas plants, wind farms, and solar installations, Philippe co-founded Green Data Science to bridge the gap between advanced AI research and practical energy industry applications. His work focuses on developing interpretable machine learning models that provide actionable insights for plant operators and decision-makers.
Areas of Expertise
- Machine Learning & Deep Learning for Energy Systems
- SCADA Data Processing & Analysis
- Kolmogorov-Arnold Neural Networks (KAN)
- Predictive Maintenance & Anomaly Detection
- Financial Forecasting for Renewable Energy
Our Values
Scientific Rigor
Every recommendation we make is grounded in solid mathematics and validated against real-world data. We challenge our own models and never rely on black-box solutions.
Privacy by Design
As a GDPR-compliant organization, we guarantee your data is anonymized and processed on dedicated servers within the European Union. We only store what is necessary, when it is necessary.
Sustainable Impact
We measure our success not just by the performance of our models, but by the real-world reduction in carbon emissions and improvement in energy efficiency that our solutions enable.
