FranceEnergy and utilities2019 – ongoingENGIE
Darwin renewable-asset monitoring and predictive maintenance
ENGIE deployed Darwin across more than 15,000 MW of renewable assets
Problem
ENGIE needed to monitor renewable plants and improve their availability and production performance.
Approach
ENGIE developed Darwin using Microsoft Azure services, including machine learning. The software combined real-time plant monitoring with forecasting and predictive maintenance. Microsoft reported that Darwin had already improved availability and production performance on some assets.
Results
Takeaway
The reported production gains applied to some assets. They were not a measured improvement across the entire deployed portfolio.
Categories
- Size
- Large enterprise
- Industry
- Energy and utilities
- Business function
- Production and quality
- Innovation type
- Process innovation
- AI technology
- Predictive analytics and ML
- Adoption
- Built in-house
- Evidence
- Vendor case study
Sources
Source summaries
Microsoft described Darwin as deployed across more than 15,000 MW of assets worldwide. It reported improved plant availability and production gains of up to a few percent on some assets, without a portfolio-wide result.
Full details are in the original sources.
