EDP Renováveis Improves Wind Energy Forecasting with Machine Learning
“We were able to significantly reduce deviation costs between the wind power forecast and the real production, resulting in millions of euros saved per year.”
Key Outcomes
- Accessed a wide collection of algorithms for machine learning
- Estimated energy lost due to ice accretion in blades to find wind farm locations
- Safely installed wind turbines in locations prone to strong storms
- Saved millions of euros by reducing deviation costs
EDP Renováveis is a global leader in the renewable energy sector and the world’s fourth-largest wind energy producer. As wind farms are installed in increasingly complex locations, resource assessment and energy production becomes more and more challenging.
To overcome these challenges, EDP Renováveis used MATLAB® to find patterns between meteorological data and measurements recorded in the wind turbine. They developed a suite of applications to apply machine learning to these patterns that enabled them reduce the error in wind power forecast, estimate potential losses due to ice in cold climates, and study the effect of weather on wind and production in large regions of the planet.
