- Category: Articles
Machine Learning Techniques Reduce Uncertainty in Long-Term Performance Reference
EWC Weather Consult, a German pioneer in the optimisation of weather data, has developed a long- term correction method for wind measurements giving far superior results. By using machine learning processes EWC has created a method that successfully minimises yield uncertainties. The new method makes it possible to use non-linear corrections, and by doing so the error in the yield estimates on a wind time-series can be reduced to only 3% on average, even for very complex sites. This is half the error level achieved using the matrix method and one-fifth of the error associated with sector-based linear regression in site assessments.
By Jon Meis, Managing Director, EWC Weather Consult, Germany

By Jon Meis, Managing Director, EWC Weather Consult, Germany
- Category: Articles
A Cloud-Based Modelling Software Uses a New Technique to Extend the Remaining Life of Gearboxes

By Stephen Steen, Manager, New Business Development, Sentient Science, USA
- Category: Articles
Gamesa’s G128-5.0MW Offshore Turbine Prototype

By Michaela O’Donohoe and Francisco Maza, Gamesa Corporación Tecnológica, Spain
- Category: Articles
Using Oil Analysis Data from Wind Turbine Gearboxes

By Richard Russo, Kevin Harrington and Sandra Legay, ExxonMobil Fuels & Lubricants
- Category: Articles
A New Generation of AC/DC/AC Converters

By Frits Ogg, Renewable Energy Consultant, The Netherlands
- Category: Articles
Tethered Tools Can Increase Safety and Productivity on Wind Farms

By John Martell, Product Manager, Snap-on Industrial, USA
- Category: Articles
How Geophysics and Multiple Perspectives Could Revolutionise the Wind Industry

By Daniel Kramer, Environmental Division Manager, Neil O. Anderson & Associates, USA
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