German energy company Kaiserwetter Energy Asset Management announced that its cloud-based, data analytics as a service (DAaaS) Aristoteles platform can now predict wind turbine failures before they occur. Developed in partnership with SAP, Aristoteles uses predictive analytics and machine learning to turn complex and unstructured technical, financial and meteorological data into actionable, real-time intelligence for investors and financial institutions to minimize investment risks and maximize monetary returns.
With recent SAP advancements in machine learning, Aristoteles is now able to utilize historical technical data from wind turbines to feed continuously learning algorithms that can detect wind turbine failures in advance. Traditionally, the operational yield of renewable energy assets has been hard to predict, and reliable information about the technical status of assets has been hard to come by. By anticipating and remedying technical and operational problems before they impact asset output, owners, operators and investors can now minimize the impact of reduced yields.
“Predicting renewable energy asset failures before they occur is a significant AI achievement in the energy sector—this is a huge advantage for Aristoteles users,” said Hanno Schoklitsch, CEO of Kaiserwetter. “I’m very proud of the collaborative work between Kaiserwetter and SAP to make this capability a reality.”
The new feature was demonstrated earlier this month by Kaiserwetter CEO Hanno Schoklitsch and SAP Head of Machine Learning Markus Noga at SAP TechEd Barcelona. Both companies are now working on extending the predictive feature to other forms of renewable energy generation, including solar, hydro, biogas and biomass.
“Kaiserwetter has been an outstanding partner and is on the forefront of AI use in the renewable energy sector,” said Markus Noga, SAP Head of Machine Learning. “As we continue to advance our machine learning capabilities, Kaiserwetter’s customers will benefit from new capabilities and additional functionality.”
News item from Kaiserwetter
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