Predict, Greenbyte Energy Cloud’s new innovative feature is now on commercial release and available for new and potential users. Predict enables wind farm operators and owners to avoid unscheduled downtime and decrease unforeseen expenditures.
Predict uses statistical models, artificial neural networks and machine learning to identify wind turbine component failures before they occur. The feature alarms users on changes in temperature that indicate need for maintenance. Predict’s advanced statistical models developed by Greenbyte’s Head of Research, Dr. Pramod Bangalore have been optimized for high accuracy and put to vigorous testing.
Predict estimates the expected temperature for critical components, compares that estimated data to the actual measured values, and enables intelligent and early detection of developing failures. The feature detects faults 2 to 9 months in advance, has achieved 94% accuracy, and the potential saving for a portfolio of 350 MW is 1.6 GWh per year. Accordingly, the possibility for lost production saving due to downtime reaches 12% with Predict.
Developing Predict has been a journey of knowledge for Greenbyte and an evidence of innovation for the industry. Director of Technology, Mikael Baros has been describing the Artificial Intelligence and machine learning part of the journey in a thrilling blog series The Greenbyte recipe for Artificial Intelligence in renewable energy Part 1 and Part 2.
Greenbyte is proud to deliver the latest technologies adapted to the needs of the users and the renewable energy industry, and is humbled to enable professionals create a more sustainable world in the most efficient way. We believe that knowledge is a resource to be shared openly we invite you to dig into it!
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