Predict uses statistical models, artificial neural networks and machine learning to identify wind turbine component failures before they occur. The feature notifies 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 in collaboration with Greenbyte’s Head of Technology, Mikael Baros, been put to vigorous testing to ensure high accuracy.
The pilot study on Predict detected faults two to nine months in advance, achieved 94 percent accuracy and showed a 23 percent reduction of cost. Early indication for component failure can aid in reducing downtime and increasing component life.
Dr. Bangalore is holding a webinar on Predict August 29th, 2018, where he will unveil the science behind the technology. This webinar is a valuable source of knowledge for users of Greenbyte Energy Cloud, industry professionals and data scientists alike. Interesting parties can sign up to attend the webinar here.
