Sempra Renewables Selects Ensemble Energy Predictive Analytics Platform
Ensemble Energy announced today that they have been selected by Sempra Renewables to extend a pilot project using Ensemble Energy’s predictive analytics platform to reduce cost and increase energy production in Sempra’s wind turbine fleet.
Sempra began working on a pilot with Ensemble Energy in 2017. The pilot focused on two specific component reliability issues that were driving unplanned maintenance costs at one of Sempra’s projects. Ensemble Energy wind turbine engineers and data scientists worked closely with the Sempra team to build predictive models of the selected components using advanced machine learning and artificial intelligence techniques. The predictive models developed by Ensemble Energy utilized existing sources of data, with no additional hardware required. Upon receiving indications of anomalies from the Ensemble Energy predictive analytics platform, Sempra performed inspections of the turbine components, and confirmed the accuracy of Ensemble Energy’s notifications. In one case, the Ensemble Energy anomaly notification allowed Sempra to perform a maintenance action that prevented a significant failure, resulting in a very large cost savings.
“Detecting operational and maintenance issues on our system in a timely manner is very important to Sempra Renewables and our stakeholders,” said Darren Weim, director of operations at Sempra Renewables.
Ensemble Energy’s advanced analytics capability coupled with their wind turbine engineering expertise, are helping us identify and address potential issues well in advance, thereby supporting our maintenance and reliability goals.
Dr. Sandeep Gupta, CEO of Ensemble Energy stated that “Combining machine learning with domain expertise is the key to making the best decisions for predictive maintenance.” Dr. Gupta also commented on the working relationship between Sempra Renewables and Ensemble Energy, noting that ”Sempra Renewables has been an incredible partner for Ensemble. They are committed to using the very best tools and practices maximize production and minimize costs, and we could not be prouder to be helping them to do just that.”