Canadian tech firm Clir Renewables has developed software which uses layered machine learning to identify underperforming wind turbines and the causes. Using layered machine learning the company has created an application that works along with other algorithms in the software to analyse turbine data and classify the information based on the reason for underperformance. The application works alongside algorithms in the software to analyse and classify data. Clir’s technology is based on a combination of data from weather stations around the world as well as satellite images taken by satellites that can be used for analysis. Additionally, the application spotlights hardware or software changes which could have prompted the slowdown.
Canadian tech firm Clir Renewables has developed software which uses layered machine learning to identify underperforming wind turbines and the causes. The application works alongside algorithms in the software to analyse and classify data. Clir’s technology is based on a combination of data from weather stations around the world as well as satellite images taken by satellites that can be used for analysis. Additionally, the application spotlights hardware or software changes which could have prompted the slowdown.