Wind power’s time has come.
As reported in Capgemini’s 26th World Energy Markets Observatory (WEMO), wind power has seen 10% growth rates in recent years, and reached 30% of EU electricity generation.
As part of its ascendence, the wind energy sector has invested heavily in cloud-based data analytics and AI. In doing so, it anticipated big gains from real-time optimized performance, reduced downtime, and extended turbine lifespan – and ultimately new tech-style, service oriented business models where wind turbines sell digital services and data-driven insights.
But the industry has not seen this promised value. Why?
Many exciting AI and Machine Learning models could deliver the promised value to wind, as this paper will show in Part 1. But – for reasons we will discuss – few such models are suited to running in the cloud. At the same time, the rapid growth of data centers needed for the cloud is creating environmental concerns.
Moving from a centralized cloud approach to an Edge approach – where wind turbines deploy computing capabilities and AI models on the turbine itself – could unlock a whole host of benefits to both OEMs and developers. It could also create entirely new business models. We will discuss both in Part 2.
