Over the past decade, wind farms have become an important source of carbon-free electricity as the cost of turbines has plummeted and adoption has surged.
However, the variable nature of wind itself makes it an unpredictable energy source – less useful than one that can reliably deliver power at a set time.
In search of a solution to this problem, Google, in collaboration with its Britain-based Artificial Intelligence (AI) subsidiary DeepMind has developed a system to predict wind power output 36 hours ahead of actual generation.
DeepMind and Google started applying machine learning algorithms to 700 megawatts of wind power capacity in the central US. These wind farms – part of Google’s global fleet of renewable energy projects – collectively generate as much electricity as is needed by a medium-sized city.
Using a neural network trained on widely available weather forecasts and historical turbine data, the researchers configured the DeepMind system to predict wind power output 36 hours ahead of actual generation. This machine learning has boosted the value of Google’s wind energy projects by roughly 20 per cent.
Google said that these type of predictions can boost the value of wind energy and can strengthen the business case for wind power and drive further adoption of carbon-free energy on electric grids worldwide.
Reference- Economic Times, Google PR