Grid Raven Secures €1.5M Grant from Enterprise Estonia

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Georg Rute, CEO

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Grid Raven has been awarded €1.5 million by Enterprise Estonia under the Programme for Applied Research. This substantial funding will support a 2.5-year project aimed at improving Grid Raven's technologies and expanding its global impact. The grant will primarily fund two key areas of development:

Machine Learning for Wind Prediction: Grid Raven will further refine its AI-driven wind prediction model, ensuring its applicability and accuracy on a global scale in various geographies including mountainous areas.

Digital Twin Technology: The funding will also support the continued development of Grid Raven's digital twin of the power grid. This sophisticated virtual model is the basis with which Grid Raven provides Dynamic Line Rating services to transmission owners, allowing for better planning, management, and optimization of grid resources.

The complete cost of the project is €2.3 million, of which €800k will be financed by Grid Raven. With the help of the additional funding the company plans to expand its technical team, adding eight new members before the end of the year.

Georg Rute, CEO of Grid Raven, expressed his enthusiasm about the grant: "Thanks to the grant from EIS, we will be able to build a world-class technical team here in Tallinn, Estonia, and achieve our vision of helping increase grid capacity by a third, globally by 2030."

The support from Enterprise Estonia, the government agency responsible for developing the country's economy and achieving strategic goals, underscores the importance of Grid Raven's mission. By investing in technologies that optimize grid capacity, Estonia is positioning itself at the forefront of the global transition to clean energy.

This grant not only represents a vote of confidence in Grid Raven's innovative approach but also highlights Estonia's commitment to fostering technologies that address global challenges. As Grid Raven continues to grow and develop its solutions, the impact on global grid capacity and the acceleration of clean energy adoption could be substantial.

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