At Gridraven, I work with a wide range of geospatial data—weather observations, digital elevation models (DEM), and numerical weather prediction (NWP) models. My job is finding, cleaning, and preparing these datasets to train machine learning models. The end goal? Precise wind forecasts that can help optimize the performance of high-voltage power lines.
Markus Lippus and the team at GridRaven are rethinking grid potential through advanced AI solutions, using machine learning to unlock real-time flexibility in power transmission.
Introducing three key members who drive our projects forward: Eneli Toodu, Sten Buldas, and Juri Krainjukov.
Growing the team to pioneer smarter power grids with AI and innovation.
GridRaven has demonstrated a 50% improvement in accuracy for predicting wind speeds in forested areas.
Every line and every span must be accounted with to increase capacity.
Exploring the parameters that affect our wind predictions.
Ambient Adjusted Ratings can lead to a reduction of capacity.
A single span limits the entire line.
Accurate ratings would help reduce congestion in Texas.
Lack of grid capacity is slowing down affordable energy.
Accurate line ratings ensure safety while increasing capacity.
Predicting wind in sheltered areas.
More transmission capacity means lower prices.
Lack of transmission capacity is slowing the energy transition.
Mari joins as Senior Power Systems Expert and Ingvar as Data Scientist.
Using satellite data to improve line ratings.
A year-long cooperation centered on sharing expertise.
The funding will be used to expand GridRaven's technical team in Tallinn.
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