AI-based Power Production Models for Increased Wind Farm Efficiency

Wind energy is a promising source of power but is not easy to utilize effectively. Wind farms consist of many turbines that have complex interactions with each other and their surroundings. Factors such as terrain, wind trail effect (wake) between turbines, and ice accumulation on the blades influence the amount of power generated. Predicting the power output of wind farms typically relies on time-consuming simulations, but an emerging paradigm based on AI can drastically speed up prediction methods while maintaining their reliability. In this project we will develop new methods that use AI trained on real-world data to get accurate prediction of wind farm power output at a low computational cost. As the turbines and their relationships can be seen as a graph, we will use Graph Neural Networks (GNNs) to model them. Our method can have a big impact as the number of wind farms keeps growing, improving their efficiency and planning, and enabling more sustainable and affordable energy.