Spatiotemporal dependencies in wind energy production

Supervisor: Maria Astefanoaei
Semester: Fall 2021
Tags: spatial data analysis, graph neural networks, Python, timeseries data

The integration of wind power in the energy grid is dependent on accurate production forecasts. The power output curves between neighbouring wind farms are often correlated temporally and spatially, but currently, these spatiotemporal dependencies are under-utilised in prediction models. Graph neural networks allow for modelling these dependencies. In this project the student will implement a prediction model using a newly released Python library for temporal geometric graph neural networks in order to analyse dependencies across wind farms at different locations in Denmark.