Doctoral candidate Lars Ødegaard Bentsen is defending the thesis "Deep Learning for Offshore Wind Park Modelling and Forecasting" for the degree of Philosophiae Doctor
In the global effort to combat climate change, wind energy is of paramount importance in transitioning away from fossil fuels. But wind power comes with a challenge: It is not always blowing when we need it.
Time and place: – ,
Department of Technology Systems, Room 402
Lars Ødegaard Bentsen will defend his thesis "Deep Learning for Offshore Wind Park Modelling and Forecasting" at ITS on May 22nd.
That is where advanced prediction systems come in. Imagine a wind farm as a complex puzzle of turbines, each affecting the others. In this work, we study how deep learning enables us to account for these turbine interactions, resulting in more precise wind turbine power predictions. By looking at data from multiple locations over time, we show how graph neural networks and complex Transformer architectures can help improve our forecasts even further. We've also come up with new ways to handle tricky data gaps, making our predictions more reliable.
