Join the trial lecture - May 22nd at 10:15 AM (room 402, Department of Technology Systems)
"How wind power forecasts are used in applications in different time frames, from seconds to days ahead".
Conferral summary
For å bekjempe klimaendringer er det essensielt å forbedre påliteligheten til fornybare energikilder som vindkraft. Arbeidet setter søkelys på bruk av dyp læring for å utvikle nøyaktige prognoser for vindkraft, inkludert modellering av interaksjoner mellom vindturbiner og værprognoser i nær fremtid. Forskingen viser til relevante arkitekturer og metoder for mer nøyaktige prognosemodeller, samt fleksible modeller for irregulære datasett og metoder for sannsynlighetsprognoser.
Main research findings
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's not always blowing when we need it. That's 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.
Furthermore, a key takeaway from this research is not only the utilisation of advanced techniques to improve wind energy forecasting but also the importance of incorporating probabilistic prediction models. These models provide valuable insights into the uncertainty associated with wind power, aiding in better planning for successful system integration. Ultimately, these advanced techniques empower us to harness wind energy more effectively, offering hope for a greener future.
Chair of defence
Prof. Øivind Kure, University of Oslo
Adjudication committee
1. Opponent: Prof. Georges Kariniotakis, Mines Paris
2. Opponent: Dr. Corinna Möhrlen, WEPROG
Chair of committee: Prof. Josef Noll, University of Oslo
Supervisors
Supervisor: Paal Engelstad, University of Oslo
Co-supervisor: Narada Dilp Warakagoda, University of Oslo
Co-supervisor: Roy Stenbro, IFE
Candidate contact information
LinkedIn: Lars' LinkedIn
E-post: lars.nbe@hotmail.com
Mob.nr: +47 97139979
For more information
PhD-coordinator: Yvonne Baade
Request for thesis (pdf)