Dittmer, AntjeAntjeDittmerWerner, HerbertHerbertWerner2026-10-212026-10-212026-05-01Journal of Physics: Conference Series 3224 (3): 032085 (2026)https://hdl.handle.net/11420/65380This paper investigates how the selection of data points from a simulated wind field, combined with nonlinear lifting functions, influences wind turbine power predictions. The objective is to develop accurate linear models within the Koopman operator framework suitable for real-time Model Predictive Control. Using Dynamic Mode Decomposition, we compare three data configurations from a high-fidelity simulation: a dense wind field with 50,000 points, a dataset restricted to turbine locations, and a sparse dataset including two additional streamwise positions. Results demonstrate that models using data from well-placed sensors can outperform those using dense wind field data. Furthermore, while turbine-only data initially yields lower accuracy, we show that physically motivated Koopman lifting functions effectively compensate for missing spatial information. By incorporating these additional prediction features, turbine-only models achieve prediction accuracies comparable to those utilizing the complete wind field data, which relies on information density unavailable for real-world turbine control.en1742-659620263IOP Publishinghttps://creativecommons.org/licenses/by/4.0/Technology::621: Applied Physics::621.3: Electrical Engineering, Electronic EngineeringNatural Sciences and Mathematics::530: PhysicsJournal Article10.1088/1742-6596/3224/3/03208510.15480/882.18947