This thesis evaluates AI-based weather forecasting models—specifically GraphCast and Pangu-Weather—against traditional numerical weather prediction models like the ECMWF High-Resolution Model (HRES) and the Aspire Meso and LES models, focusing on the Netherlands due to its dense observational network and wind energy infrastructure. The study examines operational considerations, including computational costs and hardware requirements, highlighting that while AI models offer significant computational efficiency during inference, they require substantial resources for training and have limitations in local adaptability and variable inclusion (e.g., Pangu-Weather lacks precipitation data). Performance analysis demonstrates that GraphCast consistently outperforms HRES, Pangu-Weather, and Aspire Meso across various meteorological variables and lead times. Despite the advantages, the models exhibit baseline errors due to shared data sources like ERA5, leading to correlated errors that limit the effectiveness of ensemble forecasting. The research underscores the potential of AI models to enhance forecast accuracy and reduce imbalance costs in wind energy production but also emphasizes challenges related to the black-box nature of AI models, data bottlenecks, and limitations imposed by neural scaling laws, where larger models do not necessarily yield better performance. Recommendations for future work include incorporating more observational data, enhancing model modularity and adaptability, improving temporal resolution, expanding variable ranges, emphasizing validation against observational data, and exploring advanced ensemble techniques. The findings suggest that while AI-based models like GraphCast and Pangu-Weather hold significant promise for advancing weather forecasting, addressing limitations in data quality, model architecture, and operational flexibility is crucial for realizing their full potential in operational settings.