Optimizing Wind Farm Layouts Using Machine Learning and CFD Simulations

Abstract: This research explores the optimization of wind farm layouts by integrating machine learning (ML) techniques with computational fluid dynamics (CFD) simulations. The placement of wind turbines significantly influences energy yield due to wake effects and aerodynamic interactions. This study employs supervised learning algorithms to predict wake losses and applies CFD tools to simulate flow behavior in various layout configurations. A regression-based optimization framework is developed to identify optimal turbine positions, with predictive performance validated against real-world data. Results indicate a potential increase in annual energy production (AEP) by up to 14% compared to conventional grid layouts. The findings demonstrate that ML-driven layout planning, augmented by CFD validation, offers a scalable and efficient approach for next-generation wind farm design. Keywords: Wind Farm Optimization, Machine Learning, CFD Simulation, Wake Loss Prediction, Energy Yield, Turbine Placement, Regression Analysis