DEVELOPMENT OF AN EFFECTIVE WIND POWER MODELLING TOOL FOR PREDICTING WIND TURBINE POWER PRODUCTION IN ONSHORE AND OFFSHORE WIND FARMS

The accurate prediction of wind turbine power production is critical for optimizing the performance and economic viability of both onshore and offshore wind farms. This study presents the development of an effective wind power modeling tool designed to enhance power output predictions by integrating advanced meteorological data, turbine characteristics, and site-specific conditions. The model incorporates key parameters such as wind speed, air density, turbulence intensity, and wake effects to improve prediction accuracy. Utilizing computational fluid dynamics (CFD) and machine learning techniques, the tool accounts for variations in terrain, atmospheric stability, and offshore-specific factors such as sea surface roughness and wave interactions. Validation against real-world operational data from multiple wind farms demonstrates strong correlation between predicted and actual power outputs, with improved reliability over conventional methods.The developed tool provides a robust framework for wind farm planners and operators to optimize turbine placement, assess energy yield, and improve grid integration strategies. Its adaptability to diverse geographical and climatic conditions makes it a valuable asset for advancing wind energy efficiency in both onshore and offshore environments.