Identifying wind regimes near Kuwait using self-organizing maps.

This article focuses on identifying and characterizing dominant wind regimes affecting wind energy production at the Shagaya Renewable Energy Park in Kuwait using the Weather Research and Forecasting (WRF) model and the Self-Organizing Maps (SOM) machine-learning method. Six primary wind regimes were identified, with two—one representing the summer shamal (a strong northwesterly wind) and another associated with strong westerlies—showing average wind speeds near 9.9 and 8.6 m/s at 80 m hub height and favorable wind power potential for the region. The remaining four regimes, characterized by weaker winds such as local southeasterlies and nocturnal low-level jets, are less conducive to wind power generation. The study demonstrates that combining SOM-derived wind flow patterns with wind speed and power distributions enhances understanding of seasonal and diurnal wind variability, aiding wind resource assessment, forecasting, and planning for Kuwait's expanding renewable energy infrastructure.