A comparative study of machine learning and Kriging: Improving wind resource assessment in data-scarce, monsoon-affected regions

Key Points

• Wind resource assessment accuracy improves with machine learning and kriging in challenging climates.
• A comparative analysis found machine learning methods increased prediction accuracy by over 15%.
• Observational analysis assessed wind data in data-scarce, monsoon-affected regions over multiple seasons.
• Findings highlight the need for innovative assessment techniques in regions facing data limitations.