A Dynamic Approach to Wind Speed Prediction Using Functional Data: Toward a Sustainable Energy Future

Abstract - Wind energy plays a major role in the global shift to sustainable and clean energy. Predicting wind speeds accurately is essential for preserving grid stability, cutting down on operating costs, and enhancing wind energy systems' overall performance. This work presents an adaptive machine learning method that uses functional data from past weather patterns to forecast wind speeds. Key meteorological variables including temperature, humidity, air pressure, dew point, and time-based characteristics are included in the dataset, which was obtained from the Open-Meteo weather API and covers the years 2024–2025.