WIND SPEED PREDICTION WITH MACHINE LEARNING

Abstract

Wind speed prediction is crucial for efficient wind energy generation, weather forecasting, and climate modeling. This study presents a novel approach to predicting wind speed using machine learning algorithms and meteorological data. A dataset of historical wind speed measurements and corresponding meteorological variables (temperature, humidity, atmospheric pressure, etc.) is utilized to train and evaluate the models. We compare the performance of various machine learning algorithms, including linear regression, decision trees, and random forests, to predict wind speeds. The model is trained and validated using a dataset of historical wind speed and climate data. The results show that the proposed model achieves a high degree of accuracy, with a mean absolute error of 0.5m/s. The model’s performance is also compared with traditional statistical methods, demonstrating the superiority of the machine learning approach. The project demonstrates the potential of which can have significant implications for wind energy production, weather forecasting, and climate modeling