Predicting Wind Speed Using Machine Learning Models

Accurate wind speed prediction is crucial for optimizing wind energy generation and ensuring effective integration into power systems. Reliable forecasts enable better adaptation of power grids to renewable energy sources, refining overall efficiency and sustainability. This study explores the use of machine learning techniques for wind speed forecasting by using meteorological data, such as temperature, air pressure, wind direction, and height. Multiple regression models, which would include K-Nearest Neighbours (KNN), Random Forest, Decision Tree, Support Vector Regression (SVR), and Linear Regression are compared to assess the models Mean Squared Error (MSE) and R2 scores of a real-world wind speed datasets to predict wind speed. Among all Random Forest model performed the best, proving its adaptability in identifying complex relationships with MSE values of 0.27 and 0.998 respectively, KNN and Decision Tree also formed modest results. The results demonstrate how ensemble-based models, such as Random Forest, can accurately forecast wind speed, which makes them appropriate for optimizing renewable energy. In order to improve wind speed prediction for energy applications, this study offers insights into model performance and potential paths for future research.