Enhancing Short-Term Wind Power Forecasting Through Advanced Machine Learning Algorithms: A Comparative Study of Random Forests, Gradient Boosting, and LSTM Networks Against Traditional Linear Regression Models

Integrating wind energy into the power grid is becoming increasingly prevalent, but it poses variability-a key operational challenge to grid operators. Precise short-term wind power forecasting will help ensure grid stability, optimize energy storage, and enable efficient power dispatch. This work, therefore, compares high-end machine learning algorithms, such as Random Forests, Gradient Boosting Machines, and Long Short-Term Memory networks, against traditional baselines comprising Linear Regression models. The use of real data from the WIND Toolkit dataset demonstrates that advanced machine learning techniques are much better positioned to model complex nonlinear relationships and temporal dependencies that are representative of wind power. That highlights that the results present a significant reduction in forecast errors, with Random Forests and LSTM networks resulting in the highest level of predictive accuracy. This is because minimizing the forecasting errors enhances the models by providing better grid stability and energy storage management. The study further contributes towards a scalable and practical machine learning framework that could easily be integrated into real-time energy management systems. Future research on the application of these models to spatiotemporal forecasting and hybrid deep learning architectures will further enhance the reliability of wind power prediction, enabling seamless integration of renewable energy into modern power grids.