Harnessing the Power of Advanced Deep Learning Algorithms for Foretelling Wind Speed

Wind speed estimation is crucial for the stability and long-term planning of the electrical grid. Wind power has positive environmental effects and allows grid operators to anticipate energy generation trends and make real-time adjustments. This study investigates four deep learning algorithms for predicting wind speed: K-Nearest Neighbors (KNN), Extreme Gradient Boosting (XGB), Convolutional Neural Network (CNN), and Long Short-Term Memory (LSTM) networks. The LSTM model shows the best R2 value and the lowest RMSE value, demonstrating its better predictive ability. These models capture complex temporal correlations in wind speed data, demonstrating their utility as reliable time-series analysis tools. The recognition of LSTM networks as the best wind speed predictors paves the way for cutting-edge forecasting techniques and a robust energy infrastructure that can meet the demands of a changing energy landscape.