Deep learning ensemble for solar and wind power forecasting with feature engineering optimization (swift-net)

To compensate for the massive variations in power output brought on by unpredictability, enormous quantities of pricy battery storage or power reserve capacity are required. Precise prediction of the Solar Wind Power Forecasting system improves energy conversion efficiency, reduces the risk of overloading the system, and optimizes unit commitment. In this paper, design a SWIFT-Net (Solar & Wind Integrated Forecasting Technology Network) model for precise solar and wind energy forecasting. Unlike existing models, SWIFT-Net uniquely integrates a deep learning ensemble framework with a dual-stage hybrid feature engineering mechanism using Hybrid Kookaburra Optimization and Botox Optimization, which has not been previously applied in this context. This ensures the selection of the most impactful features and enhances generalization across variable weather conditions. The weighted averaging method is employed to combine solar and wind power predictions. Finally, the designed model gained results are validated with existing classifiers in terms of MSE, RMSE, and MAE, ensuring continual refinement for enhanced forecasting accuracy, reliability.