Hybrid AI-Based Framework for Renewable Energy Forecasting: One-Stage Decomposition and Sample Entropy Reconstruction with Least-Squares Regression

Accurate renewable energy forecasting is crucial for grid stability and efficient energy management. This study introduces a hybrid model that combines signal decomposition and artificial intelligence to enhance the prediction of solar radiation and wind speed. The framework uses a one-stage decomposition strategy, applying variational mode decomposition and an improved empirical mode decomposition method with adaptive noise. This process effectively extracts meaningful components while reducing background noise, improving data quality, and minimizing uncertainty. The complexity of these components is assessed using entropy-based selection to retain only the most relevant features. The refined data are then fed into advanced predictive models, including a bidirectional neural network for capturing long-term dependencies, an extreme learning machine, and a support vector regression model. These models address nonlinear patterns in the historical data. To optimize forecasting accuracy, outputs from all models are combined using a leastsquares regression technique that assigns optimal weights to each prediction. The hybrid model was tested on datasets from three geographically diverse locations, encompassing varying weather conditions. Results show a notable improvement in accuracy, achieving a root mean square error as low as 2.18 and a coefficient of determination near 0.999. Compared to traditional methods, forecasting errors were reduced by up to 30%, demonstrating the model’s effectiveness in supporting sustainable and reliable energy systems.