Abstract
Introduction: Wind speed and solar radiation are two of the most well-known and widely used renewable energy sources worldwide. Coal, natural gas, and petroleum are examples of fossil fuels that are not replenished and are thus non-renewable energy sources due to their high carbon content and the methods by which they are generated. To predict energy production of renewable sources, researchers use energy forecasting techniques based on the recent advances in machine learning approaches. Numerous prediction methods have significant drawbacks, including high computational complexity and inability to generalize for various types of sources of renewable energy sources. Methodology: In this paper, we proposed a novel approach capable of generalizing the prediction accuracy for both wind speed and solar radiation forecasting data. The proposed approach is based on a new optimization algorithm and a new stacked ensemble model. The new optimization algorithm is a hybrid of Al-Biruni Earth Radius (BER) and genetic algorithm (GA) and it is denoted by the GABER optimization algorithm. This algorithm is used to optimize the parameters of the proposed stacked ensemble model to boost the prediction accuracy and to improve the generalization capability. Results: To evaluate the proposed approach, several experiments are conducted to study its effectiveness and superiority compared to other optimization methods and forecasting models. In addition, statistical tests are conducted to assess the significance and difference of the proposed approach. The recorded results proved the proposed approach's superiority, effectiveness, generalization, and statistical significance when compared to state-of-the-art methods. Conclusions: The proposed approach is capable of predicting both wind speed and solar radiation with better generalization.