Artificial Intelligence for Forecasting Solar, Hydropower, and Wind Renewable-Energy Systems: A Comparative Analysis

Abstract

The increasing integration of renewable energy sources into modern power systems has created a strong demand for accurate and reliable forecasting methods. Solar, wind, and hydropower systems are highly dependent on meteorological and hydrological conditions; therefore, accurate forecasting is essential for grid stability, optimal energy scheduling, and operational efficiency. Artificial intelligence (AI) has emerged as a powerful and widely adopted approach for renewable energy forecasting due to its ability to model complex nonlinear relationships and extract patterns from large-scale datasets. In solar energy forecasting, multilayer perceptron (MLP) and long short-term memory (LSTM) models are commonly used to predict power generation using historical and meteorological data. Wind energy forecasting commonly employs artificial neural networks (ANNs), support vector regression (SVR), and Gaussian process regression (GPR) to model nonlinear wind behavior and quantify prediction uncertainty. Hydropower forecasting utilizes support vector regression (SVR) and hybrid models, such as Gaussian process regression combined with the cooperation search algorithm (GPR–CSA), to estimate hydrological variables and predict power output. This review compares these approaches in terms of predictive accuracy, robustness, computational efficiency, data requirements, and their ability to handle resource-specific uncertainty.