Renewable Energy Prediction Using Deep Learning Techniques

Abstract

The accurate prediction of renewable energy generation is essential for the efficient operation, planning, and optimization of modern power systems. The increasing integration of renewable sources such as solar photovoltaic (PV) and wind energy introduces uncertainty because their output depends on meteorological conditions. Traditional statistical and physics-based forecasting methods often fail to effectively capture the complex, nonlinear, and stochastic characteristics of renewable energy time-series data. Deep learning models have recently emerged as powerful tools for renewable energy forecasting due to their ability to automatically learn hierarchical representations from large-scale historical and exogenous data. This study presents a comprehensive analysis of renewable energy forecasting using deep learning techniques, focusing on short-term and medium-term prediction horizons. The paper reviews the evolution of forecasting approaches from conventional time-series models to advanced deep learning architectures such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), long short-term memory (LSTM) networks, gated recurrent units (GRUs), and hybrid deep learning models. The proposed forecasting framework integrates meteorological variables, historical power generation data, and temporal features to improve prediction accuracy and robustness. A unified deep learning–based forecasting scheme is presented, including data collection, preprocessing, feature engineering, model training, validation, and deployment. Mathematical formulations of the learning process and loss optimization are also provided to establish a theoretical foundation. Extensive experiments are conducted on real-world renewable energy datasets obtained from publicly available sources to demonstrate the superiority of deep learning models over traditional methods. Model performance is evaluated using Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE). The results show that deep learning models provide significantly better forecasting accuracy, particularly under highly variable weather conditions. The study highlights the importance of model selection, hyperparameter optimization, and data quality in renewable energy forecasting and serves as a useful reference for researchers and practitioners working on intelligent energy management and decision-support systems.