Deep Learning Methods in Energy Systems: A Renewable Energy Perspective

This paper presents a comprehensive review of deep learning applications in energy systems with a particular focus on renewable-energy-based power systems. The rapid deployment of photovoltaic (PV) and wind generation introduces significant uncertainty into power system operation and planning. Accurate forecasting of renewable generation and load, advanced energy management strategies for renewable-rich microgrids, and reliable fault detection and predictive maintenance schemes for PV plants and wind turbines are essential to guarantee secure and economic operation. In recent years, deep neural networks, convolutional neural networks (CNN), recurrent neural networks (RNN) such as long short-term memory (LSTM) and gated recurrent units (GRU), and deep reinforcement learning (DRL) algorithms have achieved state-of-the-art performance in these tasks. This review first outlines the main deep learning architectures and the characteristics of data in energy and renewable energy systems. It then surveys applications in PV and wind power forecasting, load forecasting in smart grids, DRL-based energy management in renewable-rich microgrids, and fault detection and predictive maintenance in PV and wind plants. Emerging trends such as generative models for data augmentation, physics-informed learning and explainable artificial intelligence (XAI) are also discussed. The paper concludes by highlighting open challenges related to data quality, generalization, computational cost and model interpretability, and by outlining promising directions for future research.