AI in Solar Forecasting: Advanced Machine Learning Techniques for Photovoltaic Power Prediction

Solar energy forecasting is critical for grid stability and renewable energy integration. This paper reviews artificial intelligence techniques applied to solar forecasting, focusing on advances from 2023-2026. We examine deep learning architectures including LSTM networks, CNNs, Transformer-based models, and hybrid approaches. Analysis of 242 studies reveals that hybrid CNN-LSTM models achieve 15-30% MAE reductions compared to standalone models. Deep learning excels for short-term predictions, while ensemble approaches benefit day-ahead forecasts. Key challenges include data quality, computational complexity, and model generalization. This review synthesizes methodologies, performance metrics, and future directions including transfer learning and physics-informed neural networks