Review of AI Methods for Intelligent Photovoltaic Systems in Smart Grids: Forecasting, Sizing, MPPT, and Fault Diagnosis

Abstract

The growing integration of photovoltaic (PV) energy into smart electrical grids presents significant challenges related to intermittency, fault detection, system sizing, and real-time control. This review provides a comprehensive analysis of recent advancements in Artificial Intelligence (AI), particularly Machine Learning (ML) and Deep Learning (DL), for optimizing the operation and management of PV systems within smart grids. The study categorizes and compares AI-based methodologies in energy forecasting, fault detection and diagnosis, intelligent PV system sizing, and optimization of Maximum Power Point Tracking (MPPT) techniques. Through a critical evaluation of recent literature (2020-2025), the review highlights the complementary roles of classical ML models and advanced DL architectures, including Long ShortTerm Memory (LSTM) networks, Transformer models, and hybrid approaches combining physics-based knowledge with datadriven learning. Moreover, heuristic optimization algorithms such as Particle Swarm Optimization (PSO) are discussed for their contribution to enhancing convergence speed and accuracy in MPPT and diagnostic applications. This review not only synthesizes state-of-the-art AI strategies but also identifies key technical gaps related to data availability, computational complexity, and model interpretability. Emerging solutions such as federated learning, edge computing, and Explainable AI (XAI) are discussed as promising pathways toward scalable, trustworthy, and autonomous PV energy systems. The study aims to support researchers and practitioners in designing robust, real-time AI-based frameworks for next-generation renewable energy integration.