Applications of IoT and Machine Learning in Photovoltaic (PV) Systems: A Comprehensive Review

Abstract

Photovoltaic (PV) system monitoring, optimization, and control have completely changed as a result of the convergence of internet of things (IoT) and machine learning (ML) technologies. While IoT makes it possible to gather, transmit, and store electrical and environmental data, ML offers intelligent data analysis for prediction and adaptive decision-making. This review provides a comprehensive analysis of recent advances in the application of IoT as well as ML for improving PV performance and efficiency. It examines the IoT hardware and communication architectures and highlights their roles in achieving high-resolution and real-time monitoring. In addition, this paper explores the application of ML in PV systems, including power forecasting, maximum power point tracking (MPPT), fault detection, and energy management. Moreover, it analyzes the benefits and performance improvements as well as challenges and limitations of the combined IoT–ML framework with PV systems. It outlines the future directions, such as federated learning, edge intelligence, and digital-twin integration. This combination enhances the system performance by improving tracking efficiency, reducing forecasting error, and decreasing operational cost, which makes these technologies key parts of the next generation of PV systems.