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.

Photovoltaic (PV) technology is now one of the main options for expanding the power of low-carbon electricity generation. However, in practical operation, PV systems still face several persistent difficulties, including the variability of solar irradiance, gradual performance degradation, fault occurrence, suboptimal control, and the growing complexity of grid-connected operation. These issues explain why artificial intelligence (AI) has become increasingly relevant in PV research, not only as a prediction tool, but also to improve monitoring, control, diagnosis, and decision-making. This review investigates the applications of AI in the major stages of the PV system lifecycle: solar resource assessment, power forecasting, fault detection, condition monitoring, system sizing, maximum power point tracking (MPPT), and grid integration. Rather than treating these applications as separate research topics, the review attempts to connect them through the common factors that determine their practical value: data quality, sensing configuration, model complexity, physical operating conditions, and deployment constraints. The reviewed studies indicate that AI-based MPPT methods can achieve tracking efficiencies close to 99%, while recent forecasting models, particularly LSTM, CNN–LSTM, and transformer-based architectures, can reduce prediction errors under changing weather conditions. At the same time, PV fault detection is moving beyond electroluminescence image classification toward more practical multimodal strategies that combine infrared thermography, RGB and drone imagery, electrical measurements, and SCADA/IoT data. Nevertheless, the progress reported in the literature should be interpreted with caution. Many proposed models are still evaluated on limited or non-standardized datasets, and their performance may decrease when they are transferred to different PV technologies, climates, fault severities, or operating conditions. Other recurring limitations include class imbalance, high computational cost, weak generalization, and the limited interpretability of deep-learning models. For this reason, hybrid neural networks, explainable AI, physics-informed learning, edge-AI, federated learning, and quantum machine learning are discussed as possible directions for making AI-based PV solutions more reliable and deployable. This review aims to critically synthesize recent advances and remaining gaps in order to support the practical integration of AI into efficient, reliable, and sustainable PV systems.

The digitalization of photovoltaic (PV) systems can support sustainable energy deployment by improving operational efficiency, system visibility, and energy extraction. However, many existing Internet of Things (IoT)-enabled solutions address monitoring and maximum power point tracking (MPPT) separately or depend on proprietary platforms, remote cloud services, and relatively costly hardware, which may restrict their accessibility and replication in small-scale and resource-constrained applications. This study presents the implementation and laboratory-scale experimental evaluation of an edge-IoT architecture that integrates real-time PV monitoring, embedded adaptive MPPT control, local data management, and visualization using low-cost hardware and open-source software. The proposed architecture combines an ESP32 microcontroller with a Raspberry Pi (RPi) local server to enable environmental and electrical sensing, edge-based control, message queuing telemetry transport (MQTT) communication, local data storage, and interactive visualization through the open-source Node-RED, InfluxDB, and Grafana platforms. An adaptive perturb-and-observe (AP&O) algorithm is implemented on the ESP32 to dynamically adjust the duty cycle of a DC–DC boost converter in response to changing operating conditions. The system is experimentally evaluated using a PV test bench equipped with a custom boost converter and sensing modules measuring eleven electrical and environmental parameters. The architecture achieved an average communication latency of 193 ± 23 ms and an average MPPT efficiency of 97.3 ± 0.54%. It also provided a power gain of 0.7 ± 0.5% compared with the conventional fixed-step perturb-and-observe method. By combining local processing, open-source software, low-cost components, and integrated monitoring and control, the proposed system reduces dependence on external cloud infrastructure while supporting responsive and accessible PV energy management. These results demonstrate its potential as a replicable technological framework for improving the operational sustainability and digital management of small-scale PV installations.

This paper presents a low-cost embedded platform integrating photovoltaic (PV) energy monitoring, maximum power point tracking (MPPT), and Internet of Things (IoT) communication for real-time control and performance evaluation. The proposed system is built around an ESP32 microcontroller with dual INA219 sensors and a custom-designed DC–DC boost converter operating at 20 kHz. Two MPPT strategies, perturb and observe (P&O) and fuzzy logic control (FLC), are implemented and experimentally compared under identical outdoor conditions. Experimental results obtained under natural irradiance demonstrate that the FLC approach achieves faster convergence and reduced steady-state oscillations compared to P&O. Specifically, FLC improves tracking efficiency from 82.5% to 84%, reduces duty cycle ripple, and increases average extracted power by approximately 1.5%. The system also enables real-time acquisition of I–V and P–V characteristics through duty-cycle sweeping. The proposed architecture integrates sensing, control, and IoT telemetry using MQTT and a ThingsBoard dashboard, enabling continuous monitoring with negligible communication latency relative to the MPPT update period. This work contributes a reproducible, low-cost testbed that bridges embedded control, power electronics, and IoT-based monitoring, with potential applications in both education and rapid prototyping of smart PV systems.

Parameter estimation of photovoltaic (PV) models is crucial for the theoretical analysis and performance evaluation of PV cells and modules, with the objective of enhancing their efficiency and reliability, thereby supporting the long-term sustainability of solar energy systems. Nevertheless, the nonlinear and multimodal characteristics of PV models make the task of accurate parameter estimation challenging. This paper proposes an improved differential evolution algorithm, named opposition-based parent selection differential evolution (OBPSDE), to enhance the reliability and robustness of PV parameter estimation. The method integrates a parent-selection mechanism with an opposition-based learning strategy to exploit both solution quality and population diversity during the search process. The proposed method is evaluated using measured data from several PV cells and modules (RTC France, PVM752GaAs, PWP201, and STP6-120/36) for parameter estimation of the double-diode model (DDM). Its performance is compared with standard DE, DE variants, and four metaheuristic algorithms using statistical metrics including root mean square error (RMSE), individual absolute error (IAE), and mean absolute error (MAE). The results indicate that OBPSDE achieves stable performance, competitive computational cost, and improved convergence behavior, with RMSE values of 6.93726 × 10^–4 for RTC France, 5.89070 × 10^–5 for PVM752GaAs, 1.93772 × 10^–3 for PWP201, and 1.39519 × 10^–2 for STP6-120/36. Additionally, the improved parameter estimation accuracy may support more reliable performance prediction and analysis of PV systems, contributing to effective PV system modeling and diagnostic applications.