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.

The Lisbon "Calçada" sidewalks have a traditional style made of small flat and irregular cobblestones, representing magnificent mosaics. This work presents the characterization and identification of the mineralogical phases of sidewalk stones, originating from Lisbon, Portugal. Samples referenced MEKPBL (white) and MEKPNL (grey-black) were studied by means of Raman spectroscopy, ATR-FTIR and XRD. All Raman spectra show a strong band around 1089 cm-1 which can be attributed to the v1-symmetric stretching mode of (CO3)2-, indicating that calcite is the main crystal phase in both samples. All Raman spectra of MEKPNL show also graphite fingerprints localized around 1329 cm-1 and 1607 cm-1, which are characteristic respectively of the D and G peaks of the carbonaceous material. These carbonaceous materials are the substance responsible of the grey-black color to these stones. Furthermore, the Raman spectra of the MEKPNL sample show also a weak Raman band located at 460 cm-1 which can be attributed to the symmetrical stretching of Si-O-Si, characteristic of the α-quartz. Furthermore, ATR-FTIR and XRD analyses supported the Raman results.

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.

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.