As solar energy adoption increases, monitoring photovoltaic (PV) systems continues to pose significant challenges, especially when aiming for cost-effective, scalable solutions. This study presents a real-time Internet of Things (IoT)-based monitoring system for PV plants, designed to enhance operational efficiency, predictive maintenance, and long-term performance. Implemented using low-cost Espressif Systems 32-bit (ESP32) microcontrollers and sensors, the system captures a range of environmental and electrical variables, transmitting data seamlessly to cloud-based spreadsheets via Google Sheets. Validation using reference meteorological data confirmed measurement reliability and highlighted the system's robustness under diverse field conditions. Beyond monitoring, the proposed architecture demonstrates high adaptability, offering potential for fault alerting through anomaly detection and serving as a dynamic database for performance analytics and system optimization. Challenges related to sensor calibration and environmental interference were addressed, with solutions proposed to strengthen future implementations. By merging real-time sensing with intelligent data structuring, this research highlights the scalability and scientific relevance of IoT in renewable energy applications, providing a foundation for smart, automated, and resilient PV system management.
Souza et al. (Mon,) studied this question.
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