Analysis of solar panel characteristics

Reliable monitoring of photovoltaic (PV) modules is essential for assessing energy generation performance, diagnosing degradation, and ensuring long-term system reliability. Solar panels experience variations in output characteristics due to changing irradiance, temperature, environmental conditions, and aging effects. Conventional monitoring techniques rely on manual measurement or bulky instrumentation, which lack real-time visibility and are unsuitable for continuous data logging. This paper presents a data-driven Internet of Things (IoT)–based methodology for real-time solar panel characteristic monitoring and analytics. The proposed system utilizes an ESP32 microcontroller interfaced with an INA219 voltage–current sensor to acquire live measurements of panel voltage, current, and instantaneous power. The data is timestamped using NTP synchronization and transmitted to a Firebase Real-Time Database for cloud storage. A Flutter-based Android application retrieves the data to provide live dashboards, historical charts, and CSV export functionality for one-hour intervals or the complete operational dataset. Time-series data collected from the system enables computation of analytical metrics such as daily energy generation, peak-power duration, stability under irradiance variation, and long-term performance trends. Experimental evaluation on a 11 W SLP011-12 solar module demonstrates accurate sensing, stable wireless data transfer, and effective visualization of more than 2,000+ recorded samples. The contributions of this work include: (1) a low-cost, scalable IoT architecture for continuous PV monitoring, (2) automated cloud-synchronized data logging with precise timestamping, (3) an interactive mobile application for real-time analytics and dataset export, and (4) a foundation for future machine-learning–based performance prediction and fault diagnosis. This system provides an efficient research and industrial tool for solar panel condition assessment and long-term energy monitoring.