SOLTRACK: Dual-Axis Solar Tracking System Dataset

The SOLTRACK-25 dataset was collected from an experimental Dual-Axis Solar Tracking System (SLS) developed to optimize the orientation of a photovoltaic (PV) panel toward the sun in real time. The system was designed to evaluate and benchmark various solar tracking control strategies under diverse seasonal, environmental, and illumination conditions. Experiments were conducted throughout 2025 in New Cairo, Egypt (30° 2′ 57.017″ N, 31° 27′ 39.804″ E), a region characterized by high solar irradiance and moderate wind, ideal for analyzing real-world tracking performance.

The setup comprises four Light Dependent Resistor (LDR) sensors mounted around the panel aperture to detect directional irradiance, a Witmotion IMU sensor to measure actual platform orientation (tilt/elevation and azimuth), and two stepper motors—one for the daily (azimuth) axis and one for the seasonal (elevation/zenith) axis—driven via a dual-chain mechanism. An Arduino Mega microcontroller forms the system’s core, interfacing with sensors and motor drivers to ensure synchronized data acquisition and motion control.

The controller collects data with a 15-second sampling period, logging time (s), ambient temperature (°C), irradiance (W/m²), humidity (%), LDR1–LDR4 voltages (top, right, down, left), and tracked azimuth and zenith angles. Each day includes 3 264 records at 15-second intervals, stored in Excel (.xlsx) format, with each sheet representing one full day of continuous operation. Data were collected during four representative months—January, February, July, and August 2025—to capture distinct solar elevation geometries and seasonal environmental effects.

SOLTRACK-25 supports research in advanced solar-tracking controllers correlation between tracking accuracy and Maximum Power Point Tracking (MPPT), and the analysis of environmental disturbances such as shading, wind, and humidity. It is also suitable for applying artificial-intelligence and machine-learning models in predictive tracking, irradiance forecasting, and adaptive control optimization.