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ZENODO
Dataset . 2025
License: CC BY
Data sources: ZENODO
ZENODO
Dataset . 2025
License: CC BY
Data sources: Datacite
ZENODO
Dataset . 2025
License: CC BY
Data sources: Datacite
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SOLTRACK: Dual-Axis Solar Tracking System Dataset

Authors: Abdellatif Hamed IBRAHIM, Ahmed; Mourad, Raouf Mahmoud; Ismaiel, Amr; Shaaban, Sameh;

SOLTRACK: Dual-Axis Solar Tracking System Dataset

Abstract

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.

Keywords

Solar Energy, Deep learning, Renewable Energy

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
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