IOT-BASED DUAL-AXIS SOLAR TRACKING SYSTEM

Abstract

The transition toward renewable energy sources is imperative for addressing escalating global energy demands. However, static solar panels experience significant energy yield reductions due to cosine losses as the angle of incidence of solar radiation continuously changes. This study presents a framework based on the Internet of Things (IoT) for an automated dual-axis solar tracking system designed to continuously optimize the orientation of a photovoltaic (PV) panel. An Arduino Uno microcontroller architecture was developed and programmed to utilize a sensor array comprising four Light Dependent Resistors (LDRs) and two servomotors. This model can distinguish the spatial light intensity gradients across four quadrants to dynamically actuate the panel along both azimuth and elevation axes. The proposed model uses a photometric differential control logic framework with a built-in hysteresis threshold to improve generalization, prevent motor hunting, and reduce parasitic power consumption. The network architecture incorporates an ESP8266 Wi-Fi module, operating within a four-layer IoT model, to facilitate real-time telemetry and remote monitoring. We evaluated model performance using standard electrical metrics such as output power, voltage, and overall module efficiency across different times of the day. Experimental results show high tracking performance, yielding up to a 44% to 45% increase in total daily energy generation compared to static installations, with particularly pronounced efficiency gains during the morning and evening hours. A comparative analysis confirms that the proposed closed-loop framework is superior in reliability and energy capture. The system has potential for smart agriculture, off-grid systems, and residential micro-grids, helping operators monitor efficiency remotely. This study highlights the effectiveness of simple embedded systems and supports their use in automated green energy solutions. Future work will focus on integrating Maximum Power Point Tracking (MPPT) and machine learning algorithms for predictive weather tracking.