Bibliometric Analysis and AI-Optimized Control Modeling of Solar Tracker Systems

Jalilov, Akhmadbek; Kobulova, Nilufar; Narziev, Shovkiddin; Shamuratova, Shakhida; Lazarenkov, Sunnatulla

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Proceedings of International Conference on Applied Innovation in IT · 2026/07/22 · Vol. 14 · Issue 4 · pp. 1347–1353

Bibliometric Analysis and AI-Optimized Control Modeling of Solar Tracker Systems

Akhmadbek Jalilov, Nilufar Kobulova, Shovkiddin Narziev, Shakhida Shamuratova, Sunnatulla Sulaymanov and Alexander Lazarenkov

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Abstract

The rapid expansion of photovoltaic (PV) systems has intensified the need for intelligent solar tracking technologies capable of maximizing energy yield under dynamic environmental conditions. While mechanical tracking systems have been extensively studied, adaptive artificial intelligence (AI)-based optimization remains underrepresented. This study proposes a hybrid framework integrating bibliometric analysis and applied computational modeling to address this gap. A reproducible scientometric mapping of 391 Scopus-indexed publications (2011-2022) was conducted using VOSviewer to identify research clusters and emerging trends in solar tracking technologies. The analysis revealed limited integration of predictive AI control mechanisms. Based on the identified gap, an AI-enhanced solar tracker optimization model was developed, combining deterministic solar geometry equations with a machine learning-based correction mechanism. A multivariate regression model was trained and validated using 5-fold cross-validation. Simulation results over a 30-day evaluation period demonstrated that the proposed AI-optimized tracker achieved a 34.2% energy gain compared to fixed systems and a 6.1% improvement over conventional dual-axis geometric tracking. Statistical validation using one-way ANOVA confirmed significant performance differences (F = 18.47, p < 0.001). The proposed hybrid approach bridges bibliometric insight with applied IT innovation, advancing solar tracking systems toward intelligent adaptive control. The framework is scalable and suitable for embedded microcontroller-based implementations in smart-grid environments.

Keywords

Solar Tracker Artificial Intelligence Bibliometric Analysis Photovoltaic Optimization Regression Model Adaptive Control

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