Optimization of Photovoltaic Systems via AI-Based Solar Tracking and MPPT: Trends, Challenges, and Bibliometric Insights

Hamza Rafik¹, Oussama Khouili², Mohamed Louzazni¹, Petru Adrian Cotfas³, Daniel Tudor Cotfas^3,*
1 Science Engineer Laboratory for Energy, National School of Applied Sciences, Chouaib Doukkali University, El Jadida, Morocco
2 LTI Laboratory, National School of Applied Sciences, Chouaib Doukkali University, El Jadida, Morocco
3 Electronics and Computers Department, IESC Faculty, Transilvania University of Brasov, Brasov, Romania
* Corresponding Author: Daniel Tudor Cotfas. Email: email
(This article belongs to the Special Issue: Advanced Computational Methods and AI algorithms for Renewable Energy)

Computer Modeling in Engineering & Sciences https://doi.org/10.32604/cmes.2026.084256

Received 19 April 2026; Accepted 29 June 2026; Published online 10 August 2026

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Abstract

The rapid expansion of photovoltaic (PV) technologies has necessitated the enhancement of energy conversion efficiency by developing more and more sophisticated control and optimization techniques. In particular, novel MPPT methods combined with solar tracking systems and AI approaches emerge as a promising solution to surmount the barriers of the conventional PV systems. This research presents a critical assessment of the recent developments in the research area of PV systems with MPPT algorithms, solar tracking mechanisms, and AI-based techniques. Therefore, papers with publication years from 2021 to 2025 were selected using Web of Science Core Collection. Then, a series of bibliometric metrics were used in this paper, including publication output, citation impact, collaboration patterns, and keyword co-occurrence, to highlight the evolution of this research area. The significant growth of scientific productivity is depicted in the paper, together with the strong contribution of both emerging and developed economies, in addition to rising interest in intelligent control strategies, machine learning, and hybrid optimization methodologies. As a result, these findings provide important insights regarding recent research developments and future challenges and offer a useful reference for researchers and practitioners interested in high-efficiency PV systems.

Keywords

Maximum power point tracking; photovoltaic systems; intelligent control; artificial neural networks; metaheuristic optimization; power electronics; dc–dc converters; renewable energy integration; solar tracker

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Optimization of Photovoltaic Systems via AI-Based Solar Tracking and MPPT: Trends, Challenges, and Bibliometric Insights

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