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Article

Computational Intelligence-Based Modeling of UAV-Integrated PV Systems

by
Mohammad Hosein Saeedinia
,
Shamsodin Taheri
* and
Ana-Maria Cretu
Department of Computer Science and Engineering, Université du Québec en Outaouais Gatineau, Gatineau, QC J8X 3X7, Canada
*
Author to whom correspondence should be addressed.
Solar 2025, 5(4), 45; https://doi.org/10.3390/solar5040045
Submission received: 10 July 2025 / Revised: 20 August 2025 / Accepted: 25 September 2025 / Published: 3 October 2025
(This article belongs to the Special Issue Efficient and Reliable Solar Photovoltaic Systems: 2nd Edition)

Abstract

The optimal utilization of UAV-integrated photovoltaic (PV) systems demands accurate modeling that accounts for dynamic flight conditions. This paper introduces a novel computational intelligence-based framework that models the behavior of a moving PV system mounted on a UAV. A unique mathematical approach is developed to translate UAV flight dynamics, specifically roll, pitch, and yaw, into the tilt and azimuth angles of the PV module. To adaptively estimate the diode ideality factor under varying conditions, the Grey Wolf Optimization (GWO) algorithm is employed, outperforming traditional methods like Particle Swarm Optimization (PSO). Using a one-year environmental dataset, multiple machine learning (ML) models are trained to predict maximum power point (MPP) parameters for a commercial PV panel. The best-performing model, Rational Quadratic Gaussian Process Regression (RQGPR), demonstrates high accuracy and low computational cost. Furthermore, the proposed ML-based model is experimentally integrated into an incremental conductance (IC) MPPT technique, forming a hybrid MPPT controller. Hardware and experimental validations confirm the model’s effectiveness in real-time MPP prediction and tracking, highlighting its potential for enhancing UAV endurance and energy efficiency.
Keywords: grey wolf optimization; machine learning; PV modeling; moving PV; UAV grey wolf optimization; machine learning; PV modeling; moving PV; UAV

Share and Cite

MDPI and ACS Style

Saeedinia, M.H.; Taheri, S.; Cretu, A.-M. Computational Intelligence-Based Modeling of UAV-Integrated PV Systems. Solar 2025, 5, 45. https://doi.org/10.3390/solar5040045

AMA Style

Saeedinia MH, Taheri S, Cretu A-M. Computational Intelligence-Based Modeling of UAV-Integrated PV Systems. Solar. 2025; 5(4):45. https://doi.org/10.3390/solar5040045

Chicago/Turabian Style

Saeedinia, Mohammad Hosein, Shamsodin Taheri, and Ana-Maria Cretu. 2025. "Computational Intelligence-Based Modeling of UAV-Integrated PV Systems" Solar 5, no. 4: 45. https://doi.org/10.3390/solar5040045

APA Style

Saeedinia, M. H., Taheri, S., & Cretu, A.-M. (2025). Computational Intelligence-Based Modeling of UAV-Integrated PV Systems. Solar, 5(4), 45. https://doi.org/10.3390/solar5040045

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