article · Energy Science & Engineering
An enhanced social network search algorithm, known as ESNSA, accurately estimates parameters for solar photovoltaic modules. The method adapts an optimisation technique inspired by social network user behaviours by adding a powerful exploitation strategy and an adaptable parameter to guide later computational iterations. Tested on single, double, and triple diode models across mono-crystalline, multicrystalline, and polycrystalline panels, the algorithm minimises the root-mean-square error between experimental and calculated data. When applied to the mono-crystalline STM6(40/36) module, the method achieves minimal root-mean-square errors and very low standard error values across all three diode models. The updated algorithm also demonstrates faster convergence, reaching optimal values in substantially fewer iterations than the original technique, while showing greater robustness and consistency than other published methods.
Precise mathematical modelling of solar photovoltaic panels is essential for predicting and optimising their energy output. By minimising the discrepancy between real-world measurements and model simulations, this improved optimisation algorithm allows for more accurate parameter estimation across diverse panel types. This enables better prediction of solar panel behaviour and energy production, supporting more reliable system design and computational analysis in solar energy research.
The algorithm can assist solar energy engineers and software developers who build computational tools for photovoltaic simulation, testing, and performance modelling. The work represents an applied and computationally tested algorithmic development validated against experimental panel data. While the numerical method is validated on standard test modules, its direct commercial use depends on integration into commercial solar design suites, engineering software, or digital twin platforms.
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Abstract In this paper, an enhanced social network search algorithm (ESNSA) has been proposed to model the solar photovoltaic (PV) modules accurately and efficiently. The proposed algorithm is introduced to minimize the least root‐mean‐square error (RMSE) between the calculated and experimental data for the single, double, and triple diode models of Kyocera KC200GT, STM6(40/36), and Photowatt‐PWP201 modules. The original SNSA was inspired by users on social networks and their many moods, including imitation, conversation, disputation, and innovation mood. Two strategies are presented for the ESNSA. The first strategy is the powerful exploitation strategy (PES), which is intended to increase the SNSA's performance by boosting searching around the best view of all users. The second strategy is to suggest an adaptable parameter to aid in the exploitation of iterations in the end. Diverse comparisons and statistical analyses for validation purposes are carried out for mono‐crystalline STM6(40/36), multicrystalline KC200GT, and polycrystalline photowatt‐PWP201 modules. The comparative studies and statistical measures show the consistency and accurateness of the proposed ESNSA. As a numerical application, for the mono‐crystalline STM6(40/36) PV module, the proposed ESNSA achieves the least RMSE of 1.751631E−3, 1.769953E−3, and 1.696504E−3, respectively for the three models. Also, it shows high robustness compared to the original SNSA as it acquires the least standard errors for the three models of 2.56E−18, 1.76E−6, and 1.24E−5, respectively. Moreover, the proposed ESNSA provides a higher convergence speed where it is approximately reached to the least RMSE in less than 60%, 50%, and 60% of the iterations for the three models, respectively. Nevertheless, the proposed ESNSA provides better performance than miscellaneous published approaches in minimizing the RMSE, with high robust indices.
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DOI: 10.1002/ese3.1109
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