Artificial Intelligence-Based Deep Learning Model for the Performance Enhancement of Photovoltaic Panels in Solar Energy Systems

This study looks into artificial intelligence methods for scaling solar power systems, such as standalone, grid-connected, and hybrid systems, in order to lessen environmental effect. When all essential information is provided, conventional sizing methods may be a feasible alternative. It is impossi...

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Bibliographic Details
Main Authors: Radhey Shyam Meena, Anoop Singh, Shilpa Urhekar, null RohitBhakar, Neeraj Kumar Garg, Mohammad Israr, D. P. Kothari, C. Chiranjeevi, Prasath Srinivasan
Format: Article
Language:English
Published: Wiley 2022-01-01
Series:International Journal of Photoenergy
Online Access:http://dx.doi.org/10.1155/2022/3437364
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Summary:This study looks into artificial intelligence methods for scaling solar power systems, such as standalone, grid-connected, and hybrid systems, in order to lessen environmental effect. When all essential information is provided, conventional sizing methods may be a feasible alternative. It is impossible to apply typical procedures in instances where data is unavailable. The new suggested artificial intelligence model employing multilayered perceptrons is employed for sizing solar systems, and this model functions on current photovoltaic modules that incorporate hybrid-sizing models; so, they should not be rejected entirely. In this work, the convergence speed of the proposed model for single diode, two diodes, and three diodes are the comparison factors to estimate the performance of the proposed model.
ISSN:1687-529X