R. Meena, Ashutosh Kumar Singh, Shilpa Urhekar, RohitBhakar, N. K. Garg, Mohammad Israr, D. Kothari, C. Chiranjeevi, Prasath Srinivasan
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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 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.
期刊介绍:
International Journal of Photoenergy is a peer-reviewed, open access journal that publishes original research articles as well as review articles in all areas of photoenergy. The journal consolidates research activities in photochemistry and solar energy utilization into a single and unique forum for discussing and sharing knowledge.
The journal covers the following topics and applications:
- Photocatalysis
- Photostability and Toxicity of Drugs and UV-Photoprotection
- Solar Energy
- Artificial Light Harvesting Systems
- Photomedicine
- Photo Nanosystems
- Nano Tools for Solar Energy and Photochemistry
- Solar Chemistry
- Photochromism
- Organic Light-Emitting Diodes
- PV Systems
- Nano Structured Solar Cells