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Enhancing Solar Cell Efficiency: AI Solutions for Sustainable Energy
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Author(s): Harpreet Kaur Channi (Guru Nanak Dev Engineering College, India)
Copyright: 2026
Pages: 34
Source title:
The Future of Green Energy: Storage, Materials, Alternative Fuels, and Net-Zero Strategies
Source Author(s)/Editor(s): Shaik Mazhar Hussain (Middle East College, Oman), Shafiq Ul Rehman (Kingdom University, Bahrain), Anupam Srivastav (Middle East College, Oman), Anilloy Augustine Frank (Presidency University, India)and Ahmed Nawaz Hakro (Middle East College, Oman)
DOI: 10.4018/979-8-3373-3226-0.ch006
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Abstract
This study explores the integration of nanotechnology and artificial intelligence (AI) to enhance solar photovoltaic (PV) system performance. A simulation-based methodology was applied to a 3 kW rooftop system in Ludhiana, Punjab, using quantum dots and carbon nanotubes to improve light absorption and charge transport. AI models, including artificial neural networks (ANN), were employed for energy forecasting and real-time optimization. Results show a 22% increase in annual energy output for the nano-enhanced system over conventional PV, with improved accuracy under variable weather conditions. The ANN model achieved high prediction accuracy (R2 = 0.985), while AI-assisted MPPT improved power tracking efficiency. This hybrid approach demonstrates significant potential for scalable, efficient, and climate-resilient solar systems suitable for smart grids and decentralized applications in semi-urban regions. The findings support the advancement of intelligent, material-optimized renewable energy systems aligned with sustainability goals.
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