Citation :

MD Niyaz Ali Khan, Dr. Mohd Muazzam, 2025. "Smart Solar Cells: Integrating Artificial Neural Networks with Nanotechnology and IoT for Superior Energy Conversion Efficiency" ESP International Journal of Advancements in Science & Technology (ESP-IJAST)  Volume 3, Issue 3: 37-47.

Abstract :

This study presents an advanced solar cell system integrating Artificial Neural Networks (ANN) with nanotechnology and Internet of Things (IoT) for superior energy conversion efficiency and intelligent control. While conventional Proportional-Integral (PI) controllers and Fuzzy Logic Controllers (FLC) have demonstrated improvements in solar system performance, they face limitations in handling complex, multi-variable, and highly nonlinear solar systems with time-varying environmental parameters. To overcome these challenges, this research proposes an ANN-based controller that autonomously learns optimal control strategies from historical data and adapts in real-time to dynamic operating conditions. The ANN controller leverages its pattern recognition and predictive capabilities to optimize voltage and current regulation across diverse environmental scenarios including rapid irradiance variations, temperature fluctuations, and partial shading conditions. Experimental results demonstrate that the ANN-integrated system achieves 6-8% higher efficiency compared to FLC-based systems and 8-11% improvement over conventional PI controllers, particularly during unpredictable weather transitions and low-light conditions. The integration of aluminum nanoparticles further enhances light absorption and charge carrier mobility, synergizing with the ANN's adaptive control to maximize power extraction. The ability to continuously collect information to train artificial neural networks (ANNs), optimize the system parameters predictive scheduling as well as fault detection, become possible with the real-time monitoring of the Internet of Things (IoT). Assessing performance, the ANN controllers not only showed remarkable improvement in voltage regulation, with error margins reduced by 40% to 50%, and response times improved to adaptations of less than one millisecond, but also demonstrated improvement in the accuracy of the Maximum Power Point Tracking (MPPT) to values above 99.2%.

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Keywords :

Artificial Neural Networks, Solar Cells, Nanotechnology, IoT, Deep Learning, MPPT, Voltage Regulation, Energy Efficiency, Intelligent Control Systems, Adaptive Control.