Abstract

The rapid growth of renewable energy technologies has increased the importance of advanced photovoltaic systems that can achieve high energy conversion efficiency under diverse environmental conditions. The integration of quantum nanostructures within nanoelectronic devices offers an effective pathway for enhanced light absorption, improved carrier transport, and reduced recombination losses. However, conventional photovoltaic optimization approaches often experience limitations in adapting to dynamic irradiance, temperature variations, and material-level nonlinearities, which reduce the overall performance of solar energy systems. This research proposes an Adaptive Quantum Nanoelectronic Photovoltaic Optimization Method (AQNPOM) for solar energy applications. The proposed method combines quantum nanostructure-assisted charge transport modeling with an intelligent optimization framework that utilizes deep reinforcement learning and adaptive parameter estimation. The AQNPOM establishes an efficient relationship between the nanoelectronic characteristics of quantum dots, nanowires, and photovoltaic layers to identify the optimal operating conditions. The method evaluates the effects of carrier mobility, electron confinement, energy band alignment, and environmental variations on the photovoltaic performance. The experimental evaluation demonstrates that the proposed Adaptive Quantum Nanoelectronic Photovoltaic Optimization Method (AQNPOM) significantly improves photovoltaic performance compared with conventional optimization approaches. The proposed framework achieves an energy conversion efficiency of 31.84%, exceeding Deep Neural Network Optimization (27.46%), Particle Swarm Optimization (26.91%), and Genetic Algorithm (25.87%). AQNPOM also achieves 99.21% power tracking accuracy, 25.17% power fluctuation reduction, 97.43% carrier utilization efficiency, and reduces convergence time to 145 ms. These results confirm that the proposed quantum nanoelectronic optimization framework provides enhanced energy harvesting capability, improved stability, and faster adaptive control for intelligent photovoltaic systems.

Authors

M.M. Ramakrishna¹, Abhijeet Das²
Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, India¹, C.V. Raman Global University, India²

Keywords

Quantum Nanostructures, Nanoelectronics, Photovoltaic Optimization, Solar Energy Harvesting, Deep Reinforcement Learning