Accurate prophecy of photovltaic-segmented thermoelectric generator's performance using a neural network that feeds on finite element-generated data

Abstract

To further enhance the photovoltaic–thermoelectric system efficiency, this paper proposes a new hybrid system design comprising a segmented thermoelectric generator and aluminum heat sink directly lapped to the back plate of a photovoltaic cell operating under Nigerian transient and fluctuating weather conditions. The performance evaluation and optimization of the hybrid system design is conducted using a numerical model setup in ANSYS software and the optimized parameters include the thermoelectric leg height and cross-sectional area, skutterudite content, ceramic height, fin and fin base heights, convective film coefficient, and solar concentration ratio while the performance indices are the system power generation rate and the system efficiency. Finally, a Bayesian regularized artificial neural network with 10 neurons in the hidden layer is proposed to overcome the lengthy computational time and energy needed to conduct the numerical-inspired system optimization. Results are that the proposed system design improved the efficiency of the conventional photovoltaic cell integrated with regular unsegmented thermoelectric generators by 96% at a solar concentration of 5. Additionally, the numerical-inspired optimization was able to improve the conventional system power output and efficiency by 45.7% and 45.9%, respectively, compared to the unoptimized system when operated under peak sunshine conditions. Finally, the Bayesian regularized neural network with a low mean squared error of 9.7 × 10−14 after 471 iterations and perfect regression correlations for training, testing, and all processes provided a perfect fit of the numerical-generated data, being 201 times faster than the conventional numerical optimization scheme.