Enhancing Optimization of Photovoltaic Devices Performance using Artificial Neural Network (ANN) Comparing with Thermoelectric Generators (TEGs) to Improve Power Efficiency

AIM: The objective of the study is to enhance optimization of photovoltaic devices performance using Artificial Neural network(ANN) compared with Thermoelectric generators (TEGs) to improve power efficiency.An artificial neural network (ANN) is suggested as a replacement for the time-consuming traditional finite element methodologies (FEMs) intended to maximize the performance. A segmented traditional TEG is deployed to provide better cooling in a PV (photo-voltaic) setup. this case,pretest power analysis was done with 80% and the sample size for the two groups was 20 and each group having the size of 10 was taken. Its show that the Thermoelectric generators (TEGs) is 91% and that the ANN more efficient than the traditional TEG and is 94.44% faster performance.There is a statistical 2 tailed significance difference in the power efficiency for two algorithms is 0.002(p<0.05) by performing the independent sample test. ANN was offered as a substantially faster method of optimising the performance of photovoltaic devices than traditional FEM.