An approach towards building robust neural networks models using multilayer perceptron through experimentation on different photovoltaic thermal systems

The study is aimed at training an artificial neural network to determine the specific Photo voltaic thermal (PV/T) system that will function optimally in a specific environmental situation by optimizing the input parameters. In this regard, four different PV/T systems based on the arrangement of the tubes and the presence of the copper absorber sheet, namely, vertically oscillating tube, vertically oscillating sheet tube, rectangular spiral tube and horizontally oscillating tube PV/T’s were considered. In the experimental study, wind velocity, solar irradiation, ambient temperature, relative humidity, and inlet temperature to the PV/T are considered the influencing factors. The response factors for evaluating the performance of the PV/T system were temperature outlet and power output. Water was used as the working fluid for the thermal management of the systems for all four test cases. The investigation discloses that the rectangular spiral tube photovoltaic thermal system is the best among the studied systems as it maintains both average thermal efficiency and average electrical efficiency at 50.7% and 12.15%, respectively, considering the trade-off between electrical and thermal efficiency. The ideal design can then be selected for a specific area that will be more appropriate with its environmental fluctuations by approximating the production for each scenario. The artificial neural network was designed to be capable of being successfully trained for this application based on Artificial Neural Network (ANN), Multilayer Perceptron using Levenberg Marquardt learning algorithm through MATLAB software based on the experimental data. The ANN model for the rectangular spiral tube PV/T system achieved an exceptional overall R-value of 0.99843 among the proposed prediction models. For the artificial neural network model formulated for vertically oscillating tube, vertically oscillating sheet tube and horizontally oscillating tube PV/T system, the R-value was found to be 0.99807, 0.9943, and 0.99593, respectively. In the training phase, the MSE for vertically oscillating tube, vertically oscillating sheet tube, rectangular spiral tube and horizontally oscillating tube PV/T’s using the multilayer perceptron artificial neural network models were found to be 1.1328, 3.5082, 0.3134 and 1.1328, respectively. For the same PV/T’s models, the MSE values for Cross validation phase were found to be 1.8497, 3.9072, 5.1419, and 4.6402, respectively. Finally, the suggested linear prediction models assist in quickly and easily determining the ideal conditions for every solar system while lowering the error in furcating future results.