A comparison of neural networks for prediction of generation of thermal energy of Flat Plate Vacuum solar thermal collectors (#1081)

Authors

Arellanos-Tafur, Elmer

Rojas-Arquiñego, Felix

Damas Niño, Marcelo

Abstract

This research performs a comparative analysis of the precision level of time series neural networks using the NARX, NAR, and input-output models for predicting the thermal energy generated by flat-plate vacuum solar collectors y(t) based on specific time series neural network models x(t). For the prediction analysis of each model, the neural network was constructed, followed by the phases of training, validation, and testing to obtain the respective predictions. The prediction level of each implemented model was then determined through linear regression analysis, which indicated how well the generated output was related to the targets. Finally, the prediction levels of the three models were compared to determine which model had a better precision for predicting the thermal energy generation of flat-plate vacuum solar collectors.