Improvement of the classical artificial neural network simulation model of the parabolic trough solar collector outlet temperature and thermal efficiency using the conformable activation functions

The outlet temperature and the thermal efficiency predictions of the PTSC are essential parameters in the solar thermal power system. Therefore, it is crucial to have a prediction model that can predict its spatiotemporal behavior to the greatest extent possible. For that, the present study provides improved models of the classical ANN model by using ELU, swish, softplus, logsig, and tansig functions through the exponential transfer function to improve the outlet temperature prediction performance and thermal efficiency of PTSC. The PTSC's outlet temperature simulation showed that the ANN model of topology 6-2-1 with the conformable swish transfer function (cswish) was the best model among nine other studied models, with an R2 and R2adj of 0.9988, and MAE of 0.0052. The simulation of PTSC's thermal efficiency proved that the conformable functions logsig, tansig, ELU and the classical ELU function involved in the ANN model for 6-3-1 achieved better results with approximately 95% precision. With the non-integer activation functions, was reduced a neuron in the hidden layer, which leads to the simplicity of the prediction model compared to the use of the classical activation functions. The proposed activation functions do not require high-performance features according to their execution times. Therefore, it could be a good alternative for engineers to apply in other thermal energy systems to make their prediction models more accessible and practical in the control and optimization process.

Automated summary

This study provides improved models of the classical ANN model to predict the outlet temperature and thermal efficiency of PTSC. The results show that using specific transfer and activation functions in the ANN model can achieve better prediction performance.