Artificial neural network simulation and development of a predictive model to anticipate performance of a hybrid plant combined with PVT solar system

This article uses the neural network method and numerical data for predicting the performance of an integrated energy system. The studied system is an integrated energy system consisting of a gas turbine with an air preheater and subsystems such as the Kalina cycle, supercritical carbon oxide cycle and hydrogen production. In this unit, a photovoltaic thermal (PVT) module has been used to generate electric power, as well as heating and starting the organic Rankine cycle. In addition, a Proton exchange membrane (PEM) electrolyzer to produce hydrogen is integrated. To propose a model that can predict the behavior of the integrated system by changing the parameters, the important outputs of the system such as energy efficiency, exergy efficiency, exergy destruction rate and net output power have been extracted in the form of functions according to the input parameters namely T₁₁, P₁₁, P₁₈, R_p. The results show that the suggested functions of four outputs have the acceptable value for mean square error and regression coefficient which show high accuracy. The influence of each variable on each other and their interaction on the responses are drawn and adequately explained using three-dimensional response surface diagrams. The analysis represented that the amount of exergy destruction in the high-pressure ratio is at its maximum value with the increase in the temperature of point 4. In addition, it is at its maximum value in the high-pressure ratio with different changes in the pressure of point 11. Generally, the p-values for predicted values less than 0.001 represent that the model is statistically considerable at the 99 % confidence level. The p-values for suggested functions show a high level of confidence.