Implementation of artificial intelligence for prediction performance of solar thermal system

A related input parameter is used in this case study to forecast solar thermal systems (STS) capabilities and compare which artificial neural network (ANN) algorithms other intelligence (AI) methods have the most reliable predictor for STS performance. In order gauge performance of STS, research aims implement AI predicting by comparing ANN technique with methods. Three different training are Levenberg-Marquardt (LM), scaled conjugate gradient (SCG) Bayesian regularization (BR) considered research. This will identify acceptable parameters best use Previous on demonstrates that efficiency has been estimated using parameters. The results show prediction LM algorithm