An Algorithm to Predict Accurate Output Power of a Glass-Glass (Semitransparent) Solar Thermal Module Using Artificial Neural Network
Ruby Beniwal, Tiwari G.N, Gupta H.O · International Journal of Engineering and Technology · 2017
This paper presents an algorithm to predict output power or performance parameters i.e., open circuit voltage and short circuit current of a glass-glass (G-G)i.e., semitransparentsolar thermal module very close to the experimental values.The predicted performance parameters by the proposed algorithm have been found closer to the experimental values or actual parameters than those computed by the artificial neural network (ANN) and analytical approach alone.The proposed algorithm uses ANN to reduce the root mean square error (RMSE)upto 100% between performance parameters of the prototype solar cell model under study due to ANN and analytical approaches alone.Solar irradiation andsolar-cell temperature are the essential parameters for design, prediction and performance analysis of any photovoltaic solar energy system.Therefore, solar irradiation,and solar cell temperature have been taken as input parameters in the proposed algorithm, ANN model and analytical model.