Modelling a solar-assisted air-conditioning system installed in CIESOL building using an artificial neural network

This paper proposes Artificial Neural Networks (ANN) to model a solar-assisted air-conditioning system installed in the Solar Energy Research Center (CIESOL). This system consists mainly of the single-effect LiBr-H20 absorption chiller fed by water provided from either solar collectors or hot water storage tanks. The present work describes the total solar cooling systems based on absorption chiller and provided only with solar collectors. The experimental data were collected during the cooling period of 2008. ANN was used with the main goal of predicting the efficiency of the chiller and global system using the lowest number of input variables. The configuration 7-8-4 (7 inputs, 8 hidden and 4 output neurons) was found to be the optimal topology. The results demonstrate the accuracy ANN’s predictions with a Root Mean Square Error (RMSE) of less than 1.9% and practically null deviation, which can be considered very satisfactory.