Open AccessPerformance Analysis of Hybrid Cooling Systems Using Artificial Neural Network
Author(s) -
D.B. Jani
Publication year - 2020
Publication title -
global journal of energy technology research updates
Language(s) - English
Resource type - Journals
ISSN - 2409-5818
DOI - 10.15377/2409-5818.2020.07.2
Subject(s) - artificial neural network , coefficient of performance , desiccant , vapor compression refrigeration , test data , reliability (semiconductor) , experimental data , air conditioning , water cooling , correlation coefficient , computer science , simulation , engineering , power (physics) , artificial intelligence , machine learning , mathematics , gas compressor , mechanical engineering , meteorology , statistics , refrigerant , thermodynamics , physics , programming language
In the present study, an artificial neural network (ANN) model for a solid desiccant – vapor compression hybrid air-conditioning system is developed to predict the cooling capacity, power input and coefficient of performance (COP) of the system. This paper also describes the experimental test set up for collecting the required experimental test data. The experimental measurements are taken at steady-state conditions while varying the input parameters like air stream flow rates and regeneration temperature. Most of the experimental test data (80%) were used for training the ANN model while the remaining (20%) were used for the testing of the ANN model. Experimental data were collected during the cooling period of March to September. The outputs predicted from the ANN model have a high coefficient of correlation (R>0.988) in predicting the system performance. The results show that the ANN model can be applied successfully and can provide high accuracy and reliability for predicting the performance of the hybrid desiccant cooling systems.