Hot water temperature prediction using a dynamic neural network for absorption chiller application in Indonesia. (December 2018)

• Record Type: Journal Article
• Authors: Nasruddin, ; Sholahudin, ; Idrus Alhamid, M.; Saito, Kiyoshi
• Is Part Of: Sustainable energy technologies and assessments. Volume 30(2018)
• Page Start: 114
• Page End: 120
• Publication Date: 2018-12
• Subjects: Neural network -- Absorption chiller -- Solar radiation -- Hot water; Renewable energy sources -- Periodicals; Energy development -- Technological innovations -- Periodicals; Electric power production -- Periodicals; Energy storage -- Periodicals; 333.79
• DOI: 10.1016/j.seta.2018.09.006
• Languages: English
• ISSNs: 2213-1388

Abstract

Abstract: Weather condition particularly for solar radiation and dry bulb temperature has important role in absorption chiller performance. In this paper hot water temperature prediction in generator inlet of absorption chiller has been conducted under various weather conditions. Dry bulb temperature and global horizontal radiation are selected as predictors. Three artificial neural network (ANN) types including feed forward back-propagation, cascade forward back-propagation, and Elman back propagation models have been investigated for prediction. Moreover, numbers of neuron and time delay effects were analyzed to achieve an accurate prediction. The results show that hot water temperature in generator inlet can be predicted precisely using a feed forward back propagation neural network with the configuration of a three hour delayed input on radiation, current dry bulb temperature, seven neurons, tan-sigmoid transfer function and Bayesian regularization algorithm. The prediction results perform a good agreement between predicted and experimental values. The error resulting from training and validation is 3.1 °C and 2.6 °C with a coefficient of variation at 4.4% and 3.5% respectively.