Please use this identifier to cite or link to this item:
https://hdl.handle.net/20.500.14279/1327
Title: | Artificial neural networks used for the performance prediction of a thermosiphon solar water heater | Authors: | Panteliou, Sofia Dentsoras, Argiris Kalogirou, Soteris A. Panteliou, Sofia Dentsoras, Argiris |
Major Field of Science: | Engineering and Technology | Field Category: | Environmental Engineering | Keywords: | Artificial Neural Networks (ANN) | Issue Date: | 2-Sep-1999 | Source: | Renewable Energy,1999, vol. 18, no. 1, pp. 87-99 | Volume: | 18 | Issue: | 1 | Start page: | 87 | End page: | 99 | Journal: | Renewable Energy | Abstract: | Artificial Neural Networks (ANN) are widely accepted as a technology offering an alternative way to tackle complex and ill-defined problems. They can learn from examples, are fault tolerant, are able to deal with non-linear problems, and once trained can perform prediction at high speed. ANNs have been used in diverse applications and they have shown to be particularly effective in system modelling as well as for system identification. The objective of this work is to train an artificial neural network (ANN) to learn to predict the performance of a thermosiphon solar domestic water heating system. This performance is measured in terms of the useful energy extracted and of the stored water temperature rise. An ANN has been trained using performance data for four types of systems, all employing the same collector panel under varying weather conditions. In this way the network was trained to accept and handle a number of unusual cases. The data presented as input were, the storage tank heat loss coefficient (U-value), the type of system (open or closed), the storage volume, and a total of fifty-four readings from real experiments of total daily solar radiation, total daily diffuse radiation, ambient air temperature, and the water temperature in storage tank at the beginning of the day. The network output is the useful energy extracted from the system and the water temperature rise. The statistical coefficient of multiple determination (R2-value) obtained for the training data set was equal to 0.9914 and 0.9808 for the two output parameters respectively. Both values are satisfactory because the closer R2-value is to unity the better is the mapping. Unknown data for all four systems were subsequently used to investigate the accuracy of prediction. These include performance data for the systems considered for the training of the network at different weather conditions. Predictions with maximum deviations of 1 MJ and 2.2°C were obtained respectively. Random data were also used both with the performance equations obtained from the experimental measurements and with the artificial neural network to predict the above two parameters. The predicted values thus obtained were very comparable. These results indicate that the proposed method can successfully be used for the estimation of the performance of the particular thermosiphon system at any of the different types of configuration used here. The greatest advantage of the present model is the capacity of the network to learn from examples and thus gradually improve its performance. This is done by embedding experimental knowledge in the network. | URI: | https://hdl.handle.net/20.500.14279/1327 | ISSN: | 09601481 | DOI: | 10.1016/S0960-1481(98)00787-3 | Rights: | © Elsevier | Type: | Article | Affiliation : | Higher Technical Institute Cyprus | Publication Type: | Peer Reviewed |
Appears in Collections: | Άρθρα/Articles |
CORE Recommender
SCOPUSTM
Citations
120
checked on Nov 9, 2023
WEB OF SCIENCETM
Citations
20
93
Last Week
0
0
Last month
1
1
checked on Oct 29, 2023
Page view(s) 20
480
Last Week
0
0
Last month
6
6
checked on Aug 28, 2024
Google ScholarTM
Check
Altmetric
Items in KTISIS are protected by copyright, with all rights reserved, unless otherwise indicated.