Thermodynamic analysis of absorption systems using artificial neural network
Author
Abstract
Suggested Citation
DOI: 10.1016/j.renene.2005.03.011
Download full text from publisher
As the access to this document is restricted, you may want to search for a different version of it.
References listed on IDEAS
- Kalogirou, Soteris A., 2004. "Optimization of solar systems using artificial neural-networks and genetic algorithms," Applied Energy, Elsevier, vol. 77(4), pages 383-405, April.
- Kalogirou, Soteris A. & Bojic, Milorad, 2000. "Artificial neural networks for the prediction of the energy consumption of a passive solar building," Energy, Elsevier, vol. 25(5), pages 479-491.
- Kalogirou, Soteris A., 2000. "Long-term performance prediction of forced circulation solar domestic water heating systems using artificial neural networks," Applied Energy, Elsevier, vol. 66(1), pages 63-74, May.
- Kalogirou, Soteris A & Panteliou, Sofia & Dentsoras, Argiris, 1999. "Artificial neural networks used for the performance prediction of a thermosiphon solar water heater," Renewable Energy, Elsevier, vol. 18(1), pages 87-99.
- Kalogirou, Soteris A., 2000. "Applications of artificial neural-networks for energy systems," Applied Energy, Elsevier, vol. 67(1-2), pages 17-35, September.
Citations
Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
Cited by:
- Rosiek, S. & Batlles, F.J., 2010. "Modelling a solar-assisted air-conditioning system installed in CIESOL building using an artificial neural network," Renewable Energy, Elsevier, vol. 35(12), pages 2894-2901.
- Mohanraj, M. & Jayaraj, S. & Muraleedharan, C., 2012. "Applications of artificial neural networks for refrigeration, air-conditioning and heat pump systems—A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(2), pages 1340-1358.
- Souliotis, M. & Kalogirou, S. & Tripanagnostopoulos, Y., 2009. "Modelling of an ICS solar water heater using artificial neural networks and TRNSYS," Renewable Energy, Elsevier, vol. 34(5), pages 1333-1339.
- Labus, J. & Hernández, J.A. & Bruno, J.C. & Coronas, A., 2012. "Inverse neural network based control strategy for absorption chillers," Renewable Energy, Elsevier, vol. 39(1), pages 471-482.
- Lazrak, Amine & Boudehenn, François & Bonnot, Sylvain & Fraisse, Gilles & Leconte, Antoine & Papillon, Philippe & Souyri, Bernard, 2016. "Development of a dynamic artificial neural network model of an absorption chiller and its experimental validation," Renewable Energy, Elsevier, vol. 86(C), pages 1009-1022.
- Álvarez, María E. & Hernández, José A. & Bourouis, Mahmoud, 2016. "Modelling the performance parameters of a horizontal falling film absorber with aqueous (lithium, potassium, sodium) nitrate solution using artificial neural networks," Energy, Elsevier, vol. 102(C), pages 313-323.
- Colorado, D. & Hernández, J.A. & Rivera, W. & Martínez, H. & Juárez, D., 2011. "Optimal operation conditions for a single-stage heat transformer by means of an artificial neural network inverse," Applied Energy, Elsevier, vol. 88(4), pages 1281-1290, April.
- Arslan, Oguz, 2011. "Power generation from medium temperature geothermal resources: ANN-based optimization of Kalina cycle system-34," Energy, Elsevier, vol. 36(5), pages 2528-2534.
- Alosaimy, A.S. & Hamed, Ahmed M., 2011. "Theoretical and experimental investigation on the application of solar water heater coupled with air humidifier for regeneration of liquid desiccant," Energy, Elsevier, vol. 36(7), pages 3992-4001.
Most related items
These are the items that most often cite the same works as this one and are cited by the same works as this one.- Jha, Sunil Kr. & Bilalovic, Jasmin & Jha, Anju & Patel, Nilesh & Zhang, Han, 2017. "Renewable energy: Present research and future scope of Artificial Intelligence," Renewable and Sustainable Energy Reviews, Elsevier, vol. 77(C), pages 297-317.
- Ghritlahre, Harish Kumar & Prasad, Radha Krishna, 2018. "Application of ANN technique to predict the performance of solar collector systems - A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 84(C), pages 75-88.
- Sözen, Adnan & Ali Akçayol, M., 2004. "Modelling (using artificial neural-networks) the performance parameters of a solar-driven ejector-absorption cycle," Applied Energy, Elsevier, vol. 79(3), pages 309-325, November.
- Kalogirou, Soteris A., 2001. "Artificial neural networks in renewable energy systems applications: a review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 5(4), pages 373-401, December.
- Lazrak, Amine & Leconte, Antoine & Chèze, David & Fraisse, Gilles & Papillon, Philippe & Souyri, Bernard, 2015. "Numerical and experimental results of a novel and generic methodology for energy performance evaluation of thermal systems using renewable energies," Applied Energy, Elsevier, vol. 158(C), pages 142-156.
- Kalogirou, Soteris A., 2000. "Applications of artificial neural-networks for energy systems," Applied Energy, Elsevier, vol. 67(1-2), pages 17-35, September.
- He, Zhaoyu & Guo, Weimin & Zhang, Peng, 2022. "Performance prediction, optimal design and operational control of thermal energy storage using artificial intelligence methods," Renewable and Sustainable Energy Reviews, Elsevier, vol. 156(C).
- Wu, Sheng-Ju & Shiah, Sheau-Wen & Yu, Wei-Lung, 2009. "Parametric analysis of proton exchange membrane fuel cell performance by using the Taguchi method and a neural network," Renewable Energy, Elsevier, vol. 34(1), pages 135-144.
- Arslan, Oguz, 2011. "Power generation from medium temperature geothermal resources: ANN-based optimization of Kalina cycle system-34," Energy, Elsevier, vol. 36(5), pages 2528-2534.
- Kalogirou, S.A. & Mathioulakis, E. & Belessiotis, V., 2014. "Artificial neural networks for the performance prediction of large solar systems," Renewable Energy, Elsevier, vol. 63(C), pages 90-97.
- Sözen, Adnan & Arcaklioglu, Erol & Özalp, Mehmet & Yücesu, Serdar, 2005. "Performance parameters of an ejector-absorption heat transformer," Applied Energy, Elsevier, vol. 80(3), pages 273-289, March.
- Kicsiny, R. & Nagy, J. & Szalóki, Cs., 2014. "Extended ordinary differential equation models for solar heating systems with pipes," Applied Energy, Elsevier, vol. 129(C), pages 166-176.
- Altan Dombaycı, Ömer & Gölcü, Mustafa, 2009. "Daily means ambient temperature prediction using artificial neural network method: A case study of Turkey," Renewable Energy, Elsevier, vol. 34(4), pages 1158-1161.
- Zhijian Liu & Hao Li & Xinyu Zhang & Guangya Jin & Kewei Cheng, 2015. "Novel Method for Measuring the Heat Collection Rate and Heat Loss Coefficient of Water-in-Glass Evacuated Tube Solar Water Heaters Based on Artificial Neural Networks and Support Vector Machine," Energies, MDPI, vol. 8(8), pages 1-21, August.
- Vakili, Masoud & Yahyaei, Masood & Ramsay, James & Aghajannezhad, Pouria & Paknezhad, Behnaz, 2021. "Adaptive neuro-fuzzy inference system modeling to predict the performance of graphene nanoplatelets nanofluid-based direct absorption solar collector based on experimental study," Renewable Energy, Elsevier, vol. 163(C), pages 807-824.
- Ghobadian, B. & Rahimi, H. & Nikbakht, A.M. & Najafi, G. & Yusaf, T.F., 2009. "Diesel engine performance and exhaust emission analysis using waste cooking biodiesel fuel with an artificial neural network," Renewable Energy, Elsevier, vol. 34(4), pages 976-982.
- Movagharnejad, Kamyar & Mehdizadeh, Bahman & Banihashemi, Morteza & Kordkheili, Masoud Sheikhi, 2011. "Forecasting the differences between various commercial oil prices in the Persian Gulf region by neural network," Energy, Elsevier, vol. 36(7), pages 3979-3984.
- Rodríguez-Hidalgo, M.C. & Rodríguez-Aumente, P.A. & Lecuona, A. & Legrand, M. & Ventas, R., 2012. "Domestic hot water consumption vs. solar thermal energy storage: The optimum size of the storage tank," Applied Energy, Elsevier, vol. 97(C), pages 897-906.
- Wang, Zhangyuan & Yang, Wansheng & Qiu, Feng & Zhang, Xiangmei & Zhao, Xudong, 2015. "Solar water heating: From theory, application, marketing and research," Renewable and Sustainable Energy Reviews, Elsevier, vol. 41(C), pages 68-84.
- Piliougine, Michel & Elizondo, David & Mora-López, Llanos & Sidrach-de-Cardona, Mariano, 2013. "Multilayer perceptron applied to the estimation of the influence of the solar spectral distribution on thin-film photovoltaic modules," Applied Energy, Elsevier, vol. 112(C), pages 610-617.
More about this item
Keywords
Artificial neural network; Absorption heat pump; Lithium bromide–water; Lithium chloride–water; Thermodynamic properties;All these keywords.
Statistics
Access and download statisticsCorrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:renene:v:31:y:2006:i:1:p:29-43. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/renewable-energy .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.