IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v86y2016icp1009-1022.html
   My bibliography  Save this article

Development of a dynamic artificial neural network model of an absorption chiller and its experimental validation

Author

Listed:
  • Lazrak, Amine
  • Boudehenn, François
  • Bonnot, Sylvain
  • Fraisse, Gilles
  • Leconte, Antoine
  • Papillon, Philippe
  • Souyri, Bernard

Abstract

The aim of this paper is to present a methodology to model and evaluate the energy performance and outlet temperatures of absorption chillers so that users can have reliable information on the long-term performance of their systems in the desired boundary conditions before the product is installed. Absorption chillers' behaviour could be very complex and unpredictable, especially when the boundary conditions are variable. The system dynamic must therefore be included in the model. Artificial neural networks (ANNs) have proved to be suitable for handling such complex problems, particularly when the physical phenomena inside the system are difficult to model. Reliable “black box” ANN modelling is able to identify the system's global model without any advanced knowledge of its internal operating principles. Knowledge of the system's global inputs and outputs is sufficient. The methodology proposed was applied to evaluate a commercial absorption chiller. Predictions of the ANN model developed were compared, with a satisfactory degree of precision, to 2 days of experimental measures. These days were chosen to be representative of the real dynamic operating conditions of an absorption chiller. The neural model predictions are very satisfactory: absolute relative errors of the transferred energy are within 0.1–6.6%.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:renene:v:86:y:2016:i:c:p:1009-1022
    DOI: 10.1016/j.renene.2015.09.023
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960148115303025
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.renene.2015.09.023?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Şencan, Arzu & Yakut, Kemal A. & Kalogirou, Soteris A., 2006. "Thermodynamic analysis of absorption systems using artificial neural network," Renewable Energy, Elsevier, vol. 31(1), pages 29-43.
    2. 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.
    3. Khosravi, Abbas & Nahavandi, Saeid & Creighton, Doug, 2013. "Quantifying uncertainties of neural network-based electricity price forecasts," Applied Energy, Elsevier, vol. 112(C), pages 120-129.
    4. Qi, Min & Zhang, Guoqiang Peter, 2001. "An investigation of model selection criteria for neural network time series forecasting," European Journal of Operational Research, Elsevier, vol. 132(3), pages 666-680, August.
    5. 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.
    6. 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.
    7. 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.
    8. Almonacid, F. & Rus, C. & Pérez, P.J. & Hontoria, L., 2009. "Estimation of the energy of a PV generator using artificial neural network," Renewable Energy, Elsevier, vol. 34(12), pages 2743-2750.
    9. Pons, M. & Anies, G. & Boudehenn, F. & Bourdoukan, P. & Castaing-Lasvignottes, J. & Evola, G. & Le Denn, A. & Le Pierrès, N. & Marc, O. & Mazet, N. & Stitou, D. & Lucas, F., 2012. "Performance comparison of six solar-powered air-conditioners operated in five places," Energy, Elsevier, vol. 46(1), pages 471-483.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Menegon, Diego & Persson, Tomas & Haberl, Robert & Bales, Chris & Haller, Michel, 2020. "Direct characterisation of the annual performance of solar thermal and heat pump systems using a six-day whole system test," Renewable Energy, Elsevier, vol. 146(C), pages 1337-1353.
    2. Tomasz Halon & Ewa Pelinska-Olko & Malgorzata Szyc & Bartosz Zajaczkowski, 2019. "Predicting Performance of a District Heat Powered Adsorption Chiller by Means of an Artificial Neural Network," Energies, MDPI, vol. 12(17), pages 1-11, August.
    3. Guo, Wencheng & Yang, Jiandong, 2018. "Dynamic performance analysis of hydro-turbine governing system considering combined effect of downstream surge tank and sloping ceiling tailrace tunnel," Renewable Energy, Elsevier, vol. 129(PA), pages 638-651.
    4. Machado, Diogo Ortiz & Chicaiza, William D. & Escaño, Juan M. & Gallego, Antonio J. & de Andrade, Gustavo A. & Normey-Rico, Julio E. & Bordons, Carlos & Camacho, Eduardo F., 2023. "Digital twin of an absorption chiller for solar cooling," Renewable Energy, Elsevier, vol. 208(C), pages 36-51.
    5. Sochard, Sabine & Castillo Garcia, Lorenzo & Serra, Sylvain & Vitupier, Yann & Reneaume, Jean-Michel, 2017. "Modelling a solar absorption chiller using positive flash to estimate the physical state of streams and theoretical plate concept for the generator," Renewable Energy, Elsevier, vol. 109(C), pages 121-134.

    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.
    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. Tomasz Halon & Ewa Pelinska-Olko & Malgorzata Szyc & Bartosz Zajaczkowski, 2019. "Predicting Performance of a District Heat Powered Adsorption Chiller by Means of an Artificial Neural Network," Energies, MDPI, vol. 12(17), pages 1-11, August.
    7. Fernández, Eduardo F. & Almonacid, Florencia, 2014. "Spectrally corrected direct normal irradiance based on artificial neural networks for high concentrator photovoltaic applications," Energy, Elsevier, vol. 74(C), pages 941-949.
    8. Gunasekar, N. & Mohanraj, M. & Velmurugan, V., 2015. "Artificial neural network modeling of a photovoltaic-thermal evaporator of solar assisted heat pumps," Energy, Elsevier, vol. 93(P1), pages 908-922.
    9. Heng, Shye Yunn & Asako, Yutaka & Suwa, Tohru & Nagasaka, Ken, 2019. "Transient thermal prediction methodology for parabolic trough solar collector tube using artificial neural network," Renewable Energy, Elsevier, vol. 131(C), pages 168-179.
    10. Ouammi, Ahmed & Zejli, Driss & Dagdougui, Hanane & Benchrifa, Rachid, 2012. "Artificial neural network analysis of Moroccan solar potential," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(7), pages 4876-4889.
    11. 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.
    12. Almonacid, F. & Fernández, Eduardo F. & Rodrigo, P. & Pérez-Higueras, P.J. & Rus-Casas, C., 2013. "Estimating the maximum power of a High Concentrator Photovoltaic (HCPV) module using an Artificial Neural Network," Energy, Elsevier, vol. 53(C), pages 165-172.
    13. Almonacid, Florencia & Fernandez, Eduardo F. & Mellit, Adel & Kalogirou, Soteris, 2017. "Review of techniques based on artificial neural networks for the electrical characterization of concentrator photovoltaic technology," Renewable and Sustainable Energy Reviews, Elsevier, vol. 75(C), pages 938-953.
    14. Hwang, Jun Kwon & Yun, Geun Young & Lee, Sukho & Seo, Hyeongjoon & Santamouris, Mat, 2020. "Using deep learning approaches with variable selection process to predict the energy performance of a heating and cooling system," Renewable Energy, Elsevier, vol. 149(C), pages 1227-1245.
    15. 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.
    16. Mousavi, Navid & Kothapalli, Ganesh & Habibi, Daryoush & Das, Choton K. & Baniasadi, Ali, 2020. "A novel photovoltaic-pumped hydro storage microgrid applicable to rural areas," Applied Energy, Elsevier, vol. 262(C).
    17. Naseri, F. & Gil, S. & Barbu, C. & Cetkin, E. & Yarimca, G. & Jensen, A.C. & Larsen, P.G. & Gomes, C., 2023. "Digital twin of electric vehicle battery systems: Comprehensive review of the use cases, requirements, and platforms," Renewable and Sustainable Energy Reviews, Elsevier, vol. 179(C).
    18. Hemmatabady, Hoofar & Welsch, Bastian & Formhals, Julian & Sass, Ingo, 2022. "AI-based enviro-economic optimization of solar-coupled and standalone geothermal systems for heating and cooling," Applied Energy, Elsevier, vol. 311(C).
    19. Linying Yang & Teng Zhang & Peter Glynn & David Scheinker, 2021. "The development and deployment of a model for hospital-level COVID-19 associated patient demand intervals from consistent estimators (DICE)," Health Care Management Science, Springer, vol. 24(2), pages 375-401, June.
    20. Selimefendigil, Fatih & Öztop, Hakan F., 2020. "Identification of pulsating flow effects with CNT nanoparticles on the performance enhancements of thermoelectric generator (TEG) module in renewable energy applications," Renewable Energy, Elsevier, vol. 162(C), pages 1076-1086.

    Corrections

    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:86:y:2016:i:c:p:1009-1022. 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.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.