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Modelling a solar-assisted air-conditioning system installed in CIESOL building using an artificial neural network

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  • Rosiek, S.
  • Batlles, F.J.

Abstract

This paper proposes Artificial Neural Networks (ANN) to model a solar-assisted air-conditioning system installed in the Solar Energy Research Center (CIESOL). This system consists mainly of the single-effect LiBr-H20 absorption chiller fed by water provided from either solar collectors or hot water storage tanks. The present work describes the total solar cooling systems based on absorption chiller and provided only with solar collectors. The experimental data were collected during the cooling period of 2008. ANN was used with the main goal of predicting the efficiency of the chiller and global system using the lowest number of input variables. The configuration 7-8-4 (7 inputs, 8 hidden and 4 output neurons) was found to be the optimal topology. The results demonstrate the accuracy ANN’s predictions with a Root Mean Square Error (RMSE) of less than 1.9% and practically null deviation, which can be considered very satisfactory.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:renene:v:35:y:2010:i:12:p:2894-2901
    DOI: 10.1016/j.renene.2010.04.018
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    References listed on IDEAS

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    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.
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    4. 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.
    5. Rosiek, S. & Batlles, F.J., 2009. "Integration of the solar thermal energy in the construction: Analysis of the solar-assisted air-conditioning system installed in CIESOL building," Renewable Energy, Elsevier, vol. 34(6), pages 1423-1431.
    6. Bosch, J.L. & López, G. & Batlles, F.J., 2008. "Daily solar irradiation estimation over a mountainous area using artificial neural networks," Renewable Energy, Elsevier, vol. 33(7), pages 1622-1628.
    7. Atmaca, Ibrahim & Yigit, Abdulvahap, 2003. "Simulation of solar-powered absorption cooling system," Renewable Energy, Elsevier, vol. 28(8), pages 1277-1293.
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    Citations

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    Cited by:

    1. 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.
    2. Wang, Lu & Yuan, JianJuan & Qiao, Xu & Kong, Xiangfei, 2023. "Optimal rule based double predictive control for the management of thermal energy in a distributed clean heating system," Renewable Energy, Elsevier, vol. 215(C).
    3. 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.
    4. 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.
    5. 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.
    6. Rosiek, Sabina & Batlles, Francisco Javier, 2013. "Renewable energy solutions for building cooling, heating and power system installed in an institutional building: Case study in southern Spain," Renewable and Sustainable Energy Reviews, Elsevier, vol. 26(C), pages 147-168.
    7. Trigo-González, Mauricio & Cortés-Carmona, Marcelo & Marzo, Aitor & Alonso-Montesinos, Joaquín & Martínez-Durbán, Mercedes & López, Gabriel & Portillo, Carlos & Batlles, Francisco Javier, 2023. "Photovoltaic power electricity generation nowcasting combining sky camera images and learning supervised algorithms in the Southern Spain," Renewable Energy, Elsevier, vol. 206(C), pages 251-262.

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