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Generation of ambient temperature hourly time series for some Spanish locations by artificial neural networks

Author

Listed:
  • Almonacid, F.
  • Pérez-Higueras, P.
  • Rodrigo, P.
  • Hontoria, L.

Abstract

In this paper, an artificial neural network (ANN) is used for the generation of ambient temperature hourly time series for some Spanish locations. The model was trained and tested with ten locations and different years of data. Results show that the proposed artificial neural network provides a better approach than other methods. The aim of this paper is to provide a complete description of this ANN so that, it can be used by anyone avoiding all the design, training and testing process again.

Suggested Citation

  • Almonacid, F. & Pérez-Higueras, P. & Rodrigo, P. & Hontoria, L., 2013. "Generation of ambient temperature hourly time series for some Spanish locations by artificial neural networks," Renewable Energy, Elsevier, vol. 51(C), pages 285-291.
    • Handle: RePEc:eee:renene:v:51:y:2013:i:c:p:285-291
    DOI: 10.1016/j.renene.2012.09.022
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    References listed on IDEAS

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    1. Tasadduq, Imran & Rehman, Shafiqur & Bubshait, Khaled, 2002. "Application of neural networks for the prediction of hourly mean surface temperatures in Saudi Arabia," Renewable Energy, Elsevier, vol. 25(4), pages 545-554.
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    1. Fernández, Eduardo F. & Talavera, D.L. & Almonacid, Florencia M. & Smestad, Greg P., 2016. "Investigating the impact of weather variables on the energy yield and cost of energy of grid-connected solar concentrator systems," Energy, Elsevier, vol. 106(C), pages 790-801.
    2. Meschede, Henning & Dunkelberg, Heiko & Stöhr, Fabian & Peesel, Ron-Hendrik & Hesselbach, Jens, 2017. "Assessment of probabilistic distributed factors influencing renewable energy supply for hotels using Monte-Carlo methods," Energy, Elsevier, vol. 128(C), pages 86-100.
    3. Manuel Angel Gadeo-Martos & Antonio Jesús Yuste-Delgado & Florencia Almonacid Cruz & Jose-Angel Fernandez-Prieto & Joaquin Canada-Bago, 2019. "Modeling a High Concentrator Photovoltaic Module Using Fuzzy Rule-Based Systems," Energies, MDPI, vol. 12(3), pages 1-22, February.
    4. Rodrigo, P. & Fernández, E.F. & Almonacid, F. & Pérez-Higueras, P.J., 2014. "Review of methods for the calculation of cell temperature in high concentration photovoltaic modules for electrical characterization," Renewable and Sustainable Energy Reviews, Elsevier, vol. 38(C), pages 478-488.
    5. 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.
    6. Rodrigo, P. & Fernández, Eduardo F. & Almonacid, F. & Pérez-Higueras, P.J., 2013. "Outdoor measurement of high concentration photovoltaic receivers operating with partial shading on the primary optics," Energy, Elsevier, vol. 61(C), pages 583-588.
    7. Rodrigo, P. & Fernández, E.F. & Almonacid, F. & Pérez-Higueras, P.J., 2013. "Models for the electrical characterization of high concentration photovoltaic cells and modules: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 26(C), pages 752-760.

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