IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v13y2020i13p3493-d381059.html
   My bibliography  Save this article

Very Short-Term Power Forecasting of High Concentrator Photovoltaic Power Facility by Implementing Artificial Neural Network

Author

Listed:
  • Yaser I. Alamin

    (CIESOL Research Center on Solar Energy, Universidad de Almería, 04120 Almería, Spain)

  • Mensah K. Anaty

    (School of Renewable Energies and Petroleum Studies, Technopolis, International University of Rabat, LERMA, Sala el Jadida 11000, Morocco
    Renewable Energy and Environment Laboratory, Faculty of Science, Ibn Tofail University, Kenitra 14000, Morocco)

  • José Domingo Álvarez Hervás

    (CIESOL Research Center on Solar Energy, Universidad de Almería, 04120 Almería, Spain)

  • Khalid Bouziane

    (School of Renewable Energies and Petroleum Studies, Technopolis, International University of Rabat, LERMA, Sala el Jadida 11000, Morocco)

  • Manuel Pérez García

    (CIESOL Research Center on Solar Energy, Universidad de Almería, 04120 Almería, Spain)

  • Reda Yaagoubi

    (School of Geomatics and Surveying Engineering, Hassan II Agronomic and Veterinary Institute, Rabat 10101, Morocco)

  • María del Mar Castilla

    (CIESOL Research Center on Solar Energy, Universidad de Almería, 04120 Almería, Spain)

  • Merouan Belkasmi

    (School of Renewable Energies and Petroleum Studies, Technopolis, International University of Rabat, LERMA, Sala el Jadida 11000, Morocco)

  • Mohammed Aggour

    (Renewable Energy and Environment Laboratory, Faculty of Science, Ibn Tofail University, Kenitra 14000, Morocco)

Abstract

Concentrator photovoltaic (CPV) is used to obtain cheaper and more stable renewable energy. Methods which predict the energy production of a power system under specific circumstances are highly important to reach the goal of using this system as a part of a bigger one or of making it integrated with the grid. In this paper, the development of a model to predict the energy of a High CPV (HCPV) system using an Artificial Neural Network (ANN) is described. This system is located at the University of Rabat. The performed experiments show a quick prediction with encouraging results for a very short-term prediction horizon, considering the small amount of data available. These conclusions are based on the processes of obtaining the ANN models and detailed discussion of the results, which have been validated using real data.

Suggested Citation

  • Yaser I. Alamin & Mensah K. Anaty & José Domingo Álvarez Hervás & Khalid Bouziane & Manuel Pérez García & Reda Yaagoubi & María del Mar Castilla & Merouan Belkasmi & Mohammed Aggour, 2020. "Very Short-Term Power Forecasting of High Concentrator Photovoltaic Power Facility by Implementing Artificial Neural Network," Energies, MDPI, vol. 13(13), pages 1-16, July.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:13:p:3493-:d:381059
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/13/13/3493/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/13/13/3493/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. da Silva Fonseca Junior, Joao Gari & Oozeki, Takashi & Ohtake, Hideaki & Shimose, Ken-ichi & Takashima, Takumi & Ogimoto, Kazuhiko, 2014. "Regional forecasts and smoothing effect of photovoltaic power generation in Japan: An approach with principal component analysis," Renewable Energy, Elsevier, vol. 68(C), pages 403-413.
    2. Mellit, A. & Kalogirou, S.A. & Hontoria, L. & Shaari, S., 2009. "Artificial intelligence techniques for sizing photovoltaic systems: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(2), pages 406-419, February.
    3. Elyas Rakhshani & Kumars Rouzbehi & Adolfo J. Sánchez & Ana Cabrera Tobar & Edris Pouresmaeil, 2019. "Integration of Large Scale PV-Based Generation into Power Systems: A Survey," Energies, MDPI, vol. 12(8), pages 1-19, April.
    4. Yang, Dazhi & Gu, Chaojun & Dong, Zibo & Jirutitijaroen, Panida & Chen, Nan & Walsh, Wilfred M., 2013. "Solar irradiance forecasting using spatial-temporal covariance structures and time-forward kriging," Renewable Energy, Elsevier, vol. 60(C), pages 235-245.
    5. López, G. & Batlles, F.J. & Tovar-Pescador, J., 2005. "Selection of input parameters to model direct solar irradiance by using artificial neural networks," Energy, Elsevier, vol. 30(9), pages 1675-1684.
    6. 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.
    7. Emanuele Ogliari & Francesco Grimaccia & Sonia Leva & Marco Mussetta, 2013. "Hybrid Predictive Models for Accurate Forecasting in PV Systems," Energies, MDPI, vol. 6(4), pages 1-12, April.
    8. Bonanno, F. & Capizzi, G. & Graditi, G. & Napoli, C. & Tina, G.M., 2012. "A radial basis function neural network based approach for the electrical characteristics estimation of a photovoltaic module," Applied Energy, Elsevier, vol. 97(C), pages 956-961.
    9. 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.
    10. George Makrides & Bastian Zinsser & Matthew Savvas Harry Norton & George E. Georghiou, 2012. "Performance of Photovoltaics Under Actual Operating Conditions," Chapters, in: Vasilis Fthenakis (ed.), Third Generation Photovoltaics, IntechOpen.
    11. 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.
    12. Leva, S. & Dolara, A. & Grimaccia, F. & Mussetta, M. & Ogliari, E., 2017. "Analysis and validation of 24 hours ahead neural network forecasting of photovoltaic output power," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 131(C), pages 88-100.
    13. Shanks, Katie & Senthilarasu, S. & Mallick, Tapas K., 2016. "Optics for concentrating photovoltaics: Trends, limits and opportunities for materials and design," Renewable and Sustainable Energy Reviews, Elsevier, vol. 60(C), pages 394-407.
    14. 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.
    Full references (including those not matched with items on IDEAS)

    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. 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.
    2. 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.
    3. 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.
    4. Li, Guiqiang & Xuan, Qingdong & Pei, Gang & Su, Yuehong & Ji, Jie, 2018. "Effect of non-uniform illumination and temperature distribution on concentrating solar cell - A review," Energy, Elsevier, vol. 144(C), pages 1119-1136.
    5. Rodrigo, Pedro M. & Velázquez, Ramiro & Fernández, Eduardo F. & Almonacid, Florencia M. & Lay-Ekuakille, Aimé, 2018. "A method for the outdoor thermal characterisation of high-concentrator photovoltaic modules alternative to the IEC 62670-3 standard," Energy, Elsevier, vol. 148(C), pages 159-168.
    6. Almonacid, F. & Fernández, E.F. & Mallick, T.K. & Pérez-Higueras, P.J., 2015. "High concentrator photovoltaic module simulation by neuronal networks using spectrally corrected direct normal irradiance and cell temperature," Energy, Elsevier, vol. 84(C), pages 336-343.
    7. 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.
    8. Almonacid, Florencia & Rodrigo, Pedro & Fernández, Eduardo F., 2016. "Determination of the current–voltage characteristics of concentrator systems by using different adapted conventional techniques," Energy, Elsevier, vol. 101(C), pages 146-160.
    9. Rodrigo, P. & Gutiérrez, S. & Velázquez, Ramiro & Fernández, Eduardo F. & Almonacid, F. & Pérez-Higueras, P.J., 2015. "A methodology for the electrical characterization of shaded high concentrator photovoltaic modules," Energy, Elsevier, vol. 89(C), pages 768-777.
    10. Muhsen, Dhiaa Halboot & Khatib, Tamer & Nagi, Farrukh, 2017. "A review of photovoltaic water pumping system designing methods, control strategies and field performance," Renewable and Sustainable Energy Reviews, Elsevier, vol. 68(P1), pages 70-86.
    11. Zarzo, Manuel & Martí, Pau, 2011. "Modeling the variability of solar radiation data among weather stations by means of principal components analysis," Applied Energy, Elsevier, vol. 88(8), pages 2775-2784, August.
    12. Dimri, Neha & Tiwari, Arvind & Tiwari, G.N., 2019. "Comparative study of photovoltaic thermal (PVT) integrated thermoelectric cooler (TEC) fluid collectors," Renewable Energy, Elsevier, vol. 134(C), pages 343-356.
    13. Fernández, Eduardo F. & Pérez-Higueras, P. & Almonacid, F. & Ruiz-Arias, J.A. & Rodrigo, P. & Fernandez, J.I. & Luque-Heredia, I., 2015. "Model for estimating the energy yield of a high concentrator photovoltaic system," Energy, Elsevier, vol. 87(C), pages 77-85.
    14. Rodrigo, P.M. & Talavera, D.L. & Fernández, E.F. & Almonacid, F.M. & Pérez-Higueras, P.J., 2019. "Optimum capacity of the inverters in concentrator photovoltaic power plants with emphasis on shading impact," Energy, Elsevier, vol. 187(C).
    15. García-Domingo, B. & Piliougine, M. & Elizondo, D. & Aguilera, J., 2015. "CPV module electric characterisation by artificial neural networks," Renewable Energy, Elsevier, vol. 78(C), pages 173-181.
    16. Lan, Hai & Zhang, Chi & Hong, Ying-Yi & He, Yin & Wen, Shuli, 2019. "Day-ahead spatiotemporal solar irradiation forecasting using frequency-based hybrid principal component analysis and neural network," Applied Energy, Elsevier, vol. 247(C), pages 389-402.
    17. Benhammane, Mousaab & Notton, Gilles & Pichenot, Grégoire & Voarino, Philippe & Ouvrard, David, 2021. "Overview of electrical power models for concentrated photovoltaic systems and development of a new operational model with easily accessible inputs," Renewable and Sustainable Energy Reviews, Elsevier, vol. 135(C).
    18. Yadav, Amit Kumar & Chandel, S.S., 2017. "Identification of relevant input variables for prediction of 1-minute time-step photovoltaic module power using Artificial Neural Network and Multiple Linear Regression Models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 77(C), pages 955-969.
    19. Chen, Zhicong & Yu, Hui & Luo, Linlu & Wu, Lijun & Zheng, Qiao & Wu, Zhenhui & Cheng, Shuying & Lin, Peijie, 2021. "Rapid and accurate modeling of PV modules based on extreme learning machine and large datasets of I-V curves," Applied Energy, Elsevier, vol. 292(C).
    20. Stéphanie Monjoly & Maina André & Rudy Calif & Ted Soubdhan, 2019. "Forecast Horizon and Solar Variability Influences on the Performances of Multiscale Hybrid Forecast Model," Energies, MDPI, vol. 12(12), pages 1-20, June.

    More about this item

    Keywords

    HCPV; power prediction; RBF; ANN;
    All these keywords.

    Statistics

    Access and download statistics

    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:gam:jeners:v:13:y:2020:i:13:p:3493-:d:381059. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.