IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v27y2002i4p647-666.html
   My bibliography  Save this article

Prediction of global daily solar radiation using higher order statistics

Author

Listed:
  • Safi, S.
  • Zeroual, A.
  • Hassani, M.

Abstract

The main concern of the present paper is to present and to analyse two procedures for modelling daily global solar radiation. The first one uses the clearness index techniques and the second one uses a totally different type of approach for taking in consideration important properties of such data, including non-Gaussian shape and non-stationarity. This procedure uses the difference between the extraterrestrial and the observed daily global radiation denoted “lost solar component”. Both procedures are based on higher order statistics for generating the global solar radiation using mainly a random process. The prediction results show that the sequences of values generated have the same statistical characteristics as those of sequences observed. The comparison between the two methods used indicates that the developed model based on the “lost solar component” is better than the model obtained using the conventional procedure based on the clearness index.

Suggested Citation

  • Safi, S. & Zeroual, A. & Hassani, M., 2002. "Prediction of global daily solar radiation using higher order statistics," Renewable Energy, Elsevier, vol. 27(4), pages 647-666.
    • Handle: RePEc:eee:renene:v:27:y:2002:i:4:p:647-666
    DOI: 10.1016/S0960-1481(01)00153-7
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960148101001537
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/S0960-1481(01)00153-7?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. Bahel, V. & Srinivasan, R. & Bakhsh, H., 1987. "Statistical comparison of correlations for estimation of global horizontal solar radiation," Energy, Elsevier, vol. 12(12), pages 1309-1316.
    2. Bahel, V., 1987. "Statistical comparison of correlations for estimation of the diffuse fraction of global radiation," Energy, Elsevier, vol. 12(12), pages 1257-1263.
    3. Zeroual, A. & Ankrim, M. & Wilkinson, A.J., 1995. "Stochastic modelling of daily global solar radiation measured in Marrakesh, Morocco," Renewable Energy, Elsevier, vol. 6(7), pages 787-793.
    4. Bouhaddou, H. & Hassani, M.M. & Zeroual, A. & Wilkinson, A.J., 1997. "Stochastic simulation of weather data using higher order statistics," Renewable Energy, Elsevier, vol. 12(1), pages 21-37.
    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. Jebaraj, S. & Iniyan, S., 2006. "A review of energy models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 10(4), pages 281-311, August.
    2. Mohanty, Sthitapragyan & Patra, Prashanta Kumar & Sahoo, Sudhansu Sekhar, 2016. "Prediction and application of solar radiation with soft computing over traditional and conventional approach – A comprehensive review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 56(C), pages 778-796.
    3. Müller, Alfred & Reuber, Matthias, 2023. "A copula-based time series model for global horizontal irradiation," International Journal of Forecasting, Elsevier, vol. 39(2), pages 869-883.
    4. Paschen, Marius, 2016. "Dynamic analysis of the German day-ahead electricity spot market," Energy Economics, Elsevier, vol. 59(C), pages 118-128.
    5. Antonello Rosato & Rosa Altilio & Rodolfo Araneo & Massimo Panella, 2017. "Prediction in Photovoltaic Power by Neural Networks," Energies, MDPI, vol. 10(7), pages 1-25, July.
    6. Fernandez-Jimenez, L. Alfredo & Muñoz-Jimenez, Andrés & Falces, Alberto & Mendoza-Villena, Montserrat & Garcia-Garrido, Eduardo & Lara-Santillan, Pedro M. & Zorzano-Alba, Enrique & Zorzano-Santamaria,, 2012. "Short-term power forecasting system for photovoltaic plants," Renewable Energy, Elsevier, vol. 44(C), pages 311-317.
    7. Aurelia Rybak & Aleksandra Rybak & Spas D. Kolev, 2023. "Modeling the Photovoltaic Power Generation in Poland in the Light of PEP2040: An Application of Multiple Regression," Energies, MDPI, vol. 16(22), pages 1-17, November.
    8. Aurelia Rybak & Aleksandra Rybak & Jarosław Joostberens & Joachim Pielot & Piotr Toś, 2024. "Analysis of the Impact of Clean Coal Technologies on the Share of Coal in Poland’s Energy Mix," Energies, MDPI, vol. 17(6), pages 1-17, March.
    9. Chen, S.X. & Gooi, H.B. & Wang, M.Q., 2013. "Solar radiation forecast based on fuzzy logic and neural networks," Renewable Energy, Elsevier, vol. 60(C), pages 195-201.
    10. Rybak, Aurelia & Rybak, Aleksandra & Kolev, Spas D., 2024. "Development of wind energy and access to REE. The case of Poland," Resources Policy, Elsevier, vol. 90(C).
    11. Li, Yanting & Su, Yan & Shu, Lianjie, 2014. "An ARMAX model for forecasting the power output of a grid connected photovoltaic system," Renewable Energy, Elsevier, vol. 66(C), pages 78-89.
    12. Shubham Gupta & Amit Kumar Singh & Sachin Mishra & Pradeep Vishnuram & Nagaraju Dharavat & Narayanamoorthi Rajamanickam & Ch. Naga Sai Kalyan & Kareem M. AboRas & Naveen Kumar Sharma & Mohit Bajaj, 2023. "Estimation of Solar Radiation with Consideration of Terrestrial Losses at a Selected Location—A Review," Sustainability, MDPI, vol. 15(13), pages 1-29, June.
    13. Aurelia Rybak & Aleksandra Rybak & Jarosław Joostberens & Spas D. Kolev, 2022. "Cluster Analysis of the EU-27 Countries in Light of the Guiding Principles of the European Green Deal, with Particular Emphasis on Poland," Energies, MDPI, vol. 15(14), pages 1-20, July.
    14. Almorox, J. & Benito, M. & Hontoria, C., 2005. "Estimation of monthly Angström–Prescott equation coefficients from measured daily data in Toledo, Spain," Renewable Energy, Elsevier, vol. 30(6), pages 931-936.

    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. Koussa, Mustapha & Saheb-Koussa, Djohra & Hadji, Seddik, 2017. "Experimental investigation of simple solar radiation spectral model performances under a Mediterranean Algerian's climate," Energy, Elsevier, vol. 120(C), pages 751-773.
    2. Zeroual, A. & Ankrim, M. & Wilkinson, A.J., 1996. "The diffuse-global correlation: Its application to estimating solar radiation on tilted surfaces in Marrakesh, Morocco," Renewable Energy, Elsevier, vol. 7(1), pages 1-13.
    3. Anton Vernet & Alexandre Fabregat, 2023. "Evaluation of Empirical Daily Solar Radiation Models for the Northeast Coast of the Iberian Peninsula," Energies, MDPI, vol. 16(6), pages 1-18, March.
    4. Khalil, Samy A. & Shaffie, A.M., 2016. "Evaluation of transposition models of solar irradiance over Egypt," Renewable and Sustainable Energy Reviews, Elsevier, vol. 66(C), pages 105-119.
    5. Liu, Xiaoying & Xu, Yinlong & Zhong, Xiuli & Zhang, Wenying & Porter, John Roy & Liu, Wenli, 2012. "Assessing models for parameters of the Ångström–Prescott formula in China," Applied Energy, Elsevier, vol. 96(C), pages 327-338.
    6. Muzathik, A.M. & Ibrahim, M.Z. & Samo, K.B. & Wan Nik, W.B., 2011. "Estimation of global solar irradiation on horizontal and inclined surfaces based on the horizontal measurements," Energy, Elsevier, vol. 36(2), pages 812-818.
    7. Shaahid, S.M. & Elhadidy, M.A., 1994. "Wind and solar energy at Dhahran, Saudi Arabia," Renewable Energy, Elsevier, vol. 4(4), pages 441-445.
    8. Mecibah, Mohamed Salah & Boukelia, Taqiy Eddine & Tahtah, Reda & Gairaa, Kacem, 2014. "Introducing the best model for estimation the monthly mean daily global solar radiation on a horizontal surface (Case study: Algeria)," Renewable and Sustainable Energy Reviews, Elsevier, vol. 36(C), pages 194-202.
    9. Benkaciali, Saïd & Haddadi, Mourad & Khellaf, Abdellah, 2018. "Evaluation of direct solar irradiance from 18 broadband parametric models: Case of Algeria," Renewable Energy, Elsevier, vol. 125(C), pages 694-711.
    10. Olubayo M. Babatunde & Josiah L. Munda & Yskandar Hamam, 2020. "Exploring the Potentials of Artificial Neural Network Trained with Differential Evolution for Estimating Global Solar Radiation," Energies, MDPI, vol. 13(10), pages 1-18, May.
    11. Sierra, J.P. & Martín, C. & Mösso, C. & Mestres, M. & Jebbad, R., 2016. "Wave energy potential along the Atlantic coast of Morocco," Renewable Energy, Elsevier, vol. 96(PA), pages 20-32.
    12. Jahani, Babak & Dinpashoh, Y. & Raisi Nafchi, Atefeh, 2017. "Evaluation and development of empirical models for estimating daily solar radiation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 73(C), pages 878-891.
    13. Bakirci, Kadir, 2009. "Models of solar radiation with hours of bright sunshine: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(9), pages 2580-2588, December.
    14. Achour, Lazhar & Bouharkat, Malek & Assas, Ouarda & Behar, Omar, 2017. "Hybrid model for estimating monthly global solar radiation for the Southern of Algeria: (Case study: Tamanrasset, Algeria)," Energy, Elsevier, vol. 135(C), pages 526-539.
    15. Bulut, Hüsamettin & Büyükalaca, Orhan, 2007. "Simple model for the generation of daily global solar-radiation data in Turkey," Applied Energy, Elsevier, vol. 84(5), pages 477-491, May.
    16. Bilbao, J. & Miguel, A., 2013. "Contribution to the study of UV-B solar radiation in Central Spain," Renewable Energy, Elsevier, vol. 53(C), pages 79-85.
    17. Olatomiwa, Lanre & Mekhilef, Saad & Shamshirband, Shahaboddin & Petković, Dalibor, 2015. "Adaptive neuro-fuzzy approach for solar radiation prediction in Nigeria," Renewable and Sustainable Energy Reviews, Elsevier, vol. 51(C), pages 1784-1791.
    18. Kambezidis, H.D. & Psiloglou, B.E. & Karagiannis, D. & Dumka, U.C. & Kaskaoutis, D.G., 2017. "Meteorological Radiation Model (MRM v6.1): Improvements in diffuse radiation estimates and a new approach for implementation of cloud products," Renewable and Sustainable Energy Reviews, Elsevier, vol. 74(C), pages 616-637.
    19. Hassan, Jamal, 2014. "ARIMA and regression models for prediction of daily and monthly clearness index," Renewable Energy, Elsevier, vol. 68(C), pages 421-427.
    20. Khalil, Samy A. & Shaffie, A.M., 2013. "A comparative study of total, direct and diffuse solar irradiance by using different models on horizontal and inclined surfaces for Cairo, Egypt," Renewable and Sustainable Energy Reviews, Elsevier, vol. 27(C), pages 853-863.

    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:27:y:2002:i:4:p:647-666. 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.