IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v23y1986i4p269-279.html
   My bibliography  Save this article

Solar radiation model for hot dry arid climates

Author

Listed:
  • Habbane, A.Y.
  • McVeigh, J.C.
  • Cabawe, S.O.I.

Abstract

The industrialised countries have well-established solar radiation networks based on detailed observations of solar irradiance data from relatively sophisticated weather stations. However, in many regions of the developing countries the only available data consist of records of sunshine hours. There have been several approaches towards establishing a relationship between sunshine hours and solar irradiance. This paper describes how one particular formula, the Barbaro et al. model, has been modified to determine solar irradiance from sunshine hours for a number of stations located in hot dry arid climates.

Suggested Citation

  • Habbane, A.Y. & McVeigh, J.C. & Cabawe, S.O.I., 1986. "Solar radiation model for hot dry arid climates," Applied Energy, Elsevier, vol. 23(4), pages 269-279.
    • Handle: RePEc:eee:appene:v:23:y:1986:i:4:p:269-279
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/0306-2619(86)90011-5
    Download Restriction: Full text for ScienceDirect subscribers only
    ---><---

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

    Citations

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


    Cited by:

    1. Al-Alawi, S.M. & Al-Hinai, H.A., 1998. "An ANN-based approach for predicting global radiation in locations with no direct measurement instrumentation," Renewable Energy, Elsevier, vol. 14(1), pages 199-204.
    2. Jebaraj, S. & Iniyan, S., 2006. "A review of energy models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 10(4), pages 281-311, August.
    3. Cañada, Javier, 1992. "Solar radiation prediction from sunshine in eastern Spain," Renewable Energy, Elsevier, vol. 2(3), pages 305-310.
    4. Azadeh, A. & Babazadeh, R. & Asadzadeh, S.M., 2013. "Optimum estimation and forecasting of renewable energy consumption by artificial neural networks," Renewable and Sustainable Energy Reviews, Elsevier, vol. 27(C), pages 605-612.

    More about this item

    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:eee:appene:v:23:y:1986:i:4:p:269-279. 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.

    We have no bibliographic references for this item. You can help adding them by using 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.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    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.