IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v31y2006i9p1321-1332.html
   My bibliography  Save this article

Global solar irradiance in Cordoba: Clearness index distributions conditioned to the optical air mass

Author

Listed:
  • Varo, M.
  • Pedrós, G.
  • Martínez-Jiménez, P.
  • Aguilera, M.J.

Abstract

The biological and photochemical effects of solar radiation and solar energy applications make it really important to characterize the variability of this component. In view of the fact that the clearness index indicates not only the level of availability of solar radiation but also the changes in atmospheric conditions in a given location, since the classic Liu and Jordan study, many papers have dealt with its statistical distribution. Specifically, Tovar et al. [Tovar J, Olmo FJ, Alados-Arboledas L. Solar Energy 1998;62(6):387–393] proposed a model to represent the probability density distributions of the instantaneous clearness index conditioned to the optical air mass from measurements recorded in Granada (Spain). In this work, we have proved the applicability of this model in a different location, Cordoba (Spain), finding that the parameters for fitting the model depend on both the optical air mass and the geographic and climatic conditions.

Suggested Citation

  • Varo, M. & Pedrós, G. & Martínez-Jiménez, P. & Aguilera, M.J., 2006. "Global solar irradiance in Cordoba: Clearness index distributions conditioned to the optical air mass," Renewable Energy, Elsevier, vol. 31(9), pages 1321-1332.
    • Handle: RePEc:eee:renene:v:31:y:2006:i:9:p:1321-1332
    DOI: 10.1016/j.renene.2005.07.004
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960148105002168
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.renene.2005.07.004?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. 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.
    2. Santos, J.M. & Pinazo, J.M. & Cañada, J., 2003. "Methodology for generating daily clearness index index values Kt starting from the monthly average daily value K̄t. Determining the daily sequence using stochastic models," Renewable Energy, Elsevier, vol. 28(10), pages 1523-1544.
    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. Ayodele, T.R. & Ogunjuyigbe, A.S.O., 2015. "Prediction of monthly average global solar radiation based on statistical distribution of clearness index," Energy, Elsevier, vol. 90(P2), pages 1733-1742.

    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. Wang, Jianzhou & Jiang, He & Wu, Yujie & Dong, Yao, 2015. "Forecasting solar radiation using an optimized hybrid model by Cuckoo Search algorithm," Energy, Elsevier, vol. 81(C), pages 627-644.
    2. 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.
    3. Reikard, Gordon & Haupt, Sue Ellen & Jensen, Tara, 2017. "Forecasting ground-level irradiance over short horizons: Time series, meteorological, and time-varying parameter models," Renewable Energy, Elsevier, vol. 112(C), pages 474-485.
    4. Liukkonen, M. & Heikkinen, M. & Hiltunen, T. & Hälikkä, E. & Kuivalainen, R. & Hiltunen, Y., 2011. "Artificial neural networks for analysis of process states in fluidized bed combustion," Energy, Elsevier, vol. 36(1), pages 339-347.
    5. 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.
    6. 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.
    7. Roumpakias, Elias & Zogou, Olympia & Stamatelos, Anastassios, 2015. "Correlation of actual efficiency of photovoltaic panels with air mass," Renewable Energy, Elsevier, vol. 74(C), pages 70-77.
    8. Marzo, A. & Trigo-Gonzalez, M. & Alonso-Montesinos, J. & Martínez-Durbán, M. & López, G. & Ferrada, P. & Fuentealba, E. & Cortés, M. & Batlles, F.J., 2017. "Daily global solar radiation estimation in desert areas using daily extreme temperatures and extraterrestrial radiation," Renewable Energy, Elsevier, vol. 113(C), pages 303-311.
    9. Utrillas, M.P. & Marín, M.J. & Esteve, A.R. & Salazar, G. & Suárez, H. & Gandía, S. & Martínez-Lozano, J.A., 2018. "Relationship between erythemal UV and broadband solar irradiation at high altitude in Northwestern Argentina," Energy, Elsevier, vol. 162(C), pages 136-147.
    10. Corral, Nicolás & Anrique, Nicolás & Fernandes, Dalila & Parrado, Cristóbal & Cáceres, Gustavo, 2012. "Power, placement and LEC evaluation to install CSP plants in northern Chile," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(9), pages 6678-6685.
    11. Alonso, J. & Batlles, F.J., 2014. "Short and medium-term cloudiness forecasting using remote sensing techniques and sky camera imagery," Energy, Elsevier, vol. 73(C), pages 890-897.
    12. Chao-Rong Chen & Unit Three Kartini, 2017. "k-Nearest Neighbor Neural Network Models for Very Short-Term Global Solar Irradiance Forecasting Based on Meteorological Data," Energies, MDPI, vol. 10(2), pages 1-18, February.
    13. Mostafavi, Elham Sadat & Ramiyani, Sara Saeidi & Sarvar, Rahim & Moud, Hashem Izadi & Mousavi, Seyyed Mohammad, 2013. "A hybrid computational approach to estimate solar global radiation: An empirical evidence from Iran," Energy, Elsevier, vol. 49(C), pages 204-210.
    14. Reikard, Gordon & Hansen, Clifford, 2019. "Forecasting solar irradiance at short horizons: Frequency and time domain models," Renewable Energy, Elsevier, vol. 135(C), pages 1270-1290.
    15. Ayodele, T.R. & Ogunjuyigbe, A.S.O., 2015. "Prediction of monthly average global solar radiation based on statistical distribution of clearness index," Energy, Elsevier, vol. 90(P2), pages 1733-1742.
    16. Rodrigues, Eugénio & Gomes, Álvaro & Gaspar, Adélio Rodrigues & Henggeler Antunes, Carlos, 2018. "Estimation of renewable energy and built environment-related variables using neural networks – A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 94(C), pages 959-988.
    17. Yacef, R. & Benghanem, M. & Mellit, A., 2012. "Prediction of daily global solar irradiation data using Bayesian neural network: A comparative study," Renewable Energy, Elsevier, vol. 48(C), pages 146-154.
    18. Linares-Rodríguez, Alvaro & Ruiz-Arias, José Antonio & Pozo-Vázquez, David & Tovar-Pescador, Joaquín, 2011. "Generation of synthetic daily global solar radiation data based on ERA-Interim reanalysis and artificial neural networks," Energy, Elsevier, vol. 36(8), pages 5356-5365.
    19. Qingtao Li & Jianxue Wang & Yao Zhang & Yue Fan & Guojun Bao & Xuebin Wang, 2020. "Multi-Period Generation Expansion Planning for Sustainable Power Systems to Maximize the Utilization of Renewable Energy Sources," Sustainability, MDPI, vol. 12(3), pages 1-18, February.
    20. Linares-Rodriguez, Alvaro & Ruiz-Arias, José Antonio & Pozo-Vazquez, David & Tovar-Pescador, Joaquin, 2013. "An artificial neural network ensemble model for estimating global solar radiation from Meteosat satellite images," Energy, Elsevier, vol. 61(C), pages 636-645.

    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:31:y:2006:i:9:p:1321-1332. 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.