IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v60y2013icp235-245.html
   My bibliography  Save this article

Solar irradiance forecasting using spatial-temporal covariance structures and time-forward kriging

Author

Listed:
  • Yang, Dazhi
  • Gu, Chaojun
  • Dong, Zibo
  • Jirutitijaroen, Panida
  • Chen, Nan
  • Walsh, Wilfred M.

Abstract

Electricity power grid operations require information about demand and supply on a variety of timescales and areas. The advent of significant generation contributions by time variable renewable energy sources means that forecasting methods are increasingly required. Some of the earliest requirements will be for spatial-temporal estimation of solar irradiance and the resulting photovoltaic-generated electricity. Accurate forecasts represent an important step towards building a smart grid for renewable energy driven cities or regions, and to this end we develop forecasting tools that use data from ground-based irradiance sensors.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:renene:v:60:y:2013:i:c:p:235-245
    DOI: 10.1016/j.renene.2013.05.030
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960148113002759
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.renene.2013.05.030?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. Kaplanis, S.N., 2006. "New methodologies to estimate the hourly global solar radiation; Comparisons with existing models," Renewable Energy, Elsevier, vol. 31(6), pages 781-790.
    2. J. Kruskal, 1964. "Nonmetric multidimensional scaling: A numerical method," Psychometrika, Springer;The Psychometric Society, vol. 29(2), pages 115-129, June.
    3. J. Kruskal, 1964. "Multidimensional scaling by optimizing goodness of fit to a nonmetric hypothesis," Psychometrika, Springer;The Psychometric Society, vol. 29(1), pages 1-27, March.
    4. Kwiatkowski, Denis & Phillips, Peter C. B. & Schmidt, Peter & Shin, Yongcheol, 1992. "Testing the null hypothesis of stationarity against the alternative of a unit root : How sure are we that economic time series have a unit root?," Journal of Econometrics, Elsevier, vol. 54(1-3), pages 159-178.
    5. Dickey, David A & Fuller, Wayne A, 1981. "Likelihood Ratio Statistics for Autoregressive Time Series with a Unit Root," Econometrica, Econometric Society, vol. 49(4), pages 1057-1072, June.
    6. Al-Sadah, Fayez H & Ragab, Farouk M & Arshad, Mirza K, 1990. "Hourly solar radiation over Bahrain," Energy, Elsevier, vol. 15(5), pages 395-402.
    7. Gneiting T., 2002. "Nonseparable, Stationary Covariance Functions for Space-Time Data," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 590-600, June.
    8. Rehman, Shafiqur & Ghori, Saleem G, 2000. "Spatial estimation of global solar radiation using geostatistics," Renewable Energy, Elsevier, vol. 21(3), pages 583-605.
    9. Baig, A. & Akhter, P. & Mufti, A., 1991. "A novel approach to estimate the clear day global radiation," Renewable Energy, Elsevier, vol. 1(1), pages 119-123.
    10. Righini, R. & Grossi Gallegos, H. & Raichijk, C., 2005. "Approach to drawing new global solar irradiation contour maps for Argentina," Renewable Energy, Elsevier, vol. 30(8), pages 1241-1255.
    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. Dong, Zibo & Yang, Dazhi & Reindl, Thomas & Walsh, Wilfred M., 2013. "Short-term solar irradiance forecasting using exponential smoothing state space model," Energy, Elsevier, vol. 55(C), pages 1104-1113.
    2. Trapero, Juan R., 2016. "Calculation of solar irradiation prediction intervals combining volatility and kernel density estimates," Energy, Elsevier, vol. 114(C), pages 266-274.
    3. Hanany Tolba & Nouha Dkhili & Julien Nou & Julien Eynard & Stéphane Thil & Stéphane Grieu, 2020. "Multi-Horizon Forecasting of Global Horizontal Irradiance Using Online Gaussian Process Regression: A Kernel Study," Energies, MDPI, vol. 13(16), pages 1-23, August.
    4. Trapero, Juan R. & Kourentzes, Nikolaos & Martin, A., 2015. "Short-term solar irradiation forecasting based on Dynamic Harmonic Regression," Energy, Elsevier, vol. 84(C), pages 289-295.
    5. Guili Liao & Qimeng Liu & Rongmao Zhang & Shifang Zhang, 2022. "Rank test of unit‐root hypothesis with AR‐GARCH errors," Journal of Time Series Analysis, Wiley Blackwell, vol. 43(5), pages 695-719, September.
    6. Xiaojie Xu, 2017. "The rolling causal structure between the Chinese stock index and futures," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 31(4), pages 491-509, November.
    7. repec:kap:iaecre:v:17:y:2011:i:2:p:157-168 is not listed on IDEAS
    8. Jan Babecký & Fabrizio Coricelli & Roman Horváth, 2009. "Assessing Inflation Persistence: Micro Evidence on an Inflation Targeting Economy," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, vol. 59(2), pages 102-127, June.
    9. Hu, Junjie & López Cabrera, Brenda & Melzer, Awdesch, 2021. "Advanced statistical learning on short term load process forecasting," IRTG 1792 Discussion Papers 2021-020, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    10. Kun Guo & Wei-Xing Zhou & Si-Wei Cheng & Didier Sornette, 2011. "The US Stock Market Leads the Federal Funds Rate and Treasury Bond Yields," PLOS ONE, Public Library of Science, vol. 6(8), pages 1-9, August.
    11. Isabel Cortés-Jiménez & Manuel Artís, 2005. "The role of the tourism sector in economic development - Lessons from the Spanish experience," ERSA conference papers ersa05p488, European Regional Science Association.
    12. Philippe Andrade & Catherine Bruneau, 2002. "Excess returns, portfolio choices and exchange rate dynamics. The yen/dollar case, 1980–1998," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 64(3), pages 233-256, July.
    13. Hondroyiannis, George & Papapetrou, Evangelia, 2001. "Demographic changes, labor effort and economic growth: empirical evidence from Greece," Journal of Policy Modeling, Elsevier, vol. 23(2), pages 169-188, February.
    14. Francis Ahking, 2003. "Efficient unit root tests of real exchange rates in the post-Bretton Woods era," Economics Bulletin, AccessEcon, vol. 6(7), pages 1-12.
    15. Fabian Knorre & Martin Wagner & Maximilian Grupe, 2021. "Monitoring Cointegrating Polynomial Regressions: Theory and Application to the Environmental Kuznets Curves for Carbon and Sulfur Dioxide Emissions," Econometrics, MDPI, vol. 9(1), pages 1-35, March.
    16. Ashis Kumar Pradhan & Gourishankar S Hiremath, 2020. "Do external commercial borrowings and financial development affect exports?," Cogent Business & Management, Taylor & Francis Journals, vol. 7(1), pages 1796269-179, January.
    17. Fredj Jawadi & Catherine Bruneau & Nadia Sghaier, 2009. "Nonlinear Cointegration Relationships Between Non‐Life Insurance Premiums and Financial Markets," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 76(3), pages 753-783, September.
    18. Derek Bond & Michael J. Harrison & Edward J. O'Brien, 2005. "Testing for Long Memory and Nonlinear Time Series: A Demand for Money Study," Trinity Economics Papers tep20021, Trinity College Dublin, Department of Economics.
    19. Wang, Peijie & Brand, Steven, 2015. "A new approach to estimating value–income ratios with income growth and time-varying yields," European Journal of Operational Research, Elsevier, vol. 242(1), pages 182-187.
    20. Frédérique Bec & Mélika Ben Salem & Marine Carrasco, 2010. "Detecting Mean Reversion in Real Exchange Rates from a Multiple Regime star Model," Annals of Economics and Statistics, GENES, issue 99-100, pages 395-427.
    21. Ahmed, R. & Sreeram, V. & Mishra, Y. & Arif, M.D., 2020. "A review and evaluation of the state-of-the-art in PV solar power forecasting: Techniques and optimization," Renewable and Sustainable Energy Reviews, Elsevier, vol. 124(C).

    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:60:y:2013:i:c:p:235-245. 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.