IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v2y1992i6p611-624.html
   My bibliography  Save this article

Stochastic modelling of daily beam irradiation

Author

Listed:
  • Callegari, M.
  • Festa, R.
  • Ratto, C.F.

Abstract

A dynamical statistical analysis of the daily sum of the beam irradiation measured, on a horizontal surface, in Genoa, Italy, has been done using a 9-year time series, with two substantially different methods: the Markov chain model and the first order autoregressive model. In the first case, the data range has been divided into five different equiprobable classes or “states”. The sequential characteristics of the obtained discrete time series have been described by four “seasonal” 5 × 5 transition matrices between the states of the process. Yearly series of daily beam irradiation have been simulated by associating suitable values of irradiation to every state of the chain. In the second case, data have been first modified in order to obtain a standard Normal frequency distribution; an autoregressive process of order 1 has been fitted to the transformed series. The autoregressive parameter has been estimated keeping it time invariant. Synthetic sequences of daily solar irradiations have been generated with the fitted model. The reliability both of the Markov chain model and of AR(1) model has been verified by comparing the artificial series to the empirical one. The autoregressive model has shown an appreciable superiority in reproducing the stochastic law of the daily sums of beam irradiation with respect to the Markov chain model.

Suggested Citation

  • Callegari, M. & Festa, R. & Ratto, C.F., 1992. "Stochastic modelling of daily beam irradiation," Renewable Energy, Elsevier, vol. 2(6), pages 611-624.
  • Handle: RePEc:eee:renene:v:2:y:1992:i:6:p:611-624
    DOI: 10.1016/0960-1481(92)90027-Z
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/096014819290027Z
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/0960-1481(92)90027-Z?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. Parzen, Emanuel & Pagano, Marcello, 1979. "An approach to modeling seasonally stationary time series," Journal of Econometrics, Elsevier, vol. 9(1-2), pages 137-153, January.
    2. Festa, R. & Jain, S. & Ratto, C.F., 1992. "Stochastic modelling of daily global irradiation," Renewable Energy, Elsevier, vol. 2(1), pages 23-34.
    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. Festa, R. & Jain, S. & Ratto, C.F., 1992. "Stochastic modelling of daily global irradiation," Renewable Energy, Elsevier, vol. 2(1), pages 23-34.
    2. Ratto, C.F. & Festa, R., 1993. "A procedure for evaluating the influence of weather Markovianity on the storage behaviour of solar systems," Renewable Energy, Elsevier, vol. 3(8), pages 951-960.
    3. Craggs, C & Conway, E & Pearsall, N.M, 1999. "Stochastic modelling of solar irradiance on horizontal and vertical planes at a northerly location," Renewable Energy, Elsevier, vol. 18(4), pages 445-463.
    4. Maafi, A. & Adane, A., 1998. "Analysis of the performances of the first-order two-state Markov model using solar radiation properties," Renewable Energy, Elsevier, vol. 13(2), pages 175-193.

    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. Łukasz Lenart, 2017. "Examination of Seasonal Volatility in HICP for Baltic Region Countries: Non-Parametric Test versus Forecasting Experiment," Central European Journal of Economic Modelling and Econometrics, Central European Journal of Economic Modelling and Econometrics, vol. 9(1), pages 29-67, March.
    2. Łukasz Lenart & Błażej Mazur, 2016. "On Bayesian Inference for Almost Periodic in Mean Autoregressive Models," FindEcon Chapters: Forecasting Financial Markets and Economic Decision-Making, in: Magdalena Osińska (ed.), Statistical Review, vol. 63, 2016, 3, edition 1, volume 63, chapter 1, pages 255-272, University of Lodz.
    3. Yorghos Tripodis & Jeremy Penzer, 2009. "Modelling time series with season-dependent autocorrelation structure," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 28(7), pages 559-574.
    4. Bentarzi, Mohamed, 1998. "Model-Building Problem of Periodically Correlatedm-Variate Moving Average Processes," Journal of Multivariate Analysis, Elsevier, vol. 66(1), pages 1-21, July.
    5. Ratto, C.F. & Festa, R., 1993. "A procedure for evaluating the influence of weather Markovianity on the storage behaviour of solar systems," Renewable Energy, Elsevier, vol. 3(8), pages 951-960.
    6. L. Tang & Q. Shao, 2014. "Efficient Estimation For Periodic Autoregressive Coefficients Via Residuals," Journal of Time Series Analysis, Wiley Blackwell, vol. 35(4), pages 378-389, July.
    7. Castro, Glaysar & Girardin, Valerie, 2002. "Maximum of entropy and extension of covariance matrices for periodically correlated and multivariate processes," Statistics & Probability Letters, Elsevier, vol. 59(1), pages 37-52, August.
    8. Qin Shao & Robert Lund, 2004. "Computation and Characterization of Autocorrelations and Partial Autocorrelations in Periodic ARMA Models," Journal of Time Series Analysis, Wiley Blackwell, vol. 25(3), pages 359-372, May.
    9. Richard M. Todd, 1989. "Periodic linear-quadratic methods for modeling seasonality," Staff Report 127, Federal Reserve Bank of Minneapolis.
    10. Eugen Ursu & Pierre Duchesne, 2009. "Estimation and model adequacy checking for multivariate seasonal autoregressive time series models with periodically varying parameters," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 63(2), pages 183-212, May.
    11. Basawa, I. V. & Lund, Robert & Shao, Qin, 2004. "First-order seasonal autoregressive processes with periodically varying parameters," Statistics & Probability Letters, Elsevier, vol. 67(4), pages 299-306, May.
    12. Roy, Roch & Saidi, Abdessamad, 2008. "Aggregation and systematic sampling of periodic ARMA processes," Computational Statistics & Data Analysis, Elsevier, vol. 52(9), pages 4287-4304, May.
    13. Kamal, Lalarukh & Jafri, Yasmin Zahra, 1999. "Stochastic modeling and generation of synthetic sequences of hourly global solar irradiation at Quetta, Pakistan," Renewable Energy, Elsevier, vol. 18(4), pages 565-572.
    14. Zervas, P.L. & Sarimveis, H. & Palyvos, J.A. & Markatos, N.C.G., 2008. "Prediction of daily global solar irradiance on horizontal surfaces based on neural-network techniques," Renewable Energy, Elsevier, vol. 33(8), pages 1796-1803.
    15. Aleksandra Grzesiek & Prashant Giri & S. Sundar & Agnieszka WyŁomańska, 2020. "Measures of Cross‐Dependence for Bidimensional Periodic AR(1) Model with α‐Stable Distribution," Journal of Time Series Analysis, Wiley Blackwell, vol. 41(6), pages 785-807, November.
    16. Łukasz Lenart & Mateusz Pipień, 2015. "Empirical Properties of the Credit and Equity Cycle within Almost Periodically Correlated Stochastic Processes - the Case of Poland, UK and USA," Central European Journal of Economic Modelling and Econometrics, Central European Journal of Economic Modelling and Econometrics, vol. 7(3), pages 169-186, September.
    17. Festa, R. & Jain, S. & Ratto, C.F., 1992. "Stochastic modelling of daily global irradiation," Renewable Energy, Elsevier, vol. 2(1), pages 23-34.
    18. Ballestrín, Jesús & Polo, Jesús & Martín-Chivelet, Nuria & Barbero, Javier & Carra, Elena & Alonso-Montesinos, Joaquín & Marzo, Aitor, 2022. "Soiling forecasting of solar plants: A combined heuristic approach and autoregressive model," Energy, Elsevier, vol. 239(PE).
    19. Lenart, Łukasz, 2013. "Non-parametric frequency identification and estimation in mean function for almost periodically correlated time series," Journal of Multivariate Analysis, Elsevier, vol. 115(C), pages 252-269.
    20. 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.

    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:renene:v:2:y:1992:i:6:p:611-624. 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.