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Prediction of Hydropower Energy Price Using G mes-Maravall Seasonal Model

Author

Listed:
  • Arash Jamalmanesh

    (Phd Candidate of Economics, International Campus, Ferdowsi University of Mashhad, Iran,)

  • Mahdi Khodaparast Mashhadi

    (Department of Economics, Faculty of Economics and Administrative Sciences, Ferdowsi University of Mashhad, Iran,)

  • Ahmad Seifi

    (Department of Economics, Faculty of Economics and Administrative Sciences, Ferdowsi University of Mashhad, Iran,)

  • Mohammad Ali Falahi

    (Department of Economics, Faculty of Economics and Administrative Sciences, Ferdowsi University of Mashhad, Iran.)

Abstract

The present research is aimed at investigating the possibility of predicting average monthly electricity prices and presenting a model for predicting electricity price in Iranian market considering unique characteristics of electricity as a commodity. For this purpose, time series data on average monthly electricity price during 2006 2015 was used. Firstly, unit root test was used to investigate stationarity of time series of electricity price. Then, using G mes-Maravall model, an ARIMA model was estimated for predicating electricity price in Iranian market using energy purchase data from a hydropower plant. The model was run utilizing SEATS (Signal Extraction in ARIMA Time Series) and TARMO ( Time Series Regression with ARIMA Noise, Missing Observations, and Outliers ) programs. For this purpose, energy purchase data from three Karun river hydropower plants (Khuzestan Province, Iran) was used.

Suggested Citation

  • Arash Jamalmanesh & Mahdi Khodaparast Mashhadi & Ahmad Seifi & Mohammad Ali Falahi, 2018. "Prediction of Hydropower Energy Price Using G mes-Maravall Seasonal Model," International Journal of Energy Economics and Policy, Econjournals, vol. 8(2), pages 81-88.
    • Handle: RePEc:eco:journ2:2018-02-10
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    References listed on IDEAS

    as
    1. Bell, William R & Hillmer, Steven C, 1984. "Issues Involved with the Seasonal Adjustment of Time Series: Reply," Journal of Business & Economic Statistics, American Statistical Association, vol. 2(4), pages 343-349, October.
    2. Darbellay, Georges A. & Slama, Marek, 2000. "Forecasting the short-term demand for electricity: Do neural networks stand a better chance?," International Journal of Forecasting, Elsevier, vol. 16(1), pages 71-83.
    3. Bell, William R & Hillmer, Steven C, 1984. "Issues Involved with the Seasonal Adjustment of Economic Time Series," Journal of Business & Economic Statistics, American Statistical Association, vol. 2(4), pages 291-320, October.
    4. Victor Gómez & Agustin Maravall & Daniel Peña, 1999. "Missing observations in ARIMA models: Skipping strategy versus outlier approach," Working Papers 9701, Banco de España.
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    More about this item

    Keywords

    Electricity Prices; Hydropower; Seasonal G mes-Maravall Model;
    All these keywords.

    JEL classification:

    • Q41 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Demand and Supply; Prices
    • Q43 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy and the Macroeconomy

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