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Application of ARIMA Modelling for the Forecasting of Solar, Wind, Spot and Options Electricity Prices: The Australian National Electricity Market

Author

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  • Yasir Alsaedi

    (Department of Mathemati cs, Umm Al-Qura University, Makkah, Saudi Arabia,)

  • Gurudeo Anand Tularam

    (Environmental Futures Research Institute, Griffith University, Australia,)

  • Victor Wong

    (Department of Accounting, Finance and Economics, Griffith University, Australia.)

Abstract

This study aims to develop autoregressive integrated moving average (ARIMA) models to predict the solar, wind, spot and options pricing over the next two years, with historical data being used in a univariate manner to understand market behaviour in terms of trends. The assessment is made in the context of the Australian National Electricity Market (ANEM). The ARIMA models predict the future values of the monthly solar, wind, spot and options prices for various Australian states using time-series data from January 2006 to March 2018. The results show increases from 30.46 40.42% for the spot electricity prices and from 14.80 15.13% for the options electricity prices in the ANEM with a two-year horizon. The results further show that wind prices are expected to increase by an average of 5.43%. However, the results also show that the average solar electricity prices will decrease by 67.7%.

Suggested Citation

  • Yasir Alsaedi & Gurudeo Anand Tularam & Victor Wong, 2019. "Application of ARIMA Modelling for the Forecasting of Solar, Wind, Spot and Options Electricity Prices: The Australian National Electricity Market," International Journal of Energy Economics and Policy, Econjournals, vol. 9(4), pages 263-272.
    • Handle: RePEc:eco:journ2:2019-04-33
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    Cited by:

    1. Renato Fernandes & Isabel Soares, 2022. "Reviewing Explanatory Methodologies of Electricity Markets: An Application to the Iberian Market," Energies, MDPI, vol. 15(14), pages 1-17, July.
    2. Yasir Alsaedi & Gurudeo Anand Tularam & Victor Wong, 2021. "Impact of the Nature of Energy Management and Responses to Policies Regarding Solar and Wind Pricing: A Qualitative Study of the Australian Electricity Markets," International Journal of Energy Economics and Policy, Econjournals, vol. 11(3), pages 191-205.
    3. Sylvia Mardiana & Ferdinand Saragih & Martani Huseini, 2020. "Forecasting Gasoline Demand in Indonesia Using Time Series," International Journal of Energy Economics and Policy, Econjournals, vol. 10(6), pages 132-145.
    4. Yasir Alsaedi & Gurudeo Anand Tularam & Victor Wong, 2020. "Impact of Solar and Wind Prices on the Integrated Global Electricity Spot and Options Markets: A Time Series Analysis," International Journal of Energy Economics and Policy, Econjournals, vol. 10(2), pages 337-353.

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    More about this item

    Keywords

    Electricity pricing; ARIMA model; Forecasting.;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications
    • Q47 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy Forecasting

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