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Artificial Neural Networks for Spot Electricity Price Forecasting: A Review

Author

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  • S. Vijayalakshmi

    (Department of Finance, IBS Hyderabad, IFHE University (a Deemed to-be-University under Section 3 of UGC Act 1956), Hyderabad, Andhra Pradesh, India,)

  • G. P. Girish

    (Department of Finance, IBS Hyderabad, IFHE University (a Deemed to-be-University under Section 3 of UGC Act 1956), Hyderabad, Andhra Pradesh, India)

Abstract

In this study we review literature related to short-term forecasting of spot electricity prices using artificial neural networks (ANN) in deregulated competitive power markets. With accurate price forecasts, power market participants can maximize their profits and meet their power commitments using a proper combination of power purchase agreements, bilateral trade and buying/selling electricity through power exchanges in a judicious, efficient and effective manner. ANN models may truly be an answer to short-term electricity spot price forecasting viz. time-series econometric models.

Suggested Citation

  • S. Vijayalakshmi & G. P. Girish, 2015. "Artificial Neural Networks for Spot Electricity Price Forecasting: A Review," International Journal of Energy Economics and Policy, Econjournals, vol. 5(4), pages 1092-1097.
  • Handle: RePEc:eco:journ2:2015-04-22
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    References listed on IDEAS

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    1. Guthrie, Graeme & Videbeck, Steen, 2007. "Electricity spot price dynamics: Beyond financial models," Energy Policy, Elsevier, vol. 35(11), pages 5614-5621, November.
    2. Karakatsani, Nektaria V. & Bunn, Derek W., 2008. "Forecasting electricity prices: The impact of fundamentals and time-varying coefficients," International Journal of Forecasting, Elsevier, vol. 24(4), pages 764-785.
    3. G. P. Girish & P. Sashikala & Bharath Supra & Anitha Acharya, 2015. "Renewable Energy Certifi cate Trading through Power Exchanges in India," International Journal of Energy Economics and Policy, Econjournals, vol. 5(3), pages 805-808.
    4. Crespo Cuaresma, Jesús & Hlouskova, Jaroslava & Kossmeier, Stephan & Obersteiner, Michael, 2004. "Forecasting electricity spot-prices using linear univariate time-series models," Applied Energy, Elsevier, vol. 77(1), pages 87-106, January.
    5. Bowden, Nicholas & Payne, James E., 2008. "Short term forecasting of electricity prices for MISO hubs: Evidence from ARIMA-EGARCH models," Energy Economics, Elsevier, vol. 30(6), pages 3186-3197, November.
    6. Knittel, Christopher R. & Roberts, Michael R., 2005. "An empirical examination of restructured electricity prices," Energy Economics, Elsevier, vol. 27(5), pages 791-817, September.
    7. Misiorek Adam & Trueck Stefan & Weron Rafal, 2006. "Point and Interval Forecasting of Spot Electricity Prices: Linear vs. Non-Linear Time Series Models," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 10(3), pages 1-36, September.
    8. Weron, Rafal & Misiorek, Adam, 2008. "Forecasting spot electricity prices: A comparison of parametric and semiparametric time series models," International Journal of Forecasting, Elsevier, vol. 24(4), pages 744-763.
    9. Rafal Weron & Adam Misiorek, 2005. "Forecasting Spot Electricity Prices With Time Series Models," Econometrics 0504001, University Library of Munich, Germany.
    10. Hickey, Emily & Loomis, David G. & Mohammadi, Hassan, 2012. "Forecasting hourly electricity prices using ARMAX–GARCH models: An application to MISO hubs," Energy Economics, Elsevier, vol. 34(1), pages 307-315.
    11. G. P. Girish & S. Vijayalakshmi, 2015. "Role of Energy Exchanges for Power Trading in India," International Journal of Energy Economics and Policy, Econjournals, vol. 5(3), pages 673-676.
    12. Rafal Weron, 2006. "Modeling and Forecasting Electricity Loads and Prices: A Statistical Approach," HSC Books, Hugo Steinhaus Center, Wroclaw University of Technology, number hsbook0601.
    13. Rafal Weron & Adam Misiorek, 2006. "Short-term electricity price forecasting with time series models: A review and evaluation," HSC Research Reports HSC/06/01, Hugo Steinhaus Center, Wroclaw University of Technology.
    14. Apostolos Serletis & Akbar Shahmoradi, 2007. "Measuring and Testing Natural Gas and Electricity Markets Volatility: Evidence from Alberta's Deregulated Markets," World Scientific Book Chapters,in: Quantitative And Empirical Analysis Of Energy Markets, chapter 16, pages 205-220 World Scientific Publishing Co. Pte. Ltd..
    15. Kristiansen, Tarjei, 2012. "Forecasting Nord Pool day-ahead prices with an autoregressive model," Energy Policy, Elsevier, vol. 49(C), pages 328-332.
    16. Weron, Rafał, 2014. "Electricity price forecasting: A review of the state-of-the-art with a look into the future," International Journal of Forecasting, Elsevier, vol. 30(4), pages 1030-1081.
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    Cited by:

    1. repec:eee:eneeco:v:66:y:2017:i:c:p:228-237 is not listed on IDEAS
    2. G P Girish & Aviral Kumar Tiwari, 2016. "A comparison of different univariate forecasting models forSpot Electricity Price in India," Economics Bulletin, AccessEcon, vol. 36(2), pages 1039-1057.

    More about this item

    Keywords

    Artificial Neural Networks; Spot Electricity; Short-term; Forecasting; Power Exchange; Review;

    JEL classification:

    • C01 - Mathematical and Quantitative Methods - - General - - - Econometrics
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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