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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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    Cited by:

    1. Mirakyan, Atom & Meyer-Renschhausen, Martin & Koch, Andreas, 2017. "Composite forecasting approach, application for next-day electricity price forecasting," Energy Economics, Elsevier, vol. 66(C), pages 228-237.
    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.
    3. Qiao, Weibiao & Yang, Zhe, 2020. "Forecast the electricity price of U.S. using a wavelet transform-based hybrid model," Energy, Elsevier, vol. 193(C).
    4. Richter, Lucas & Lehna, Malte & Marchand, Sophie & Scholz, Christoph & Dreher, Alexander & Klaiber, Stefan & Lenk, Steve, 2022. "Artificial Intelligence for Electricity Supply Chain automation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 163(C).

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    More about this item

    Keywords

    Artificial Neural Networks; Spot Electricity; Short-term; Forecasting; Power Exchange; Review;
    All these keywords.

    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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