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Short-term forecasting of prices for the Russian wholesale electricity market based on neural networks

Author

Listed:
  • I. Yu. Zolotova

    (Higher School of Economics)

  • V. V. Dvorkin

    (Higher School of Economics)

Abstract

The article considers the possibility of using neural networks for the short-term forecasting of electricity prices in the day-ahead market (DAM) based on factors strictly determined for the forecast period. A set of six factors has been determined, which allows an hourly forecast of the DAM price to be constructed for a month in each of the four seasons with a high accuracy. The proposed model shows low average errors in forecasting the price for each hour of the month and in turn allows possible significant price deviations to be anticipated.

Suggested Citation

  • I. Yu. Zolotova & V. V. Dvorkin, 2017. "Short-term forecasting of prices for the Russian wholesale electricity market based on neural networks," Studies on Russian Economic Development, Springer, vol. 28(6), pages 608-615, November.
    • Handle: RePEc:spr:sorede:v:28:y:2017:i:6:d:10.1134_s1075700717060144
    DOI: 10.1134/S1075700717060144
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    References listed on IDEAS

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    1. Hyndman, Rob J. & Koehler, Anne B., 2006. "Another look at measures of forecast accuracy," International Journal of Forecasting, Elsevier, vol. 22(4), pages 679-688.
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