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Predicting Power Deviation in the Turkish Power Market Based on Adaptive Factor Impacts

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Artificial Intelligence for Knowledge Management (AI4KM 2021)

Part of the book series: IFIP Advances in Information and Communication Technology ((IFIPAICT,volume 614))

Abstract

Energy market models are generally focused on energy balancing using the optimum energy mix. In countries where the energy markets are not fully liberalised, the State Regulators reflect any cost of being off-balance on the utility companies and this affects the consumers as well. The right short term prediction of the market trends is beneficial both to optimise the physical energy flow and commercial revenue balance for suppliers and utility companies. This study is aimed to predict the sign trends in the power market by selecting the influencing factors adaptive to the conditions of the day ahead, 10 h, 5 h, 2 h and 1 h before the electricity balance is active. There are numerous factors consisting of weather conditions, resource costs, operation costs, renewable energy conditions, regulations, etc. with a considerable impact on the predictions. The contribution of this paper is to choose the factors with the highest impacts using the Genetic Algorithm (GA) with Akaike Information Criteria (AIC), which are then used as input of a Recursive Neural Network (RNN) model for forecasting the deviation trends. The proposed hybrid method does not only reduce the prediction errors but also avoid dependency on expert knowledge. Hence this paper will allow both the market regulator and the suppliers to take precautions based on a confident prediction.

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Author information

Authors and Affiliations

  1. Eurasia Institute of Earth Sciences, Istanbul Technical University, 34467, Istanbul, Turkey

    Denizhan Guven

  2. Axpo Turkey, 34340, Besiktas, Istanbul, Turkey

    Avni Ozozen

  3. Energy Institute, Istanbul Technical University, 34467, Istanbul, Turkey

    Gülgün Kayakutlu & M. Ozgur Kayalica

Authors
  1. Denizhan Guven
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  2. Avni Ozozen
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  3. Gülgün Kayakutlu
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  4. M. Ozgur Kayalica
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Corresponding author

Correspondence to Denizhan Guven .

Editor information

Editors and Affiliations

  1. University of Reims Champagne-Ardenne, Reims, France

    Eunika Mercier-Laurent

  2. Istanbul Technical University, Istanbul, Turkey

    M. Özgür Kayalica

  3. Wroclaw University of Economics and Business, Wroclaw, Poland

    Mieczyslaw Lech Owoc

Appendix

Appendix

Table 3. Potential inputs
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Guven, D., Ozozen, A., Kayakutlu, G., Kayalica, M.O. (2021). Predicting Power Deviation in the Turkish Power Market Based on Adaptive Factor Impacts. In: Mercier-Laurent, E., Kayalica, M.Ö., Owoc, M.L. (eds) Artificial Intelligence for Knowledge Management. AI4KM 2021. IFIP Advances in Information and Communication Technology, vol 614. Springer, Cham. https://doi.org/10.1007/978-3-030-80847-1_14

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  • DOI: https://doi.org/10.1007/978-3-030-80847-1_14

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-80846-4

  • Online ISBN: 978-3-030-80847-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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