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Artificial neural network and SARIMA based models for power load forecasting in Turkish electricity market

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  • Ömer Özgür Bozkurt
  • Göksel Biricik
  • Ziya Cihan Tayşi

Abstract

Load information plays an important role in deregulated electricity markets, since it is the primary factor to make critical decisions on production planning, day-to-day operations, unit commitment and economic dispatch. Being able to predict the load for a short term, which covers one hour to a few days, equips power generation facilities and traders with an advantage. With the deregulation of electricity markets, a variety of short term load forecasting models are developed. Deregulation in Turkish Electricity Market has started in 2001 and liberalization is still in progress with rules being effective in its predefined schedule. However, there is a very limited number of studies for Turkish Market. In this study, we introduce two different models for current Turkish Market using Seasonal Autoregressive Integrated Moving Average (SARIMA) and Artificial Neural Network (ANN) and present their comparative performances. Building models that cope with the dynamic nature of deregulated market and are able to run in real-time is the main contribution of this study. We also use our ANN based model to evaluate the effect of several factors, which are claimed to have effect on electrical load.

Suggested Citation

  • Ömer Özgür Bozkurt & Göksel Biricik & Ziya Cihan Tayşi, 2017. "Artificial neural network and SARIMA based models for power load forecasting in Turkish electricity market," PLOS ONE, Public Library of Science, vol. 12(4), pages 1-24, April.
    • Handle: RePEc:plo:pone00:0175915
    DOI: 10.1371/journal.pone.0175915
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    References listed on IDEAS

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    1. Hahn, Heiko & Meyer-Nieberg, Silja & Pickl, Stefan, 2009. "Electric load forecasting methods: Tools for decision making," European Journal of Operational Research, Elsevier, vol. 199(3), pages 902-907, December.
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    2. Davut Solyali, 2020. "A Comparative Analysis of Machine Learning Approaches for Short-/Long-Term Electricity Load Forecasting in Cyprus," Sustainability, MDPI, vol. 12(9), pages 1-34, April.
    3. Niu, Dongxiao & Yu, Min & Sun, Lijie & Gao, Tian & Wang, Keke, 2022. "Short-term multi-energy load forecasting for integrated energy systems based on CNN-BiGRU optimized by attention mechanism," Applied Energy, Elsevier, vol. 313(C).
    4. Petropoulos, Fotios & Apiletti, Daniele & Assimakopoulos, Vassilios & Babai, Mohamed Zied & Barrow, Devon K. & Ben Taieb, Souhaib & Bergmeir, Christoph & Bessa, Ricardo J. & Bijak, Jakub & Boylan, Joh, 2022. "Forecasting: theory and practice," International Journal of Forecasting, Elsevier, vol. 38(3), pages 705-871.
      • Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
    5. Nyoni, Thabani, 2019. "Modeling and forecasting demand for electricity in Zimbabwe using the Box-Jenkins ARIMA technique," MPRA Paper 96903, University Library of Munich, Germany.
    6. E. V. Balatskii & N. A. Ekimova & M. A. Yurevich, 2019. "Short-Term Inflation Projection Based on Marker Models," Studies on Russian Economic Development, Springer, vol. 30(5), pages 498-506, September.
    7. Gulay, Emrah & Duru, Okan, 2020. "Hybrid modeling in the predictive analytics of energy systems and prices," Applied Energy, Elsevier, vol. 268(C).
    8. Samer Chaaraoui & Matthias Bebber & Stefanie Meilinger & Silvan Rummeny & Thorsten Schneiders & Windmanagda Sawadogo & Harald Kunstmann, 2021. "Day-Ahead Electric Load Forecast for a Ghanaian Health Facility Using Different Algorithms," Energies, MDPI, vol. 14(2), pages 1-22, January.
    9. Lianhui Li & Hongguang Wang, 2018. "A VVWBO-BVO-based GM (1,1) and its parameter optimization by GRA-IGSA integration algorithm for annual power load forecasting," PLOS ONE, Public Library of Science, vol. 13(5), pages 1-20, May.
    10. Siti Aisyah & Arionmaro Asi Simaremare & Didit Adytia & Indra A. Aditya & Andry Alamsyah, 2022. "Exploratory Weather Data Analysis for Electricity Load Forecasting Using SVM and GRNN, Case Study in Bali, Indonesia," Energies, MDPI, vol. 15(10), pages 1-17, May.
    11. Yang, Yandong & Li, Shufang & Li, Wenqi & Qu, Meijun, 2018. "Power load probability density forecasting using Gaussian process quantile regression," Applied Energy, Elsevier, vol. 213(C), pages 499-509.
    12. Damilola Elizabeth Babatunde & Ambrose Anozie & James Omoleye, 2020. "Artificial Neural Network and its Applications in the Energy Sector An Overview," International Journal of Energy Economics and Policy, Econjournals, vol. 10(2), pages 250-264.
    13. Jun-Lin Lin & Yiqing Zhang & Kunhuang Zhu & Binbin Chen & Feng Zhang, 2020. "Asymmetric Loss Functions for Contract Capacity Optimization," Energies, MDPI, vol. 13(12), pages 1-13, June.

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