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Dynamic Hybrid Model for Short-Term Electricity Price Forecasting

Author

Listed:
  • Marin Cerjan

    (HEP Trgovina d.o.o., Ulica grada Vukovara 37, Zagreb HR-10000, Croatia)

  • Marin Matijaš

    (Petrol d.o.o., Oreškovićeva 6/h, Zagreb HR-10010, Croatia)

  • Marko Delimar

    (University of Zagreb, Faculty of Electrical Engineering and Computing, Unska 3, Zagreb HR-10000, Croatia)

Abstract

Accurate forecasting tools are essential in the operation of electric power systems, especially in deregulated electricity markets. Electricity price forecasting is necessary for all market participants to optimize their portfolios. In this paper we propose a hybrid method approach for short-term hourly electricity price forecasting. The paper combines statistical techniques for pre-processing of data and a multi-layer (MLP) neural network for forecasting electricity price and price spike detection. Based on statistical analysis, days are arranged into several categories. Similar days are examined by correlation significance of the historical data. Factors impacting the electricity price forecasting, including historical price factors, load factors and wind production factors are discussed. A price spike index (CWI) is defined for spike detection and forecasting. Using proposed approach we created several forecasting models of diverse model complexity. The method is validated using the European Energy Exchange (EEX) electricity price data records. Finally, results are discussed with respect to price volatility, with emphasis on the price forecasting accuracy.

Suggested Citation

  • Marin Cerjan & Marin Matijaš & Marko Delimar, 2014. "Dynamic Hybrid Model for Short-Term Electricity Price Forecasting," Energies, MDPI, vol. 7(5), pages 1-15, May.
    • Handle: RePEc:gam:jeners:v:7:y:2014:i:5:p:3304-3318:d:36241
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    References listed on IDEAS

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    1. Huisman, Ronald & Huurman, Christian & Mahieu, Ronald, 2007. "Hourly electricity prices in day-ahead markets," Energy Economics, Elsevier, vol. 29(2), pages 240-248, March.
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    Cited by:

    1. Lucheng Hong & Wantao Shu & Angela C. Chao, 2018. "Recurrence Interval Analysis on Electricity Consumption of an Office Building in China," Sustainability, MDPI, vol. 10(2), pages 1-15, January.
    2. Marin Cerjan & Ana Petričić & Marko Delimar, 2019. "HIRA Model for Short-Term Electricity Price Forecasting," Energies, MDPI, vol. 12(3), pages 1-32, February.
    3. Zoran Gligorić & Svetlana Štrbac Savić & Aleksandra Grujić & Milanka Negovanović & Omer Musić, 2018. "Short-Term Electricity Price Forecasting Model Using Interval-Valued Autoregressive Process," Energies, MDPI, vol. 11(7), pages 1-17, July.
    4. Ricardo Faia & Tiago Pinto & Zita Vale & Juan Manuel Corchado, 2017. "An Ad-Hoc Initial Solution Heuristic for Metaheuristic Optimization of Energy Market Participation Portfolios," Energies, MDPI, vol. 10(7), pages 1-18, June.
    5. Otilia Elena Dragomir & Florin Dragomir & Veronica Stefan & Eugenia Minca, 2015. "Adaptive Neuro-Fuzzy Inference Systems as a Strategy for Predicting and Controling the Energy Produced from Renewable Sources," Energies, MDPI, vol. 8(11), pages 1-15, November.
    6. Agrawal, Rahul Kumar & Muchahary, Frankle & Tripathi, Madan Mohan, 2019. "Ensemble of relevance vector machines and boosted trees for electricity price forecasting," Applied Energy, Elsevier, vol. 250(C), pages 540-548.
    7. Simon Pezzutto & Gianluca Grilli & Stefano Zambotti & Stefan Dunjic, 2018. "Forecasting Electricity Market Price for End Users in EU28 until 2020—Main Factors of Influence," Energies, MDPI, vol. 11(6), pages 1-18, June.
    8. Dimitrios Kontogiannis & Dimitrios Bargiotas & Aspassia Daskalopulu & Athanasios Ioannis Arvanitidis & Lefteri H. Tsoukalas, 2022. "Error Compensation Enhanced Day-Ahead Electricity Price Forecasting," Energies, MDPI, vol. 15(4), pages 1-21, February.
    9. Ping Jiang & Feng Liu & Yiliao Song, 2016. "A Hybrid Multi-Step Model for Forecasting Day-Ahead Electricity Price Based on Optimization, Fuzzy Logic and Model Selection," Energies, MDPI, vol. 9(8), pages 1-27, August.
    10. Claudio Monteiro & L. Alfredo Fernandez-Jimenez & Ignacio J. Ramirez-Rosado, 2015. "Explanatory Information Analysis for Day-Ahead Price Forecasting in the Iberian Electricity Market," Energies, MDPI, vol. 8(9), pages 1-23, September.
    11. Leehter Yao & Fazida Hanim Hashim & Chien-Chi Lai, 2020. "Dynamic Residential Energy Management for Real-Time Pricing," Energies, MDPI, vol. 13(10), pages 1-15, May.
    12. Chuntian Cheng & Bin Luo & Shumin Miao & Xinyu Wu, 2016. "Mid-Term Electricity Market Clearing Price Forecasting with Sparse Data: A Case in Newly-Reformed Yunnan Electricity Market," Energies, MDPI, vol. 9(10), pages 1-22, October.
    13. Antonio Bello & Javier Reneses & Antonio Muñoz, 2016. "Medium-Term Probabilistic Forecasting of Extremely Low Prices in Electricity Markets: Application to the Spanish Case," Energies, MDPI, vol. 9(3), pages 1-27, March.
    14. Chi Zhang & Zhengning Pu & Jiasha Fu, 2018. "The Recurrence Interval Difference of Power Load in Heavy/Light Industries of China," Energies, MDPI, vol. 11(1), pages 1-20, January.
    15. Feng, Qianqian & Sun, Xiaolei & Hao, Jun & Li, Jianping, 2021. "Predictability dynamics of multifactor-influenced installed capacity: A perspective of country clustering," Energy, Elsevier, vol. 214(C).
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