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Enhanced Forecasting Approach for Electricity Market Prices and Wind Power Data Series in the Short-Term

Author

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  • Gerardo J. Osório

    (C-MAST, University of Beira Interior, Covilhã 6201-001, Portugal)

  • Jorge N. D. L. Gonçalves

    (INESC TEC and the Faculty of Engineering of the University of Porto, Porto 4200-465, Portugal)

  • Juan M. Lujano-Rojas

    (C-MAST, University of Beira Interior, Covilhã 6201-001, Portugal
    INESC-ID, Instituto Superior Técnico, University of Lisbon, Lisbon 1049-001, Portugal)

  • João P. S. Catalão

    (C-MAST, University of Beira Interior, Covilhã 6201-001, Portugal
    INESC TEC and the Faculty of Engineering of the University of Porto, Porto 4200-465, Portugal
    INESC-ID, Instituto Superior Técnico, University of Lisbon, Lisbon 1049-001, Portugal)

Abstract

The uncertainty and variability in electricity market price (EMP) signals and players’ behavior, as well as in renewable power generation, especially wind power, pose considerable challenges. Hence, enhancement of forecasting approaches is required for all electricity market players to deal with the non-stationary and stochastic nature of such time series, making it possible to accurately support their decisions in a competitive environment with lower forecasting error and with an acceptable computational time. As previously published methodologies have shown, hybrid approaches are good candidates to overcome most of the previous concerns about time-series forecasting. In this sense, this paper proposes an enhanced hybrid approach composed of an innovative combination of wavelet transform (WT), differential evolutionary particle swarm optimization (DEEPSO), and an adaptive neuro-fuzzy inference system (ANFIS) to forecast EMP signals in different electricity markets and wind power in Portugal, in the short-term, considering only historical data. Test results are provided by comparing with other reported studies, demonstrating the proficiency of the proposed hybrid approach in a real environment.

Suggested Citation

  • Gerardo J. Osório & Jorge N. D. L. Gonçalves & Juan M. Lujano-Rojas & João P. S. Catalão, 2016. "Enhanced Forecasting Approach for Electricity Market Prices and Wind Power Data Series in the Short-Term," Energies, MDPI, vol. 9(9), pages 1-19, August.
    • Handle: RePEc:gam:jeners:v:9:y:2016:i:9:p:693-:d:77035
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    References listed on IDEAS

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    Cited by:

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    2. Jagienka Rześny-Cieplińska & Agnieszka Szmelter-Jarosz, 2020. "Environmental Sustainability in City Logistics Measures," Energies, MDPI, vol. 13(6), pages 1-29, March.
    3. Ilias G. Marneris & Pandelis N. Biskas & Anastasios G. Bakirtzis, 2017. "Stochastic and Deterministic Unit Commitment Considering Uncertainty and Variability Reserves for High Renewable Integration," Energies, MDPI, vol. 10(1), pages 1-25, January.
    4. Shuai Liu & Zhong-Kai Feng & Wen-Jing Niu & Hai-Rong Zhang & Zhen-Guo Song, 2019. "Peak Operation Problem Solving for Hydropower Reservoirs by Elite-Guide Sine Cosine Algorithm with Gaussian Local Search and Random Mutation," Energies, MDPI, vol. 12(11), pages 1-24, June.

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