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Locational Marginal Price Forecasting Using SVR-Based Multi-Output Regression in Electricity Markets

Author

Listed:
  • Sergio Cantillo-Luna

    (Faculty of Engineering, Universidad Autónoma de Occidente, Cali 760030, Colombia
    These authors contributed equally to this work.)

  • Ricardo Moreno-Chuquen

    (Faculty of Engineering, Universidad Autónoma de Occidente, Cali 760030, Colombia
    These authors contributed equally to this work.)

  • Harold R. Chamorro

    (Department of Electrical Engineering, KTH, Royal Institute of Technology, 11428 Stockholm, Sweden)

  • Jose Miguel Riquelme-Dominguez

    (Department of Electrical Engineering, Escuela Tecnica Superior de Ingenieros Industriales, Universidad Politecnica de Madrid, 28006 Madrid, Spain)

  • Francisco Gonzalez-Longatt

    (Department of Electrical Engineering, Information Technology and Cybernetics, University of South-Eastern Norway, 3918 Porsgrunn, Norway)

Abstract

Electricity markets provide valuable data for regulators, operators, and investors. The use of machine learning methods for electricity market data could provide new insights about the market, and this information could be used for decision-making. This paper proposes a tool based on multi-output regression method using support vector machines (SVR) for LMP forecasting. The input corresponds to the active power load of each bus, in this case obtained through Monte Carlo simulations, in order to forecast LMPs. The LMPs provide market signals for investors and regulators. The results showed the high performance of the proposed model, since the average prediction error for fitting and testing datasets of the proposed method on the dataset was less than 1%. This provides insights into the application of machine learning method for electricity markets given the context of uncertainty and volatility for either real-time and ahead markets.

Suggested Citation

  • Sergio Cantillo-Luna & Ricardo Moreno-Chuquen & Harold R. Chamorro & Jose Miguel Riquelme-Dominguez & Francisco Gonzalez-Longatt, 2022. "Locational Marginal Price Forecasting Using SVR-Based Multi-Output Regression in Electricity Markets," Energies, MDPI, vol. 15(1), pages 1-14, January.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:1:p:293-:d:716239
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    References listed on IDEAS

    as
    1. Mingxing Wu & Zhilin Lu & Qing Chen & Tao Zhu & En Lu & Wentian Lu & Mingbo Liu, 2020. "A Two-Stage Algorithm of Locational Marginal Price Calculation Subject to Carbon Emission Allowance," Energies, MDPI, vol. 13(10), pages 1-20, May.
    2. Diego Larrahondo & Ricardo Moreno & Harold R. Chamorro & Francisco Gonzalez-Longatt, 2021. "Comparative Performance of Multi-Period ACOPF and Multi-Period DCOPF under High Integration of Wind Power," Energies, MDPI, vol. 14(15), pages 1-15, July.
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    Cited by:

    1. Jun Dong & Xihao Dou & Aruhan Bao & Yaoyu Zhang & Dongran Liu, 2022. "Day-Ahead Spot Market Price Forecast Based on a Hybrid Extreme Learning Machine Technique: A Case Study in China," Sustainability, MDPI, vol. 14(13), pages 1-24, June.
    2. Sergio Cantillo-Luna & Ricardo Moreno-Chuquen & David Celeita & George Anders, 2023. "Deep and Machine Learning Models to Forecast Photovoltaic Power Generation," Energies, MDPI, vol. 16(10), pages 1-24, May.

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