IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i1p293-d716239.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/1/293/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/15/1/293/
    Download Restriction: no
    ---><---

    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ragab El-Sehiemy & Abdallah Elsayed & Abdullah Shaheen & Ehab Elattar & Ahmed Ginidi, 2021. "Scheduling of Generation Stations, OLTC Substation Transformers and VAR Sources for Sustainable Power System Operation Using SNS Optimizer," Sustainability, MDPI, vol. 13(21), pages 1-24, October.
    2. Wei Li & Tengfei Zhu & Xiaoyu Li & Jianzhang Dong & Jun Liu, 2022. "Recommending Advanced Deep Learning Models for Efficient Insect Pest Detection," Agriculture, MDPI, vol. 12(7), pages 1-17, July.
    3. Mete Şeref Ahunbay & Martin Bichler & Johannes Knörr, 2023. "Challenges in Designing Electricity Spot Markets," NBER Chapters, in: New Directions in Market Design, National Bureau of Economic Research, Inc.
    4. Ning Ren & Xiufan Zhang & Decheng Fan, 2022. "Influencing Factors and Realization Path of Power Decarbonization—Based on Panel Data Analysis of 30 Provinces in China from 2011 to 2019," IJERPH, MDPI, vol. 19(23), pages 1-24, November.
    5. Yixin Huang & Xinyi Liu & Zhi Zhang & Li Yang & Zhenzhi Lin & Yangqing Dan & Ke Sun & Zhou Lan & Keping Zhu, 2020. "Multi-Stage Transmission Network Planning Considering Transmission Congestion in the Power Market," Energies, MDPI, vol. 13(18), pages 1-22, September.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:15:y:2022:i:1:p:293-:d:716239. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.