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

Sergio Cantillo-Luna, Ricardo Moreno-Chuquen, Harold R. Chamorro, Jose Miguel Riquelme-Dominguez and Francisco Gonzalez-Longatt
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Sergio Cantillo-Luna: Faculty of Engineering, Universidad Autónoma de Occidente, Cali 760030, Colombia
Ricardo Moreno-Chuquen: Faculty of Engineering, Universidad Autónoma de Occidente, Cali 760030, Colombia
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

Energies, 2022, vol. 15, issue 1, 1-14

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.

Keywords: electricity markets; locational marginal price (LMP); machine learning; multi-output regression (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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