Locational marginal price forecasting using svr-based multi-output regression in electricity markets

dc.creatorMoreno Chuquen, Ricardo
dc.creatorChamorro, Harold R.
dc.creatorRiquelme Domínguez, José Miguel
dc.creatorGonzález Longatt, Francisco
dc.creatorCantillo Luna, Cantillo Luna, Sergio Sergio
dc.date.accessioned2023-04-10T20:30:40Z
dc.date.accessioned2023-06-06T14:28:03Z
dc.date.available2023-04-10T20:30:40Z
dc.date.available2023-06-06T14:28:03Z
dc.date.created2023-04-10T20:30:40Z
dc.date.issued2022
dc.identifier1996-1073
dc.identifierhttps://hdl.handle.net/10614/14654
dc.identifierUniversidad Autónoma de Occidente
dc.identifierRepositorio Educativo Digital
dc.identifierhttps://red.uao.edu.co/
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/6649398
dc.description.abstractElectricity 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
dc.languageeng
dc.publisherMDPI
dc.publisherBasel
dc.relation14
dc.relation1
dc.relation1
dc.relation15
dc.relationCantillo Luna, S., Moreno Chuquen, R., Chamorro, H.R., Riquelme Domínguez, J.M., González Longatt, F. (2022). Locational marginal price forecasting using svr-based multi-output regression in electricity markets. Energies 15 (1), pp. 1-14.
dc.relationEnergies
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dc.rightshttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rightsAtribución-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0)
dc.rightsDerechos Reservados Revista Energies MDPI
dc.rightsDerechos reservados - MDPI, 2022
dc.titleLocational marginal price forecasting using svr-based multi-output regression in electricity markets
dc.typeArtículo de revista


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