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

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

• Autores: Moreno Chuquen, Ricardo; Chamorro, Harold R.; Riquelme Domínguez, José Miguel; González Longatt, Francisco; Cantillo Luna, Sergio Alejandro
• Tipo de recurso: Article of journal
• Fecha de publicación: 2022
• Institución: Universidad Autónoma de Occidente
• Repositorio: RED: Repositorio Educativo Digital UAO
• Idioma: eng
• OAI Identifier: oai:red.uao.edu.co:10614/14654
• Acceso en línea: https://hdl.handle.net/10614/14654; https://red.uao.edu.co/
• Palabra clave: Aprendizaje profundo (Aprendizaje automático); Deep learning (Machine learning); Electricity markets; Llocational marginal price (LMP); Machine learning; Multi-output regression
• Rights: openAccess
• License: Derechos Reservados Revista Energies MDPI