Forecast of the demand for hourly electric energy by artificial neural networks

Obtaining an accurate forecast of the energy demand is fundamental to support the several decision processes of the electricity service agents in a country. For market operators, a greater precision in the short-term load forecasting implies a more efficient programming of the electricity generation resources, which means a reduction in costs. In the long term, it constitutes a main indicator for the generation of investment signals for future installed capacity. This research proposes a prognostic model for the demand of electrical energy in Bogota, Colombia at hourly level in a full week, through Artificial Neural Network.

• Autores: Viloria, Amelec; RONCALLO PICHON, ALBERTO DE JESUS; Hernandez-P, Hugo; REDONDO BILBAO, OSMAN ENRIQUE; Pineda, Omar; Vargas, Jesús
• Tipo de recurso: Article of journal
• Fecha de publicación: 2020
• Institución: Corporación Universidad de la Costa
• Repositorio: REDICUC - Repositorio CUC
• Idioma: eng
• OAI Identifier: oai:repositorio.cuc.edu.co:11323/7772
• Acceso en línea: https://hdl.handle.net/11323/7772; https://doi.org/10.1007/978-981-15-3125-5_46; https://repositorio.cuc.edu.co/
• Palabra clave: Forecasting; Electric load; Artificial neural networks
• Rights: openAccess
• License: Attribution-NonCommercial-NoDerivatives 4.0 International