Predicting short-term electricity demand through artificial neural network

Forecasting the consumption of electric power on a daily basis allows considerable money savings for the supplying companies, by reducing the expenses in generation and operation. Therefore, the cost of forecasting errors can be of such magnitude that many studies have focused on minimizing the fore...

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Autores:
Viloria, Amelec
García Guliany, Jesús
Varela Izquierdo, Noel
Pineda, Omar
Hernández Palma, Hugo
Valero, Lesbia
Marín-González, Freddy
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:11323/7752
Acceso en línea:
https://hdl.handle.net/11323/7752
https://doi.org/10.1007/978-981-15-2612-1_14
https://repositorio.cuc.edu.co/
Palabra clave:
Primary feeder
Demand short-term electricity prognosis
Neural networks
Forecast accuracy
Rights
openAccess
License
Attribution-NonCommercial-NoDerivatives 4.0 International