Título A hybrid model based on symbolic regression and neural networks for electricity load forecasting
Autores Dimoulkas I. , Herre L. , Khastieva D. , Nycander E. , Amelin M. , MAZIDI, PEYMAN
Publicación externa No
Medio International Conference on the European Energy Market
Alcance Conference Paper
Naturaleza Científica
Web https://www.scopus.com/inward/record.uri?eid=2-s2.0-85055476841&doi=10.1109%2fEEM.2018.8469901&partnerID=40&md5=bbf1a461d5a326e596c2b220b6008f02
Fecha de publicacion 01/01/2018
ISI 000482771100114
Scopus Id 2-s2.0-85055476841
DOI 10.1109/EEM.2018.8469901
Abstract This paper proposes a hybrid model for electricity load forecasting. Symbolic regression is initially used to automatically create a regression model of the load. Then the explanatory variables and their transformations that have been selected in the model are used as input in an artificial neural network that is trained to predict the electricity load at the output. Therefore symbolic regression operates as a feature selection-creation method and forecasting is done by the artificial neural network. The proposed hybrid model has been successfully used in an electricity load forecasting competition. © 2018 IEEE.
Palabras clave Commerce; Electric power plant loads; Forecasting; Neural networks; Power markets; Regression analysis; Electricity load; Electricity load forecasting; Explanatory variables; Hybrid model; Load forecasting; Regression model; Symbolic regression; Electric load forecasting
Miembros de la Universidad Loyola

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