| Title | A hybrid model based on symbolic regression and neural networks for electricity load forecasting |
|---|---|
| Authors | Dimoulkas I. , Herre L. , Khastieva D. , Nycander E. , Amelin M. , MAZIDI, PEYMAN |
| External publication | No |
| Means | Int. Conf. European Energy Market, EEM |
| Scope | Conference Paper |
| Nature | Científica |
| Web | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85055476841&doi=10.1109%2fEEM.2018.8469901&partnerID=40&md5=bbf1a461d5a326e596c2b220b6008f02 |
| Publication date | 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. |
| Keywords | 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 |
| Universidad Loyola members |
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