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- W2892965516 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." @default.
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- W2892965516 date "2018-06-01" @default.
- W2892965516 modified "2023-09-30" @default.
- W2892965516 title "A Hybrid Model Based on Symbolic Regression and Neural Networks for Electricity Load Forecasting" @default.
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- W2892965516 doi "https://doi.org/10.1109/eem.2018.8469901" @default.
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