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Electricity consumption forecasting has vital importance for the energyplanning of a country. Of the enabling machine learning models, support vectorregression (SVR) has been widely used to set up forecasting models due to itssuperior generalization for unseen data. However, one key procedure for thepredictive modeling is feature selection, which might hurt the predictionaccuracy if improper features were selected. In this regard, a modifieddiscrete particle swarm optimization (MDPSO) was employed for feature selectionin this study, and then MDPSO-SVR hybrid mode was built to predict futureelectricity consumption. Compared with other well-established counterparts,MDPSO-SVR model consistently performs best in two real-world electricityconsumption datasets, which indicates that MDPSO for feature selection canimprove the prediction accuracy and the SVR equipped with the MDPSO can be apromised alternative for electricity consumption forecasting.

A novel MDPSO-SVR hybrid model for feature selection in electricity consumption forecasting