  Electricity consumption forecasting has vital importance for the energy
planning of a country. Of the enabling machine learning models, support vector
regression (SVR) has been widely used to set up forecasting models due to its
superior generalization for unseen data. However, one key procedure for the
predictive modeling is feature selection, which might hurt the prediction
accuracy if improper features were selected. In this regard, a modified
discrete particle swarm optimization (MDPSO) was employed for feature selection
in this study, and then MDPSO-SVR hybrid mode was built to predict future
electricity consumption. Compared with other well-established counterparts,
MDPSO-SVR model consistently performs best in two real-world electricity
consumption datasets, which indicates that MDPSO for feature selection can
improve the prediction accuracy and the SVR equipped with the MDPSO can be a
promised alternative for electricity consumption forecasting.
