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

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.

Multivariate Empirical Mode Decomposition based Hybrid Model for Day-ahead Peak Load Forecasting

Accurate day-ahead peak load forecasting is crucial not only for power dispatching but also has a great interest to investors and energy policy maker as well as government. Literature reveals that 1% error drop of forecast can reduce 10 million pounds operational cost. Thus, this study proposed a novel hybrid predictive model built upon multivariate empirical mode decomposition (MEMD) and support vector regression (SVR) with parameters optimized by particle swarm optimization (PSO), which is able to capture precise electricity peak load. The novelty of this study mainly comes from the application of MEMD, which enables the multivariate data decomposition to effectively extract inherent information among relevant variables at different time frequency during the deterioration of multivariate over time. Two real-world load data sets from the New South Wales (NSW) and the Victoria (VIC) in Australia have been considered to verify the superiority of the proposed MEMD-PSO-SVR hybrid model. The quantitative and comprehensive assessments are performed, and the results indicate that the proposed MEMD-PSO-SVR method is a promising alternative for day-ahead electricity peak load forecasting.