Energy consumption forecasting for the nonferrous metallurgy industry using hybrid support vector regression with an adaptive state transition algorithm

Background/introduction: The nonferrous metallurgy industry is a major energy consumer in China, and accurate energy consumption forecasting for the nonferrous metallurgy industry can help government policymakers with energy planning. Methods: For this purpose, a hybrid support vector regression (HSVR) with an adaptive state transition algorithm (ASTA) named ASTA-HSVR is proposed to forecast energy consumption in the nonferrous metallurgy industry. The proposed support vector regression (SVR) model consists of a linear weighting of ϵ-SVR and ν-SVR. The ASTA was developed to optimize the parameters of the HSVR. Results: Two cases of energy consumption from the nonferrous metallurgy industry in China are used to demonstrate the performance of the proposed method. The results indicate that the ASTA-HSVR method is superior to other methods. Conclusions: In this study, a hybrid support vector regression with an adaptive state transition algorithm (ASTA-HSVR) was developed and successfully applied to energy consumption forecasting for the nonferrous metallurgy industry. However, it should be noted that the outliers were not considered in this study. In the future, we expect to extend the ASTA-HSVR method to include energy consumption forecasting problems with outliers.