Electrical energy consumption forecasting in oil refining industry using support vector machines and particle swarm optimization

Authors:
Milena R. Petkovic;Milan R. Rapaic;Boris B. Jakovljevic
Affiliations:
Computing and Control Department, University of Novi Sad, Serbia;Computing and Control Department, University of Novi Sad, Serbia;Computing and Control Department, University of Novi Sad, Serbia
Venue:
WSEAS Transactions on Information Science and Applications
Year:
2009

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Abstract

In this paper, Support Vector Machines (SVMs) are applied in predicting electrical energy consumption in the atmospheric distillation of oil refining at a particular oil refinery. During cross-validation process of the SVM training Particle Swarm Optimization (PSO) algorithm was utilized in selection of free SVM kernel parameters. Incorporation of PSO into SVM training process has greatly enhanced the quality of prediction. Furthermore, various (different) kernel functions were used and optimized in the process of forming the SVM models.