Energy consumption forecasting in process industry using support vector machines and particle swarm optimization

Authors:
Milena R. Petković;Milan R. Rapaić;Boris B. Jakovljević
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:
MMACTEE'09 Proceedings of the 11th WSEAS international conference on Mathematical methods and computational techniques in electrical engineering
Year:
2009

Citing 6
Cited 0

Learning and Soft Computing: Support Vector Machines, Neural Networks, and Fuzzy Logic Models

Learning and Soft Computing: Support Vector Machines, Neural Networks, and Fuzzy Logic Models
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond

Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
A tutorial on support vector regression

Statistics and Computing
Response modeling with support vector machines

Expert Systems with Applications: An International Journal
Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients

IEEE Transactions on Evolutionary Computation
System reliability forecasting by support vector machines with genetic algorithms

Mathematical and Computer Modelling: An International Journal

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, Support Vector Machines (SVMs) are applied in predicting energy consumption in the first phase 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 parameters, widths of radial basis functions to be exact. Incorporation of PSO into SVM training process has greatly enhanced the quality of prediction.