Proceedings of the 2016 International Conference on Computer Engineering and Information Systems

Short Term Load Forecasting Using Core Vector Regression Trained with Particle Swarm Optimization

Authors
Xin Sun, Xin Zhang
Corresponding Author
Xin Sun
Available Online November 2016.
DOI
https://doi.org/10.2991/ceis-16.2016.60How to use a DOI?
Keywords
short term load forecasting; core vector regression; PSO; kernel parameter
Abstract
Short term load forecasting is very essential to the operation of electricity companies. In this paper, we propose a new method for short term load forecasting trained by PSO and Core Vector Regression (CVR). The CVR algorithm extend Core Vector Machine algorithm to the regression setting by generalizing the underlying minimum enclosing ball problem. In this paper, we use particle swarm optimization (PSO) to optimize the parameters of the CVR. Experiments show that the PSO optimized method has comparable performance with SVR (Support Vector Regression), but is much faster and produces much fewer support vectors on very large data sets.
Open Access
This is an open access article distributed under the CC BY-NC license.

Download article (PDF)

Proceedings
2016 International Conference on Computer Engineering and Information Systems
Part of series
Advances in Computer Science Research
Publication Date
November 2016
ISBN
978-94-6252-283-1
DOI
https://doi.org/10.2991/ceis-16.2016.60How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Xin Sun
AU  - Xin Zhang
PY  - 2016/11
DA  - 2016/11
TI  - Short Term Load Forecasting Using Core Vector Regression Trained with Particle Swarm Optimization
BT  - 2016 International Conference on Computer Engineering and Information Systems
PB  - Atlantis Press
UR  - https://doi.org/10.2991/ceis-16.2016.60
DO  - https://doi.org/10.2991/ceis-16.2016.60
ID  - Sun2016/11
ER  -