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

DOI

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.

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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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  - Proceedings of the 2016 International Conference on Computer Engineering and Information Systems
PB  - Atlantis Press
SP  - 300
EP  - 304
SN  - 2352-538X
UR  - https://doi.org/10.2991/ceis-16.2016.60
DO  - 10.2991/ceis-16.2016.60
ID  - Sun2016/11
ER  -