Short Term Load Forecasting Using Core Vector Regression Trained with Particle Swarm Optimization
- Xin Sun, Xin Zhang
- Corresponding Author
- Xin Sun
Available Online November 2016.
- https://doi.org/10.2991/ceis-16.2016.60How to use a DOI?
- short term load forecasting; core vector regression; PSO; kernel parameter
- 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.
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 SN - 2352-538X UR - https://doi.org/10.2991/ceis-16.2016.60 DO - https://doi.org/10.2991/ceis-16.2016.60 ID - Sun2016/11 ER -