A Short-term Load Forecasting Based on Support Vector Regression
- 10.2991/meic-15.2015.240How to use a DOI?
- Load Forecasting; Support Vector Machine; Machine Learning; Telecommunication; Regression Model
Load forecasting has become a significant part in national power system strategy management. In this paper, the Support Vector Regression (SVR) for Short-term Load Forecasting (STLF) is presented to predict the primacy of the industry power in the electricity composition. The Support Vector Machine (SVM) is introduced to learn a regression model from training samples with relaxation factors. Our experimental data come from a real-time data acquisition system, which is running for industrial users in a city of Eastern China. As input to the regression model, the feature vector of training samples combines meteorological factors with power system data collected from meters. In order to study the effect of different kernel functions on the accuracy of prediction, this paper respectively tests the linear, polynomial kernel function and Radial Basis Function (RBF). We evaluate the method with two types of predictions, discrete prediction of random samples and continuous prediction of sequential samples. The results indicate that the linear regression model is suitable to forecast with a high fitting degree. However, in the continuous date power prediction, the polynomial kernel function shows preferable prediction ability from the impact of emergencies.
- © 2015, 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/).
Cite this article
TY - CONF AU - Yu Lu PY - 2015/04 DA - 2015/04 TI - A Short-term Load Forecasting Based on Support Vector Regression BT - Proceedings of the 2015 International Conference on Mechatronics, Electronic, Industrial and Control Engineering PB - Atlantis Press SP - 1055 EP - 1059 SN - 2352-5401 UR - https://doi.org/10.2991/meic-15.2015.240 DO - 10.2991/meic-15.2015.240 ID - Lu2015/04 ER -