Huang Xuncheng1, Pang Wenchen2, Zhao Dengfu2, Wang Xifan2
(1. Institute of Microelectronics, Xidian University, Xi'an 710071, China; 2. School of
Electrical Engineering, Xi'an Jiaotong University, Xi'an 710049, China)
Abstract: A new
approach for short term load forecasting based on online-learning support vector machine
(SVM) algorithm is presented. The conventional implementations of support vector machine
are usually inefficient for online learning because one must retrain from scratch as the
training set is modified to ensure the forecasting accuracy. An accurate online-learning
support vector machine algorithm, which efficiently updates a trained regression function
whenever a sample is added to or removed from the training set, is proposed. The practical
examples show that the online-learning support vector machine algorithm outperforms the
conventional SVM with higher computing rate as well as better generalization.
Keywords: short term load forecasting; support vector machine; online learning