摘要
提出了基于支撑向量机在线学习方法的短期负荷预测,该方法克服了传统的支撑向量机负荷预测当训练样本集合改变时为了保证预测精度必需重新进行训练来得到新的回归函数的缺点.充分利用支撑向量机解的稀疏性和前一次的训练结果,提出了递增和递减算法,直接修改原有回归函数的系数来得到新回归函数.实例计算表明,该方法与传统支撑向量机方法相比,具有计算速度快,推广能力强的显著特点,在相同预测精度下,计算速度提高了近两个数量级.
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.
出处
《西安交通大学学报》
EI
CAS
CSCD
北大核心
2005年第4期412-416,共5页
Journal of Xi'an Jiaotong University
基金
国家自然科学基金重点资助项目(59937150
60075001).
关键词
短期负荷预测
支撑向量机
在线学习
Decision support systems
Electric power systems
Learning systems
Matrix algebra
Online systems
