A NEW PROBABILISTIC PREDICTION APPROACH BASED ON LOCAL V-SUPPORT VECTOR REGRESSION

摘要：

In this paper, a general prediction methodology is proposed which can provide a good service to the related investigations in probabilistic prediction. In particular, the proposed model has the ability to deal with both the deterministic prediction and probabilistic prediction of noisy time series. By means of the proposed approach, local V-support vector regression (L- V-SVR) model is exploited to suppress noise disturbance in deterministic prediction (points prediction), and the error intervals, which avoid the distributional assumptions of error, can be gained by using nonparametric kernel estimation (NPKE). Then forecasting confidence intervals (FCIs) are obtained by combining the deterministic prediction results and error intervals. Furthermore, joint forecasting confidence intervals (JFCIs) are proposed to improve the prediction reliability. Finally, a comparison of the proposed model and normal distribution-assumed model is performed through simulations by applying them to a real power system, and the validity and practicability of the proposed model is illustrated.

关键词： Probabilistic Prediction Deterministic Prediction Local V-support Vector Regression (L-V-SVR) Nonparametric Kernel Estimation (NPKE) Confidence Intervals (CIs)

作者: YONG-MING ZHANG LIE CHEN WEI-GUI QI HAI-YAN TANG

作者单位: Department of Electrical Engineering and Automation, Harbin Institute of Technology, Harbin, 150001, China

会议类型: 国际会议

会议名称: 2008 International Conference on Machine Learning and Cybernetics(2008机器学习与控制论国际会议)

会议地点: 昆明

会议语种:英文

页码: 728-733

在线出版日期: 2008-07-12（万方平台首次上网日期，不代表论文的发表时间）