Title :
Load forecasting based on kernel-based orthogonal projections to latent structures
Author :
Lingcai Kong ; Yanpeng Ma
Author_Institution :
Dept. of Math. & Phys., North China Electr. Power Univ., Baoding, China
Abstract :
The Kernel-based orthogonal projections to latent structures (K-OPLS) model is a recent novel data analysis method for both regression and classification. Compared with the classical orthogonal projections to latent structures (OPLS), it utilizes the kernel Gram matrix as a replacement of descriptor matrix to use the partial least squares (PLS) model. This enables it can effectively improve predictive performance, considerably in such situations where strong non-linear relationships between descriptor and response variables while retaining the OPLS model framework. In this paper, we first introduce the K-OPLS model. And then, a load forecasting model based on K-OPLS is proposed.
Keywords :
least squares approximations; load forecasting; K-OPLS; Kernel-based orthogonal projections; PLS model; load forecasting; orthogonal projections to latent structures; partial least squares model; Autoregressive processes; Data models; Kernel; Load forecasting; Load modeling; Mathematical model; Predictive models; kernel PLS; load forecasting; orthogonal signal correction; partial least square;
Conference_Titel :
Electronic and Mechanical Engineering and Information Technology (EMEIT), 2011 International Conference on
Conference_Location :
Harbin, Heilongjiang
Print_ISBN :
978-1-61284-087-1
DOI :
10.1109/EMEIT.2011.6023132
