Load Forecasting based on Kernel-based Orthogonal Projections to Latent Structures
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.
load forecasting partial least square orthogonal signal correction kernel PLS
Lingcai Kong Yanpeng Ma
Department of Mathematics and Physics North China Electric Power University Baoding, 071003, China Department of Mathematics and Physics North China Electric Power niversity Baoding, 071003, China
国际会议
哈尔滨
英文
4451-4454
2011-08-12（万方平台首次上网日期，不代表论文的发表时间）