Title :
Research of Annual Electricity Demand Forecasting Based on Kernel Partial Least Squares Regression
Author :
Jianxin Shen ; Shanlin Yang
Author_Institution :
Sch. of Manage., Hefei Univ. of Technol., Hefei, China
Abstract :
An important feature of the smart grid electricity is using prediction of high-precision power consumption for intelligent deployment, highly accurate forecasting of the power information is a key indicator of intelligent network. In this paper, we implement Gaussian kernel function to transform nonlinear regression of a low-dimensional to the linear regression in high-dimensional space. The power load forecasting model based on kernel partial least squares regression, can overcome the adverse effects of the nonlinear factors on the prediction model. Application of Jiangsu Province from 2006 to 2008 industrial electricity consumption data were verified, showing that the power load forecasting based on kernel partial least squares regression compared to linear partial least squares regression, has better prediction performance.
Keywords :
Gaussian processes; demand forecasting; least squares approximations; load forecasting; power consumption; regression analysis; smart power grids; transforms; Gaussian kernel function; Jiangsu Province; annual smart grid electricity demand forecasting; high-precision power consumption prediction; industrial electricity consumption data; intelligent deployment; kernel partial least squares regression; nonlinear regression transform; power information forecasting; power load forecasting; Analytical models; Data models; Electricity; Forecasting; Kernel; Predictive models; Smart grids; Kernel Partial Least Squares; annual electricity consumption; forecasts; smart grid;
Conference_Titel :
Control Engineering and Communication Technology (ICCECT), 2012 International Conference on
Conference_Location :
Liaoning
Print_ISBN :
978-1-4673-4499-9
DOI :
10.1109/ICCECT.2012.237
