Short-Term Power Load Forecasting Using Least Squares Support Vector Machines (LS-SVM)

摘要

Accurate forecasting of electricity load has been one of the most important issues in the electricity industry. Modern data mining methods have played a crucial role in forecasting electricity load. Support vector machines (SVMs) have been successfully employed to solve nonlinear regression and time series problems. Based on the Nystrom approximation and the primal-dual formulation of the Least Squares Support Vector Machines (LS-SVM), it becomes possible to apply a nonlinear model to a large scale regression problem. With an active selection of support vectors based on quadratic Renyi entropy criteria, approximation of the nonlinear mapping induced by the kernel matrix. The methodology is applied to the case of load forecasting in Inner Mongolia of China.