A quadratic $$\nu $$ ν -support vector regression approach for load forecasting

Abstract

Abstract This article focuses on electric load forecasting, which is a challenging task in the energy industry. In this paper, a novel kernel-free $$\nu $$ ν -support vector regression model is proposed for electric load forecasting. The proposed model produces a reduced quadratic surface for nonlinear regression. A feature weighting strategy is adopted to estimate the relevance of the features in the load history. To reduce the effects of outliers in the load history, a weight is assigned to represent the relative importance of each data point. Some computational experiments are conducted on some public benchmark data sets to show the superior performance of the proposed model over some widely used regression models. The results of some extensive computational experiments on the electric load data from the Global Energy Forecasting Competition 2012 and the ISO New England demonstrate better average accuracy of the proposed model.

Keywords

• Kernel-free support vector regression
• Electric load forecasting
• Machine learning
• Weighted support vector regression
• Feature weighting