Optimal training subset in a support vector regression electric load forecasting model

This paper presents an optimal training subset for support vector regression (SVR) under deregulated power, which has a distinct advantage over SVR based on the full training set, since it solves the problem of large sample memory complexity O(N2) and prevents over-fitting during unbalanced data regression. To compute the proposed optimal training subset, an approximation convexity optimization framework is constructed through coupling a penalty term for the size of the optimal training subset to the mean absolute percentage error (MAPE) for the full training set prediction. Furthermore, a special method for finding the approximate solution of the optimization goal function is introduced, which enables us to extract maximum information from the full training set and increases the overall prediction accuracy. The applicability and superiority of the presented algorithm are shown by the half-hourly electric load data (48 data points per day) experiments in New South Wales under three different sample sizes. Especially, the benefit of the developed methods for large data sets is demonstrated by the significantly less CPU running time.

An optimal training subset for support vector regression (SVR) is obtained by constructing an approximation convexity optimization framework, which has a distinct advantage over SVR based on the full training set, since it solves the problem of large sample memory complexity O(N2) and prevents over-fitting during unbalanced data regression. Especially, the benefit of the developed methods for large data sets is demonstrated by the significantly less CPU running time.Figure optionsDownload as PowerPoint slideHighlights
► We present an optimal training subset for support vector regression (SVR) under deregulated power.
► The proposed model solves the problem of large sample memory complexity O(N2).
► The proposed model can tolerate more redundant information compared with SVR model.
► It takes significantly less CPU running time to train the proposed model under medium and large sample sizes.
► The obtained results confirm the applicability and superiority of the developed model.