Electricity Demand Forecasting based on Support Vector Regression

Abstract

Forecasting of electricity demand have difficulty in adapting to abrupt weather changes along with a radical shift in major regional and global climates. This has lead to increasing attention to research on the immediate and accurate forecasting model. Technically, this implies that a model requires only a few input variables all of which are easily obtainable, and its predictive performance is comparable with other competing models. To meet the ends, this paper presents an energy demand forecasting model that uses the variable selection or extraction methods of data mining to select only relevant input variables, and employs support vector regression method for accurate prediction. Also, it proposes a novel performance measure for time-series prediction, shift index, followed by description on preprocessing procedure. A comparative evaluation of the proposed method with other representative data mining models such as an auto-regression model, an artificial neural network model, an ordinary support vector regression model was carried out for obtaining the forecast of monthly electricity demand from 2000 to 2008 based on data provided by Korea Energy Economics Institute. Among the models tested, the proposed method was shown promising results than others. 1)

The Potential Implications of the Introduction of Bioelectricity in South Korea

“…Furthermore, demand forecasts based on the DOLS estimation are generally in line with the projections of South Korea's Second National Energy Plan. Lee and Shin [15] focus on electricity demand. They present an electricity demand forecasting model that employs the variable selection and feature extraction methods of data mining to select only relevant input variables and uses the support vector regression method for making accurate predictions.…”