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Mathematical Problems in Engineering
Volume 2016, Article ID 9895639, 10 pages
http://dx.doi.org/10.1155/2016/9895639
Research Article

A Hybrid Model of EMD and PSO-SVR for Short-Term Load Forecasting in Residential Quarters

Department of Economics and Management, North China Electric Power University, Baoding 071003, China

Received 14 April 2016; Revised 14 October 2016; Accepted 23 November 2016

Academic Editor: Marco Mussetta

Copyright © 2016 Xiping Wang and Yaqi Wang. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Short-term load forecasting plays a vital role in the daily operational management of power utility. To improve the forecasting accuracy, this paper proposes a hybrid EMD-PSO-SVR forecasting model for short-term load forecasting based on empirical mode decomposition (EMD), support vector regression (SVR), and particle swarm optimization (PSO), also considering the effects of temperature, weekends, and holidays. EMD is used to decompose the residential electric load data into a number of intrinsic mode function (IMF) components and one residue; then SVR is constructed to forecast these IMFs and residual value individually. In order to gain optimization parameters of SVR, PSO is implemented to automatically perform the parameter selection in SVR modeling. Then all of these forecasting values are reconstructed to produce the final forecasting result for residential electric load data. Compared with the results from the EMD-SVR model, traditional SVR model, and PSO-SVR model, the result indicates that the proposed EMD-PSO-SVR model performs more effectively and more stably in forecasting the residential short-term load.