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

Xiping Wang,Yaqi Wang

doi:10.1155/2016/9895639

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.

Highlights

Considering that electricity cannot be stored, the accurate forecasting of electric load has a significant effect on reliability of power systems and the economic development of society
After being decomposed by empirical mode decomposition (EMD), the two residential electric load data series are decomposed into five independent intrinsic mode function (IMF) and one residue component, respectively, as illustrated in Figure 3 (Case I and Case II)
The decomposed IMFs components and one residue for each residential quarter from the previous step are used in PSOSVR model construction

Summary

Introduction

Considering that electricity cannot be stored, the accurate forecasting of electric load has a significant effect on reliability of power systems and the economic development of society. The short-term load forecasting plays a vital role in the daily operational management of power utility, such as energy transfer scheduling, unit commitment, and load dispatch [1, 2]. As an integral part of the daily operational management of power utility, accuracy prediction of the short-term electric load in residential quarters is of great significance to urban power network planning and the electric power market operating. The residential load is influenced by many factors, such as holidays, weekends, and temperature [4, 5]. All of these factors lead to the load of residential quarters with more variability, higher randomness, and lower similarities in history load curves. The short-term load forecasting for residential quarters is a complex task and worthy of further studies

Methods

Results

Conclusion