A Hybrid Forecasting Model Based on Empirical Mode Decomposition and the Cuckoo Search Algorithm: A Case Study for Power Load

Jiani Heng,Jianzhou Wang,Xuejing Zhao,Chen Wang

doi:10.1155/2016/3205396

Abstract

Power load forecasting always plays a considerable role in the management of a power system, as accurate forecasting provides a guarantee for the daily operation of the power grid. It has been widely demonstrated in forecasting that hybrid forecasts can improve forecast performance compared with individual forecasts. In this paper, a hybrid forecasting approach, comprising Empirical Mode Decomposition, CSA (Cuckoo Search Algorithm), and WNN (Wavelet Neural Network), is proposed. This approach constructs a more valid forecasting structure and more stable results than traditional ANN (Artificial Neural Network) models such as BPNN (Back Propagation Neural Network), GABPNN (Back Propagation Neural Network Optimized by Genetic Algorithm), and WNN. To evaluate the forecasting performance of the proposed model, a half-hourly power load in New South Wales of Australia is used as a case study in this paper. The experimental results demonstrate that the proposed hybrid model is not only simple but also able to satisfactorily approximate the actual power load and can be an effective tool in planning and dispatch for smart grids.

Highlights

The short-term power load forecasting is very important for the stable operation of the system
The results illustrate that the CSAWNN model is more practical than the GABPNN model in forecasting power load
Assessment of the power load as accurately and quickly as possible is the primary objective in power load forecasting

Summary

Introduction

The short-term power load forecasting is very important for the stable operation of the system. Many shortterm power load forecasting methods have been proposed, and these methods can be mainly divided into three categories: conventional methods, modern forecasting methods, and hybrid forecasting methods. Conventional methods include multiple linear regression analysis [4, 5], time series [6, 7], state space models [8], general exponential smoothing [9], and knowledge-based methods. These methods cannot provide appropriate nonlinear mathematical relationships to express actual power loads. A detailed introduction of the three categories is given below

Methods

Discussion

Conclusion