Short-Term Wind Speed Forecasting Using Decomposition-Based Neural Networks Combining Abnormal Detection Method

Xuejun Chen,Yufeng Yang,Wenchao Hu,Jing Zhao

doi:10.1155/2014/984268

Abstract

As one of the most promising renewable resources in electricity generation, wind energy is acknowledged for its significant environmental contributions and economic competitiveness. Because wind fluctuates with strong variation, it is quite difficult to describe the characteristics of wind or to estimate the power output that will be injected into the grid. In particular, short-term wind speed forecasting, an essential support for the regulatory actions and short-term load dispatching planning during the operation of wind farms, is currently regarded as one of the most difficult problems to be solved. This paper contributes to short-term wind speed forecasting by developing two three-stage hybrid approaches; both are combinations of the five-three-Hanning (53H) weighted average smoothing method, ensemble empirical mode decomposition (EEMD) algorithm, and nonlinear autoregressive (NAR) neural networks. The chosen datasets are ten-minute wind speed observations, including twelve samples, and our simulation indicates that the proposed methods perform much better than the traditional ones when addressing short-term wind speed forecasting problems.

Highlights

Renewable energy is considered to be the most promising alternative energy resource and plays a significant role in securing a long-term sustainable energy supply while reducing global atmospheric emissions [1]
The second involves an initial reconstruction of the signal by adding the noisefiltered intrinsic model functions (IMFs), followed by the modeling and forecasting of the reconstructed signal with a nonlinear autoregressive (NAR) neural network, obtaining the final forecast result
The average mean absolute percentage error (MAPE) of the HEN2 model is 8.71%, which is the lowest MAPE among the considered approaches as calculated for the twelve simulated samples. This outcome leads to reductions of 38.25%, 36.57%, and 13.73%, according to MAPE, respectively, when it is compared with the NAR, 53H-NAR, and ensemble empirical mode decomposition (EEMD)-NAR results

Summary

Introduction

Renewable energy is considered to be the most promising alternative energy resource and plays a significant role in securing a long-term sustainable energy supply while reducing global atmospheric emissions [1]. It is well known that short-term wind speed series are difficult to model or predict, mainly due to their strong and random variation within a short time scale For this reason, data processing is necessary to filter the abnormal values, which greatly impact the model’s performance, and to extract valid information from the raw dataset for wind speed modeling and forecasting. The second method is to reconstruct the signal first by adding the noise-filtered IMFs, followed by the modeling and forecasting of the reconstructed signal with a NAR neural network to obtain the final forecasting result Both approaches to model combination enhance the model’s performance significantly with respect to short-term wind speed forecasting, the second combination performs better in most cases.

Methods

Case Study

15 Training 10 5 0

Findings

Conclusions