A Hybrid Model Based on Multi-Stage Principal Component Extraction, GRU Network and KELM for Multi-Step Short-Term Wind Speed Forecasting

Feng Zou,Renming Wang,Ping Fang,Wenlong Fu,Dongzhen Xiong

doi:10.1109/access.2020.3043812

Abstract

Accurate wind speed forecasting exerts a critical role in energy conversion and management of wind power. In term of this purpose, a hybrid model based on multi-stage principal component extraction, kernel extreme learning machine (KELM) and gated recurrent unit (GRU) network is developed in this paper, where the multi-stage principal component extraction combines complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), singular spectrum analysis (SSA) and phase space reconstruction (PSR). Firstly, CEEMDAN is employed to decompose the raw wind speed data into a sequence of intrinsic mode functions (IMFs) and a residual component. Then the principal components and residual components of all IMFs are captured by SSA. Meanwhile, all residual components obtained by CEEMDAN decomposition and SSA processing are added to form a new component. Subsequently, PSR is utilized to construct each forecasting component obtained by CEEMDAN-SSA into the input and output of training set and testing set for the prediction model. Later, KELM and GRU neural network are conducted to predict the high-frequency and low-frequency components, respectively. Eventually, the prediction values of each component are accumulated to acquire the final prediction result. To evaluate the performance of the proposed model, four datasets from Sotavento Galicia wind farm are adopted to conduct experimental research. The experimental results manifest that the proposed model achieves higher accuracy of multi-step prediction than other comparative models.

Highlights

With the express development of the world economy, great changes have taken place in the energy structure
The main contributions of this study can be summarized as follows: 1) Considering the nonlinear and non-stationarity of raw wind speed data, the multi-stage principal component extraction method based on CEEMDAN and singular spectrum analysis (SSA) is utilized to promote the predictive capability of proposed hybrid model for multi-step wind speed forecasting effectively
In order to establish an accurate multi-step short-term wind speed forecasting model, the hybrid model including multi-stage principal component extraction, gated recurrent unit (GRU) neural network and kernel extreme learning machine (KELM) is proposed in this paper

Summary

INTRODUCTION

With the express development of the world economy, great changes have taken place in the energy structure. Artificial intelligence (AI) technology develops rapidly and has been widely applied in many fields and achieves fine effects These models mainly include back propagation neural network (BPNN) [18], support vector machine (SVM) [19], [20], extreme learning machine (ELM) [21], Gaussian Process Regression (GPR) [22] as well as long short-term memory network (LSTM) [23], and many of which are applied for studying wind speed forecasting. The main contributions of this study can be summarized as follows: 1) Considering the nonlinear and non-stationarity of raw wind speed data, the multi-stage principal component extraction method based on CEEMDAN and SSA is utilized to promote the predictive capability of proposed hybrid model for multi-step wind speed forecasting effectively. The conclusion of this paper is summarized in the last section

METHODOLOGY

SINGULAR SPECTRUM ANALYSIS

PHASE SPACE RECONSTRUCTION

KERNEL EXTREME LEARNING MACHINE

EXPERIMENTAL ANALYSIS OF MULTI-STEP FORECASTING

CONCLUSION