A Hybrid Approach for Multi-Step Wind Speed Forecasting Based on Multi-Scale Dominant Ingredient Chaotic Analysis, KELM and Synchronous Optimization Strategy

Kai Wang,Wenlong Fu,Jianzhong Zhou,Tie Chen,Jiawen Tan,Yanhe Xu

doi:10.3390/su11061804

Abstract

Accurate wind speed prediction plays a significant role in reasonable scheduling and the safe operation of the power system. However, due to the non-linear and non-stationary traits of the wind speed time series, the construction of an accuracy forecasting model is difficult to achieve. To this end, a novel synchronous optimization strategy-based hybrid model combining multi-scale dominant ingredient chaotic analysis and a kernel extreme learning machine (KELM) is proposed, for which the multi-scale dominant ingredient chaotic analysis integrates variational mode decomposition (VMD), singular spectrum analysis (SSA) and phase-space reconstruction (PSR). For such a hybrid structure, the parameters in VMD, SSA, PSR and KELM that would affect the predictive performance are optimized by the proposed improved hybrid grey wolf optimizer-sine cosine algorithm (IHGWOSCA) synchronously. To begin with, VMD is employed to decompose the raw wind speed data into a set of sub-series with various frequency scales. Later, the extraction of dominant and residuary ingredients for each sub-series is implemented by SSA, after which, all of the residuary ingredients are accumulated with the residual of VMD, to generate an additional forecasting component. Subsequently, the inputs and outputs of KELM for each component are deduced by PSR, with which the forecasting model could be constructed. Finally, the ultimate forecasting values of the raw wind speed are calculated by accumulating the predicted results of all the components. Additionally, four datasets from Sotavento Galicia (SG) wind farm have been selected, to achieve the performance assessment of the proposed model. Furthermore, six relevant models are carried out for comparative analysis. The results illustrate that the proposed hybrid framework, VMD-SSA-PSR-KELM could achieve a better performance compared with other combined models, while the proposed synchronous parameter optimization strategy-based model could achieve an average improvement of 25% compared to the separated optimized VMD-SSA-PSR-KELM model.

Highlights

In recent decades, wind energy has become more and more important as an emerging renewable energy source
To improve multi-step prediction performance, a novel hybrid based on a multi-scale dominant ingredient chaotic analysis, the kernel extreme learning machine (KELM)- and IHGWOSCA-based synchronous optimization strategy, is proposed in this paper
The proposed model possesses a structure of variational mode decomposition (VMD)-singular spectrum analysis (SSA)-phase space reconstruction (PSR)-KELM, of which the parameters in each module was synchronously optimized by the proposed IHGWOSCA algorithm

Summary

Introduction

Wind energy has become more and more important as an emerging renewable energy source. Over the past few decades, a great variety of methods have been developed to achieve wind speed prediction, which can be roughly divided into four categories [2]: physical models, conventional statistical models, spatial correlation models, and artificial intelligence (AI) models. The drawbacks of the long operation time and the large amount of computing resources make such models difficult to construct. Another popular forecasting approach, namely statistical models could extract potential information contained in the historical wind speed series, among which autoregressive (AR) [4], autoregressive moving average (ARMA) [5], and autoregressive integrated moving average (ARIMA) [6] have been widely investigated. To enhance the generalization performance of ELM, the regularization coefficient is employed to solve optimization problems, as well as replacing hidden nodes by kernel functions, weakening the randomness of the predicted results [15]

Methods

Findings

Discussion

Conclusion