A Novel Hybrid Model Based on an Improved Seagull Optimization Algorithm for Short-Term Wind Speed Forecasting

Xin Chen,Yuanlu Li,Xiong Xiong,Xiaoling Ye,Yingchao Zhang,Fanghong Zhang

doi:10.3390/pr9020387

Abstract

Wind energy is a clean energy source and is receiving widespread attention. Improving the operating efficiency and economic benefits of wind power generation systems depends on more accurate short-term wind speed predictions. In this study, a new hybrid model for short-term wind speed forecasting is proposed. The model combines variational modal decomposition (VMD), the proposed improved seagull optimization algorithm (ISOA) and the kernel extreme learning machine (KELM) network. The model adopts a hybrid modeling strategy: firstly, VMD decomposition is used to decompose the wind speed time series into several wind speed subseries. Secondly, KELM optimized by ISOA is used to predict each decomposed subseries. The ISOA technique is employed to accurately find the best parameters in each KELM network such that the predictability of a single KELM model can be enhanced. Finally, the prediction results of the wind speed sublayer are summarized to obtain the original wind speed. This hybrid model effectively characterizes the nonlinear and nonstationary characteristics of wind speed and greatly improves the forecasting performance. The experiment results demonstrate that: (1) the proposed VMD-ISOA-KELM model obtains the best performance for the application of three different prediction horizons compared with the other classic individual models, and (2) the proposed hybrid model combining the VMD technique and ISOA optimization algorithm performs better than models using other data preprocessing techniques.

Highlights

To achieve global clean energy development, reduce greenhouse gas emissions and prevent the crisis of the depletion of nonrenewable fossil energy reserves, the large-scale use of clean energy has become a global energy development trend [1,2]
Considering the noisy and highly nonlinear features of real wind speed data, this paper mainly proposes an optimized hybrid forecasting strategy based on variational modal decomposition (VMD), kernel extreme learning machine (KELM)
In order to further improve the prediction, the two parameters of KELM were optimized by the proposed improved seagull optimization algorithm (ISOA) algorithm

Summary

Introduction

To achieve global clean energy development, reduce greenhouse gas emissions and prevent the crisis of the depletion of nonrenewable fossil energy reserves, the large-scale use of clean energy has become a global energy development trend [1,2]. Among the various widely used new energies, wind energy is used worldwide due to its wide energy distribution, pollution-free nature and sustainability, and it is of great significance to tap into the potential of wind energy to adjust the traditional energy structure. According to a report released by the Global Wind Energy Association (GWEC) in 2019, the global installed capacity of wind power in 2019 was 60.4 GW, reaching a total of 651 GW. 2019, China’s cumulative installed wind power capacity reached 210 MW [3]. The chaotic, random and intermittent characteristics of wind speed pose considerable challenges to power systems. The violent fluctuation of wind power in a short period of time causes a short-term imbalance of the power system, which may cause the power system to collapse

Methods

Results

Conclusion