scholarly journals A Hybrid Model Based on A Modified Optimization Algorithm and An Artificial Intelligence Algorithm for Short-Term Wind Speed Multi-Step Ahead Forecasting

2018 ◽  
Vol 10 (5) ◽  
pp. 1443 ◽  
Author(s):  
Zonggui Yao ◽  
Chen Wang
Keyword(s):  
Artificial Intelligence ◽  
Wind Speed ◽  
Hybrid Model ◽  
Optimization Algorithm ◽  
Short Term ◽  
Model Based ◽  
Intelligence Algorithm ◽  
Artificial Intelligence Algorithm

A hybrid model based on synchronous optimisation for multi-step short-term wind speed forecasting

2018 ◽  
Vol 215 ◽  
pp. 131-144 ◽  
Author(s):  
Chaoshun Li ◽  
Zhengguang Xiao ◽  
Xin Xia ◽  
Wen Zou ◽  
Chu Zhang
Keyword(s):  
Wind Speed ◽  
Hybrid Model ◽  
Short Term ◽  
Wind Speed Forecasting ◽  
Model Based

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

Processes ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 387
Author(s):  
Xin Chen ◽  
Yuanlu Li ◽  
Yingchao Zhang ◽  
Xiaoling Ye ◽  
Xiong Xiong ◽  
...  
Keyword(s):  
Wind Speed ◽  
Hybrid Model ◽  
Optimization Algorithm ◽  
Clean Energy ◽  
Economic Benefits ◽  
Operating Efficiency ◽  
Short Term ◽  
Wind Speed Forecasting ◽  
Model Combining ◽  
Modeling Strategy

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.

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