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

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 222931-222943
Author(s):  
Feng Zou ◽  
Wenlong Fu ◽  
Ping Fang ◽  
Dongzhen Xiong ◽  
Renming Wang
Keyword(s):  
Wind Speed ◽  
Hybrid Model ◽  
Principal Component ◽  
Short Term ◽  
Wind Speed Forecasting ◽  
Model Based ◽  
Multi Stage ◽  
Component Extraction

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

Multi-step wind speed forecasting based on hybrid multi-stage decomposition model and long short-term memory neural network

2020 ◽  
Vol 213 ◽  
pp. 112869 ◽  
Author(s):  
Sinvaldo Rodrigues Moreno ◽  
Ramon Gomes da Silva ◽  
Viviana Cocco Mariani ◽  
Leandro dos Santos Coelho
Keyword(s):  
Neural Network ◽  
Wind Speed ◽  
Short Term Memory ◽  
Short Term ◽  
Term Memory ◽  
Wind Speed Forecasting ◽  
Decomposition Model ◽  
Multi Stage ◽  
Long Short Term Memory

scholarly journals Short-Term Wind Speed Prediction Using EEMD-LSSVM Model

2017 ◽  
Vol 2017 ◽  
pp. 1-22 ◽  
Author(s):  
Aiqing Kang ◽  
Qingxiong Tan ◽  
Xiaohui Yuan ◽  
Xiaohui Lei ◽  
Yanbin Yuan
Keyword(s):  
Wind Speed ◽  
Empirical Mode Decomposition ◽  
Hybrid Model ◽  
Superposition Principle ◽  
Support Vector ◽  
Short Term ◽  
Wind Speed Forecasting ◽  
Wind Speed Prediction ◽  
Mode Decomposition ◽  
Speed Prediction

Hybrid Ensemble Empirical Mode Decomposition (EEMD) and Least Square Support Vector Machine (LSSVM) is proposed to improve short-term wind speed forecasting precision. The EEMD is firstly utilized to decompose the original wind speed time series into a set of subseries. Then the LSSVM models are established to forecast these subseries. Partial autocorrelation function is adopted to analyze the inner relationships between the historical wind speed series in order to determine input variables of LSSVM models for prediction of every subseries. Finally, the superposition principle is employed to sum the predicted values of every subseries as the final wind speed prediction. The performance of hybrid model is evaluated based on six metrics. Compared with LSSVM, Back Propagation Neural Networks (BP), Auto-Regressive Integrated Moving Average (ARIMA), combination of Empirical Mode Decomposition (EMD) with LSSVM, and hybrid EEMD with ARIMA models, the wind speed forecasting results show that the proposed hybrid model outperforms these models in terms of six metrics. Furthermore, the scatter diagrams of predicted versus actual wind speed and histograms of prediction errors are presented to verify the superiority of the hybrid model in short-term wind speed prediction.

Short-term wind speed forecasting using a hybrid model

Energy ◽  
2017 ◽  
Vol 119 ◽  
pp. 561-577 ◽  
Author(s):  
Ping Jiang ◽  
Yun Wang ◽  
Jianzhou Wang
Keyword(s):  
Wind Speed ◽  
Hybrid Model ◽  
Short Term ◽  
Wind Speed Forecasting

Noise model based ν-support vector regression with its application to short-term wind speed forecasting

2014 ◽  
Vol 57 ◽  
pp. 1-11 ◽  
Author(s):  
Qinghua Hu ◽  
Shiguang Zhang ◽  
Zongxia Xie ◽  
Jusheng Mi ◽  
Jie Wan
Keyword(s):  
Wind Speed ◽  
Support Vector Regression ◽  
Noise Model ◽  
Support Vector ◽  
Short Term ◽  
Wind Speed Forecasting ◽  
Model Based
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