Application of hybrid model based on empirical mode decomposition, novel recurrent neural networks and the ARIMA to wind speed prediction

2021 ◽  
Vol 233 ◽  
pp. 113917
Author(s):  
Ming-De Liu ◽  
Lin Ding ◽  
Yu-Long Bai
Keyword(s):  
Neural Networks ◽  
Wind Speed ◽  
Empirical Mode Decomposition ◽  
Hybrid Model ◽  
Recurrent Neural Networks ◽  
Wind Speed Prediction ◽  
Model Based ◽  
Mode Decomposition ◽  
Speed Prediction

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.

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