A Hybrid Model for Short-Term Wind Speed Forecasting Based on Wavelet Analysis and RBF Neural Network

2013 ◽  
pp. 173-181 ◽  
Author(s):  
Xiao-bin Huang ◽  
Pei-lin Mao ◽  
Xiao-peng Dong ◽  
Hao-yuan Tang
Keyword(s):  
Neural Network ◽  
Wind Speed ◽  
Wavelet Analysis ◽  
Hybrid Model ◽  
Rbf Neural Network ◽  
Short Term ◽  
Wind Speed Forecasting

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.

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