Data mining-assisted short-term wind speed forecasting by wavelet packet decomposition and Elman neural network

2018 ◽  
Vol 175 ◽  
pp. 136-143 ◽  
Author(s):  
Chuanjin Yu ◽  
Yongle Li ◽  
Huoyue Xiang ◽  
Mingjin Zhang
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Wind Speed ◽  
Wavelet Packet ◽  
Wavelet Packet Decomposition ◽  
Short Term ◽  
Elman Neural Network ◽  
Wind Speed Forecasting

scholarly journals Short-Term Wind Speed Forecasting Using Meta Learning-based Elman Neural Network

2021 ◽  
Vol 2068 (1) ◽  
pp. 012045
Author(s):  
M. Madhiarasan
Keyword(s):  
Neural Network ◽  
Wind Speed ◽  
Wind Energy ◽  
Least Mean Square ◽  
Time Data ◽  
Short Term ◽  
Elman Neural Network ◽  
Wind Speed Forecasting ◽  
Generalization Problem ◽  
Meta Learning

Abstract Adequate power provision to the customer and wind energy penetration into the electrical grid is necessitated for accurate wind speed forecasting in the short-term horizon to realize the scheduling, unit commitment, and control. According to the various meteorological parameters, the wind speed and energy production from wind energy are affected. Therefore, the author performs the multi-inputs associated Meta learning-based Elman Neural Network (MENN) forecasting model to overcome the uncertainty and generalization problem. The proposed forecasting approach applicability evaluated with real-time data concerning wind speed forecasting on a short-term time scale. Performance analysis reveals that the meta learning-based Elman neural network is robust and conscious than the existing methods, with a least mean square error of 0.0011.

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

Intelligent Identification of Flow Regime Based on a Novel Neural Network

2014 ◽  
Vol 635-637 ◽  
pp. 1715-1718
Author(s):  
Qiang Wang
Keyword(s):  
Neural Network ◽  
Frequency Domain ◽  
Flow Regime ◽  
Recurrent Neural Network ◽  
Pressure Difference ◽  
Wavelet Packet ◽  
Wavelet Packet Decomposition ◽  
Elman Neural Network ◽  
Flow Regime Identification ◽  
Novel Method

A noveol neural network of Elman is typically dynamic recurrent neural network. A novel method of flow regime identification based on Elman neural network and wavelet packet decomposition is proposed in this paper. Above all, the collected pressure-difference fluctuation signals are decomposed by the four-layer wavelet packet, and the decomposed signals in various frequency bands are obtained within the frequency domain. Then the wavelet packet energy eigenvectors of flow regimes are established. At last the wavelet packet energy eigenvectors are input into Elman neural network and flow regime intelligent identification can be performed.

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