scholarly journals Chaotic Analysis and Prediction of Wind Speed Based on Wavelet Decomposition

Processes ◽  
2021 ◽  
Vol 9 (10) ◽  
pp. 1793
Author(s):  
Li Lin ◽  
Dandan Xia ◽  
Liming Dai ◽  
Qingsong Zheng ◽  
Zhiqin Qin
Keyword(s):  
Wind Speed ◽  
Wavelet Decomposition ◽  
Low Frequency ◽  
Prediction Method ◽  
Fractal Dimensions ◽  
Filter Method ◽  
Wind Speed Data ◽  
Wind Speed Prediction ◽  
Speed Prediction ◽  
Kalman Filter Method

Studying the characteristics of wind speed is essential in wind speed prediction. Based on long-term observed wind speed data, fractal dimension analysis of wind speed was first conducted at different scales, and persistence in wind speed was evaluated based on fractal dimensions in this paper. To propose a more accurate model for wind speed prediction, the wavelet decomposition method was applied to separate the high-frequency dynamics of wind speed data from the low-frequency dynamics. Chaotic behaviors were studied for each decomposed component using the largest Lyapunov exponents method. A proposed hybrid prediction method combining wavelet decomposition, a chaotic prediction method and a Kalman filter method was investigated for short-term wind speed prediction. Simulation results showed that the proposed method can significantly improve prediction accuracy.

A Hybrid Short-Term Wind Speed Forecasting Model Based on Wavelet Decomposition and Extreme Learning Machine

2013 ◽  
Vol 860-863 ◽  
pp. 361-367 ◽  
Author(s):  
Yi Hui Zhang ◽  
He Wang ◽  
Zhi Jian Hu ◽  
Kai Wang ◽  
Yan Li ◽  
...  
Keyword(s):  
Wind Speed ◽  
Extreme Learning Machine ◽  
Wavelet Decomposition ◽  
Forecasting Model ◽  
Short Term ◽  
Machine Model ◽  
Wind Speed Prediction ◽  
Speed Prediction ◽  
Learning Machine ◽  
Short Term Prediction

This paper studied the short-term prediction of wind speed by means of wavelet decomposition and Extreme Learning Machine. Wind speed signal was decomposed into several sequences by wavelet decomposition to reduce the non-stationary. Secondly, the phase space reconstructed was used to mine sequences characteristics, and then an improved extreme learning machine model of each component was established. Finally, the results of each component forecast superimposed to get the final result. The simulation result verified that the hybrid model effectively improved the wind speed prediction accuracy.

A novel wind speed prediction method: Hybrid of correlation-aided DWT, LSSVM and GARCH

2018 ◽  
Vol 174 ◽  
pp. 28-38 ◽  
Author(s):  
Yan Jiang ◽  
Guoqing Huang ◽  
Xinyan Peng ◽  
Yongle Li ◽  
Qingshan Yang
Keyword(s):  
Wind Speed ◽  
Prediction Method ◽  
Wind Speed Prediction ◽  
Speed Prediction

scholarly journals Research on Hybrid Wind Speed Prediction System Based on Artificial Intelligence and Double Prediction Scheme

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-22
Author(s):  
Ying Nie ◽  
He Bo ◽  
Weiqun Zhang ◽  
Haipeng Zhang
Keyword(s):  
Wind Speed ◽  
Prediction Method ◽  
Prediction System ◽  
Wind Speed Prediction ◽  
Interval Prediction ◽  
Prediction Scheme ◽  
Prediction Module ◽  
Speed Prediction ◽  
Nonlinear Integration ◽  
Power Generation Systems

Wind energy analysis and wind speed modeling have a significant impact on wind power generation systems and have attracted significant attention from many researchers in recent decades. Based on the inherent characteristics of wind speed, such as nonlinearity and randomness, the prediction of wind speed is considered to be a challenging task. Previous studies have only considered point prediction or interval measurement of wind speed separately and have not combined these two methods for prediction and analysis. In this study, we developed a novel hybrid wind speed double prediction system comprising a point prediction module and interval prediction module to compensate for the shortcomings of existing research. Regarding point prediction in the developed double prediction system, a novel nonlinear integration method based on a backpropagation network optimized using the multiobjective evolutionary algorithm based on decomposition was successfully implemented to derive the final prediction results, which enable further improvement of the accuracy of point prediction. Based on point prediction results, we propose an interval prediction method that constructs different intervals according to the classification of different data features via fuzzy clustering, which provides reliable interval prediction results. The experimental results demonstrate that the proposed system outperforms existing methods in engineering applications and can be used as an effective technology for power system planning.

scholarly journals Multi-time scale wind speed prediction based on WT-bi-LSTM

2020 ◽  
Vol 309 ◽  
pp. 05011
Author(s):  
Jinyong Xiang ◽  
Zhifeng Qiu ◽  
Qihan Hao ◽  
Huhui Cao
Keyword(s):  
Wind Speed ◽  
Wavelet Decomposition ◽  
Wind Farm ◽  
Short Term Memory ◽  
Short Term ◽  
Wind Speed Prediction ◽  
Proposed Model ◽  
Speed Prediction ◽  
Long Short Term Memory ◽  
Auto Regression

The accurate and reliable wind speed prediction can benefit the wind power forecasting and its consumption. As a continuous signal with the high autocorrelation, wind speed is closely related to the past and future moments. Therefore, to fully use the information of two direction, an auto-regression model based on the bi-directional long short term memory neural network model with wavelet decomposition (WT-bi-LSTM) is built to predict the wind speed at multi-time scales. The proposed model are validated by using the actual wind speed series from a wind farm in China. The validation results demonstrated that, compared with other four traditional models, the proposed strategy can effectively improve the accuracy of wind speed prediction.

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