A novel wind speed prediction method based on robust local mean decomposition, group method of data handling and conditional kernel density estimation

2019 ◽  
Vol 200 ◽  
pp. 112099 ◽  
Author(s):  
Yan Jiang ◽  
Shuoyu Liu ◽  
Liuliu Peng ◽  
Ning Zhao
Keyword(s):  
Wind Speed ◽  
Density Estimation ◽  
Prediction Method ◽  
Kernel Density ◽  
Group Method ◽  
Data Handling ◽  
Decomposition Group ◽  
Wind Speed Prediction ◽  
Speed Prediction ◽  
Local Mean

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 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 new method of wind speed prediction based on weighted optimal fuzzy c-means and modular extreme learning machine

2018 ◽  
Vol 42 (5) ◽  
pp. 447-457 ◽  
Author(s):  
Chao Pan ◽  
Qide Tan ◽  
Benshuang Qin
Keyword(s):  
Wind Speed ◽  
Extreme Learning Machine ◽  
Wind Farm ◽  
Simulated Annealing Algorithm ◽  
Prediction Models ◽  
Prediction Method ◽  
Fuzzy C Means ◽  
Wind Speed Prediction ◽  
Speed Prediction ◽  
Learning Machine

According to the characteristics of randomness, volatility, and unpredictability of wind speed, this article provides a new wind speed prediction method which includes three modules that are attribute weighting module, intelligent optimization clustering module, and wind speed prediction module based on extreme learning machine. First, the Pearson coefficient values of the attribute matrix elements are calculated and weighted considering the fluctuation characteristics of time series and influences of different weather attributes on the wind speed. Then the fuzzy c-means clustering method optimized by genetic simulated annealing algorithm is carried out on the weighted attribute matrix to cluster. Furthermore, several kinds of wind speed prediction models are built using the extreme learning machine to forecast short-term wind speed. The research on wind speed prediction is carried out by the measured data of wind farm in America (N39.91°, W105.29°). And the results show that the new prediction method of wind speed proposed in this article has higher prediction accuracy.

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