Feature selection in wind speed prediction systems based on a hybrid coral reefs optimization – Extreme learning machine approach

2014 ◽  
Vol 87 ◽  
pp. 10-18 ◽  
Author(s):  
S. Salcedo-Sanz ◽  
A. Pastor-Sánchez ◽  
L. Prieto ◽  
A. Blanco-Aguilera ◽  
R. García-Herrera
Keyword(s):  
Feature Selection ◽  
Wind Speed ◽  
Coral Reefs ◽  
Extreme Learning Machine ◽  
Wind Speed Prediction ◽  
Speed Prediction ◽  
Prediction Systems ◽  
Learning Machine

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.

scholarly journals A Method Based on Numerical Wind Field and Extreme Learning Machine for Typhoon Wind Speed Prediction of Wind Farm

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Hong Xu ◽  
Wan-Yu Wang
Keyword(s):  
Wind Speed ◽  
Simulation Model ◽  
Extreme Learning Machine ◽  
Wind Farm ◽  
Wind Farms ◽  
Wind Speed Prediction ◽  
Speed Prediction ◽  
Learning Machine ◽  
Speed Simulation ◽  
Typhoon Wind

Typhoon wind speed prediction is of great significance for it can help prevent wind farms from damages caused by frequent typhoon disasters in coastal areas. However, most researches on wind forecast are either for meteorological application or for normal weather. Therefore, this paper proposes a systematic method based on numerical wind field and extreme learning machine for typhoon wind speed prediction of wind farms. The proposed method mainly consists of three parts, IGA-YanMeng typhoon numerical simulation model, typhoon status prediction model, and wind speed simulation model based on an extreme learning machine. The IGA-YanMeng typhoon numerical simulation model can greatly enrich typhoon wind speed data according to historical typhoon parameters. The typhoon status prediction model can predict the status of typhoons studied in the next few hours. And wind speed simulation model simulates the average wind speed magnitude/direction at 10 m height of each turbine in the farm according to the predicted status. The end of this paper presents a case study on a wind farm located in Guangdong province that suffered from the super typhoon Mangkhut landed in 2018. The results verified the feasibility and effectiveness of the proposed method.

scholarly journals Wind speed prediction using extreme learning machine and neural network for resolving uncertainty in microgrids

2021 ◽  
Vol 1010 ◽  
pp. 012033
Author(s):  
A Seprijanto ◽  
M Syai’in ◽  
D F U Putra ◽  
N H Rohiem ◽  
N P U Putra ◽  
...  
Keyword(s):  
Neural Network ◽  
Wind Speed ◽  
Extreme Learning Machine ◽  
Wind Speed Prediction ◽  
Speed Prediction ◽  
Learning Machine

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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