scholarly journals Hybrid Short Term Wind Speed Forecasting Using Variational Mode Decomposition and a Weighted Regularized Extreme Learning Machine

Energies ◽  
2016 ◽  
Vol 9 (12) ◽  
pp. 989 ◽  
Author(s):  
Nantian Huang ◽  
Chong Yuan ◽  
Guowei Cai ◽  
Enkai Xing
Keyword(s):  
Wind Speed ◽  
Extreme Learning Machine ◽  
Variational Mode Decomposition ◽  
Short Term ◽  
Wind Speed Forecasting ◽  
Mode Decomposition ◽  
Learning Machine

Ultra-short-term wind speed prediction based on variational mode decomposition and optimized extreme learning machine

2021 ◽  
pp. 0309524X2110385
Author(s):  
Lian Lian ◽  
Kan He
Keyword(s):  
Wind Speed ◽  
Prediction Model ◽  
Extreme Learning Machine ◽  
Prediction Accuracy ◽  
Good Prediction ◽  
Variational Mode Decomposition ◽  
Short Term ◽  
Mode Decomposition ◽  
Whale Optimization ◽  
Learning Machine

The main purpose of this paper is to improve the prediction accuracy of ultra-short-term wind speed. It is difficult to predict the ultra-short-term wind speed because of its unstable, non-stationary and non-linear. Aiming at the unstable and non-stationary characteristics of ultra-short-term wind speed, the variational mode decomposition algorithm is introduced to decompose the ultra-short-term wind speed data, and a series of stable and stationary components with different frequencies are obtained. The extreme learning machine with good prediction performance and real-time performance is selected as the prediction model of decomposed components. In order to solve the problem of random setting of input weights and bias of extreme learning machine, whale optimization algorithm is used to optimize extreme learning machine to improve the regression performance. The performance of the developed prediciton model is verified by real ultra-short-term wind speed sample data. Five prediction models are selected as the comparison model. Through the comparison between the predicted value and the actual value, the prediction error and its histogram distribution, eight performance indicators, and Pearson’s test correlation coefficient, the results show that the proposed prediction model has high prediction accuracy.

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