ν-Support Vector Regression Model Based on Gauss-Laplace Mixture Noise Characteristic for Wind Speed Prediction

Zhang, Shiguang; Zhou, Ting; Sun, Lin; Wang, Wei; Wang, Chuan; Mao, Wentao

doi:10.3390/e21111056

Cited by 3 publications

(4 citation statements)

References 48 publications

(54 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For the linear model, we want to find a linear regression function

. When dealing with some nonlinear problems, some specific methods are given ([ 53 ]): the input vector

is mapped by a non-linear mapping

(take a prior distribution) to the high dimensional feature space H ( H is Hilbert space), induced by the nonlinear kernel function

is the inner product in H .…”

Section: Tlssvr Model Of G-l Mixed Noise Characteristicsmentioning

confidence: 99%

“…When dealing with some nonlinear problems, some specific methods are given ( [53]): the input vector A i ∈ R n is mapped by a non-linear mapping Φ: R n → H (take a prior distribution) to the high dimensional feature space H (H is Hilbert space), induced by the nonlinear kernel function K(A i , A j ) = (Φ(A i ) • Φ(A j )) (i, j = 1, 2, . .…”

Section: Tlssvr Model Of G-l Mixed Noise Characteristicsmentioning

confidence: 99%

See 1 more Smart Citation

Twin Least Square Support Vector Regression Model Based on Gauss-Laplace Mixed Noise Feature with Its Application in Wind Speed Prediction

Zhang

Liu

Wang

et al. 2020

Entropy

Self Cite

View full text Add to dashboard Cite

In this article, it was observed that the noise in some real-world applications, such as wind power forecasting and direction of the arrival estimation problem, does not satisfy the single noise distribution, including Gaussian distribution and Laplace distribution, but the mixed distribution. Therefore, combining the twin hyperplanes with the fast speed of Least Squares Support Vector Regression (LS-SVR), and then introducing the Gauss–Laplace mixed noise feature, a new regressor, called Gauss-Laplace Twin Least Squares Support Vector Regression (GL-TLSSVR), for the complex noise. Subsequently, we apply the augmented Lagrangian multiplier method to solve the proposed model. Finally, we apply the short-term wind speed data-set to the proposed model. The results of this experiment confirm the effectiveness of our proposed model.

show abstract

“…For the linear model, we want to find a linear regression function

. When dealing with some nonlinear problems, some specific methods are given ([ 53 ]): the input vector

is mapped by a non-linear mapping

(take a prior distribution) to the high dimensional feature space H ( H is Hilbert space), induced by the nonlinear kernel function

is the inner product in H .…”

Section: Tlssvr Model Of G-l Mixed Noise Characteristicsmentioning

confidence: 99%

Section: Tlssvr Model Of G-l Mixed Noise Characteristicsmentioning

confidence: 99%

Twin Least Square Support Vector Regression Model Based on Gauss-Laplace Mixed Noise Feature with Its Application in Wind Speed Prediction

Zhang

Liu

Wang

et al. 2020

Entropy

Self Cite

View full text Add to dashboard Cite

show abstract

“…Additionally, ref. [14] introduced a ν-SVR model tailored for complex noise in short-term wind speed forecasting, achieving significant performance improvements. Finally, ref.…”

Section: Introductionmentioning

confidence: 99%

Exploring Time Series Models for Wind Speed Forecasting: A Comparative Analysis

Li,

Shen

et al. 2023

Energies

View full text Add to dashboard Cite

The sustainability and efficiency of the wind energy industry rely significantly on the accuracy and reliability of wind speed forecasting, a crucial concern for optimal planning and operation of wind power generation. In this study, we comprehensively evaluate the performance of eight wind speed prediction models, spanning statistical, traditional machine learning, and deep learning methods, to provide insights into the field of wind energy forecasting. These models include statistical models such as ARIMA (AutoRegressive Integrated Moving Average) and GM (Grey Model), traditional machine learning models like LR (Linear Regression), RF (random forest), and SVR (Support Vector Regression), as well as deep learning models comprising ANN (Artificial Neural Network), LSTM (Long Short-Term Memory), and CNN (Convolutional Neural Network). Utilizing five common model evaluation metrics, we derive valuable conclusions regarding their effectiveness. Our findings highlight the exceptional performance of deep learning models, particularly the Convolutional Neural Network (CNN) model, in wind speed prediction. The CNN model stands out for its remarkable accuracy and stability, achieving the lowest mean squared error (MSE), root mean squared error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and the higher coefficient of determination (R2). This underscores the CNN model’s outstanding capability to capture complex wind speed patterns, thereby enhancing the sustainability and reliability of the renewable energy industry. Furthermore, we emphasized the impact of model parameter tuning and external factors, highlighting their potential to further improve wind speed prediction accuracy. These findings hold significant implications for the future development of the wind energy industry.

show abstract

“…Mixed

machine [ 27 ] is applied to forecast the wind speed noise, which improves performance of wind-speed prediction.

[ 28 ] models fitted by Gaussian–Laplacian (G-L) mixed noise are developed, and good performance is obtained compared with the existing regression algorithm.…”

Section: Introductionmentioning

confidence: 99%

LSSVR Model of G-L Mixed Noise-Characteristic with Its Applications

Zhang

Zhou

Sun

et al. 2020

Entropy

Self Cite

View full text Add to dashboard Cite

Due to the complexity of wind speed, it has been reported that mixed-noise models, constituted by multiple noise distributions, perform better than single-noise models. However, most existing regression models suppose that the noise distribution is single. Therefore, we study the Least square S V R of the Gaussian–Laplacian mixed homoscedastic ( G L M − L S S V R ) and heteroscedastic noise ( G L M H − L S S V R ) for complicated or unknown noise distributions. The ALM technique is used to solve model G L M − L S S V R . G L M − L S S V R is used to predict short-term wind speed with historical data. The prediction results indicate that the presented model is superior to the single-noise model, and has fine performance.

show abstract

ν-Support Vector Regression Model Based on Gauss-Laplace Mixture Noise Characteristic for Wind Speed Prediction

Cited by 3 publications

References 48 publications

Twin Least Square Support Vector Regression Model Based on Gauss-Laplace Mixed Noise Feature with Its Application in Wind Speed Prediction

Twin Least Square Support Vector Regression Model Based on Gauss-Laplace Mixed Noise Feature with Its Application in Wind Speed Prediction

Exploring Time Series Models for Wind Speed Forecasting: A Comparative Analysis

LSSVR Model of G-L Mixed Noise-Characteristic with Its Applications

Contact Info

Product

Resources

About