Wind Speed Prediction Based on Support Vector Regression Method: a Case Study of Lome-Site

2019 ◽  
Author(s):  
Koffi Agbeblewu Dotche ◽  
Adekunle Akim Salami ◽  
Koffi Mawugno Kodjo ◽  
Hadnane Ouro-Agbake ◽  
Koffi-Sa Bedja
Keyword(s):  
Wind Speed ◽  
Support Vector Regression ◽  
Regression Method ◽  
Support Vector ◽  
Wind Speed Prediction ◽  
Speed Prediction

scholarly journals Support Vector Regression Method for Wind Speed Prediction Incorporating Probability Prior Knowledge

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Jiqiang Chen ◽  
Xiaoping Xue ◽  
Minghu Ha ◽  
Daren Yu ◽  
Litao Ma
Keyword(s):  
Wind Speed ◽  
Probability Distribution ◽  
Support Vector Regression ◽  
Prior Knowledge ◽  
Historical Data ◽  
Support Vector ◽  
Support Vector Regression Model ◽  
Wind Speed Prediction ◽  
Speed Fluctuation ◽  
Speed Prediction

Prior knowledge, such as wind speed probability distribution based on historical data and the wind speed fluctuation between the maximal value and the minimal value in a certain period of time, provides much more information about the wind speed, so it is necessary to incorporate it into the wind speed prediction. First, a method of estimating wind speed probability distribution based on historical data is proposed based on Bernoulli’s law of large numbers. Second, in order to describe the wind speed fluctuation between the maximal value and the minimal value in a certain period of time, the probability distribution estimated by the proposed method is incorporated into the training data and the testing data. Third, a support vector regression model for wind speed prediction is proposed based on standard support vector regression. At last, experiments predicting the wind speed in a certain wind farm show that the proposed method is feasible and effective and the model’s running time and prediction errors can meet the needs of wind speed prediction.

Short term wind speed prediction based on evolutionary support vector regression algorithms

2011 ◽  
Vol 38 (4) ◽  
pp. 4052-4057 ◽  
Author(s):  
Sancho Salcedo-Sanz ◽  
Emilio G. Ortiz-Garcı´a ◽  
Ángel M. Pérez-Bellido ◽  
Antonio Portilla-Figueras ◽  
Luis Prieto
Keyword(s):  
Wind Speed ◽  
Support Vector Regression ◽  
Support Vector ◽  
Short Term ◽  
Wind Speed Prediction ◽  
Regression Algorithms ◽  
Speed Prediction

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

Entropy ◽  
2019 ◽  
Vol 21 (11) ◽  
pp. 1056 ◽  
Author(s):  
Shiguang Zhang ◽  
Ting Zhou ◽  
Lin Sun ◽  
Wei Wang ◽  
Chuan Wang ◽  
...  
Keyword(s):  
Wind Speed ◽  
Regression Model ◽  
Support Vector Regression ◽  
Support Vector ◽  
Noise Distribution ◽  
Support Vector Regression Model ◽  
Wind Speed Prediction ◽  
Noise Characteristics ◽  
Augmented Lagrange Multiplier ◽  
Speed Prediction

Most regression techniques assume that the noise characteristics are subject to single noise distribution whereas the wind speed prediction is difficult to model by the single noise distribution because the noise of wind speed is complicated due to its intermittency and random fluctuations. Therefore, we will present the ν -support vector regression model of Gauss-Laplace mixture heteroscedastic noise (GLM-SVR) and Gauss-Laplace mixture homoscedastic noise (GLMH-SVR) for complex noise. The augmented Lagrange multiplier method is introduced to solve models GLM-SVR and GLMH-SVR. The proposed model is applied to short-term wind speed forecasting using historical data to predict future wind speed at a certain time. The experimental results show that the proposed technique outperforms the single noise technique and obtains good performance.

An application of backtracking search optimization–based least squares support vector machine for prediction of short-term wind speed

2019 ◽  
Vol 44 (3) ◽  
pp. 266-281 ◽  
Author(s):  
Zhongda Tian ◽  
Yi Ren ◽  
Gang Wang
Keyword(s):  
Support Vector Machine ◽  
Wind Speed ◽  
Prediction Model ◽  
Least Squares ◽  
Support Vector ◽  
Short Term ◽  
Backtracking Search ◽  
Wind Speed Prediction ◽  
Search Optimization ◽  
Speed Prediction

Wind speed prediction is an important technology in the wind power field; however, because of their chaotic nature, predicting wind speed accurately is difficult. Aims at this challenge, a backtracking search optimization–based least squares support vector machine model is proposed for short-term wind speed prediction. In this article, the least squares support vector machine is chosen as the short-term wind speed prediction model and backtracking search optimization algorithm is used to optimize the important parameters which influence the least squares support vector machine regression model. Furthermore, the optimal parameters of the model are obtained, and the short-term wind speed prediction model of least squares support vector machine is established through parameter optimization. For time-varying systems similar to short-term wind speed time series, a model updating method based on prediction error accuracy combined with sliding window strategy is proposed. When the prediction model does not match the actual short-term wind model, least squares support vector machine trains and re-establishes. This model updating method avoids the mismatch problem between prediction model and actual wind speed data. The actual collected short-term wind speed time series is used as the research object. Multi-step prediction simulation of short-term wind speed is carried out. The simulation results show that backtracking search optimization algorithm–based least squares support vector machine model has higher prediction accuracy and reliability for the short-term wind speed. At the same time, the prediction performance indicators are also improved. The prediction result is that root mean square error is 0.1248, mean absolute error is 0.1374, mean absolute percentile error is 0.1589% and R2 is 0.9648. When the short-term wind speed varies from 0 to 4 m/s, the average value of absolute prediction error is 0.1113 m/s, and average value of absolute relative prediction error is 8.7111%. The proposed prediction model in this article has high engineering application value.

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