Wind speed prediction using reduced support vector machines with feature selection

The aims of this study contribute to a new hybrid model by combining ensemble empirical mode decomposition (EEMD) with multikernel function least square support vector machine (MKLSSVM) optimized by hybrid gravitation search algorithm (HGSA) for short-term wind speed prediction. In the forecasting process, EEMD is adopted to make the original wind speed data decomposed into intrinsic mode functions (IMFs) and one residual firstly. Then, partial autocorrelation function (PACF) is applied to identify the correlation between the corresponding decomposed components. Subsequently, the MKLSSVM using multikernel function of radial basis function (RBF) and polynomial (Poly) kernel function by weight coefficient is exploited as core forecasting engine to make the short-term wind speed prediction. To improve the regression performance, the binary-value GSA (BGSA) in HGSA is utilized as feature selection approach to remove the ineffective candidates and reconstruct the most relevant feature input-matrix for the forecasting engine, while real-value GSA (RGSA) makes the parameter combination optimization of MKLSSVM model. In the end, these respective decomposed subseries forecasting results are combined into the final forecasting values by aggregate calculation. Numerical results and comparable analysis illustrate the excellent performance of the EEMD-HGSA-MKLSSVM model when applied in the short-term wind speed forecasting.

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

Wind Engineering ◽

10.1177/0309524x19849843 ◽

2019 ◽

Vol 44 (3) ◽

pp. 266-281 ◽

Cited By ~ 1

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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Wind speed prediction model using singular spectrum analysis, empirical mode decomposition and convolutional support vector machine

Energy Conversion and Management ◽

10.1016/j.enconman.2018.11.006 ◽

2019 ◽

Vol 180 ◽

pp. 196-205 ◽

Cited By ~ 49

Author(s):

Xiwei Mi ◽

Hui Liu ◽

Yanfei Li

Keyword(s):

Support Vector Machine ◽

Wind Speed ◽

Prediction Model ◽

Empirical Mode Decomposition ◽

Spectrum Analysis ◽

Singular Spectrum Analysis ◽

Support Vector ◽

Wind Speed Prediction ◽

Mode Decomposition ◽

Speed Prediction

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Minimax feature selection problem for constructing a classifier using support vector machines

Computational Mathematics and Mathematical Physics ◽

10.1134/s0965542510050143 ◽

2010 ◽

Vol 50 (5) ◽

pp. 917-925

Author(s):

Yu. V. Goncharov

Keyword(s):

Feature Selection ◽

Support Vector Machines ◽

Selection Problem ◽

Support Vector ◽

Feature Selection Problem ◽

Vector Machines

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High dimensional data classification and feature selection using support vector machines

European Journal of Operational Research ◽

10.1016/j.ejor.2017.08.040 ◽

2018 ◽

Vol 265 (3) ◽

pp. 993-1004 ◽

Cited By ~ 63

Author(s):

Bissan Ghaddar ◽

Joe Naoum-Sawaya

Keyword(s):

Feature Selection ◽

Support Vector Machines ◽

High Dimensional Data ◽

Data Classification ◽

High Dimensional ◽

Support Vector ◽

Vector Machines

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Radar Emitter Signal Recognition Based on Feature Selection and Support Vector Machines

Lecture Notes in Computer Science - Advances in Intelligent Computing ◽

10.1007/11538059_74 ◽

2005 ◽

pp. 707-716 ◽

Cited By ~ 2

Author(s):

Gexiang Zhang ◽

Zhexin Cao ◽

Yajun Gu ◽

Weidong Jin ◽

Laizhao Hu

Keyword(s):

Feature Selection ◽

Support Vector Machines ◽

Support Vector ◽

Signal Recognition ◽

Vector Machines

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Research on Particle Swarm Optimization Least Square Support Vector Machine for Short-Term Wind Speed Prediction

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.511-512.927 ◽

2014 ◽

Vol 511-512 ◽

pp. 927-930

Author(s):

Shuai Zhang ◽

Hai Rui Wang ◽

Jin Huang ◽

He Liu

Keyword(s):

Support Vector Machine ◽

Particle Swarm Optimization ◽

Wind Speed ◽

Particle Swarm ◽

Least Square ◽

Support Vector ◽

Short Term ◽

Swarm Optimization ◽

Wind Speed Prediction ◽

Speed Prediction

In the paper, the forecast problems of wind speed are considered. In order to enhance the redaction accuracy of the wind speed, this article is about a research on particle swarm optimization least square support vector machine for short-term wind speed prediction (PSO-LS-SVM). Firstly, the prediction models are built by using least square support vector machine based on particle swarm optimization, this model is used to predict the wind speed next 48 hours. In order to further improve the prediction accuracy, on this basis, introduction of the offset optimization method. Finally large amount of experiments and measurement data comparison compensation verify the effectiveness and feasibility of the research on particle swarm optimization least square support vector machine for short-term wind speed prediction, Thereby reducing the short-term wind speed prediction error, very broad application prospects.

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