scholarly journals A Short-Term Wind Speed Forecasting Through Support Vector Regression Regularized by Particle Swarm Optimization

2011 ◽  
Vol 11 (4) ◽  
pp. 247-253 ◽  
Author(s):  
Seong-Jun Kim ◽  
In-Yong Seo
Keyword(s):  
Particle Swarm Optimization ◽  
Wind Speed ◽  
Support Vector Regression ◽  
Particle Swarm ◽  
Support Vector ◽  
Short Term ◽  
Swarm Optimization ◽  
Wind Speed Forecasting

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

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.

Particle Swarm Optimization-Enhanced Twin Support Vector Regression for Wind Speed Forecasting

2017 ◽  
Vol 28 (5) ◽  
pp. 905-914 ◽  
Author(s):  
Essam H. Houssein
Keyword(s):  
Particle Swarm Optimization ◽  
Wind Speed ◽  
Support Vector Regression ◽  
Renewable Energy Sources ◽  
Particle Swarm ◽  
Support Vector ◽  
Twin Support Vector Regression ◽  
Swarm Optimization ◽  
Suggested Approach ◽  
Forecasting Accuracy

Abstract Wind energy is considered one of the renewable energy sources that minimize the cost of electricity production. This article proposes a hybrid approach based on particle swarm optimization (PSO) and twin support vector regression (TSVR) for forecasting wind speed (PSO-TSVR). To enhance the forecasting accuracy, TSVR was utilized to forecast the wind speed, and the optimal settings of TSVR parameters were optimized by PSO carefully. Moreover, to estimate the performance of the suggested approach, three wind speed benchmark data of OpenEI were used as a case study. The experimental results revealed that the optimized PSO-TSVR approach is able to forecast wind speed with an accuracy of 98.9%. Further, the PSO-TSVR approach has been compared with two well-known algorithms such as genetic algorithm with TSVR and the native TSVR using radial basis kernel function. The computational results proved that the proposed approach achieved better forecasting accuracy and outperformed the comparison algorithms.

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