Cluster Computing

, Volume 22, Supplement 6, pp 15015–15021 | Cite as

Support vector machine algorithm for artificial intelligence optimization

  • Xian Tan
  • Fasheng YuEmail author
  • Xifeng Zhao


To improve the short-term power prediction accuracy, a comparative analysis of the PSO and SVM algorithm was carried out. Then, the two were combined and, the penalty factor and kernel function parameters in SVM model were optimized by the improved PSO algorithm. The SVM algorithm with optimized parameters and model were applied to predict and control and form PSO-SVM algorithm. Finally, the short-term power load was modelled and predicted based on PSO-SVM algorithm and it was compared with the conventional SVM algorithm. The results showed that the relative error of the average absolute value of PSO-SVM method was 1.62%, while the relative relative error of the average absolute value of conventional SVM using particle swarm optimization algorithm was 3.52%. It can be seen that the error adopting the new algorithm is reduced by 1.9%. It shows that the precision of the improved power load forecasting model is greatly improved.


Particle swarm optimization (PSO) Support vector machines Artificial intelligence Power load 



The authors acknowledge the National Natural Science Foundation of China (Grant: 111578109), the National Natural Science Foundation of China (Grant: 11111121005).


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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Communication & Journalism CollegeCentral China Normal UniversityWuhanChina
  2. 2.College of Literature and CommunicationHubei University for NationalitiesEnshiChina

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