Support vector machine algorithm for artificial intelligence optimization



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).


  1. 1.
    Xiao, Y., Kang, N., Hong, Y., et al.: Misalignment fault diagnosis of DFWT based on IEMD energy entropy and PSO-SVM. Entropy 19(1), 6–21 (2017)CrossRefGoogle Scholar
  2. 2.
    Du, J., Liu, Y., Yu, Y., et al.: A prediction of precipitation data based on support vector machine and particle swarm optimization (PSO-SVM) algorithms. Algorithms 10(2), 57–68 (2017)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Long, Y., Du, Z.J., Wang, W.D., et al.: PSO-SVM-based online locomotion mode identification for rehabilitation robotic exoskeletons. Sensors 16(9), 1408 (2016)CrossRefGoogle Scholar
  4. 4.
    Nieto, P.J.G., García-Gonzalo, E., Sánchez, A.B., et al.: Air quality modeling using the PSO-SVM-based approach, MLP neural network, and M5 model tree in the metropolitan area of Oviedo (Northern Spain). Environ. Model. Assess. 4, 1–19 (2017)Google Scholar
  5. 5.
    Kapoor, N., Ohri, J.: Fuzzified PSO-SVM controller for motion control of robotic manipulator. Int. J. Ind. Syst. Eng. 24(3), 361 (2016)Google Scholar
  6. 6.
    Zhou, C., Yin, K., Cao, Y., et al.: Application of time series analysis and PSO–SVM model in predicting the Bazimen landslide in the Three Gorges Reservoir, China. Eng. Geol. 204, 108–120 (2016)CrossRefGoogle Scholar
  7. 7.
    Hayat, M,, Tahir, M.: PSO fuzzy SVM-TMH: identification of transmembrane helix segments using ensemble feature space by incorporated fuzzy support vector machine. Mol. Biosyst. 11(8), 2255–2262 (2015)Google Scholar
  8. 8.
    Hameed, S.S., Hassan, R., Muhammad, F.F.: Selection and classification of gene expression in autism disorder: use of a combination of statistical filters and a GBPSO-SVM algorithm. PLoS ONE 12(11), e0187371 (2017)CrossRefGoogle Scholar
  9. 9.
    Guo, X., Guo, X., Su, J.: Improved support vector machine short-term power load forecast model based on particle swarm optimization parameters. J. Appl. Sci. 13(9), 1467–1472 (2013)CrossRefGoogle Scholar
  10. 10.
    Selakov, A., Mellon, S., Bekut, D.: Hybrid PSO-SVM method for short-term load forecasting during periods with significant temperature variations in city of Burbank. Appl. Soft Comput. 16(3), 80–88 (2014)CrossRefGoogle Scholar
  11. 11.
    Li, H.Z., Guo, S., Li, C.J., et al.: A hybrid annual power load forecasting model based on generalized regression neural network with fruit fly optimization algorithm. Knowl.-Based Syst. 37(2), 378–387 (2013)CrossRefGoogle Scholar
  12. 12.
    Lee, W.J., Hong, J.: A hybrid dynamic and fuzzy time series model for mid-term power load forecasting. Int. J. Electr. Power Energy Syst. 64, 1057–1062 (2015)CrossRefGoogle Scholar

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© 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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