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Academic Journal of Computing & Information Science, 2021, 4(5); doi: 10.25236/AJCIS.2021.040501.

Prediction of global electricity generation by using least squares support vector regression with sparrow search algorithm


Yuanjie Xu

Corresponding Author:
Yuanjie Xu

North University of China, School of Electrical and Control Engineering, Taiyuan, Shanxi, 030051, China


In order to accurately predict the development of global electricity generation, this paper presents a prediction model of least squares support vector machine (LS-SVR) based on Sparrow search optimization algorithm (SSA), and obtains the fitting curve through iterative optimization of the hyperparameters. The results show that the prediction accuracy of the least squares support vector regression machine (SSA_LSSVR) based on the sparrow search optimization algorithm is higher, and it can predict the development trend of global power production more accurately.


Global electricity generation, Sparrow search optimization algorithm, Least squares support vector machine, Time series prediction

Cite This Paper

Yuanjie Xu. Prediction of global electricity generation by using least squares support vector regression with sparrow search algorithm. Academic Journal of Computing & Information Science (2021), Vol. 4, Issue 5: 1-7. https://doi.org/10.25236/AJCIS.2021.040501.


[1] H.S. Hippert, C.E. Pedreira, R.C. Souza, Neural networks for short-term load forecasting: A review and evaluation, IEEE Trans. Power Syst. 16 (1) (2001) 44–55.

[2] F.J. Chang, Y.C. Chen, Estuary water-stage forecasting by using radial basis function neural network, J. Hydrol. 270 (1/2) (2003) 158–166.

[3] M.H. Wang, C.P. Hung, Novel grey model for the prediction of trend of dissolved gases in oil-filled power apparatus, Electr. Power Syst. Res. 67 (1) (2003) 53–58.

[4] S.W. Fei, Y. Sun, Forecasting dissolved gases content in power transformer oil based on support vector machine with genetic algorithm, Electr. Power Syst. Res. 78 (3) (2008) 507–514.

[5] F.E.H. Tay, L.J. Cao, Application of support vector machines in financial time series forecasting, Omega-Int. J. Manage. Sci. 29 (4) (2001) 309–317.

[6] Q. Wu, The forecasting model based on wavelet v-support vector machine, Expert Syst. Appl. 36 (4) (2009) 7604–7610.

[7] V.N. Vapnik, the Nature of Statistical Learning Theory, Springer-Verlag, New York, 1998.

[8] J.A.K. Suykens, T. Van Gestel, J. De Brabanter, B. De Moor, J. Vandewalle, Least Squares Support Vector Machines, World Scientific, Singapore, 2002.

[9] T. Van Gestel, J.A.K. Suykens, D.E. Baestaens, Financial time series prediction using least squares support vector machines within the evidence framework, IEEE Trans. Neural Netw. 12 (4) (2001) 809–821.

[10] Z. Yang, X.S. Gu, X.Y. Liang, L.C. Ling, Genetic algorithm-least squares support vector regression based predicting and optimizing model on carbon fiber composite integrated conductivity, Mater. Des. 31 (3) (2010) 1042–1049.