A Combined Model Based on Neural Networks, LSSVM and Weight Coefficients Optimization for Short-Term Electric Load Forecasting

  • Caihong LiEmail author
  • Zhaoshuang He
  • Yachen Wang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9998)


As an essential energy in the daily life, electricity which is difficult to store has become a hot issue in power system. Short-term electric load forecasting (STLF) which is regarded as a vital tool helps electric power companies make good decisions. It can not only guarantee adequate energy supply but also avoid unnecessary wastes. Although there exists quantity of forecasting methods, most of them are not able to make accurate predictions. Therefore, a forecasting method with high accuracy is particularly important. In this paper, a combined model based on neural networks and least squares support vector machine (LSSVM) is proposed to improve the forecasting accuracy. At first, three forecasting methods named generalized regression neural network (GRNN), Elman, LSSVM are utilized to forecast respectively. Among them, simulate anneal (SA) arithmetic is used to optimize GRNN. Then, SA is employed to determine the weight coefficients of each individual method. At last, multiplying all the three forecasting results with the corresponding weights, the final result of the combined model can be attained. Using the electric load data of Queensland of Australia as experimental simulation, case studies show that the proposed combined model works well for STLF and the results prove more accurate.


Generalized regression neural network Elman Least squares support vector machine simulated annealing algorithm Short-term electric load forecasting 



The authors would like to thank the Natural Science Foundation of PR of China (61073193,61300230), the Key Science and Technology Foundation of Gansu Province (1102FKDA010), the Natural Science Foundation of Gansu Province (1107RJZA188), and the Science and Technology Support Program of Gansu Province (1104GKCA037) for supporting this research.


  1. 1.
    Masa-Bote, D., Castillo-Cagigal, M., et al.: Improving photvoltaics grid integration through short time forecasting and self-consumption. Appl. Energy 125, 103–113 (2014)CrossRefGoogle Scholar
  2. 2.
    Hong, W.C.: Electric load forecasting by seasonal recurrent SVR (support vector regression) with chaotic artificial bee colony algorithm. Energy 36, 5568–5578 (2011)CrossRefGoogle Scholar
  3. 3.
    Li, S., Wang, P., Goel, L.: Short-term load forecasting by wavelet transform and evolutionary extreme learning machine. Electr. Power Syst Res. 122, 96–103 (2015)CrossRefGoogle Scholar
  4. 4.
    Deihimi, A., Showkati, H.: Application of echo state networks in short-term electric load forecasting. Energy 39, 327–340 (2012)CrossRefGoogle Scholar
  5. 5.
    Zhang, R., Dong, Z.Y., Xu, Y., Meng, K., Wong, K.P.: Short-term load forecasting of Australian national electricity market by an ensemble model of extreme learning machine. Gener. Transm. Distrib. IET 7, 391–397 (2013)CrossRefGoogle Scholar
  6. 6.
    Kandil, N., Wamkeue, R., Saad, M., Georges, S.: An efficient approach for short term load forecasting using artificial neural networks. Int. J. Electr. Power Energy Syst. 28(8), 525–530 (2006)CrossRefGoogle Scholar
  7. 7.
    Jin, M., Zhou, X., Zhang, Z.M., Tentzeris, M.M.: Short-term power load forecasting using grey correlation contest modeling. Expert Syst. Appl. 39, 773–779 (2012)CrossRefGoogle Scholar
  8. 8.
    Moghram, I., Rahman, S.: Analysis and evaluation of five short-term load forecasting techniques. IEEE Trans. Power Syst. 4(4), 1484–1494 (1989)CrossRefGoogle Scholar
  9. 9.
    Amina, M., Kodogiannis, V.S., Petrounias, I., Tomtsis, D.: A hybrid intelligent approach for the prediction of electricity consumption. Int. J. Electr. Power Energy Syst. 43(1), 99–108 (2012)CrossRefGoogle Scholar
  10. 10.
    Xiao, L., Wang, J., Yang, X., Xiao, L.: A hybrid model based on data preprocessing for electrical power forecasting. Electr. Power Energy Syst. 64, 311–327 (2015)CrossRefGoogle Scholar
  11. 11.
    Liu, N., Tang, Q., Zhang, J.H., Fan, W., Liu, J.: A hybrid forecasting model with parameter optimization for short-term load forecasting of micro-grid. Appl. Energy 129, 336–345 (2014)CrossRefGoogle Scholar
  12. 12.
    Wang, J.Z., Zhu, S.L., Zhang, W.Y., Lu, H.Y.: Combined modeling for electric load forecasting with adaptive partical swarm optimization. Energy 35(4), 1671–1678 (2010)CrossRefGoogle Scholar
  13. 13.
    Xiao, Y., Liu, J.J., et al.: A neuro-fuzzy combination model based on singular spectrum analysis for air transport demand forecasting. J. Air Transp. Manage. 39, 1–11 (2014)CrossRefGoogle Scholar
  14. 14.
    Souptick, C., Sanjay, G., Dilip, K.P.: A combined neural network and genetic algorithm based approach for optimally designed femoral implant having improved primary stability. Appl. Soft Comput. 38, 296–307 (2016)CrossRefGoogle Scholar
  15. 15.
    Xia, C.H., Lei, B.J., Wang, H.P., Li, J.N.: GRNN short-term load forecasting model and virtual instrument design. Energy Procedia 13, 9150–9158 (2011)CrossRefGoogle Scholar
  16. 16.
    Chelgani, S.C., Jorjani, E.: Microwave irradiation pretreatment and peroxyacetic acid desulfurzation of coal and application of GRNN simultaneous predictor. Fuel 90(11), 3156–3163 (2011)CrossRefGoogle Scholar
  17. 17.
    Li, H.Z., Guo, S., Li, C.J., Sun, J.Q.: A hybrid annual power load forecasting model based on generalized neural network with fruit fly optimization algorithm. Knowl. Based Syst. 37, 378–387 (2013)CrossRefGoogle Scholar
  18. 18.
    Kelo, S., Dudul, S.: A wavelet Elman neural network for short-term load prediction under the influence of temperature. Electr. Power Energy Syst. 43, 1063–1071 (2012)CrossRefGoogle Scholar
  19. 19.
    Song, Q.: On the weight convergence of Elman networks. IEEE Trans. Neural Netw. 21(3), 96–101 (2010)Google Scholar
  20. 20.
    Li, X., Chen, G., Chen, Z., Yuan, Z.: Chaotifying linear Elman networks. IEEE Trans. Neural Netw. 13(5), 1193–1199 (2002)CrossRefGoogle Scholar
  21. 21.
    Shayeghi, H., Ghasemi, A.: Day-ahead electricity prices forecasting by a modified CGSA technique and hybrid WT in LSSVM based scheme. Energy Convers. Manage. 74, 482–491 (2013)CrossRefGoogle Scholar
  22. 22.
    Zhang, M., Fu, L.: Unbiased least squares support vector machine with polynomial kernel. In: 8th IEEE International Conference on Signal Processing (ICSP-2006), vol. 3, Guilin, China, pp. 16–20 (2006)Google Scholar
  23. 23.
    Xie, L., Zheng, H., Zhang, L.Z.: Electricity price forecasting by clustering-LSSVM. In: Proceedings of the International Power, Engineering Conference, pp. 697–702 (2007)Google Scholar
  24. 24.
    Hong, W.C.: Traffic flow forecasting by seasonal SVR with chaotic simulated annealing algorithm. Neurocomputing 74, 2096–2107 (2011)CrossRefGoogle Scholar
  25. 25.
    Lv, P., Yuan, L., Zhang, J.: Clound theory-based simulated annealing algorithm and application. Eng. Appl. Artif. Intell. 22, 742–749 (2009)CrossRefGoogle Scholar
  26. 26.
    Pai, P.F., Hong, W.C.: Support vector machines with simulated annealing algorithms in electricity load forecasting. Energy Convers. Manage. 46(17), 2669–2688 (2005)CrossRefGoogle Scholar
  27. 27.
    Chandra, R.: Competition and collaboration in cooperative coevolution of Elman recurrent neural networks for time-series prediction. IEEE Trans. Neural Netw. Learn. Syst. 26, 3123–3136 (2015)MathSciNetCrossRefGoogle Scholar
  28. 28.
    Specht, D.F.: A general regression neural network. IEEE Trans. Neural Netw. 2(6), 568–576 (1991)CrossRefGoogle Scholar
  29. 29.
    Suykens, J.A.K., Van Gestel, T., De Brabanter, J., et al.: Least Square Support Vector Machines. World Scientific, Singapore (2002)CrossRefzbMATHGoogle Scholar
  30. 30.
    An, N., Zhao, W., Wang, J., et al.: Using multi-output feedforward nerual network with empirical mode decomposition based signal filtering for electricity demand forecasting. Energy 49, 279–288 (2013)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.School of Information Science and EngineeringLanzhou UniversityLanzhouPeople’s Republic of China

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