References

• 1. 1)
     • 32. Hong, T., Pinson, P., Fan, S.: ‘Global energy forecasting competition 2012’, Int. J. Forecast., 2014, 30, pp. 357–363 (doi: 10.1016/j.ijforecast.2013.07.001).
  2. 2)
     • 28. Song, Y.D., Li, X.H., Cai, W.C.: ‘Adaptive and fault-tolerant reactive power compensation in power systems via multilevel statcoms’, Int. J. Innovative Comput. Inf. Control, 2013, 9, (8), pp. 3403–3413.
  3. 3)
     • 3. Song, Y.D., Zhang, Z.Y., Li, P.et al.: ‘Robust adaptive variable speed control of wind power systems without wind speed measurement’, J. Renew. Sustain. Energy, 2013, 5, (6), Article ID 107450, p. 12 (doi: 10.1063/1.4840035).
  4. 4)
     • 23. Chang, P., Fan, C., Lin, J.: ‘Monthly electricity demand forecasting based on a weighted evolving fuzzy neural network approach’, Electr. Power Energy Syst., 2011, 33, pp. 17–27 (doi: 10.1016/j.ijepes.2010.08.008).
  5. 5)
     • 25. Pai, P.-F., Hongi, W.-C.: ‘Support vector machines with simulated annealing algorithms in electricity load forecasting’, Energy Convers. Manage., 2005, 46, (17), pp. 2669–2688 (doi: 10.1016/j.enconman.2005.02.004).
  6. 6)
     • 9. Huang, C.M., Huang, C.J., Wang, M.L.: ‘A particle swarm optimization to identifying the ARMAX model for short-term load forecasting’, IEEE Trans. Power Syst., 2005, 20, pp. 1126–1133 (doi: 10.1109/TPWRS.2005.846106).
  7. 7)
     • 4. Saber, A.Y., Shareef, A.A.: ‘Load forecasting of a desert: a computational intelligence approach’, Proc. 2009. ISAP '09. 15th Int. Conf. on Intelligent System Applications to Power Systems, 2009, pp. 1–7.
  8. 8)
     • 14. Fan, S., Hyndman, R.J.: ‘Short-term load forecasting based on a semi-parametric additive models’, IEEE Trans. Power Syst., 2012, 27, pp. 134–141 (doi: 10.1109/TPWRS.2011.2162082).
  9. 9)
     • 15. Goude, Y., Nedellec, R., Kong, N.: ‘Local short and middle term electricity load forecasting with semi-parametric additive models’, IEEE Trans. Smart Grid, 2014, 5, pp. 440–446 (doi: 10.1109/TSG.2013.2278425).
  10. 10)
      • 30. Hou, M.Z., Duan, G.R., Guo, M.S.: ‘New versions of Barbalat's lemma with applications’, J. Control Theory Appl., 2010, 8, (4), pp. 545–547 (doi: 10.1007/s11768-010-8178-z).
  11. 11)
      • 8. Stockman, M., Ramli, R.S.E., Awad, M.et al.: ‘An asymmetrical and quadratic support vector regression loss function for Beirut short term load forecast’, Proc. 2012 IEEE Int. Conf. on Systems, Man, and Cybernetics (SMC), 2012, vol. 12, pp. 651–656.
  12. 12)
      • 5. Amjady, N., Keynia, F., Zareipour, H.: ‘Short-term load forecast of microgrids by a new bilevel prediction strategy’, IEEE Trans. Smart Grid, 2010, 1, (3), pp. 286–294 (doi: 10.1109/TSG.2010.2078842).
  13. 13)
      • 1. Chen, B.-J., Chang, M.-W., Lin, C.-J.: ‘Included in your digital subscription load forecasting using support vector Machines: a study on EUNITE competition 2001’, IEEE Trans. Power Syst., 2004, 19, (4), pp. 1821–1830 (doi: 10.1109/TPWRS.2004.835679).
  14. 14)
      • 19. Niu, D.X., Wang, Y., Wu, D.D.: ‘Power load forecasting using support vector machine and ant colony optimization’, Expert Syst. Appl., 2010, 37, pp. 2531–2539 (doi: 10.1016/j.eswa.2009.08.019).
  15. 15)
      • 7. Lin, C.T., Chou, L.D.: ‘Incorporating stock index in a support vector regression model to improve short term load forecasting accuracy’, Proc. 2012 Int. Symp. on Computer, Consumer and Control (IS3C), 2012, pp. 634–637.
  16. 16)
      • 2. Ceperic, E., Ceperic, V., Baric, A.: ‘A strategy for short-term load forecasting by support vector regression machines’, IEEE Trans. Power Syst., 2013, 28, (4), pp. 4356–4364 (doi: 10.1109/TPWRS.2013.2269803).
  17. 17)
      • 12. Shankar, R., Chatterjee, K., Chatterjee, T.K.: ‘A very short-term load forecasting using Kalman filter for load frequency control with economic load dispatch’, J. Eng. Sci. Technol. Rev., 2012, 5, pp. 97–103.
  18. 18)
      • 33. Tashman, L.J.: ‘Out-of-sample tests of forecasting accuracy: an analysis and review’, Int. J. Forecast., 2000, 16, (4), pp. 437–450 (doi: 10.1016/S0169-2070(00)00065-0).
  19. 19)
      • 34. Moghram, L., Rahrnan, S.: ‘Analysis and evaluation of five short-term load forecasting techniques’, IEEE Trans. Power Syst., 1989, 4, (4), pp. 1484–1491 (doi: 10.1109/59.41700).
  20. 20)
      • 31. Wang, J.Z., Xiao, L., Shi, J.: ‘The combination forecasting of electricity price based on price spikes processing: a case study in south Australia’, Abstract Appl. Anal., 2014, 2014, Article ID 172306, p. 12.
  21. 21)
      • 29. Song, Y.D., Shen, Z.X., Dai, D.L.et al.: ‘Short-term load forecasting in electrical power systems via trajectory tracking and error correcting approach’, J. Renew. Sustain. Energy, 2014, 6, Article ID 013112, p. 16 (doi: 10.1063/1.4861784).
  22. 22)
      • 26. Shi, J., Guo, J.M., Zheng, S.T.: ‘Evaluation of hybrid forecasting approaches for wind speed and power generation time series’, Renew. Sustain. Energy Rev., 2012, 16, pp. 3471–3480 (doi: 10.1016/j.rser.2012.02.044).
  23. 23)
      • 25. Khashei, M., Bijari, M.: ‘A novel hybridization of artificial neural networks and ARIMA models for time series forecasting’, Appl. Soft Comput., 2011, 11, pp. 2664–2675 (doi: 10.1016/j.asoc.2010.10.015).
  24. 24)
      • 17. Khosravi, A., Nahavandi, S., Creighton, D.et al.: ‘Interval type-2 fuzzy logic systems for load forecasting: a comparative study’, IEEE Trans. Power Syst., 2012, 27, pp. 1274–1282 (doi: 10.1109/TPWRS.2011.2181981).
  25. 25)
      • 2. Song, Y.D., Dhinakaran, B.: ‘Nonlinear variable speed control of wind turbines’, Proc. 1999 IEEE Int. Conf. on Control Applications, 1999, vol. 1, pp. 814–819.
  26. 26)
      • 16. Hinojosa, V.H., Hoese, A.: ‘Short-term load forecasting using fuzzy inductive reasoning and evolutionary algorithms’, IEEE Trans. Power Syst., 2010, 25, pp. 565–574 (doi: 10.1109/TPWRS.2009.2036821).
  27. 27)
      • 11. Romani, L.A.S., de Avila, A.M.H., Chino, D.Y.T.et al.: ‘A new time series mining approach applied to multitemporal remote sensing imagery’, IEEE Trans. Geosci. Remote Sens., 2013, 51, pp. 140–150 (doi: 10.1109/TGRS.2012.2199501).
  28. 28)
      • 10. Nataraja, C., Gorawar, M.B., Shilpa, G.N.et al.: ‘Short term load forecasting using time series analysis: a case study for Karnataka, India’, Int. J. Eng. Sci. Innovative Technol., 2012, 1, pp. 45–53.
  29. 29)
      • 1. Matija, M., Suykens, J.A.K., Krajcar, S.: ‘Load forecasting using a multivariate meta-learning system’, Expert Syst. Appl., 2013, 40, pp. 4427–4437 (doi: 10.1016/j.eswa.2013.01.047).
  30. 30)
      • 13. Peng, J.C., Nair, N.C.: ‘Enhancing Kalman filter for tracking ringdown electromechanical oscillations’, IEEE Trans. Power Syst., 2012, 27, (2), pp.1042–1050 (doi: 10.1109/TPWRS.2011.2169284).
  31. 31)
      • 22. Zhao, Y.G., Luh, P.B., Bomgardner, C.et al.: ‘Short-term load forecasting: multi-level wavelet neural networks with holiday corrections’, Proc. 2009 IEEE Power and Energy Society General Meeting, 2009, pp. 1–7.
  32. 32)
      • 21. Hippert, H.S., Pedreira, C.E., Souza, R.C.: ‘Neural networks for short-term load forecasting: a review and evaluation’, IEEE Trans. Power Syst., 2001, 16, (1), pp. 44–55 (doi: 10.1109/59.910780).
  33. 33)
      • 24. Jain, A., Kumar, A.M.: ‘Evaluation of hybrid forecasting approaches for wind speed and power generation time series’, Appl. Soft Comput., 2007, 7, pp. 585–592 (doi: 10.1016/j.asoc.2006.03.002).
  34. 34)
      • 27. D'az-Robles, L.A., Ortega, J.C., Fu, J.S.: ‘A hybrid ARIMA and artificial neural networks model to forecast particulate matter in urban areas: the case of Temuco, Chile’, Atmos. Environ., 2008, 42, pp. 8331–8340 (doi: 10.1016/j.atmosenv.2008.07.020).