A hybrid application algorithm based on the support vector machine and artificial intelligence: An example of electric load forecasting

Applied Mathematical Modelling

Volumn 39, Issue 9, 2015, Pages 2617-2632

  Chen, Yanhua ^a   Yang, Yi ^a   Liu, Chaoqun ^a   Li, Caihong ^a   Li, Lian ^a  

^a LANZHOU UNIVERSITY   (China)

Author keywords

Electric load forecasting; Empirical mode decomposition; LSSVM; PSO; Seasonal adjustment

Indexed keywords

COMMERCE; ELECTRIC POWER PLANT LOADS; FORECASTING; LEAST SQUARES APPROXIMATIONS; PARTICLE SWARM OPTIMIZATION (PSO); SIGNAL FILTERING AND PREDICTION; SUPPORT VECTOR MACHINES;

COMBINED FORECASTING; EMPIRICAL MODE DECOMPOSITION; FORECASTING ACCURACY; HYBRID APPLICATIONS; LEAST SQUARES SUPPORT VECTOR MACHINES; LSSVM; MARKET PARTICIPANTS; SEASONAL ADJUSTMENTS;

ELECTRIC LOAD FORECASTING;

EID: 84928213604     PISSN: 0307904X     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.apm.2014.10.065     Document Type: Article

References (41)

1. Al-Hamadi H.M., Soliman S.A. Short-term electric load forecasting based on Kalman filtering algorithm with moving window weather and load model. Electr. Power Syst. Res. 2004, 68(1):47-59.
2. Senjyu T., Mandal P., Uezato K., et al. Next day load curve forecasting using hybrid correction method. IEEE Trans. Power Syst. 2005, 20(1):102-109.
3. Yang X., Yuan J., Yuan J., et al. An improved WM method based on PSO for electric load forecasting. Expert Syst. Appl. 2010, 37(12):8036-8041.
4. Lou C.W., Dong M.C. Modeling data uncertainty on electric load forecasting based on type-2 fuzzy logic set theory. Eng. Appl. Artif. Intel. 2012, 25(8):1567-1576.
5. Kucukali S., Baris K. Turkey's short-term gross annual electricity demand forecast by fuzzy logic approach. Energy Policy 2010, 38(5):2438-2445.
6. Wang J., Chi D., Wu J., et al. Chaotic time series method combined with particle swarm optimization and trend adjustment for electricity demand forecasting. Expert Syst. Appl. 2011, 38(7):8419-8429.
7. Vilar J.M., Cao R., Aneiros G. Forecasting next-day electricity demand and price using nonparametric functional methods. Int. J. Electr. Power Energy Syst. 2012, 39(1):48-55.
8. Wang C., Grozev G., Seo S. Decomposition and statistical analysis for regional electricity demand forecasting. Energy 2012, 41(1):313-325.
9. Abu-Shikhah N., Elkarmi F. Medium-term electric load forecasting using singular value decomposition. Energy 2011, 36(7):4259-4271.
10. Moghram I.S., Rahman S. Analysis and evaluation of five short-term load forecasting techniques. IEEE Trans. Power Syst. 1989, 4(4):1484-1491.
11. Bates J.M., Granger C.W.J. The combination of forecasts. OR 1969, 451-468.
12. Dickinson J.P. Some comments on the combination of forecasts. Oper. Res. Q. 1975, 205-210.
13. Li H., Guo S., Li C., et al. A hybrid annual power load forecasting model based on generalized regression neural network with fruit fly optimization algorithm. Knowl. Based Syst. 2013, 37:378-387.
14. Zhang W.Y., Hong W.C., Dong Y., et al. Application of SVR with chaotic GASA algorithm in cyclic electric load forecasting. Energy 2012, 45(1):850-858.
15. Xiao Z., Ye S.J., Zhong B., et al. BP neural network with rough set for short term load forecasting. Expert Syst. Appl. 2009, 36(1):273-279.
16. Kheirkhah A., Azadeh A., Saberi M., et al. Improved estimation of electricity demand function by using of artificial neural network, principal component analysis and data envelopment analysis. Comput. Ind. Eng. 2013, 64(1):425-441.
17. Chang P.C., Fan C.Y., Lin J.J. Monthly electricity demand forecasting based on a weighted evolving fuzzy neural network approach. Int. J. Electr. Power Energy Syst. 2011, 33(1):17-27.
18. Wang J., Wang J., Li Y., et al. Techniques of applying wavelet de-noising into a combined model for short-term load forecasting. Int. J. Electr. Power Energy Syst. 2014, 62:816-824.
19. Che J.X. A novel hybrid model for bi-objective short-term electric load forecasting. Int. J. Electr. Power Energy Syst. 2014, 61:259-266.
20. Hong W.C. Chaotic particle swarm optimization algorithm in a support vector regression electric load forecasting model. Energy Convers. Manage. 2009, 50(1):105-117.
21. Wang J., Lu H., Dong Y., et al. The model of chaotic sequences based on adaptive particle swarm optimization arithmetic combined with seasonal term. Appl. Math. Model. 2012, 36(3):1184-1196.
22. Shayeghi H., Ghasemi A. Day-ahead electricity prices forecasting by a modified CGSA technique and hybrid WT in LSSVM based scheme. Energy Convers. Manage. 2013, 74:482-491.
23. Xu H., Chen G. An intelligent fault identification method of rolling bearings based on LSSVM optimized by improved PSO. Mech. Syst. Signal Process. 2013, 35(1):167-175.
24. Zhang J., Tan Z., Yang S. Day-ahead electricity price forecasting by a new hybrid method. Comput. Ind. Eng. 2012, 63(3):695-701.
25. Boudraa A.O., Cexus J.C. EMD-based signal filtering. IEEE Trans. Instrum. Measure. 2007, 56(6):2196-2202.
26. Guo Z., Zhao W., Lu H., et al. Multi-step forecasting for wind speed using a modified EMD-based artificial neural network model. Renew. Energy 2012, 37(1):241-249.
27. Dong Y., Wang J., Jiang H., et al. Short-term electricity price forecast based on the improved hybrid model. Energy Convers. Manage. 2011, 52(8):2987-2995.
28. Cochrane J.L., Zeleny M. Multiple Criteria Decision Making 1973, University of South Carolina Press.
29. Zeleny M. Multiple Criteria Decision Making 1982, McGraw-Hill, New York.
30. Saaty T.L. The Analytical Hierarchy Process 1990, RWS Publications, Pittsburgh.
31. Yoon K.P., Hwang C.L. Multiple Attribute Decision Making: An Introduction 1995, Sage Publications.
32. Deng J.L. Introduction to grey system theory. J. Grey Syst. 1989, 1(1):1-24.
33. Wu J.H., Wen K.-L., You M.-L. A multi decision making based on modified grey relational grade. J. Grey Syst. 1999, 11(4):381-387.
34. Huang N.E., Shen Z., Long S.R., et al. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proc. R. Soc. Lond. Ser. A Math. Phys. Eng. Sci. 1988, 454(1971):903-995.
35. Qi K., He Z., Zi Y. Cosine window-based boundary processing method for EMD and its application in rubbing fault diagnosis. Mech. Syst. Signal Process. 2007, 21(7):2750-2760.
36. Suykens J.A.K., Van Gestel T., De Brabanter J., De Moor B., Vandewalle J. Least Square Support Vector Machines 2002, World Scientific, Singapore.
37. R.C. Eberhart, J. Kennedy, A new optimizer using particle swarm theory, in: Proceedings of the Sixth International Symposium on Micro Machine and Human Science, vol. 1, 1995, pp. 39-43.
38. Wu H.H. A comparative study of using grey relational analysis in multiple attribute decision making problems. Qual. Eng. 2002, 15(2):209-217.
39. Chang T.C., Wen K.L., Chang H.T., et al. A new method for evaluation of design alternatives based on the fuzzy gray relational analysis. 1999 IEEE International Fuzzy Systems Conference Proceedings, FUZZ-IEEE'99 1999, vol. 2:708-713. IEEE.
40. Ju-Long D. Control problems of grey systems. Syst. Control Lett. 1982, 1(5):288-294.
41. An N., Zhao W., Wang J., et al. Using multi-output feedforward neural network with empirical mode decomposition based signal filtering for electricity demand forecasting. Energy 2013, 49:279-288.