Modelling and short-term forecasting of daily peak power demand in Victoria using two-dimensional wavelet based SDP models

International Journal of Electrical Power and Energy Systems

Volumn 30, Issue 9, 2008, Pages 511-518

  Truong, Nguyen Vu ^a   Wang, Liuping ^a   Wong, Peter K C ^b  

^a RMIT UNIVERSITY   (Australia)

^b Alinta   (Australia)

Author keywords

Nonlinear system modelling; Short term load forecast; State dependent parameters

Indexed keywords

DYNAMICAL SYSTEMS; ELECTRIC NETWORK ANALYSIS; ELECTRIC POWER SYSTEMS; ERROR ANALYSIS; FISCHER-TROPSCH SYNTHESIS; FORECASTING; MATHEMATICAL MODELS; MULTIRESOLUTION ANALYSIS; NONLINEAR SYSTEMS; PLANNING; SCHRODINGER EQUATION; TWO DIMENSIONAL;

HISTORICAL DATA; INPUT AND OUTPUT; MEAN ABSOLUTE PREDICTION ERROR; NON-LINEAR DYNAMIC SYSTEMS; NON-LINEARITIES; NONLINEAR SYSTEM MODELLING; PEAK POWERS; POWER DEMANDS; POWER SYSTEM OPERATIONS; SECURITY ANALYSIS; SHORT TERM LOAD FORECAST; SHORT-TERM FORECASTING; STATE DEPENDENT PARAMETERS; STATE VARIABLES; STATE-DEPENDENT; TIME PERIODS; TWO-DIMENSIONAL WAVELET; VICTORIA , AUSTRALIA; WAVELET SERIES;

ELECTRIC LOAD FORECASTING;

EID: 53149095418     PISSN: 01420615     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ijepes.2008.04.006     Document Type: Article

References (29)

1. Truong NV, Wang L, Young PC. Nonlinear system modeling using nonparametric identification and linear wavelet estimation of SDP models. In: Proc. 45th IEEE conf. on decision and control, San Diego, USA; 2006. p. 2523-8.
2. Truong N.V., Wang L., and Young P.C. Nonlinear system modeling using nonparametric identification and linear wavelet estimation of SDP models. Int J Control 80 5 (2007) 774-788
3. Truong NV, Wang L, Huang JM. Nonlinear modeling of a magnetic bearing using SDP model and linear wavelet parameterization. In: Proc. 2007 American control conference, New York, USA; 2007. p. 2254-9.
4. Truong NV, Wang L. Nonlinear system identification using two dimensional wavelet based SDP models. Int J Control [submitted for publication].
5. Young C. Stochastic, dynamic modelling and signal processing: time variable and state dependent parameter estimation. In: Fitzgerald W.J., Walden A., Smith R., and Young P.C. (Eds). Nonlinear and nonstationary signal processing (2000), Cambridge University Press, Cambridge 74-114
6. Young P.C. The identification and estimation of nonlinear stochastic systems. In: Mees A.I. (Ed). Nonlinear dynamics and statistics (2001), Birkhauser, Boston 127-166
7. Young P.C., McKenna P., and Bruun J. Identification of nonlinear stochastic systems by state dependent parameter estimation. Int J Control 74 (2001) 1837-1857
8. Young PC. Comment on 'a quasi-ARMAX approach to modelling nonlinear systems' by J. Hu et al. Int J Control 2001;74:1767-71.
9. Ho K.L. Short-term load forecasting of Taiwan power system using a knowledge based expert system. IEEE Trans Power Syst 5 4 (1990) 1214-1219
10. Rahman S., and Hazim O. Load forecasting for multiple sites: development of an expert system based technique. Electr Power Syst Res 39 3 (1996) 161-169
11. Huang S., and Shih K. Application of a fuzzy model for short-term load forecast with group method of data handling enhancement. Int J Electr Power Energ Syst 24 (2002) 631-638
12. Al-Kandari A.M., Soliman S.A., and El-Hawary M.E. Fuzzy short-term electric forecasting. Int J Electr Power Energ Syst 26 (2004) 111-122
13. Niebur D. Artificial Neural Networks in the power industry, survey and applications. Neural Networks World 6 (1995) 945-950
14. Senjyu T, Mandal P, Uezato K, Funabashi T. Next day load curve forecasting using recurrent neural network structure. In: IEE proceedings - generation, transmission and distribution, vol. 151; 2004. p. 388-94.
15. Beccali M., Cellura M., Lo Brano V., and Marvuglia A. Forecasting daily urban electric load profiles using artificial neural networks. Energ Convers Manage 45 (2004) 2879-2900
16. Baczynski D, Parol M. Influence of artificial neural network structure on quality of short-term electric energy consumption forecast. In: IEE proceedings - generation, transmission and distribution, vol. 151; 2004. p. 241-5.
17. Saini LM, Soni MK. Artificial neural network based peak load forecasting using Levenberg-Marquardt and quasi-Newton methods. In: IEE proceedings - generation, transmission and distribution, vol. 149; 2002. p. 578-84.
18. Kumluca A., and Erkmen I. A hybrid learning for neural networks applied to short term load forecasting. Neurocomputing 51 (2003) 495-500
19. Desouky AA, Elkateb MM. Hybrid adaptive techniques for electric-load forecast using ANN and ARIMA. In: IEE proceedings - generation, transmission and distribution, vol. 147; 2000. p. 213-7.
20. Liang R., and Cheng C. Short-term forecasting by a neuro-fuzzy based approach. Int J Electr Power Energ Syst 24 2 (2002) 103-111
21. Gao R., and Tsoukalas L.H. Neural-wavelet methodology for load forecasting. J Intell Robotics Syst 31 (2001) 149-157
22. Kermanshashi B., and Iwamiya H. Up to year 2020 load forecasting using neural nets. Int J Electr Power Energ Syst 24 (2002) 789-797
23. Bunn D.W., and Farmer E.D. Comparative models for electrical load forecasting (1985), John Wiley, New York
24. Moghram I., and Rahman S. Analysis and evaluation of five short-term load forecasting techniques. IEEE Trans Power Syst 4 4 (1989) 1484-1491
25. Bunn D.W., and Vassilopoulos A.I. Comparison of seasonal estimation methods in multi-item short-term forecasting. Int J Forecasting 15 (1999) 431-443
26. Al-Hamadi H.M., and Soliman S.A. Short-term electric load forecasting based on Kalman filtering algorithm with moving window weather and load model. Electr Power Syst Res 68 1 (2004) 47-59
27. Taylor J.W. Short-term electricity demand forecasting using double seasonal exponential smoothing. J Oper Res Soc 54 (2003) 799-804
28. Taylor J.W., and Buizza R. Using weather ensemble predictions in electricity demand forecasting. Int J Forecasting 19 (2003) 57-70
29. Asber D., Lefebvre S., Asber J., Saad M., and Desbiens C. Non-parametric short-term load forecasting. Int J Electr Power Energ Syst 29 (2007) 630-635