Dynamical prediction and pattern mapping in short-term load forecasting

International Journal of Electrical Power and Energy Systems

Volumn 30, Issue 1, 2008, Pages 73-82

  Aguirre, Luis Antonio ^a   Rodrigues, Daniela D ^a   Lima, Silvio T ^a   Martinez, Carlos Barreira ^a  

^a FEDERAL UNIVERSITY OF MINAS GERAIS   (Brazil)

Author keywords

Dynamical prediction; Load forecasting; Neural networks; Nonlinear models; Surrogate data analysis

Indexed keywords

DATA REDUCTION; MATHEMATICAL MODELS; NEURAL NETWORKS; PATTERN RECOGNITION; PROBLEM SOLVING; REAL TIME SYSTEMS;

DYNAMICAL PREDICTION; NONLINEAR MODELS; PATTERN MAPPING; SHORT-TERM LOAD FORECASTING; SURROGATE DATA ANALYSIS;

ELECTRIC LOAD FORECASTING;

EID: 37449029959     PISSN: 01420615     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ijepes.2007.11.001     Document Type: Article

References (26)

1. Chow T.W.S., and Leung C.T. Nonlinear autoregressive integrated neural network model for short-term load forecasting. IEEE Proc Gener Transm Distrib 143 5 (1996) 500-506
2. Darbellay G.A., and Slama M. Forecasting the short-term demand for electricity. Do neural networks stand a better chance?. Int J Forecasting 16 (2000) 71-83
3. Dash P.K., Satpathy H.P., and Liew A.C. A real-time short-term peak and average load forecasting system using a self-organizing fuzzy neural network. Eng Appl Artif Intell 11 (1998) 307-316
4. Drezga I., and Rahman S. Input variable selection for AA-based short-term load forecasting. IEEE Trans Power Syst 13 4 (1999) 1238-1244
5. Farmer J.D., and Sidorowich J.J. Predicting chaotic time series. Phys Rev Lett 59 8 (1987) 845-848
6. Gomes M.E.D., Souza A.V.P., Guimarães H.N., and Aguirre L.A. Evidences of determinism in HRV signals. Chaos 10 2 (2000) 398-410
7. Haykin S. Neural networks: a comprehensive foundation. 2nd ed. (1999), Prentice Hall, New Jersey
8. Hippert H.S., Pedreira C.E., and Souza R.C. Neural networks for short-term load forecasting: a review and evaluation. IEEE Trans Power Syst 16 1 (2001) 44-55
9. Huang S.J., and Shih K.R. Application of a fuzzy model for short-term load forecast with group method of data handling enhancement. Electr Power Energ Syst 24 (2002) 631-638
10. Khotanzad A., Afkhami-Rohani R., Lu T.L., Abaye A., Davis M., and Maratukulam D.J. ANNSTLF - artificial neural network-based electric load forecasting system. IEEE Trans Neural Networks 8 4 (1997) 835-846
11. Khotanzad A., Afkhami-Rohani R., and Maratukulam D. ANNSTLF - artificial neural network short-term load forecaster: generation three. IEEE Trans Power Syst 13 4 (1998) 1413-1422
12. Kim C., Yu I., and Song Y.H. Kohonen neural network and wavelet transform based approach to short-term load forecasting. Electric Power Syst Res 63 (2002) 169-176
13. Kodogiannis V.S., and Anagnostakis E.M. A study of advanced learning algorithms for short-term load forecasting. Eng Appl Artif Intell 12 (1999) 159-173
14. Kodogiannis V.S., and Anagnostakis E.M. Soft computing based techniques for short-term load forecasting. Fuzzy Set Syst 128 (2002) 413-426
15. Liang R.H., and Cheng C.C. Short-term load forecasting by a neuro-fuzzy based approach. Electr Power Energ Syst 24 (2002) 103-111
16. Metaxiotis K., Kagiannas A., Askounis D., and Psarras J. Artificial intelligence in short term electricity load forecasting: a state-of-the-art survey for the researcher. Energ Convers Manage 44 (2003) 1525-1534
17. Peng T.M., Hubele N.F., and Karady G.G. An adaptive neural network approach to one-week ahead load forecasting. IEEE Trans Power Syst 8 3 (1993) 1195-1203
18. Ramanathan R., Engle R., Granger C.W.J., Vahid-Araghi F., and Brace C. Short-run forecasts of electricity loads and peaks. Int J Forecasting 13 (1997) 161-174
19. Reis A.J.R., and Alves da Silva A.P. Feature extraction via multi resolution analysis for short-term load forecasting. IEEE Trans Power Syst 20 1 (2005) 189-198
20. Rodrigues, D.D. (2002). Forecasting the supply of electricity: a study from the standpoint of the power bus (in Portuguese). Master's Thesis, PPGEE, Universidade Federal de Minas Gerais, Belo Horizonte, Brasil.
21. Saligari G.R., and Snyder R.D. Trends, lead times and forecasting. Int J Forecasting 13 (1997) 477-488
22. Small M., and Judd K. Detecting periodicity in experimental data using linear modeling techniques. Phys Rev E 59 2 (1999) 1379-1385
23. Small M., and Tse C.K. Applying the method of surrogate data to cyclic time series. Physica D 164 3-4 (2002) 187-201
24. Theiler J., Eubank S., Longtin A., Galdrijian B., and Farmer J.D. Testing for nonlinearity in time series: the method of surrogate data. Physica D 58 (1992) 77-94
25. Zhang B.L., and Dong Z.Y. An adaptive neural-wavelet model for short-term load forecasting. Electr Power Syst Res 59 (2001) 121-129
26. Zhang G.P., and Qi M. Neural network forecasting for seasonal and trend time series. Eur J Oper Res 160 (2005) 501-514