Electricity demand and spot price forecasting using evolutionary computation combined with chaotic nonlinear dynamic model

International Journal of Electrical Power and Energy Systems

Volumn 32, Issue 2, 2010, Pages 108-116

  Unsihuay Vila, C ^a   Zambroni de Souza, A C ^a   Marangon Lima, J W ^a   Balestrassi, P P ^a  

^a FEDERAL UNIVERSITY OF ITAJUBÁ   (Brazil)

Author keywords

Electricity markets; Evolutionary strategy optimization; Nonlinear chaotic dynamics; Predict (Matlab code); Price forecasting; Short term load forecasting

Indexed keywords

CHAOTIC DYNAMICS; ELECTRICITY MARKET; EVOLUTIONARY STRATEGIES; MATLAB CODE; PRICE FORECASTING; SHORT TERM LOAD FORECASTING;

BACKPROPAGATION; COSTS; EVOLUTIONARY ALGORITHMS; IDENTIFICATION (CONTROL SYSTEMS); MATHEMATICAL MODELS; MATLAB; NEURAL NETWORKS; OPTIMIZATION; TIME SERIES;

ELECTRIC LOAD FORECASTING;

EID: 70450270789     PISSN: 01420615     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ijepes.2009.06.018     Document Type: Article

References (47)

1. Mandal P., Senjyu T., and Funabashi T. Forecast several hours ahead electricity prices and loads in deregulated markets. Energy Convers Manage 47 (2006) 2128-2142
2. Conejo A.J., Plazas M.A., Espinola R., and Molina A.B. Day-ahead electricity price forecasting using the wavelet transform and ARIMA models. IEEE Trans Power Syst 20 2 (2005) 1035-1042
3. Conejo A., Contreras J., Espi{dotless}́nola R., and Plazas M. Forecasting electricity prices for a day-ahead pool-based electric energy market. Int J Forecasting 21 (2005) 435-462
4. Metaxiotis K., Kagiannas A., and Askounis D. Artificial intelligence in short-term electric load forecasting: a state of-the-art survey for the researcher. Energy Convers Manage 4 (2003) 1525-1534
5. Dillon TS, Morsztyn K, Phua K. Short term load forecasting using adaptive pattern recognition and self-organizing techniques. In: Proceedings 5th world power system computation conference (PSCC-5). Cambridge; 1975. p. 1-15.
6. Sestito S., Leung S., and Dillon T.S. Short term load forecasting using an adaptive neural network. J Electr Power Energy Syst 13 4 (1991) 186-192
7. Choi J-G, Park J-K, Kim K-H, Kim J-C. A daily peak load forecasting system using a chaotic time series. In: Proceedings of the international conference on intelligent systems applications to power systems ISAP; 1996. p. 283-7.
8. Mori H, Urano S. Short-term load forecasting with chaos time series analysis. In: International conference on intelligent systems applications to power systems ISAP; 1996. p. 283-7.
9. Iokibe T., Fujimoto Y., Kanke M., and Suzuki S. Short-term prediction of chaotic time series by local fuzzy reconstruction method. J Intell Fuzzy Syst 5 (1997) 3-21
10. Drezga I., and Rahman S. Phase-space based short-term load forecasting for deregulated electric power industry Neural Networks. Int Joint Conf IJCNN 5 (1999) 3405-3409
11. Michanos S.P., Tsakoumis A.C., Fessas P., Vladov S.S., and Mladenov V.M. Short-term load forecasting using a chaotic time series. Int Symp Signals, Circuit Syst SCS 2 (2003) 437-440
12. Kawauchi H, Sasaki H, Ishikawa F, Sugihara H. A development of real time load forecasting system based on chaos theory. In: 6th international conference on advances in power system control, operation and management, vol. 2; 2003. p. 541-6.
13. Jiang C., Wang C., and Ma Y. Short-term load nonlinear forecasting with high-embedded dimensions using wavelet decomposing and chaos theory. The IASTED-2004 Conf Eur Power Energy Syst (2004) 1-6
14. Zhang H.-S.W.S. Power load forecasting with least squares support vector machines and chaos theory. Int Conf Power Syst (2005) 1-6
15. Liao G.-C. Hybrid chaos search genetic algorithm and meta heuristics method for short-term load forecasting. Electr Eng 88 (2006) 1-6
16. Liu Z., Xie X., Zhang D., and Liu H. Local partial least squares multi-step model for short-term load forecasting. IEICE Trans Fundam E89-A (2006) 1-6
17. Tian L, Noore A. Short-term load forecasting using optimized neural network with genetic algorithm. In: 8th international conference on probabilistic methods applied to power systems. Iowa: Iowa State University Ames; September 2004. p. 12-6.
18. Li Y, Li B, Fang T. Short-term load forecast based on fuzzy wavelet support vector machines. In: Proceedings of the 5th world congress on intelligent control and automation. Hangzhou, PR China; 15-19 June 2004.
19. Contreras J., Espi{dotless}́nola R., Nogales F.J., and Conejo A.J. ARIMA models to predict next-day electricity prices. IEEE Trans Power Syst 18 3 (2003) 1014-1020
20. Nogales F.J., Contreras J., Conejo A.J., and Espi{dotless}́nola R. Forecasting next-day electricity prices by time series models. IEEE Trans Power Syst 17 2 (2002) 342-348
21. Aggarwal S.K., Saini L.M., and Kumar A. Electricity price forecasting in deregulated markets: A review and evaluation. Int J Electr Power Energy Syst 31 1 (2009) 13-22
22. Szkuta B.R., Sanabria L.A., and Dillon T.S. Electricity price short-term forecasting using artificial neural networks. IEEE Trans Power Syst (PES) 14 3 (1999) 851-857
23. Rodri{dotless}́guez C.P., and Anders G.J. Energy price forecasting in the Ontario competitive power system market. IEEE Trans Power Syst 19 1 (2004) 366-374
24. Pao H.-T. Forecasting electricity market pricing using artificial neural networks. Energy Convers Manage 48 3 (2007) 907-912
25. Garcia R.C., Contreras J., van Akkeren M., and Garcia J.B.C. GARCH forecasting model to predict day-ahead electricity prices. Trans Power Syst 20 2 (2005) 867-874
26. Pingan Z, Xiaohong G. Fuzzy modeling for electricity market price forecasting. In: Proceedings of the 3rd world congress on intelligent control and automation, vol. 3; 28 June-2 July 2000. p. 2262-6.
27. Kim C.-I., Yu I.-K., and Song Y.H. Prediction of system marginal price of electricity using wavelet transform analysis. Energy Convers Manage 43 (2002) 1839
28. Amjady N., and Keynia F. Day ahead price forecasting of electricity markets by a mixed data model and hybrid forecast method. Int J Electr Power Energy Syst 30 9 (2008) 533-546
29. Yang H., and Duan X. Chaotic characteristics of electricity price and its forecasting model. IEEE-CCECE Can Conf Electr Comput Eng 1 (2003) 659-662
30. Guangjian L., Sangao H., and Junliang D. The chaotic property of system marginal price and its forecasting. Proc CSEE 23 5 (2003) 6-8
31. Wu W, Zhou J-Z, Yu J, Zhu C-J, Yang J-J. Prediction of spot market prices of electricity using chaotic time series. In: Proceeding of the 3rd international conference on machine learning and cybemetics. Shanghai; 26-29 August 2004. p. 1-6.
32. Liu Z, Yang H, Lai M. Electricity price forecasting model based on chaos theory. In: Power engineering conference, IPEC; 2005. p. 1-5.
33. Varadan V., Leung H., and Bossé E. Dynamical Model Reconstruction and Accurate Prediction of Power-Pool Time Series. IEEE Trans Instrum Meas 55 1 (2006) 1-10
34. Kantz H., and Schreiber T. Nonlinear time series analysis (2002), Cambridge University Press
35. Abarbanel H.D.I., and Parlitz U. Nonlinear analysis of time series data. In: Schelter B., Winterhalder M., and Timmer J. (Eds). Handbook of time series analysis (2006), Wiley-VCH Verlag, Weinheim 5-33
36. Merkwirth C., Parlitz U., and Lauterborn W. Fast exact approximate nearest neighbor searching for nonlinear signal processing. Phys Rev E 62 2 (2000) 2089-2097
37. McNames J. Innovations in local modeling for time series prediction. PhD thesis, Stanford University; May 2000.
38. Babovic V, Keijzer M, Stefansson M. Optimal embedding using evolutionary algorithms. In: Proceedings of 4th international conference on hydroinformatics. Iowa City, Iowa: Iowa Institute of Hydraulic Research; 2000. p. 1-6.
39. Phoon K.K., Islam M.N., Liaw C.Y., and Liong S.Y. Practical inverse approach for forecasting nonlinear hydrological time series. J Hydrol Eng 7 2 (2002) 116-128
40. Merkwirth C, Parlitz U, Wedekind I, Lauterborn W. Matlab software package for nonlinear time series analysis tstool. Advances in enzymology, vol. 2. Germay; 2008. Available from: http://www.physik3.gwdg.de/tstool/.
41. Bäck T. Evolutionary algorithms in theory and practice. 1st ed. (1996), Oxford University Press, New York
42. Indepent system operator - ISO New England. Available from: http://www.iso-ne.com.
43. The Alberta electric system operator. Available from: http://www.aeso.ca.
44. Compañía Operadora del Mercado Español de Electricidad (Spanish market operator). Available from: http://www.omel.es.
45. Data mining, statistical analysis, quality control - STATISTICA. Software, StatSoft Inc.; 2005. Available: http://www.statsoftinc.com/
46. Box G.E.P., and Jenkins G.M. Time series analysis: forecasting and control (1976), Holden-Day, San Francisco, CA
47. Balestrassi PP, Popova E, Paiva AP, Marangon-Lima JW. Design of experiments on neural network's training for nonlinear time series forecasting. J Neurocomput; 2009. doi:10.1016/j.neucom.2008.02.002.