A non-linear multivariable regression model for midterm energy forecasting of power systems

Electric Power Systems Research

Volumn 77, Issue 12, 2007, Pages 1560-1568

  Tsekouras, G J ^a   Dialynas, E N ^a   Hatziargyriou, N D ^a   Kavatza, S ^a  

^a NATIONAL TECHNICAL UNIVERSITY OF ATHENS   (Greece)

Author keywords

Energy forecasting; Non linear multivariable regression model

Indexed keywords

ELECTRIC POWER SYSTEMS; MATHEMATICAL MODELS; REGRESSION ANALYSIS;

ENERGY FORECASTING; NON-LINEAR MULTIVARIABLE REGRESSION MODEL;

ELECTRIC LOAD FORECASTING;

EID: 34548382234     PISSN: 03787796     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.epsr.2006.11.003     Document Type: Article

References (43)

1. Egolioglu F., Mohamad A.A., and Guven H. Economic variables and electricity consumption forecasting in Northern Cyprus. Energy 26 (2001) 355-362
2. Mohamed Z., and Bodger P. Forecasting electricity consumption in New Zealand using economic and demographic variables. Energy 30 (2005) 1833-1843
3. Al-Garni A.Z., Zubair S.M., and Nizami J.S. A regression model for electric energy consumption forecasting in eastern Saudi Arabia. Energy 19 (1994) 1043-1049
4. Chern W.S., and Just R.E. Assessing the need for power: a regional econometric model. Energy Econ. 4 (1982) 232-239
5. Yang M., and Yu X. China's rural electricity market-a quantitative analysis. Energy 29 (2004) 961-977
6. Bajay S.V. Long-term electricity demand forecasting models: a review of methodologies. Electr. Power Syst. Res. 6 (1983) 243-257
7. Haida T., and Muto S. Regression based peak load forecasting using a transformation technique. IEEE Trans. Power Syst. 9 (1994) 1788-1794
8. Small J.L. Joint estimation of household and heat-pump electricity demand. IEEE Trans. Power Syst. 9 (1994) 413-419
9. Vlahovic V.M., and Vujosevic I.M. Long-term forecasting: a critical review of direct-trend extrapolation methods. Electr. Power Energy Syst. 9 (1987) 2-8
10. Pardo A., Meneu V., and Valor E. Temperature and seasonality influences on Spanish electricity load. Energy Econ. 24 (2002) 55-70
11. Mirasgedis S., Safaridis Y., Georgopoulou E., Lalas D.P., Moschovits M., Karagiannis F., and Papakonstantinou D. Models for mid-term electricity demand forecasting incorporating weather influences. Energy 31 (2006) 208-227
12. Willis H.L., and Northcote-Green J.E.D. Comparison tests of fourteen distribution load forecasting methods. IEEE Trans. Power Appl. Syst. 103 (1984) 1190-1197
13. Willis H.L., Powell R.W., and Wall D.L. Load transfer coupling regression curve fitting for distribution load forecasting. IEEE Trans. Power Appl. Syst. 103 (1984) 1070-1076
14. Padmakumari K., Mohandas K.P., and Thiruvengadam S. Long-term distribution forecasting using neuro-fuzzy computations. Electr. Power Energy Syst. 21 (1999) 315-322
15. Chow M., Zhu J., and Tram H. Application of fuzzy multi-objective decision making in spatial load forecasting. IEEE Trans. Power Syst. 13 (1998) 1185-1190
16. Yeh E.C., and Tram H. Information integration in computerized distribution system planning. IEEE Trans. Power Syst. 12 (1997) 1008-1013
17. Wu H.C., and Lu C.N. A data mining approach for spatial modeling in small area load forecast. IEEE Trans. Power Syst. 17 (2002) 516-521
18. Saab S., Badr E., and Nasr G. Univariate modeling and forecasting of energy consumption: the case of electricity in Lebanon. Energy 26 (2001) 1-14
19. Da X., Jiangyan Y., and Jilai Y. The physical series algorithm of mid-long term load forecasting of power systems. Electr. Power Syst. Res. 53 (2000) 31-37
20. Goh T.N., Choi S.S., and Chen S.B. Forecasting of electricity demand by end-use characteristics. Electr. Power Syst. Res. 6 (1983) 177-183
21. Barakat E.H. Modeling of nonstationary time-series data. Part I. Data with regular periodic trends. Electr. Power Energy Syst. 23 (2001) 57-62
22. Barakat E.H. Modeling of nonstationary time-series data. Part II. Dynamic periodic trends. Electr. Power Energy Syst. 23 (2001) 63-68
23. Yalcinoz T., and Eminoglu U. Short term and medium term power distribution load forecasting by neural networks. Energy Convers. Manage. 46 (2005) 1393-1405
24. Doveh Ε., Feigin P., Greig D., and Hyams L. Experience with FNN models for medium term power demand predictions. IEEE Trans. Power Syst. 17 (2002) 538-546
25. Kermanshahi B., and Iwamiya H. Up to year 2020 load forecasting using neural nets. Electr. Power Energy Syst. 24 (2002) 789-797
26. Pai P.F., and Hong W.C. Support vector machines with simulated annealing algorithms in electricity load forecasting. Energy Convers. Manage. 46 (2005) 2669-2688
27. Abdel-Aal R.E., Al-Garni A.Z., and Al-Nassar Y.N. Modeling and forecasting monthly electric energy consumption in Eastern Saudi Arabia using abductive networks. Energy 22 (1997) 911-921
28. Chen G.J., Li K.K., Chung T.S., Sun H.B., and Tang G.Q. Application of an innovative combined forecasting method in power system load forecasting. Electr. Power Syst. Res. 59 (2001) 131-137
29. Ozturk H.K., Ceylan H., Canyurt O.E., and Hepbasli A. Electricity estimation using genetic algorithm approach: a case study of Turkey. Energy 30 (2005) 1003-1012
30. Lee D.G., Lee B.W., and Chang S.H. Genetic programming model for long-term forecasting of electric power demand. Electr. Power Syst. Res. 40 (1997) 17-22
31. Kandil M.S., El-Debeiky S.M., and Hasanien N.E. Long-term load forecasting for fast developing utility using a knowledge-based expert system. IEEE Trans. Power Syst. 17 (2002) 491-496
32. Kandil M.S., El-Debeiky S.M., and Hasanien N.E. The implementation of long-term forecasting strategies using a knowledge-based expert system: Part II. Electr. Power Syst. Res. 58 (2001) 19-25
33. Kandil M.S., El-Debeiky S.M., and Hasanien N.E. Overview and comparison of long-term forecasting techniques for a fast developing utility: Part I. Electr. Power Syst. Res. 58 (2001) 11-17
34. Badri M.A., Al-Mutawa A., Davis D., and Davis D. EDSSF: A decision support system (DSS) for electricity peak-load forecasting. Energy 22 (1997) 579-589
35. Pai P.F., and Hong W.C. Forecasting regional electricity load based on recurrent support vector machines with genetic algorithms. Electr. Power Syst. Res. 74 (2005) 417-425
36. Jia N.X., Yokoyama R., Zhou Y.C., and Gao Z.Y. A flexible long-term load forecasting approach based on new dynamic simulation theory-GSIM. Electr. Power Energy Syst. 23 (2001) 549-556
37. Yao A.W.L., Chi S.C., and Chen J.H. An improved Grey-based approach for electricity demand forecasting. Electr. Power Syst. Res. 67 (2003) 217-224
38. Levi V.A. A medium-term probabilistic model for forecasting loads with a small proportion of air-conditioners. Electr. Power Syst. Res. 29 (1994) 57-67
39. McSharry P.E., Bouwman S., and Bloemhof G. Probabilistic forecasts of the magnitude and timing of peak electricity demand. IEEE Trans. Power Syst. 20 (2005) 1166-1172
40. Al-Hamadi H.M., and Soliman S.A. Long-term/mid-term electric load forecasting based on short-term correlation and annual growth. Electr. Power Syst. Res. 74 (2005) 353-361
41. Hand D., Manilla H., and Smyth: P. Principles of Data Mining (2001), The MIT Press, Cambridge, MA
42. 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 (2001) 44-55
43. Tsekouras G.J., Elias Ch.N., Kavatza S., and Contaxis G.C. A hybrid non-linear regression midterm energy forecasting method using data mining. Proceedings of the IEEE Bologna Power Tech Conference. Bologna, Italy, June 23-26 (2003)