1. © Univariate Modeling And Forecasting Of Monthly Energy Demand Time Series Using Abductive And Neural Networks Abdel-Aal, RE PERGAMON-ELSEVIER SCIENCE LTD, COMPUTERS INDUSTRIAL ENGINEERING; pp: 903-917; Vol: 54 King Fahd University of Petroleum & Minerals http://www.kfupm.edu.sa Summary Neural networks have been widely used for short-term, and to a lesser degree medium and long-term, demand forecasting. In the majority of cases for the latter two applications, multivariate modeling was adopted, where the demand time series is related to other weather, socio-economic and demographic time series. Disadvantages of this approach include the fact that influential exogenous factors are difficult to determine, and accurate data for them may not be readily available. This paper uses univariate modeling of the monthly demand time series based only on data for 6 years to forecast the demand for the seventh year. Both neural and abductive networks were used for modeling, and their performance was compared. A simple technique is described for removing the upward growth trend prior to modeling the demand time series to avoid problems associated with extrapolating beyond the data range used for training. Two modeling approaches were investigated and compared: iteratively using a single next-month forecaster, and employing 12 dedicate(] models to forecast the 12 individual months directly. Results indicate better performance by the first approach, with mean percentage error (MAPE) of the order of 3% for abductive networks. Performance is superior to naive forecasts based on persistence and seasonality, and is better than results quoted in the literature for several similar applications using multivariate abductive modeling, multiple regression, and univariate ARIMA analysis. Automatic selection of only the most relevant model inputs by the abductive learning algorithm provides better insight into the modeled process and allows Copyright: King Fahd University of Petroleum & Minerals; http://www.kfupm.edu.sa

2. © constructing simpler neural network models with reduced data dimensionality and improved forecasting performance. (C) 2007 Elsevier Ltd. All rights reserved. References: 1. *ABTECH CORP, 1990, AIM US MAN 2. ABDELAAL RE, 1997, ENERGY, V22, P911 3. ABDELAAL RE, 2004, IEEE T POWER SYST, V19, P164, DOI 4. 10.1109/TPWRS.2003.820695 5. ALFUHAID AS, 1997, IEEE T POWER SYST, V12, P1524 6. ALGARNI AZ, 1994, ENERGY, V19, P1043 7. ALHAMADI HM, 2006, IEE P-GENER TRANSM D, V153, P217, DOI 8. 10.1049/ip-gtd:20050088 9. ALSABA T, 1999, ARTIF INTELL ENG, V13, P189 10. BARAKAT EH, 1989, IEE PROC-C, V136, P35 11. BARAKAT EH, 1992, IEEE T POWER SYST, V7, P1483 12. BARRON AR, 1984, SELF ORG METHODS MOD, P87 13. BIELINSKA EM, 1994, 3 IEEE C CONTR APPL, P1835 14. CHATFIELD C, 1998, P 1998 IEEE SIGN PRO, P419 15. DESILVA A, 2001, IEEE POW TECH C PORT 16. DILLON TS, 1975, P 5 POW SYST COMP C 17. ELKATEB MM, 1998, NEUROCOMPUTING, V23, P3 18. FARLOW SJ, 1984, SELF ORG METHODS MOD, P1 19. FRANCEY RJ, 2000, P 2 INT S CO2 OC JAN, P237 20. FUJIMORI S, 1998, IEEE INT S ELECT INS, P530 21. GHIASSI M, 2006, ELECTR POW SYST RES, V76, P302, DOI 22. 10.1016/j.epsr.2005.06.010 23. GONZALEZROMERA E, 2007, COMPUT IND ENG, V52, P336, DOI 24. 10.1016/j.cie.2006.12.010 25. HIPPERT HS, 2001, IEEE T POWER SYST, V16, P44 26. ISLAM SM, 1995, ELECTR POW SYST RES, V34, P1 27. KERMANSHAHI B, 2002, INT J ELEC POWER, V24, P789 28. KHOTANZAD A, 1998, IEEE T POWER SYST, V13, P1413 29. LEWIS HW, 2001, P MOUNT WORKSH SOFT, P25 30. LIU XQ, 1991, IEEE INT JOINT C NEU, P1254 31. MATSUI T, 2001, IEEE POW ENT SOC WIN, P405 32. MONTGOMERY GJ, 1991, NEUROCOMPUTING, V2, P97 33. PARK DC, 1991, IEEE T POWER SYST, V6, P442 34. SFORNA M, 1995, ELECTR POW SYST RES, V32, P1 35. SHIMAKURA Y, 1993, 2 INT FOR APPL NEUR, P233 36. THIESING FM, 1997, INT C NEUR NETW, P2125 37. TOYADA J, 1970, IEEE T POWER SYST, V89, P1678 38. VIRILI F, 2000, P INT JOINT C NEUR N, V5, P129 For pre-prints please write to: radwan@kfupm.edu.sa Copyright: King Fahd University of Petroleum & Minerals; http://www.kfupm.edu.sa

3. © Copyright: King Fahd University of Petroleum & Minerals; http://www.kfupm.edu.sa