1. 2. 3. 4. 5. 6. © modeling and forecasting electric daily peak loads using abductive networks...

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1. 2. 3. 4. 5. 6. © Modeling And Forecasting Electric Daily Peak Loads Using Abductive Networks Abdel-Aal, RE ELSEVIER SCI LTD, INTERNATIONAL JOURNAL OF ELECTRICAL POWER ENERGY SYSTEMS; pp: 133-141; Vol: 28 King Fahd University of Petroleum & Minerals http://www.kfupm.edu.sa Summary casting the daily peak load is important for secure and profitable operation rn power utilities. Machine learning techniques including neural networks hav used for this purpose. This paper proposes the alternative modeling approach ctive networks, which offers simpler and more automated model synthe lting analytical input-output models automatically select influential inputs, er insight and explanations, and allow comparison with other empirical models loped using peak load and extreme temperature data for 5 years and evaluated sixth year, a model forecasts next-day peak loads with an overall mean absolu entage error (MAPE) of 2.50%, outperforming neural network models and flat casting for the same data. Two methods are described for forecasting daily pe s up to 1 week ahead through iterative use of the next-day model or using sev cated models. Effects; of varying model complexity are considered lified analytical expressions are derived for the peak load. Proposals are ma her improving the forecasting accuracy. (C) 2005 Elsevier Ltd. All rights res References: *ABTECH CORP, 1990, AIM US MAN ABDELAAL RE, 1994, ENERGY, V19, P739 ABDELAAL RE, 1995, WEATHER FORECAST, V10, P310 ABDELAAL RE, 1997, ENERGY, V22, P911 ABDELAAL RE, 2004, IEEE T POWER SYST, V19, P164, DOI 10.1109/TPWRS.2003.820695 Copyright: King Fahd University of Petroleum & Minerals; http://www.kfupm.edu.sa

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Page 1: 1. 2. 3. 4. 5. 6. © Modeling And Forecasting Electric Daily Peak Loads Using Abductive Networks Abdel-Aal, RE ELSEVIER SCI LTD, INTERNATIONAL JOURNAL OF

1.2.3.4.5.6.

©

Modeling And Forecasting Electric Daily Peak Loads Using

Abductive

Networks

Abdel-Aal, RE

ELSEVIER SCI LTD, INTERNATIONAL JOURNAL OF ELECTRICAL POWER

ENERGY SYSTEMS; pp: 133-141; Vol: 28

King Fahd University of Petroleum & Minerals

http://www.kfupm.edu.sa

Summary

Forecasting the daily peak load is important for secure and profitable operation of

modern power utilities. Machine learning techniques including neural networks have

been used for this purpose. This paper proposes the alternative modeling approach of

abductive networks, which offers simpler and more automated model synthesis.

Resulting analytical input-output models automatically select influential inputs, give

better insight and explanations, and allow comparison with other empirical models.

Developed using peak load and extreme temperature data for 5 years and evaluated on

the sixth year, a model forecasts next-day peak loads with an overall mean absolute

percentage error (MAPE) of 2.50%, outperforming neural network models and flat

forecasting for the same data. Two methods are described for forecasting daily peak

loads up to 1 week ahead through iterative use of the next-day model or using seven

dedicated models. Effects; of varying model complexity are considered, and

simplified analytical expressions are derived for the peak load. Proposals are made for

further improving the forecasting accuracy. (C) 2005 Elsevier Ltd. All rights reserved.

References:*ABTECH CORP, 1990, AIM US MANABDELAAL RE, 1994, ENERGY, V19, P739ABDELAAL RE, 1995, WEATHER FORECAST, V10, P310ABDELAAL RE, 1997, ENERGY, V22, P911ABDELAAL RE, 2004, IEEE T POWER SYST, V19, P164, DOI10.1109/TPWRS.2003.820695

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

Page 2: 1. 2. 3. 4. 5. 6. © Modeling And Forecasting Electric Daily Peak Loads Using Abductive Networks Abdel-Aal, RE ELSEVIER SCI LTD, INTERNATIONAL JOURNAL OF

7.8.9.10.11.12.13.14.15.16.17.18.19.20.21.22.23.24.25.26.27.28.29.30.31.32.33.

©

ABOULMAGD MA, 2001, P LARG ENG SYST C PO, P105ALFUHAID AS, 1997, IEEE T POWER SYST, V12, P1524ASAR A, 1994, IEEE T CONTR SYST T, V2, P135BARRON AR, 1984, SELF ORG METHODS MOD, P87CHARYTONIUK W, 2000, P INT C EL UT DER RE, P554DASILVA APA, 2001, IEEE POW TECHN C PORDILLON TS, 1975, 5TH P PSCC, P1DILLON TS, 1991, INT J ELEC POWER, V13, P186DILLON TS, 1996, NEURAL NETWORKS APPL, P387ELDESOUKY AA, 2000, IEE P-GENER TRANSM D, V147, P213FARLOW SJ, 1984, SELF ORG METHODS MOD, P1FROST F, 1999, P 3 INT C COMP INT M, P45GROSS G, 1987, P IEEE, V75, P1558HAIDA T, 1994, IEEE T POWER SYST, V9, P1788HIPPERT HS, 2001, IEEE T POWER SYST, V16, P44HSU YY, 1991, IEE PROC-C, V138, P414KHOTANZAD A, 1998, IEEE T POWER SYST, V13, P1413LAM HK, 2001, 27 ANN C IEEE IND EL, P25LEWIS HW, 2001, P MOUNT WORKSH SOFT, P25MATSUI T, 2001, P IEEE POW ENG SOC W, P405MONTGOMERY GJ, 1990, P SPIE C APPL ART NE, P56MORIOKA Y, 1993, P 2 INT FOR APPL NEU, P60ONODA T, 1993, P 2 INT FOR APPL NEU, P284SAINI LM, 2002, IEEE T POWER SYST, V17, P907, DOI10.1109/TPWRS.2002.800992SENJYU T, 2002, IEEE T POWER SYST, V17, P113TENORIO MF, 1990, IEEE T NEURAL NETWOR, V1, P100

For pre-prints please write to: [email protected]

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