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A hybrid expert system assisting decision making for distribution system load forecasting.

Morsi, D.M.; Abbasy, N.H.; Abul Ella, M.S.

Authors

N.H. Abbasy

M.S. Abul Ella



Abstract

This paper introduces a typically intelligent hybrid expert system (ES) for an annualized distribution system load forecasting. The proposed ES has the capability of predicting the annual distribution substation load growth, and patterns of subsequent load shifts, in the case of a substation overload. Also, possible expected system expansion plans are introduced. The parameters of the load growth model are estimated for each substation. The load transfer model is chosen to follow the Weibull distribution function and to simulate different factors affecting the transfer process. The ES is developed using an artificial intelligence language (PROLOG), and is applied to Alexandria city, 66/11 kV power distribution network.

Citation

MORSI, D.M., ABBASY, N.H. and ABUL ELLA, M.S. 1994. A hybrid expert system assisting decision making for distribution system load forecasting. In Proceedings of the 1994 Mediterranean electrotechnical conference (MELECON '94), 12-14 April 1994, Antalya, Turkey. Piscataway: IEEE [online], pages 893-896. Available from: https://doi.org/10.1109/MELCON.1994.380958

Conference Name 1994 Mediterranean electrotechnical conference (MELECON '94)
Conference Location Antalya, Turkey
Start Date Apr 12, 1994
End Date Apr 14, 1994
Acceptance Date Feb 28, 1994
Online Publication Date Apr 14, 1994
Publication Date Aug 6, 2002
Deposit Date Nov 10, 2022
Publicly Available Date Nov 10, 2022
Publisher IEEE Institute of Electrical and Electronics Engineers
Book Title Proceedings of the 1994 Mediterranean electrotechnical conference (MELECON '94), 12-14 April 1994, Antalya, Turkey
ISBN 0780317726
DOI https://doi.org/10.1109/melcon.1994.380958
Keywords Expert systems; Decision making; Substations; Load modeling; Power system modeling; Hybrid intelligent systems; Load forecasting; Weibull distribution; Artificial intelligence; Cities and towns
Public URL https://rgu-repository.worktribe.com/output/1805765

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