NDLI: Long-term industrial load forecasting and planning using neural networks technique and fuzzy inference method

Content Provider	IEEE Xplore Digital Library
Author	Farahat, M.A.
Copyright Year	2004
Description	Author affiliation: Zagazig Univ., Egypt (Farahat, M.A.)
Abstract	Load forecasting plays a dominant part in the economic optimization and secure operation of electric power systems. The plans of the electric power sector have been done and developed with the aid of statistical prediction methods. Electric utility companies need monthly peak and yearly load forecasting for budget planning, maintenance scheduling and fuel management. This paper presents a new approach based on a hybrid fuzzy neural technique which combines artificial neural network and fuzzy logic modeling for long term industrial load forecasting in electrical power systems. An extensive study is carried out to find the accurate forecasting model through an application on an industrial 10th of Ramadan city in Egypt. Actual record data is used to test the proposed method. A large number of influencing factors have been examined and tested. This paper presents a fully developed system for the prediction of electric maximum demand and consumption for the future 24 months. Also very long-term load forecasting was carried out. The strength of this technique lies in its ability to reduce appreciably computational time and its comparable accuracy with other modeling techniques. The outcome of the study clearly indicates that the proposed composite model of neural network technique and fuzzy inference method can be used as an attractive and effective means for industrial monthly and yearly peak load forecasting. The test results showed very accurate forecasting with the average percentage relative error of 1.98%.
Sponsorship	IEEE PES IEE Cigre NGT EDF Energy WUHAN HI-TECH
Starting Page	368
Ending Page	372
File Size	712141
Page Count	5
File Format	PDF
ISBN	1860433650
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2004-09-06
Publisher Place	UK
Access Restriction	Subscribed
Rights Holder	UWE
Subject Keyword	Fuzzy logic Load forecasting Neural networks Power system planning Power system economics Predictive models Fuzzy neural networks Hybrid power systems Power system modeling Testing
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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