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Long-term industrial load forecasting and planning using neural networks technique and fuzzy inference method
Faculty
Science
Year:
2005
Type of Publication:
InProcceding
Pages:
Authors:
Farahat, MA
Journal:
UNIV WEST ENGLAND-UWE
Volume:
Research Area:
Computer Science; Energy \& Fuels; Engineering
ISSN
ISI:000231594700075
Keywords :
Long-term industrial load forecasting , planning using
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 terin 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. The strength of this technique lies in its ability to reduce appreciable computational time and its comparable accuracy with other modeling techniques. The outcomes of the study clearly indicate that the proposed composite model of neural network technique and fuzzy inference method can be used as attractive and effective means for the industrial monthly and yearly peak load forecasting. The test results showed very accurate forecasting with the average percentage relative error of 1.98 \%.
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