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Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/1439

Title: Short-term electric load forecasting based on a neural fuzzy network
Authors: Ling, S. H.
Leung, Frank H. F.
Lam, H. K.
Tam, Peter K. S.
Subjects: Genetic algorithm (GA)
Home networking
Load forecasting
Neural fuzzy network (NFN)
Issue Date: Dec-2003
Publisher: IEEE
Citation: IEEE transactions on industrial electronics, Dec. 2003, v. 50, no. 6, p. 1305-1316.
Abstract: Electric load forecasting is essential to improve the reliability of the ac power line data network and provide optimal load scheduling in an intelligent home system. In this paper, a short-term load forecasting realized by a neural fuzzy network (NFN) and a modified genetic algorithm (GA) is proposed. It can forecast the hourly load accurately with respect to different day types and weather information. By introducing new genetic operators, the modified GA performs better than the traditional GA under some benchmark test functions. The optimal network structure can be found by the modified GA when switches in the links of the network are introduced. The membership functions and the number of rules of the NFN can be obtained automatically. Results for a short-term load forecasting will be given.
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This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.
Type: Journal/Magazine Article
URI: http://hdl.handle.net/10397/1439
ISSN: 0278-0046
Appears in Collections:EIE Journal/Magazine Articles

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