Short-term electric load forecasting based on a neural fuzzy network
Ling, S.H.
Leung, F.H.F.
Lam, H.K.
Tam, P.K.S.
Dept. of Electron. & Inf. Eng., Hong Kong Polytech. Univ., China;
This paper appears in: Industrial Electronics, IEEE Transactions on
Publication Date: Dec. 2003
Volume: 50,
Issue: 6
On page(s): 1305- 1316
ISSN: 0278-0046
INSPEC Accession Number: 7964628
Digital Object Identifier: 10.1109/TIE.2003.819572
Current Version Published: 2004-01-08
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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