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《Power System Protection and Control》 2009-22
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Research on FNN short-term electric load forecasting based on data mining technology

CUI He-rui1, SONG Xiu-li1, GE Man-qian2 ( 1.School of Business Administration, North China Electric Power University,Baoding 071003,China; 2. Qingdao Electric Power Design Institute, Qingdao 266002,China)  
The artificial neural network model does not consider the weather and other factors in load forecasting. Based on data mining(DM), this paper proposes the improved short-term load forecasting method, by the clustering feature of DM, we search the historical daily load with the same meteorological category as the forecasting day and to compose data sequence with highly similar meteorological features, so as to enhance the accuracy of the forecasting. In view of the poor ability of ANN model to deal with the uncertainty and ambiguity information, this paper cites the theory of fuzzy to build fuzzy neural network (FNN) model. Comparing with ANN and FNN, the approach in this paper has achieved greater forecasting accuracy.
【Fund】: 国家自然科学基金项目资助(70671039)~~
【CateGory Index】: TM715
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