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《Journal of Zhejiang University(Engineering Science)》 2007-09
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Case-based reasoning for short-term load forecasting based on hierarchy matching

WANG Zhi-yong1,2,CAO Yi-jia1(1.College of Electrical Engineering,Zhejiang University,Hangzhou 310027,China;2.Zhejiang Power Corporation,Hangzhou 310007,China)  
An improved reasoning system was presented to solve the short-term load forecasting(STLF) problem in power system operation and planning by using case-based reasoning(CBR),self-organizing map(SOM) and fuzzy-rough sets method. CBR is composed of the steps of case representation,indexing,retrieval,and adaptation,and the key idea in CBR involves the use of already existing knowledge about objects or situations to predict aspects of similar objects.SOM are trained as a cluster tool in order to organize the old cases with the purpose of speeding up the CBR process.Fuzzy-rough sets method further extends the rough set concept through the use of fuzzy equivalence classes and is presented as a tool to extract principal case attributes.This method uses not only case-specific knowledge of past problems,but also uses additional knowledge derived from the clusters of cases and provides a new way for selecting proper feature subset and feature weights.The testing results on a real power system show that the proposed system is feasible and promising for STLF.
【Fund】: 高等学校博士学科点基金资助项目(20030335003)
【CateGory Index】: TM715
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【Co-citations】
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1 YAO Fei;WANG Bo;GU Ji-fa;Business School, University of Shanghai for Science and Technology;;Forecast and Analysis of Total Electricity Consumption of China Rural—Based on the Improved GM(1,1) Model of Least Square Method[J];物流工程与管理;2015-10
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9 SUN Wei;CHANG Hong;ZHAO Qiao-zhi;Department of Economics and Management,North China Electric Power University;School of Information Science and Technology,East China University of Science and Technology;;Forecasting mid-long term electricity consumption using a quantum harmony search based improved DMSFE combination model[J];电力系统保护与控制;2014-21
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