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- W2362103239 abstract "For the strong correlations among training examples,load forecasting model construction and forecasting accuracy,on the basis of researching into the load changing law,the theory and corresponding method for dynamic example organization were presented.According to the horizontal and longitudinal load changing characters,date,season and weather characters,the time classification tree and example map were constructed.Then,the examples were selected preliminarily via fuzzily dealing with weather data,and the dynamic selection of examples was completed via extracting the characteristic curve of load level changing trend by the improved self-organizing feature map(SOFM).The results of several forecasting models indicate the filtration mechanism with several levels and perspectives can offer better training examples to inhibit interference from bad examples and get higher forecasting accuracy of short-term load." @default.
- W2362103239 created "2016-06-24" @default.
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- W2362103239 date "2009-01-01" @default.
- W2362103239 modified "2023-09-23" @default.
- W2362103239 title "Dynamic Example Organization for Short-Term Load Forecasting" @default.
- W2362103239 hasPublicationYear "2009" @default.
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