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- W4362680915 abstract "Data feature analysis is the key to realize the balance of power supply and demand. There are a large number of nonlinear data in the power grid, which affects the accurate mining of short-term features. Therefore, a short-term feature mining method for power grid big data is designed based on convolutional neural network. The change law of short-term characteristics is complex and diverse, and the factors affecting short-term characteristics are analyzed. According to the potential characteristics of time-dependent changes in influencing factors, the date distance is quantified, and the grey correlation degree is used to select the best short-term similar day. The weather, temperature, date and load series of similar days are input into the convolution neural network. The information is extracted by two-layer convolution and pooling, and the short-term feature mining results of power grid data are obtained. The abnormal and missing values of the collected power load and meteorological data are processed to form an example data set for feature mining. The test results show that the design method can improve the mining accuracy, the results can reflect the development trend of power grid big data, and improve the information utilization." @default.
- W4362680915 created "2023-04-08" @default.
- W4362680915 creator A5029486050 @default.
- W4362680915 date "2022-12-16" @default.
- W4362680915 modified "2023-09-28" @default.
- W4362680915 title "Research on Power Grid Big Data Short-Term Feature Mining Method Based on Convolution Neural Network" @default.
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- W4362680915 doi "https://doi.org/10.1109/ic2ecs57645.2022.10087951" @default.
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