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- W4281783963 abstract "By examining load curves, users can be classified into different categories according to their characteristics. Such results are of great importance to the power industry and socio-economic development. This study therefore analyzes the behavior of user’s electricity consumption through clustering. Specifically, several classic clustering algorithms such as K-means, fuzzy clustering and neural network clustering are described. This is followed by an introduction to a data preprocessing method. Then, selecting an appropriate clustering algorithm as well as an optimal clustering number based on the clustering volatility and clustering accuracy index is discussed. Then, the load curve and the center representative curve are obtained by clustering. In addition, in order to eliminate the discontinuity caused by interval sampling, the moving average method is used to generate a continuous and smooth center load representative curve, and the user’s electricity consumption behavior is further analyzed." @default.
- W4281783963 created "2022-06-13" @default.
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- W4281783963 date "2022-04-22" @default.
- W4281783963 modified "2023-09-26" @default.
- W4281783963 title "A Clustering Analysis of Electricity Consumption Behavior" @default.
- W4281783963 doi "https://doi.org/10.1109/ceepe55110.2022.9783311" @default.
- W4281783963 hasPublicationYear "2022" @default.
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