With the construction of smart grid, a large number of user-side power data has been accumulated. This paper proposes a method for analyzing the user’s power behavior based on clustering algorithm. Firstly, the user load data is classified according to the season, and the user’s seasonal power characteristics are analyzed according to the typical daily load curve of the season. Then the average temperature plus load data is used as the feature, and K-means clustering algorithm is used to explore the influence of temperature and holidays on users’ electricity behavior in summer and winter respectively. This paper proposes a method of classifying and analyzing different power consumption modes of a single user, which provides data support for the subsequent load prediction model training for similar days, as well as the formulation of fine management and demand side management decisions for the power grid.
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