Power consumption behavior analysis based on cluster analysis

Xinya Yuan; Qinyuan Cai; Song Deng

doi:10.1117/12.2605823

28 October 2021 Power consumption behavior analysis based on cluster analysis

Xinya Yuan, Qinyuan Cai, Song Deng

Proceedings Volume 11884, International Symposium on Artificial Intelligence and Robotics 2021; 118841F (2021) https://doi.org/10.1117/12.2605823
Event: International Symposium on Artificial Intelligence and Robotics 2021, 2021, Fukuoka, Japan

Abstract

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.

Citation Download Citation

Xinya Yuan, Qinyuan Cai, and Song Deng "Power consumption behavior analysis based on cluster analysis", Proc. SPIE 11884, International Symposium on Artificial Intelligence and Robotics 2021, 118841F (28 October 2021); https://doi.org/10.1117/12.2605823

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KEYWORDS

Analytical research

Fuzzy logic

Feature extraction

Neural networks

Statistical analysis

Stochastic processes

Temperature metrology

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