Abstract
There are many factors affecting the user’s electricity consumption behavior in China, and the electricity usage behavior of power users is both random and periodic. Therefore, how to more accurately grasp the user’s power consumption behavior has always been an important topic for power researchers. This paper proposes to analyze the correlation between users’ weekly workdays and weekly rest days on the weekly time scale, with working days and weekly holidays. The correlation coefficient of the daily load data constructs the feature value, combines the variance with the K-means algorithm, determines the number of clusters by the cluster validity index, rapidly clusters the weekly load, and summarizes the weekly power consumption of the user based on the clustering result. The behavior category analyzes the detail the changes in the behavior of users using electricity during the week. The simulation is carried out by using MATLAB software, and the users are divided into four categories. By combining the characteristic value curve of each type of user and the typical user load curve, the characteristics of each user’s electricity consumption behavior are analyzed in detail.
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Zhao, B., Shao, B. (2020). Analysis the Consumption Behavior Based on Weekly Load Correlation and K-means Clustering Algorithm. In: Hassanien, A., Shaalan, K., Tolba, M. (eds) Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2019. AISI 2019. Advances in Intelligent Systems and Computing, vol 1058. Springer, Cham. https://doi.org/10.1007/978-3-030-31129-2_7
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DOI: https://doi.org/10.1007/978-3-030-31129-2_7
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