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• W4386248300 abstract "This paper focuses on the real-time dynamic clustering analysis of power load data based on the dynamic conditional score (DCS) model, and an autocorrelation increment fuzzy C-means clustering algorithm based on the DCS model is proposed. (1) The paper addresses the problem that current power load clustering methods, when performing time series data mining, tend to focus on capturing the mean structure while ignoring the variance characteristics of the data, making it difficult to effectively capture the structural information of time series data. The DCS model is used as the statistical model basis for clustering analysis, and the time series is clustered based on the estimated conditional moment characteristics of the model, dynamically capturing data features such as the mean, variance, and sequence correlation of time series data, effectively improving the clustering performance. (2) This paper also addresses the issue that current power load clustering methods tend to focus on static datasets of user power loads and cannot effectively handle the data stream clustering problem with time series characteristics in practical applications. The DCS model parameter dataset and the autocorrelation increment fuzzy clustering algorithm are used to conduct a dynamic data flow analysis of user electricity behaviour evolution and pattern continuous updating research for power loads. The clustering results are dynamically updated based on the user's power load data stream using the proposed algorithm, achieving research on a universal clustering model and secure and efficient algorithms in a big data environment. (3) The paper verifies the clustering performance of the proposed method using power load time series data provided by a Chinese power supply company as a case dataset. The clustering evaluation index shows that the proposed algorithm has high clustering accuracy and good clustering performance. Additionally, different power supply recommendations are proposed for different customer electricity types in the obtained clustering results to provide more personalized power services." @default.
• W4386248300 created "2023-08-30" @default.
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• W4386248300 date "2023-11-01" @default.
• W4386248300 modified "2023-10-16" @default.
• W4386248300 title "An Autocorrelation Incremental Fuzzy Clustering Framework Based on Dynamic Conditional Scoring Model" @default.
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• W4386248300 doi "https://doi.org/10.1016/j.ins.2023.119567" @default.
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