Dynamic conditional score model-based weighted incremental fuzzy clustering of consumer power load data

and sequence correlations. Consequently, this method has unsatisfactory time series clustering and low clustering accuracy. A dynamic conditional score model is constructed to analyze and extract statistical characteristic parameters of a time series to calculate the autocorrelation value of the parameter series. A weighted fuzzy C-mean clustering analysis is performed, and the obtained data weight information is used as input for incremental clustering to improve the clustering accuracy. The DCS model parameter dataset and data weight information are combined, and the clustering analysis of the consumer power load data stream is performed. The power load time series of different companies is given, and the clustering validity indices are defined for the performance analysis to verify the proposed clustering algorithm. The experimental results show that the proposed algorithm achieves satisfactory clustering and improves the performance.

Automated summary

In this study, a dynamic conditional score model is constructed to analyze and extract statistical characteristic parameters of a time series and calculate the autocorrelation value of the parameter series. A weighted fuzzy C-mean clustering analysis is performed, and the obtained data weight information is used for incremental clustering to improve clustering accuracy. Experimental results show that the proposed algorithm achieves satisfactory clustering and improves performance.