Aiming at the problem of low accuracy in directly applying traditional clustering algorithms to time series clustering in theelectricity load of universities,a DLT-Kmedoids?algorithm combining DTW distance,LB_Keogh distance and time window is proposedto improve the accuracy and efficiency of clustering algorithm applied to time series.?
The DLT - Kmedoids algorithm uses DTW tocalculate the distance between time series data instead of traditional Euclidean distance measurement,improving the accuracy of similaritymeasurement algorithms and also improving the accuracy and complexity of clustering algorithms. LB_Keogh distance is introduced tofilter out most sequences that are unlikely to be optimal matching sequences before calculating DTW distance, and DTW is used tocompare the remaining sequences one by one to further reduce the complexity?
of the algorithm. Finally,we analyze the time series data of electricity consumption in university buildings, and compare it with mainstream clustering algorithms. It is showed that the proposedalgorithm can more accurately identify similar load patterns and perform clustering analysis with higher efficiency for the clustering task ofelectricity consumption data in universities.