Method of power user classification based on distributed K-means, storage medium and classification device

A non-transitory computer-readable storage medium for storing one or more programs, wherein the one or more programs include instructions, and the instructions, when executed by a computing device, cause the computing device to execute a method of a power user classification based on distributed K-means, and the method comprises:

• S1, obtaining, by N load aggregators, power consumption data of power users managed by the respective load aggregators, the power consumption data comprising serial numbers of the power users and time series load data of the power users;
• S2, performing, by each of the load aggregators, a normalization operation on the time series load data of the power users managed by the load aggregator according to a uniform normalization criterion;
• S3, forming a N×N dimensional adjacency matrix A according to an information sharing relationship of the load aggregators;
• S4, performing, by each load aggregator, K-means clustering on the time series load data of the power users managed by the load aggregator after the normalization operation in the step S2, to obtain respective centroids and user groups characterized by the respective centroids;
• sharing, by the respective load aggregators, the centroids and the number of users under the respective centroids based on the adjacency matrix in the step S3;
• performing overall iterations repeatedly according to a corresponding function;
• and obtaining consistent centroids by multiple load aggregators;
• and
• S5, after the overall iteration ends, obtaining, by the respective load aggregators, the consistent centroids consistent with K-means centroids based on global data, to realize user classification.

A classification device, comprising:

• one or more processors;
• a memory;
• and
• one or more programs, wherein one or more programs are stored in the memory and configured to be executed by the one or more processors, and the one or more programs comprise instructions for executing a method of a power user classification based on distributed K-means, and the method comprises:
• S1, obtaining, by N load aggregators, power consumption data of power users managed by the respective load aggregators, the power consumption data comprising serial numbers of the power users and time series load data of the power users;
• S2, performing, by each of the load aggregators, a normalization operation on the time series load data of the power users managed by the load aggregator according to a uniform normalization criterion;
• S3, forming a N×N dimensional adjacency matrix A according to an information sharing relationship of the load aggregators;
• S4, performing, by each load aggregator, K-means clustering on the time series load data of the power users managed by the load aggregator after the normalization operation in the step S2, to obtain respective centroids and user groups characterized by the respective centroids;
• sharing, by the respective load aggregators, the centroids and the number of users under the respective centroids based on the adjacency matrix in the step S3;
• performing overall iterations repeatedly according to a corresponding function;
• and obtaining consistent centroids by multiple load aggregators;
• and
• S5, after the overall iteration ends, obtaining, by the respective load aggregators, the consistent centroids consistent with the K-means centroids based on global data, to realize user classification.