Abstract: A method of a power user classification based on distributed K-means, a storage medium and a classification device are provided. The method includes: obtaining, by N load aggregators, power consumption data of power users managed by respective load aggregators; performing, by each load aggregator, a normalization operation on time series load data of the power users managed by the load aggregator; forming a N×N dimensional adjacency matrix A; performing K-means clustering on normalized time series load data, to obtain the 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 A, and obtaining consistent centroids by multiple load aggregators; after an overall iteration ends, obtaining, by the respective load aggregators, the consistent centroids consistent with the K-means centroid based on global data, to realize user classification.

Abstract: A method of a power user classification based on distributed K-means, a storage medium and a classification device are provided. The method includes: obtaining, by N load aggregators, power consumption data of power users managed by respective load aggregators; performing, by each load aggregator, a normalization operation on time series load data of the power users managed by the load aggregator; forming a N×N dimensional adjacency matrix A; performing K-means clustering on normalized time series load data, to obtain the 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 A, and obtaining consistent centroids by multiple load aggregators; after an overall iteration ends, obtaining, by the respective load aggregators, the consistent centroids consistent with the K-means centroid based on global data, to realize user classification.

Abstract: The present disclosure provides a method for performing clustering on operation modes of a power system based on a sparse autoencoder. The method includes: obtaining related data of the power system; setting a training parameter, a number of hidden layers, and a number of neurons; training an autoencoder model using the related data and extracting a topological structure and a weight matrix from the model; performing cluster analysis to obtain a number of typical scenarios; and performing decoding to obtain original data at centers of respective scenarios. The present disclosure can achieve fast selection and dimensionality reduction of feature vectors representing operation modes of a power system. In view of this, the present disclosure provides a novel idea and method for selecting a feature vector representing an operation mode of a power system and generating a typical operation scenario.