Orderly power utilization management strategy based on load time series clustering
Aiming at the problem that in the current orderly power utilization planning the difference in user load characteristics is not fully considered,an orderly power utilization management strategy based on load time series clustering is proposed.First,the typical load curve shape of a single user is extracted.After that,the Canopy and k-shape time series clustering algorithms are used to solve the problem of insufficient accuracy of traditional clustering algorithms when there are many user objects,and the load time series data of different users are clustered,thereby classifying multiple users into different power consumption types.Finally,according to the load characteristics of different types of users,the targeted orderly power utilization management strategies are formulated.The actual load data of a certain regional power grid is analyzed as an example.The results show that the proposed method can more accurately classify multiple users with different load characteristics than traditional methods,so as to more effectively guide the rational formulation of orderly power utilization management strategies.
time seriesload clusteringorderly power utilizationload characteristicssimilarity measurement