Poor user identification method based on daily differential electricity consumption

Abstract

The invention discloses a poor user identification method based on daily differential electricity consumption, and relates to a poor user identification method. At present, a user group type is subjectively defined often directly according to a clustering result, so that a relatively large error exists in user group classification. The method comprises the steps: firstly, carrying out poor user statistics on an investigated user group in a questionnaire mode, then dividing the investigated user group into a training sample group and a test sample group, and meanwhile, carrying out daily electricity consumption difference calculation on users of the training sample group to obtain the daily difference electricity consumption data; and respectively extracting electricity utilization characteristic data of poor users and non-poor users from the daily differential electricity consumption data by utilizing a statistical method. On the basis, the test sample group is verified, and a poor user identification method is perfected. According to the technical scheme, the influence of subjective factors is reduced, and the user group classification accuracy is effectively improved; the recognition efficiency is high, the coverage range is wide, and a large amount of manpower and material resources are saved.

Description

technical field [0001] The invention relates to a poor user identification method, in particular to a poor user identification method based on daily differential power consumption. Background technique [0002] At this stage, the identification method for poor users is usually issued step by step from the city to the county to the village, and then the village government conducts on-site investigations to confirm the type of each household and reports the summary. Low, wasting a lot of manpower and material resources. With the rapid development of smart grids, a large amount of electricity consumption data has been accumulated. Due to the huge differences in various factors such as economic level, living habits, and living regions of different resident groups, their power consumption levels are often very different. [0003] The State Grid Power Consumption Information Collection System is a system that collects, processes and monitors power consumption information of power...

AI Technical Summary

Problems solved by technology

[0004] At present, scholars at home and abroad identify the types of user groups through the characteristics of users' electricity consumption, but most of them directly conduct cluster analysis on users based on electricity consumption data to study the characteristics of users' electricity consumption. There are two problems in this method. On the one hand, the number of clustering types is often determined artificially, which leads to difficulties in interpreting and verifying the classification results, and cannot intuitively analyze the effectiveness of the differences in power consumption characteristics of different users

On the other hand, researchers often directly define user group types subjectively based on clustering results, resulting in large errors in the classification of user groups.

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