A Consumption Habits Clustering Modeling of Distribution Terminal Loads Based on Multi-Source Data Fusion

Abstract

Aiming at the problems that are difficult to analyze due to the diverse load characteristics of distribution network users, considering the behavior characteristics of distribution terminal users, define user power consumption characteristics indicators, and study and establish a new distribution terminal load power consumption habit index system. Aiming at the problem that due to the diverse composition of end users and the randomness of user behavior, which reduces the accuracy of the analysis model of electricity consumption habits, considering the influencing factors such as user electricity consumption behavior, holidays and emergencies, a clustering algorithm based on data mining theory is used to The power distribution terminal user data is preprocessed to eliminate data noise interference, extract the feature quantities of the influencing factors of user behavior, and improve the accuracy of the analysis model. Finally, a portrait of the electricity consumption behavior of power distribution terminal users is formed, and according to the analysis results, it has a certain load forecasting ability for users.