Early Warning Method of Abnormal Electricity Consumption Behavior Based on Data Driven

In view of the huge economic losses caused to power companies by abnormal power consumption behaviors of users such as stealing and abusing electricity, based on data driven method, the daily load data of residents in the region is used to score the users′ energy consumption behavior quantitatively from the horizontal and vertical levels.Firstly, based on K-means and SVM (Support Vector Machine) classification model, the daily load data of individual residents data are compared with those of residents with similar electricity consumption behaviors to generate the user′s horizontal score.Secondly, the user load forecasting model is established by using LSTM (Long Short-Term Memory) model to realize the comparison with their own historical electricity consumption behavior and generate the user’s vertical score.Finally, according to the set weight, a comprehensive score is made. When the score is lower than a certain threshold, early warning is given.The proposed method is verified by the data of 30 users for 4 years, the accuracy of horizontal score is more than 99.9%, and the goodness of fit of vertical score is more than 95%, which proves the feasibility of the method.

Abstract licence: CC BY

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