At present, there are many kinds of electricity theft and the corresponding approaches to combat this are insufficient. Manual approaches result in a heavy staff workload and are inefficient. In this paper, the data from an electricity information acquisition system is collected and mined using Python. Based on an understanding of the business and an analysis of the information value (IV) measure, important characteristic indexes are selected and an improved decision tree algorithm is used to construct a model of power theft by users. This method effectively narrows the range of users suspected of power theft, improving the pertinence of audit, and providing strong support for reducing the financial losses of power supply enterprises and ensuring the safety of power grid operation.
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