Data-driven anti-electricity stealing intelligent early warning method
A data-driven, anti-stealing technology, applied in the field of electric power industry, can solve the problems of low accuracy of investigation and punishment of electricity theft, insufficient data mining depth, lack of digital handles, etc., to reduce personnel operation and inspection costs, and improve investigation rate , The effect of reducing operating costs
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[0036] Step 1: Extract the internal data of the power grid, plus the imported external big data, preprocess the user data, extract the features such as maximum power, power consumption, compliance rate and other characteristics to establish a user power consumption feature set, and then use a random search strategy And the wrapping algorithm, perform feature subset search, compare various search results, select the data with the best measurement performance, and construct the key feature set of user electricity consumption;
[0037] Step 2: Use the traditional industry classification method to preliminarily classify the training data, and build the must-connect constraint set ML and the non-connection constraint set CL according to the nature difference between the major industries. , voltage, current, maximum power, peak-to-valley ratio, and other key features of user electricity consumption, as well as user classification information obtained by using traditional industry cla...
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