User classification method and system based on power retail data
A technology of power users and classification methods, applied in data processing applications, market data collection, computer components, etc., can solve the problems of lack of methods and systems for classification management, reduce user management and operating costs, and stabilize clustering effects Reliable, well-documented effects
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Embodiment 1
[0066] A user classification method based on electricity retail data, comprising the steps of:
[0067] Step (1), according to the user authorization information, obtain the transaction power Q, transaction price P and actual power consumption E of N power users with the electricity sales company within a certain research period;
[0068] Step (2), calculate the user transaction amount M and the absolute value of the deviation power T:
[0069] M=Q×P
[0070] T=|E-Q|
[0071] Step (3), normalize the transaction amount and the absolute value of the deviation power, and map it to the range of [0, 1];
[0072] Step (4), based on the normalized transaction amount and the absolute value data of the deviation electricity, use the elbow method to determine the category quantity K of power users;
[0073] Step (5), according to the normalized transaction amount and the absolute value data of the deviation power and the user category K, use the K-means algorithm to cluster the N pow...
Embodiment 2
[0084] A user classification method based on electricity retail data, comprising the steps of:
[0085] Step (1), according to the user authorization information, obtain the transaction power Q, transaction price P and actual power consumption E of N power users with the electricity sales company within a certain research period;
[0086] Step (2), calculate the user transaction amount M and the absolute value of the deviation power T:
[0087] M=Q×P
[0088] T=|E-Q|
[0089] Step (3), normalize the transaction amount and the absolute value of the deviation power, and map it to the range of [0, 1];
[0090] Step (4), based on the normalized transaction amount and the absolute value data of the deviation electricity, use the elbow method to determine the category quantity K of power users;
[0091] Step (5), according to the normalized transaction amount and the absolute value data of the deviation power and the user category K, use the K-means algorithm to cluster the N pow...
Embodiment 3
[0109] Such as figure 1 As shown, a user classification system based on electricity retail data, including:
[0110] The back-end data acquisition module 101 is used to acquire the user's transaction power, transaction price and actual power consumption data within a certain research period;
[0111] The back-end data processing module 102 is used to calculate the user's transaction amount and the absolute value of the deviation power according to the data obtained by the back-end data acquisition module 101, and perform normalization processing on the transaction amount and the absolute value of the deviation power to obtain the standardized The transaction amount and the absolute value data of the deviation electricity; and then adaptively determine the category number K of power users according to the elbow method;
[0112] The back-end data classification module 103 is used to implement user classification according to the standardized data obtained by the back-end data p...
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