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Intelligent power distribution method based on electric power big data

A smart power distribution and big data technology, applied in data processing applications, other database retrieval, other database clustering/classification, etc., can solve problems such as energy waste and poor accuracy, and achieve prediction speed improvement, efficient and accurate algorithms, and guaranteed The effect of power utilization

Pending Publication Date: 2022-05-20
STATE GRID HEILONGJIANG ELECTRIC POWER COMPANY +1
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Problems solved by technology

[0003] The purpose of the present invention is to solve the problem that the existing electricity consumption forecasting models perform wide-range electricity consumption prediction according to the user's electricity consumption law, the accuracy is poor, and energy waste is easily caused, and an intelligent power distribution method based on electric power big data is provided.

Method used

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  • Intelligent power distribution method based on electric power big data
  • Intelligent power distribution method based on electric power big data
  • Intelligent power distribution method based on electric power big data

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specific Embodiment approach 1

[0043] Specific implementation mode one: the following combination figure 1 Describe this embodiment, the intelligent power distribution method based on electric power big data described in this embodiment, the method includes:

[0044] Step 1. Obtain the historical electricity consumption data of all electricity users, and preprocess the historical electricity consumption data; obtain a sample set;

[0045] Step 2, using an AP clustering algorithm based on dynamic time planning similarity to cluster the sample set to obtain n types of samples; wherein n is a positive integer;

[0046] Step 3. Drawing electricity consumption curves of n types of samples respectively, and according to the electricity consumption curves, users corresponding to the electricity consumption data are divided into three types: non-migratory birds, family-type migratory birds or non-family-type migratory birds;

[0047] Step 4. Label all sample data by type; mark them as household electricity consump...

specific Embodiment

[0084] figure 1 The flow chart of the K neighbor algorithm based on the dynamic time planning algorithm is given, which specifically includes the following steps:

[0085] Step 1: Clean the original user power to eliminate abnormal values ​​in power consumption. Use one-hot and standardized data processing methods for data processing and data mining. Perform feature engineering on the data to obtain the upper and lower quartiles, mean, variance, covariance and other characteristics of the data.

[0086] Step 1.1: Filter users with zero electricity throughout the year.

[0087] Step 1.2: Eliminate abnormal data through the quartile method, so that the normal fluctuation of the data is significant.

[0088] Step 1.3: Perform linear transformation on the original data through dispersion standardization, so that the power data value of each State Grid user is mapped to [0-1]. The formula is as follows.

[0089]

[0090] Among them, x* represents the normalized power data, ...

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Abstract

An intelligent power distribution method based on power big data belongs to the technical field of power load prediction and power distribution. The method solves the problems that an existing electricity consumption prediction model carries out wide-range electricity consumption prediction according to the electricity consumption rule of users, the accuracy is poor, and energy waste is easily caused, and comprises the steps of obtaining historical electricity consumption data of all users in a region to be subjected to power distribution, and preprocessing the historical electricity consumption data; obtaining a sample set; clustering the sample set by using an AP clustering algorithm based on dynamic time planning similarity to obtain n types of samples; respectively drawing electricity consumption curve graphs of the n types of samples, and according to the electricity consumption curve graphs, dividing users corresponding to the electricity consumption data into non-migrant birds, all-family type migrant birds or non-all-family type migrant birds; performing type labeling on all sample data; establishing a power utilization classification model by using the labeled sample data and a K-proximity classification algorithm; and acquiring the power consumption type of the user, and adjusting the power distribution strategy of the next time period according to the power consumption type of the user.

Description

technical field [0001] The invention belongs to the technical field of power load forecasting and power distribution. Background technique [0002] Power load forecasting is an important task in the power sector, providing a basis for smart power deployment, especially in thermal power plants, which need to predict the power consumption of each region in each quarter in advance, so as to provide a basis for smart power distribution. Provide an effective basis and avoid energy waste; the existing forecasting methods only predict the law of power consumption changes, and cannot realize the prediction of each user type and power consumption time, and make precise power distribution adjustments, so there are The prediction accuracy of power consumption is poor, and the power distribution is not accurate enough, which easily leads to energy waste. Contents of the invention [0003] The purpose of the present invention is to solve the problem that the existing electricity consu...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q50/06G06F16/906G06Q10/04G06K9/62H02J3/00
CPCG06Q50/06G06F16/906G06Q10/04H02J3/003G06F18/23G06F18/2413G06F18/2415Y04S10/50
Inventor 赵威张智勇王云峰方宽谭正卯李明涛赵金石于洋曹勇付鑫向哲宏
Owner STATE GRID HEILONGJIANG ELECTRIC POWER COMPANY
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