Unlock instant, AI-driven research and patent intelligence for your innovation.
Electricity stealing risk prediction method and device based on deep learning
What is Al technical title?
Al technical title is built by PatSnap Al team. It summarizes the technical point description of the patent document.
A technology of deep learning and electricity stealing, applied in the field of electricity stealing risk prediction based on deep learning, can solve the problem of low accuracy of prediction results
Inactive Publication Date: 2019-04-12
WUHAN UNIV
View PDF5 Cites 33 Cited by
Summary
Abstract
Description
Claims
Application Information
AI Technical Summary
This helps you quickly interpret patents by identifying the three key elements:
Problems solved by technology
Method used
Benefits of technology
Problems solved by technology
[0007] In view of this, the present invention provides a method and device for predicting electricity theft risks based on deep learning, to solve or at least partially solve the technical problem of low accuracy of prediction results in existing methods
Method used
the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more
Image
Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
Click on the blue label to locate the original text in one second.
Reading with bidirectional positioning of images and text.
Smart Image
Examples
Experimental program
Comparison scheme
Effect test
Embodiment 1
[0088] This embodiment provides a method for predicting the risk of stealing electricity based on deep learning, please refer to figure 1 , the method includes:
[0089] First, step S1 is performed: obtaining historical data from the actual application scene of the on-site metering equipment, and performing preprocessing on the obtained historical data.
[0090] Specifically, the historical data obtained is the original electricity consumption data. In practical applications, due to some external reasons such as personnel and equipment, sometimes there will be a lack of data items generated by one or more fields, and sometimes there will be a large amount of information. An error in the field. Therefore, the process of preprocessing the data is required, and the preprocessing includes steps such as data cleaning.
[0091] In one implementation, see image 3 , in step S1, the preprocessing of the obtained historical data specifically includes:
[0092] Step S1.1: Data sampl...
Embodiment 2
[0145] This embodiment provides a device for predicting the risk of stealing electricity based on deep learning, please refer to Figure 8 , the device consists of:
[0146] A preprocessing module 301, configured to acquire historical data from actual application scenarios of on-site metering equipment, and preprocess the acquired historical data;
[0147] A feature extraction module 302, configured to extract climate features and electricity consumption features from the preprocessed data;
[0148] The daily electricity consumption pattern prediction module 303 is used to use the extracted climate characteristics and electricity consumption characteristics as training samples, and predict the user's daily electricity consumption pattern based on the training samples and the deep belief network DBN;
[0149] The abnormal electricity consumption rate detection module 304 is used to calculate the abnormal electricity consumption rate by using the preset analytic hierarchy proce...
Embodiment 3
[0184] Based on the same inventive concept, the present application also provides a computer-readable storage medium 400, please refer to Figure 9 A computer program 411 is stored thereon, and the method in Embodiment 1 is implemented when the program is executed.
the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More
PUM
Login to View More
Abstract
The invention provides an electricity stealing risk prediction method and device based on deep learning. A solution is provided for the problems that in a traditional electricity larceny prediction method, an electricity larceny user cannot be positioned due to the fact that a topological structure in a power distribution network is not known, and the electricity larceny prediction accuracy is lowdue to the fact that a non-efficient identification feature is used in the traditional electricity larceny prediction method. The prediction method comprises the following steps: firstly, predictingthe daily power consumption mode quantity of a user by utilizing daily historical power consumption information of a low-voltage user; secondly, according to analysis of power utilization factors of the users in nearly three days of the day, an analytic hierarchy process is adopted to calculate the abnormal rate of power utilization, and finally, based on K <->, the abnormal rate of power utilization is calculated. And the Means method is used for grading the electricity stealing. Technical effects of improving prediction accuracy and realizing electricity larceny grade division are achieved.
Description
technical field [0001] The invention relates to the technical field of data mining and machine learning in computer science, in particular to a method and device for predicting electricity theft risks based on deep learning. Background technique [0002] The State Grid Corporation of China started the construction of the electricity consumption information collection system in 2010. At present, the collection coverage rate has reached 92.2% in the national power gridsystem, and 398 million smart energy meters have been installed. During the operation for about 7 years, a large amount of data of metering equipment has been accumulated. The data of multiple databases can be freely exchanged, and data mining technology can be used to conduct in-depth analysis of the characteristics of users' electricity consumption. [0003] In the process of implementing the present invention, the applicant of the present invention finds that at least the following problems exist in the met...
Claims
the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More
Application Information
Patent Timeline
Application Date:The date an application was filed.
Publication Date:The date a patent or application was officially published.
First Publication Date:The earliest publication date of a patent with the same application number.
Issue Date:Publication date of the patent grant document.
PCT Entry Date:The Entry date of PCT National Phase.
Estimated Expiry Date:The statutory expiry date of a patent right according to the Patent Law, and it is the longest term of protection that the patent right can achieve without the termination of the patent right due to other reasons(Term extension factor has been taken into account ).
Invalid Date:Actual expiry date is based on effective date or publication date of legal transaction data of invalid patent.
Login to View More 
Login to View More