CN111177208A - Power consumption abnormity detection method based on big data analysis - Google Patents
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Abstract
The invention discloses a big data analysis-based power utilization abnormity detection method, which comprises the following steps of: s1, mastering the electricity utilization rules of different types of residents, and mastering the electricity utilization rules of the residents by describing the electricity characteristics of 9 types of residential users of electricity utilization types and region types; s2, identifying abnormal electricity consumption residents, improving the marketing electricity consumption lean management level, accurately positioning outlier users from massive resident electricity consumption data, and early warning risk customer lists such as electricity price execution errors and abnormal electricity consumption. The invention innovatively provides that resident electricity utilization customers are divided into 9 parts according to electricity utilization types and region types, then the users with abnormal electricity consumption are classified and identified according to an isolated forest algorithm, the concept that the electricity utilization customers are all looked at once is broken through, different abnormal user detection methods are provided for different types and through different parameters, and therefore the accuracy rate of residents with abnormal electricity utilization is improved to a great extent.
Description
Technical Field
The invention relates to the technical field of power utilization abnormity detection, in particular to a power utilization abnormity detection method based on big data analysis.
Background
The method aims at the business pain points that the contradiction between the service strength and the number of customers is prominent, the service pressure situation tends to be serious, the electricity utilization market potential of residential customers is huge, the implementation difficulty of accurate service strategies is high and the like, deeply mining and researching residential electricity utilization customers, and fully exerting the value of a big data means in the management of residential electricity utilization business.
Disclosure of Invention
The invention aims to solve the defects in the prior art, and provides a power consumption abnormity detection method based on big data analysis, which divides residential power consumption customers into 9 parts according to power consumption types and regional types, and then classifies and identifies users with abnormal power consumption according to an isolated forest algorithm, breaks through the concept of looking at the same person for the power consumption customers in the past, and provides different abnormal user detection methods for different classes and different parameters, thereby greatly improving the accuracy of residents with abnormal power consumption.
In order to achieve the purpose, the invention adopts the following technical scheme:
the power utilization abnormity detection method based on big data analysis comprises the following steps:
s1, mastering the electricity utilization rules of different types of residents, and mastering the electricity utilization rules of the residents by describing the electricity characteristics of 9 types of residential users of electricity utilization types and region types;
s2, identifying abnormal electricity consumption residents, improving the marketing electricity consumption lean management level, accurately positioning outlier users from massive resident electricity consumption data, and early warning risk customer lists such as electricity price execution errors and abnormal electricity consumption.
S3, managing economic value generated by leakage through problems, coordinating with a business department to verify and process abnormal electricity customers, and simultaneously ensuring benefits of residents and power suppliers to block the economic value generated by leakage.
Preferably, in step S1, Oracle is used for storage in the early stage, Hive of the alternative large data platform is used for data storage in the later stage, and the obtained data is obtained by using the association ID of the wide table;
constructing a model of data by using Pycharm, Anaconda and other related integrated tool environments;
fusing multi-source data such as user file information, electric energy meter information, user meter daily freezing electric quantity information, external weather and the like: firstly, data preparation and data processing are carried out;
secondly, data rules are explored through data distribution, trend analysis and other ways, and basis is provided for subsequent modeling;
thirdly, carrying out classification on residential electricity utilization characteristic images based on a K-Shape clustering algorithm;
and fourthly, constructing an outlier recognition model based on the isolated forest algorithm in a classified mode, and recognizing outlier users.
Preferably, in step S2, the internal data mainly includes a residential electricity customer profile and a daily frozen electricity amount;
the electricity consumption customer files come from a marketing system, the daily freezing electricity quantity comes from an acquisition system, and the data are primary data, so that the data reliability is high;
the file fields of the electricity customers are complete, the missing condition does not exist, the daily frozen electricity does not have the repeated condition due to the collection reason, the missing condition of a small number of data items exists, the integrity of the whole fields is high, and the subsequent data mining analysis is not influenced;
the situation that the overall records of the power utilization customer files are repeated exists, and the GIS geographic distribution outlier customer identification is influenced due to the fact that the user address is not recorded in a standard mode; the daily freezing of the electric quantity mainly has the condition that the data is empty or the numerical value of the electric quantity is suddenly changed.
Preferably, in step S2, only the first piece of data is retained for the data with duplicate, and the rest is deleted; deleting the whole piece of data under the condition that the core field is missing; for the condition that the electric quantity data is empty or the numerical value amplitude is suddenly changed, the data is used for shifting and filling up the adjacent data;
in the daily freezing electric quantity width table, counting the missing condition of each piece of data; and determining data deletion with the data deletion dimensionality exceeding 55 dimensions according to the specific deletion condition and the condition of preventing the filling deletion value from influencing the analysis result too much.
The invention has the following beneficial effects:
1. the invention innovatively provides that residential electricity customers are divided into 9 parts according to electricity utilization types and region types, and then the users with abnormal electricity consumption are classified and identified according to an isolated forest algorithm, so that the concept that the electricity customers are all looked at once in the past is broken through, and different types are distinguished;
2. the system is low in efficiency and high in company operation cost, and in consideration of the problems, the paper provides a resident electricity utilization abnormity identification model and method based on a user isolated forest algorithm, and the system has high application value;
3. different abnormal user detection methods are provided through different parameters, so that the accuracy of residents with abnormal electricity utilization is improved to a great extent.
Drawings
FIG. 1 shows data of a part of a repeated measurement point identification of a year-day frozen electric quantity wide meter;
fig. 2 is a daily electricity quantity distribution histogram of the residential user;
FIG. 3 is a schematic diagram of the overall distribution of power consumption;
FIG. 4 illustrates the advantages and disadvantages of various alternative algorithms;
fig. 5 is a residential electricity consumption characterization table.
Detailed Description
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in detail below. In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention. This invention may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein, but rather should be construed as broadly as the present invention is capable of modification in various respects, all without departing from the spirit and scope of the present invention.
It will be understood that when an element is referred to as being "secured to" another element, it can be directly on the other element or intervening elements may also be present. When an element is referred to as being "connected" to another element, it can be directly connected to the other element or intervening elements may also be present. The terms "vertical," "horizontal," "left," "right," and the like as used herein are for illustrative purposes only and do not represent the only embodiments.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used herein, the term "and/or" includes any and all combinations of one or more of the associated listed items.
Referring to fig. 1 to 5, the power consumption abnormality detection method based on big data analysis includes the following steps:
s1, mastering the electricity utilization rules of different types of residents, and mastering the electricity utilization rules of residents by realizing the characterization of the electricity characteristics of 9 types of resident users of electricity utilization types and region types, wherein in the step S1, Oracle is used for storage in the early stage, Hive of a large data platform is selected for data storage in the later stage, and the data is acquired by using a wide table association ID;
constructing a model of data by using Pycharm, Anaconda and other related integrated tool environments;
fusing multi-source data such as user file information, electric energy meter information, user meter daily freezing electric quantity information, external weather and the like: firstly, data preparation and data processing are carried out;
secondly, data rules are explored through data distribution, trend analysis and other ways, and basis is provided for subsequent modeling;
thirdly, carrying out classification on residential electricity utilization characteristic images based on a K-Shape clustering algorithm;
fourthly, constructing an outlier recognition model based on isolated forest algorithm classification, and recognizing outlier users;
s2, identifying residents with abnormal electricity consumption, improving the marketing electricity consumption lean management level, accurately positioning outlier users from massive resident electricity consumption data, and early warning risk client lists such as electricity price execution errors and abnormal electricity consumption, wherein in the step S2, internal data mainly comprise resident electricity consumption client files and daily frozen electricity;
the electricity consumption customer files come from a marketing system, the daily freezing electricity quantity comes from an acquisition system, and the data are primary data, so that the data reliability is high;
the file fields of the electricity customers are complete, the missing condition does not exist, the daily frozen electricity does not have the repeated condition due to the collection reason, the missing condition of a small number of data items exists, the integrity of the whole fields is high, and the subsequent data mining analysis is not influenced;
the situation that the overall records of the power utilization customer files are repeated exists, and the GIS geographic distribution outlier customer identification is influenced due to the fact that the user address is not recorded in a standard mode; the daily freezing electric quantity is mainly communicated when the data is empty or the electric quantity value is suddenly changed;
only the first strip is reserved for the data with the repeated condition, and the rest are deleted; deleting the whole piece of data under the condition that the core field is missing; for the condition that the electric quantity data is empty or the numerical value amplitude is suddenly changed, the data is used for shifting and filling up the adjacent data;
and in the daily freezing electric quantity width table, counting the missing condition of each piece of data. Determining data deletion with data deletion dimensionality exceeding 55 dimensions according to specific deletion conditions and conditions for preventing excessive filling deletion values from influencing analysis results;
s3, managing economic value generated by leakage through problems, coordinating with a business department to verify and process abnormal electricity customers, and simultaneously ensuring benefits of residents and power suppliers to block the economic value generated by leakage.
The data after data processing still has time series data characteristics, but the general algorithm only relates to the data level and cannot take into account the time series characteristics. The analysis requirement shows that no classification label exists, and classification marking needs to be carried out on data by means of a clustering algorithm. A clustering algorithm is selected that is specific to the time series data. The advantages and disadvantages of each algorithm and the trial operation indexes are as follows:
FIG. 4 shows the advantages and disadvantages of each alternative algorithm
The contour coefficient is the basis for measuring whether the classification is accurate, and the closer the value is to 1, the better the classification effect is. The KShape algorithm is chosen.
The samples may optionally include electricity usage in days, weeks, and months. The KShape algorithm with the same parameters is used for testing the data set, the electricity consumption taking the day as a unit is greatly influenced by the outside world, and the fluctuation conditions are different; the dimension of the electricity consumption in the unit of a month is small, and the electricity consumption condition cannot be accurately reflected, so that the electricity consumption in the unit of a week is finally selected as a sample for model training.
The KShape algorithm has the main parameters of n _ clusters and n _ init, and the optimization of the parameters uses grid search.
The residential electricity utilization characteristics are characterized according to the electricity utilization categories and the region types, and the result is as follows:
electricity consumption characteristic describing meter for meter residents
The current anomaly detection algorithms are roughly classified into the following three categories.
The first category is to detect abnormal data based on descriptive statistics, and box line graphs are representative methods, which are simple but have serious cases of mistakenly deleting data.
The second category is cluster-based outlier detection methods. The typical methods are a BIRCH algorithm and a DBSCAN algorithm, and the defects are that the clustering algorithm is not suitable for calculation of large data volume and the calculation time is long.
The third category is based on outlier detection algorithms. The algorithm is different from the two methods, the main application of the algorithm is abnormal point detection, and the algorithms are represented by OneClassSVM and isolated forest.
By synthesizing the three classes of algorithms, we finally select the third class. As the data exploration stage shows that the data do not conform to normal distribution, the characteristic depiction shows that the data have various forms, polynomial fitting errors are large, a hyperplane needs to be trained by the data, the existing data set cannot be provided, and therefore research cannot be carried out on the basis of the OneClass support vector machine, and therefore an isolated forest algorithm is selected
The invention innovatively provides that resident electricity utilization customers are divided into 9 parts according to electricity utilization types and region types, then the users with abnormal electricity consumption are classified and identified according to an isolated forest algorithm, the concept that the electricity utilization customers are all looked at once is broken through, different abnormal user detection methods are provided for different types and through different parameters, and therefore the accuracy rate of residents with abnormal electricity utilization is improved to a great extent.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art should be considered to be within the technical scope of the present invention, and the technical solutions and the inventive concepts thereof according to the present invention should be equivalent or changed within the scope of the present invention.
Claims (4)
1. The power utilization abnormity detection method based on big data analysis is characterized by comprising the following steps of:
s1, mastering the electricity utilization rules of different types of residents, and mastering the electricity utilization rules of the residents by describing the electricity characteristics of 9 types of residential users of electricity utilization types and region types;
s2, identifying abnormal electricity consumption residents, improving the marketing electricity consumption lean management level, accurately positioning outlier users from massive resident electricity consumption data, early warning risk client lists such as electricity price execution errors and abnormal electricity consumption,
s3, managing economic value generated by leakage through problems, coordinating with a business department to verify and process abnormal electricity customers, and simultaneously ensuring benefits of residents and power suppliers to block the economic value generated by leakage.
2. The power consumption anomaly detection method based on big data analysis according to claim 1, wherein in step S1, storage is performed by Oracle at the early stage, data storage is performed by Hive of the alternative big data platform at the later stage, and the fetched data is obtained by using the wide-table association ID;
constructing a model of data by using Pycharm, Anaconda and other related integrated tool environments;
fusing multi-source data such as user file information, electric energy meter information, user meter daily freezing electric quantity information, external weather and the like: firstly, data preparation and data processing are carried out;
secondly, data rules are explored through data distribution, trend analysis and other ways, and basis is provided for subsequent modeling;
thirdly, carrying out classification on residential electricity utilization characteristic images based on a K-Shape clustering algorithm;
and fourthly, constructing an outlier recognition model based on the isolated forest algorithm in a classified mode, and recognizing outlier users.
3. The power consumption abnormality detection method based on big data analysis according to claim 1, characterized in that in said step S2, the internal data mainly contains the resident power consumption customer profile and daily frozen power;
the electricity consumption customer files come from a marketing system, the daily freezing electricity quantity comes from an acquisition system, and the data are primary data, so that the data reliability is high;
the file fields of the electricity customers are complete, the missing condition does not exist, the daily frozen electricity does not have the repeated condition due to the collection reason, the missing condition of a small number of data items exists, the integrity of the whole fields is high, and the subsequent data mining analysis is not influenced;
the situation that the overall records of the power utilization customer files are repeated exists, and the GIS geographic distribution outlier customer identification is influenced due to the fact that the user address is not recorded in a standard mode; the daily freezing of the electric quantity mainly has the condition that the data is empty or the numerical value of the electric quantity is suddenly changed.
4. The power consumption abnormality detection method based on big data analysis according to claim 1, characterized in that in step S2, only the first piece of data is retained and the rest is deleted for data with repetition; deleting the whole piece of data under the condition that the core field is missing; for the condition that the electric quantity data is empty or the numerical value amplitude is suddenly changed, the data is used for shifting and filling up the adjacent data;
in the daily freezing electric quantity width table, counting the missing condition of each piece of data; and determining data deletion with the data deletion dimensionality exceeding 55 dimensions according to the specific deletion condition and the condition of preventing the filling deletion value from influencing the analysis result too much.
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Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111507731A (en) * | 2020-06-17 | 2020-08-07 | 银联数据服务有限公司 | Abnormal data detection feature generation method and device |
| CN111767951A (en) * | 2020-06-29 | 2020-10-13 | 上海积成能源科技有限公司 | Method for discovering abnormal data by applying isolated forest algorithm in residential electricity safety analysis |
| CN112925827A (en) * | 2021-03-04 | 2021-06-08 | 南京怡晟安全技术研究院有限公司 | User property abnormity analysis method based on power acquisition Internet of things data |
| CN113125903A (en) * | 2021-04-20 | 2021-07-16 | 广东电网有限责任公司汕尾供电局 | Line loss anomaly detection method, device, equipment and computer-readable storage medium |
| CN113435494A (en) * | 2021-06-22 | 2021-09-24 | 国网江苏省电力有限公司营销服务中心 | Low-voltage resident user abnormal electricity utilization identification method and simulation system |
-
2019
- 2019-10-18 CN CN201910990554.3A patent/CN111177208A/en active Pending
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111507731A (en) * | 2020-06-17 | 2020-08-07 | 银联数据服务有限公司 | Abnormal data detection feature generation method and device |
| CN111507731B (en) * | 2020-06-17 | 2020-10-20 | 银联数据服务有限公司 | Abnormal data detection feature generation method and device |
| CN111767951A (en) * | 2020-06-29 | 2020-10-13 | 上海积成能源科技有限公司 | Method for discovering abnormal data by applying isolated forest algorithm in residential electricity safety analysis |
| CN112925827A (en) * | 2021-03-04 | 2021-06-08 | 南京怡晟安全技术研究院有限公司 | User property abnormity analysis method based on power acquisition Internet of things data |
| CN113125903A (en) * | 2021-04-20 | 2021-07-16 | 广东电网有限责任公司汕尾供电局 | Line loss anomaly detection method, device, equipment and computer-readable storage medium |
| CN113435494A (en) * | 2021-06-22 | 2021-09-24 | 国网江苏省电力有限公司营销服务中心 | Low-voltage resident user abnormal electricity utilization identification method and simulation system |
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