CN111506636A - System and method for analyzing residential electricity consumption behavior based on autoregressive and neighbor algorithm - Google Patents
System and method for analyzing residential electricity consumption behavior based on autoregressive and neighbor algorithm Download PDFInfo
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- 238000004458 analytical method Methods 0.000 claims description 16
- 238000012549 training Methods 0.000 claims description 7
- 238000013480 data collection Methods 0.000 claims description 4
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Abstract
With the development of new generation technologies such as the internet, the internet of things and wireless sensors, the construction speed of the smart grid is increased. In the construction process of the smart grid, advanced metering equipment and intelligent terminal equipment are installed and used in large quantities, and the electricity utilization modes of residents tend to be diversified. The deep perception of the actual power consumption mode of residents is important for improving the accuracy of load prediction, ensuring the normal operation of a power system, energy management and planning and community management. The invention discloses a system and a method for identifying potential safety hazards of residential electricity consumption according to historical electricity consumption data and real-time power load conditions by using an autoregressive and neighbor algorithm.
Description
Technical Field
The invention relates to the field of electric power big data analysis, in particular to a system and a method for analyzing residential electricity consumption behaviors based on autoregressive and neighbor algorithms.
Background
In recent years, with the continuous construction and development of intelligent cells, a large amount of basic electricity utilization data are accumulated, and the data not only have the characteristics of mass, high frequency, dispersion and the like, but also have relevance and similarity among the data. The electricity consumption behavior habits of the users are hidden in the electricity consumption data of the users, but the electricity consumption data are not mined and subjected to big data analysis and research at present, so that the invention provides a system and a method for residential electricity consumption behavior analysis based on autoregressive and neighbor algorithm, which can use the electricity consumption data of residents to figure the electricity consumption behaviors of the users, analyze the states of the electricity consumption behaviors and assist in intelligent community management.
Disclosure of Invention
The invention provides a system and a method for analyzing residential electricity consumption behaviors based on power load data, which are mainly applied to classifying electricity consumption laws of users and identifying potential abnormal electricity consumption conditions. The whole process comprises a data collection module, an extreme value elimination module, an autocorrelation coefficient analysis module and a real-time identification report module, which are shown in figure 1.
The electric load data collection module generally collects, stores and processes original residential electricity load data through terminal equipment such as an intelligent electric meter and the like, analyzes, integrates and corrects the data, fills up missing values and carries out standardized processing. The abnormal analysis module eliminates extreme abnormal values in the power load data, wherein the extreme abnormal values include maximum and minimum values and missing values which are formed by reading errors during data entry. And inputting the data after the abnormal analysis into an autoregressive analysis module, performing 48-hour autoregressive analysis on the load data of each power consumption unit by the autoregressive analysis module, and analyzing whether the power consumption rule of the power consumption unit is normal, relatively normal or abnormal by calculating the correlation of the same time period on different days. And finally, in a neighbor model, training the model through historical data (normal and abnormal), classifying the real-time electricity utilization data through the neighbor model, and reporting whether the user belongs to an electricity utilization abnormal user through an identification reporting module.
Drawings
Fig. 1 is a flow chart of a residential electricity consumption behavior analysis module in the implementation of the present invention.
FIG. 2 is a diagram illustrating a graph of a user profile showing normal residential electricity consumption behavior in an embodiment of the present invention.
FIG. 3 is a diagram illustrating a graph of a user profile showing a normal electricity consumption behavior of a resident in an embodiment of the present invention.
FIG. 4 is a diagram illustrating a graph of a user profile of abnormal residential electricity consumption behavior in an embodiment of the present invention.
Detailed Description
In order to make the content, the purpose, the features and the advantages of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the embodiments described below are only a part of the embodiments of the present invention, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the scope of the protection scope of the present invention.
The specific steps of the whole system operation are as follows.
Step 2, according to the processed power consumption load data obtained in the step 1, firstly, performing abnormity analysis on the power consumption data of each user, and removing extreme values:
1) the data values are:
2) assuming a gaussian distribution of data load for each user:
3) solving the corresponding parameters:
4) if it is not
Then the value is considered to be extreme.
And 3, performing autoregressive analysis on each electricity consumption unit data according to the processed data acquired in the step 2:
wherein the k value is 48, which is 48 hours, and N is the number of users.
And 4, converting the original data into the autoregressive coefficient of each user according to the result of the step 3, wherein the original data is the hourly power consumption of each user, and training a data set:
wherein
For the example feature vector, N is the number of users.
Step 5, selecting the 24 th value and the 48 th value according to the autoregressive analysis result in the step 3, and if the sum of the two values is greater than 0.4, determining that the electricity utilization behavior of the user is normal; if the sum of the two values is less than 0.4 and greater than 0.15, the electricity utilization behavior of the user is considered to be more abnormal; if the sum of the two values is less than 0.4 and greater than 0.15, the user power consumption behavior is considered abnormal:
input feature set
Respectively indicating that 1 is normal, 2 is relatively abnormal and 3 is abnormal;
outputting the class y to which the instance x belongs;
(1) according to given distanceMetric, finding and in the training set T
Nearest k points, encompassing the k points
Is recorded as
(2) In that
In accordance with a classification decision rule (e.g., majority voting)
Class (D) of
:
Wherein
To indicate a function, i.e. when
And (2) until no misclassification point exists in the training set.
And 6, when the real-time data enters the system, the following results are obtained: wherein, fig. 2 is the normal electricity utilization behavior of the identified user, fig. 3 is the relatively abnormal electricity utilization behavior of the identified user, and fig. 4 is the abnormal electricity utilization behavior of the identified user.
The invention discloses a system and a method for identifying potential safety hazards of residential electricity consumption according to historical electricity consumption data and real-time power load conditions by analyzing and mining deep data of electricity consumption loads and applying autoregressive and neighbor algorithms. The method is used for analyzing users of the same type of electricity utilization condition in a community area and establishing group representative electricity utilization condition images, so that the method can be applied to large-scale management of electricity utilization safety conditions in a specific area, the workload and the screening rate of related personnel are saved to a great extent, and the efficiency of administrative management and electricity utilization safety is improved.
While the invention has been described with reference to a preferred embodiment, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (1)
1. The invention provides a system and a method for analyzing residential electricity consumption behavior based on autoregression and neighbor algorithm, which is characterized by comprising the following steps:
step 1, a data collection module: collecting and storing the electricity consumption data of each electricity consumption unit;
the electricity load data can be recorded by adopting different frequencies, and the sum or redistribution can be hours before entering the analysis;
step 2, according to the processed power consumption load data obtained in the step 1, firstly, performing abnormity analysis on the power consumption data of each user, and removing extreme values:
1) the data values are:
2) assuming a gaussian distribution of data load for each user:
3) solving the corresponding parameters:
4) if it is not
If so, the value is considered as an extreme value;
and 3, performing autoregressive analysis on each electricity consumption unit data according to the processed data acquired in the step 2:
wherein the k value is 48 hours, N is the number of users;
and 4, converting the original data into the autoregressive coefficient of each user according to the result of the step 3, wherein the original data is the hourly power consumption of each user, and training a data set:
wherein
N is the number of users as the feature vector of the example;
step 5, selecting the 24 th value and the 48 th value according to the autoregressive analysis result in the step 3, and if the sum of the two values is greater than 0.4, determining that the electricity utilization behavior of the user is normal; if the sum of the two values is less than 0.4 and greater than 0.15, the electricity utilization behavior of the user is considered to be more abnormal; if the sum of the two values is less than 0.4 and greater than 0.15, the user power consumption behavior is considered abnormal:
input feature set
Respectively indicating that 1 is normal, 2 is relatively abnormal and 3 is abnormal;
outputting the class y to which the instance x belongs;
(1) based on the given distance measure, find and in the training set T
Nearest k points, encompassing the k points
Is recorded as
(2) In that
In accordance with a classification decision rule (e.g., majority voting)
Class (D) of
:
Wherein
To indicate a function, i.e. when
Turning to the step (2) until no misclassification point exists in the training set;
and 6, when the real-time data enters the system, the following results are obtained: wherein, fig. 2 is the normal electricity utilization behavior of the identified user, fig. 3 is the relatively abnormal electricity utilization behavior of the identified user, and fig. 4 is the abnormal electricity utilization behavior of the identified user.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111932138A (en) * | 2020-08-17 | 2020-11-13 | 上海积成能源科技有限公司 | System and method for analyzing residential electricity consumption behavior based on Gaussian mixture model and expectation maximization algorithm |
| CN113484573A (en) * | 2021-07-14 | 2021-10-08 | 国家电网有限公司 | Abnormal electricity utilization monitoring method based on energy data analysis |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106651424A (en) * | 2016-09-28 | 2017-05-10 | 国网山东省电力公司电力科学研究院 | Electric power user figure establishment and analysis method based on big data technology |
| CN109615004A (en) * | 2018-12-07 | 2019-04-12 | 江苏瑞中数据股份有限公司 | A kind of anti-electricity-theft method for early warning of multisource data fusion |
| CN110288130A (en) * | 2019-05-31 | 2019-09-27 | 国网上海市电力公司 | A kind of user power consumption prediction technique |
| CN110689279A (en) * | 2019-10-12 | 2020-01-14 | 上海积成能源科技有限公司 | System and method for analyzing potential safety hazard of residential electricity consumption based on power load data |
| CN110824270A (en) * | 2019-10-09 | 2020-02-21 | 中国电力科学研究院有限公司 | Electricity stealing user identification method and device combining transformer area line loss and abnormal events |
-
2020
- 2020-05-12 CN CN202010396667.3A patent/CN111506636A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106651424A (en) * | 2016-09-28 | 2017-05-10 | 国网山东省电力公司电力科学研究院 | Electric power user figure establishment and analysis method based on big data technology |
| CN109615004A (en) * | 2018-12-07 | 2019-04-12 | 江苏瑞中数据股份有限公司 | A kind of anti-electricity-theft method for early warning of multisource data fusion |
| CN110288130A (en) * | 2019-05-31 | 2019-09-27 | 国网上海市电力公司 | A kind of user power consumption prediction technique |
| CN110824270A (en) * | 2019-10-09 | 2020-02-21 | 中国电力科学研究院有限公司 | Electricity stealing user identification method and device combining transformer area line loss and abnormal events |
| CN110689279A (en) * | 2019-10-12 | 2020-01-14 | 上海积成能源科技有限公司 | System and method for analyzing potential safety hazard of residential electricity consumption based on power load data |
Non-Patent Citations (3)
| Title |
|---|
| 李梓欣: ""电力用户侧计量数据特征提取与窃电检测研究"", 《中国优秀硕士学位论文全文数据库 工程科技Ⅱ辑》 * |
| 李航: "《统计学习方法》", 31 March 2012 * |
| 郝方舟等: "基于高维随机矩阵的配电网用户侧用电行为分析", 《广东电力》 * |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111932138A (en) * | 2020-08-17 | 2020-11-13 | 上海积成能源科技有限公司 | System and method for analyzing residential electricity consumption behavior based on Gaussian mixture model and expectation maximization algorithm |
| CN113484573A (en) * | 2021-07-14 | 2021-10-08 | 国家电网有限公司 | Abnormal electricity utilization monitoring method based on energy data analysis |
| CN113484573B (en) * | 2021-07-14 | 2023-03-07 | 国家电网有限公司 | Abnormal electricity utilization monitoring method based on energy data analysis |
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