CN112686288A - Power consumption behavior anomaly detection method and device and computer readable storage medium - Google Patents
Power consumption behavior anomaly detection method and device and computer readable storage medium Download PDFInfo
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Abstract
The method, the system and the computer readable storage medium for detecting the abnormal electricity consumption behavior provided by the embodiment of the invention comprise the steps of acquiring historical annual energy consumption data, and sorting the historical annual energy consumption data according to a preset time period; after data cleaning is carried out on the sorted data, feature extraction is carried out on the cleaned data to obtain feature data; clustering calculation is carried out on the characteristic data by adopting a clustering algorithm to obtain an energy consumption mode; and matching the actually generated energy consumption data with the obtained energy consumption pattern to judge the abnormity of the electricity consumption behavior. The method can accurately detect the power utilization behavior and identify potential safety hazards, and the method utilizes the power utilization characteristics of power utilization customers to accurately identify power utilization abnormalities such as electricity stealing and electricity leakage with low cost and high efficiency.
Description
Technical Field
The invention relates to the technical field of building operation and maintenance, in particular to a method and a device for detecting abnormal electricity consumption behaviors and a computer readable storage medium.
Background
Abnormal behaviors of users such as electricity stealing and electricity leakage are frequently faced by the power industry, and the abnormal behaviors cause economic loss and even have potential safety production hazards.
Aiming at the problems, the traditional abnormal electricity utilization behavior monitoring is carried out by adopting a manual inspection mode and a load abnormality monitoring mode, but the method has the defects of strong manual dependence and inaccurate abnormality diagnosis. And the identification and interpretation cannot be carried out on some potential safety hazards, such as sudden abnormal low power consumption. Meanwhile, other attributes of the electricity consumers cannot be fully utilized by the method, and the judgment of electricity utilization abnormity is assisted.
Disclosure of Invention
In order to accurately detect the electricity consumption behavior and identify potential safety hazards, embodiments of the present invention provide a method and an apparatus for detecting an abnormal electricity consumption behavior, and a computer-readable storage medium. The method utilizes the electricity utilization characteristics of electricity utilization customers to accurately identify electricity utilization abnormalities such as electricity stealing and electricity leakage with low cost and high efficiency. The specific technical scheme is as follows:
in order to achieve the above object, an embodiment of the present invention provides a method for detecting abnormal behavior of electricity, including:
acquiring historical annual energy consumption data, and sorting the historical annual energy consumption data according to a preset time period;
after data cleaning is carried out on the sorted data, feature extraction is carried out on the cleaned data to obtain feature data; the characteristic data comprises extreme difference, standard deviation, daily total energy consumption and average energy consumption in a preset time period of the energy consumption data;
clustering calculation is carried out on the characteristic data by adopting a clustering algorithm to obtain an energy consumption mode; each energy utilization mode corresponds to a clustering result, and the feature data of the same cluster is the same energy utilization mode;
and matching the actually generated energy consumption data with the obtained energy consumption pattern to judge the abnormity of the electricity consumption behavior.
Further, energy consumption data extraction is carried out on the historical annual energy consumption data every 15 minutes.
Further, data cleaning is carried out on the sorted data, including statistical judgment is carried out on each 15-minute data point, and the data on the day with the distribution higher than 95% quantiles are removed.
Further, the clustering calculation of the feature data by using a clustering algorithm to obtain an energy consumption mode includes the steps:
clustering calculation is carried out on the characteristic data by adopting a clustering algorithm to obtain a plurality of clustering categories;
calculating the 15-minute-by-15-minute data corresponding to the clustering category by using a boxplot to obtain an upper bound and a lower bound of the 15-minute-by-15-minute energy consumption data;
and taking the upper and lower intervals as the energy utilization modes of the clustering categories.
Furthermore, the similarity between the actual energy consumption and each cluster is calculated by using a cosine similarity algorithm, and the energy consumption pattern for the cluster pair with the maximum similarity is used as the matching.
Furthermore, matching the actually generated energy consumption data with the obtained energy consumption pattern to judge the abnormity of the electricity consumption behavior, and the method comprises the following steps:
comparing the actual energy consumption with the lower limit of the energy consumption mode, judging whether the energy consumption condition is started, and if the actual energy consumption is lower than the lower limit of the energy consumption mode, judging that the all-day starting is not abnormal;
comparing the actual energy consumption with the upper bound of the energy consumption mode, and judging the abnormal height of the electricity consumption stage; if the actual energy utilization at a plurality of time points in the electricity utilization time period exceeds the upper limit of the energy utilization mode, judging that the energy utilization is abnormally high;
comparing the actual energy consumption with the lower bound of the energy consumption mode, and judging the abnormal low of the electricity consumption stage; if the actual energy consumption at a plurality of time points in the electricity consumption time period is lower than the lower limit of the energy consumption mode, judging that the energy consumption is abnormally low;
and comparing the actual energy consumption with the lower bound of the energy consumption mode, and judging the high energy consumption of the store-related stage abnormity.
Further, consistency scoring and abnormal grade judgment are carried out on the detection result.
A second aspect of an embodiment of the present invention provides a system for detecting an abnormal behavior of electricity, including:
the acquisition module is used for acquiring historical annual energy data and sorting the historical annual energy data according to a preset time period;
the characteristic extraction module is used for carrying out data cleaning on the sorted data and carrying out characteristic extraction on the cleaned data to obtain characteristic data; the characteristic data comprises extreme difference, standard deviation, daily total energy consumption and average energy consumption in a preset time period of the energy consumption data;
the clustering module is used for clustering and calculating the characteristic data by adopting a clustering algorithm to obtain an energy consumption mode; each energy utilization mode corresponds to a clustering result, and the feature data of the same cluster is the same energy utilization mode;
and the abnormity judgment module is used for matching the actually generated energy utilization data with the obtained energy utilization mode to judge the abnormity of the electricity utilization behavior.
Further, the method also comprises the following steps: and the data cleaning module is used for cleaning the data of the arrangement data, including performing statistical judgment on each 15-minute data point and removing the data of the day which is higher than the distribution of 95% quantiles.
The third aspect of the embodiments of the present invention further provides a computer-readable storage medium, where a computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, the processor is enabled to process the steps of the power consumption behavior abnormality detection method.
A fourth aspect of the present invention provides an electronic apparatus comprising:
a processor; and the number of the first and second groups,
a memory arranged to store computer executable instructions that, when executed, cause the processor to perform the above-described method of power usage behavior anomaly detection.
The method, the system and the computer readable storage medium for detecting the abnormal electricity consumption behavior provided by the embodiment of the invention comprise the steps of acquiring historical annual energy consumption data, and sorting the historical annual energy consumption data according to a preset time period; after data cleaning is carried out on the sorted data, feature extraction is carried out on the cleaned data to obtain feature data; clustering calculation is carried out on the characteristic data by adopting a clustering algorithm to obtain an energy consumption mode; and matching the actually generated energy consumption data with the obtained energy consumption pattern to judge the abnormity of the electricity consumption behavior. The method can accurately detect the power utilization behavior and identify potential safety hazards, and the method utilizes the power utilization characteristics of power utilization customers to accurately identify power utilization abnormalities such as electricity stealing and electricity leakage with low cost and high efficiency.
Drawings
Fig. 1 is a flowchart of a preferred implementation of a method for detecting abnormal electricity consumption behavior according to embodiment 1 of the present invention;
fig. 2 is a schematic block diagram of a structure of a preferred embodiment of a power consumption behavior abnormality detection and diagnosis system according to embodiment 2 of the present invention;
fig. 3-5 are diagrams illustrating an abnormal determination result provided by a power consumption behavior abnormality detection and diagnosis method according to an embodiment of the present invention;
in the figure: 31-energy mode upper bound; 32-energy mode lower bound; 33-actual energy.
Detailed Description
In order to clearly and thoroughly show the technical solution of the present invention, the following description is made with reference to the accompanying drawings, but the scope of the present invention is not limited thereto.
Referring to fig. 1, a flowchart of a preferred implementation of a method for detecting abnormal electricity consumption behavior according to embodiment 1 of the present invention includes the steps of:
acquiring historical annual energy consumption data, and sorting the historical annual energy consumption data according to a preset time period;
after data cleaning is carried out on the sorted data, feature extraction is carried out on the cleaned data to obtain feature data; the characteristic data comprises extreme difference, standard deviation, daily total energy consumption and average energy consumption in a preset time period of the energy consumption data;
clustering calculation is carried out on the characteristic data by adopting a clustering algorithm to obtain an energy consumption mode; each energy utilization mode corresponds to a clustering result, and the feature data of the same cluster is the same energy utilization mode;
and matching the actually generated energy consumption data with the obtained energy consumption pattern to judge the abnormity of the electricity consumption behavior.
The historical annual energy consumption data refers to energy consumption data of the year before the current year; the preset time period is set according to the requirements of the user, and in the embodiment of the invention, 15 minutes is preferred.
The data cleaning (also referred to as data screening) of the sorted data includes: and performing statistical judgment on each 15-minute data point, and removing the data of the day with the distribution of more than 95% quantiles.
The clustering algorithm preferably adopts k-means, which is a typical algorithm in the field of clustering algorithms, belongs to the known technology, and is not described herein.
The method for clustering and calculating the characteristic data by adopting a clustering algorithm to obtain an energy consumption mode comprises the following steps:
clustering calculation is carried out on the characteristic data by adopting a clustering algorithm to obtain a plurality of clustering categories; a clustering algorithm is simply an algorithm that divides outliers into other groups and data points near the center of the cluster into one class.
Calculating the 15-minute data corresponding to the clustering category by using a boxplot to obtain an upper bound and a lower bound (also called clustering range) of the 15-minute energy consumption data;
and taking the upper and lower intervals as the energy utilization modes of the clustering categories.
In the embodiment of the present invention, each of the energy consumption modes corresponds to one clustering result, the feature data of the same cluster is the same energy consumption mode, and the energy consumption modes are different according to different external meteorology, date, month, and temperature and humidity.
Further, in an alternative embodiment of the present invention, the method further includes calculating the similarity between the actual energy consumption and each cluster by using a cosine similarity algorithm, and using the energy consumption pattern for the pair of clusters with the highest similarity as the matching.
The method for calculating the similarity belongs to the conventional technology in the field, and is not described herein.
If the time period is divided into the store-opening time period and the store-closing time period 24 hours a day, the time period is divided according to the specific needs of the user, and the detailed description is not provided herein.
If a normal merchant should be opened every day, and no useful energy condition is detected when the merchant should be opened, the merchant can be judged to be abnormally opened. The judgment method is as follows: whether the energy utilization condition of the whole day (96 points) is started or not is judged, the actual energy utilization is compared with the lower limit of the energy utilization mode, and if the actual energy utilization is lower than the lower limit of the energy utilization mode, the abnormal starting of the whole day is judged.
In the time period of starting a store, if the actual energy consumption is compared with the upper bound of the energy consumption mode, judging the abnormal high of the electricity consumption stage; if the actual energy utilization at a plurality of time points in the electricity utilization time period exceeds the upper limit of the energy utilization mode, judging that the energy utilization is abnormally high;
comparing the actual energy consumption with the lower bound of the energy consumption mode, and judging the abnormal low of the electricity consumption stage; if the actual energy usage at a plurality of time points in the electricity consumption time zone is lower than the lower limit of the energy usage mode, the energy usage is determined to be abnormally low.
In the store closing stage, comparing the actual electricity consumption in the store closing stage with the lower bound of the energy consumption mode, and judging the abnormal energy consumption in the store closing stage; if the store closing stage actually uses the energy, a plurality of time points exist, and the energy is higher than the upper bound of the energy using mode. It is determined that the energy consumption for the time period of the store closing is abnormal.
Further, consistency scoring and abnormal grade judgment are carried out on the detection result.
Referring to fig. 3 to fig. 5, diagrams of the abnormality determination result provided by the power consumption behavior abnormality detection and diagnosis method according to the embodiment of the present invention are shown. In the figure, 31 and 32 are upper and lower limits of the energy use pattern, and 33 is an actual energy use curve. Referring to fig. 3, in order to indicate that the energy consumption in the electricity consumption stage is abnormally low, it can be seen from the figure that the actual energy consumption is lower than the lower bound of the energy consumption mode between one point in the afternoon and two points, between three points in the afternoon and four points, and between seven points in the afternoon and nine points, that is, it is determined that the energy consumption is abnormally low in these several time periods, the output score is 0, and the abnormality level is 2. Similarly, referring to fig. 4, from three pm to ten pm, the actual energy usage is higher than the upper bound of the energy usage pattern, and it is determined that there is abnormally high energy usage in this time period, the output score is 0, and the abnormality level is 2. Referring to fig. 5, the actual energy usage throughout the day is below the lower bound of the energy usage mode, which indicates that no electricity is used throughout the day, and the normal merchant actually uses electricity every day, so it is determined that there is an all-day unopened anomaly. The score 0 is output and the abnormality level is 0.
The method, the system and the computer readable storage medium for detecting the abnormal electricity consumption behavior provided by the embodiment of the invention comprise the steps of acquiring historical annual energy consumption data, and sorting the historical annual energy consumption data according to a preset time period; after data cleaning is carried out on the sorted data, feature extraction is carried out on the cleaned data to obtain feature data; clustering calculation is carried out on the characteristic data by adopting a clustering algorithm to obtain an energy consumption mode; and matching the actually generated energy consumption data with the obtained energy consumption pattern to judge the abnormity of the electricity consumption behavior. The method can accurately detect the power utilization behavior and identify potential safety hazards, and the method utilizes the power utilization characteristics of power utilization customers to accurately identify power utilization abnormalities such as electricity stealing and electricity leakage with low cost and high efficiency.
A second aspect of the embodiment of the present invention provides a system for detecting an abnormal electrical behavior, and referring to fig. 2, a block diagram of a schematic structure of a preferred implementation of the system for detecting and diagnosing an abnormal electrical behavior according to the embodiment 2 of the present invention is shown, and the system includes:
the acquisition module is used for acquiring historical annual energy data and sorting the historical annual energy data according to a preset time period;
the characteristic extraction module is used for carrying out data cleaning on the sorted data and carrying out characteristic extraction on the cleaned data to obtain characteristic data; the characteristic data comprises extreme difference, standard deviation, daily total energy consumption and average energy consumption in a preset time period of the energy consumption data;
the clustering module is used for clustering and calculating the characteristic data by adopting a clustering algorithm to obtain an energy consumption mode; each energy utilization mode corresponds to a clustering result, and the feature data of the same cluster is the same energy utilization mode;
and the abnormity judgment module is used for matching the actually generated energy utilization data with the obtained energy utilization mode to judge the abnormity of the electricity utilization behavior.
Further, the method also comprises the following steps: and the data cleaning module is used for cleaning the data of the arrangement data, including performing statistical judgment on each 15-minute data point and removing the data of the day which is higher than the distribution of 95% quantiles.
The third aspect of the embodiments of the present invention further provides a computer-readable storage medium, where a computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, the processor is enabled to process the steps of the power consumption behavior abnormality detection method.
A fourth aspect of the present invention provides an electronic apparatus comprising:
a processor; and the number of the first and second groups,
a memory arranged to store computer executable instructions that, when executed, cause the processor to perform the above-described method of power usage behavior anomaly detection.
The embodiment of the invention provides a method and a system for detecting abnormal electricity consumption behaviors and a computer readable storage medium. Acquiring historical annual energy consumption data, and sorting the historical annual energy consumption data according to a preset time period; after data cleaning is carried out on the sorted data, feature extraction is carried out on the cleaned data to obtain feature data; clustering calculation is carried out on the characteristic data by adopting a clustering algorithm to obtain an energy consumption mode; and matching the actually generated energy consumption data with the obtained energy consumption pattern to judge the abnormity of the electricity consumption behavior. The method can accurately detect the power utilization behavior and identify potential safety hazards, and the method utilizes the power utilization characteristics of power utilization customers to accurately identify power utilization abnormalities such as electricity stealing and electricity leakage with low cost and high efficiency.
Although the invention has been described in detail above with reference to a general description and specific examples, it will be apparent to one skilled in the art that modifications or improvements may be made thereto based on the invention. Accordingly, such modifications and improvements are intended to be within the scope of the invention as claimed.
Claims (10)
1. A power consumption behavior abnormity detection method is characterized by comprising the following steps:
acquiring historical annual energy consumption data, and sorting the historical annual energy consumption data according to a preset time period;
after data cleaning is carried out on the sorted data, feature extraction is carried out on the cleaned data to obtain feature data; the characteristic data comprises extreme difference, standard deviation, daily total energy consumption and average energy consumption in a preset time period of the energy consumption data;
clustering calculation is carried out on the characteristic data by adopting a clustering algorithm to obtain an energy consumption mode; each energy utilization mode corresponds to a clustering result, and the feature data of the same cluster is the same energy utilization mode;
and matching the actually generated energy consumption data with the obtained energy consumption pattern to judge the abnormity of the electricity consumption behavior.
2. The method for detecting abnormal behavior of electricity consumption according to claim 1, further comprising performing data extraction on the historical annual energy consumption data every 15 minutes; and carrying out consistency scoring and grade output on the abnormality.
3. The method for detecting the abnormal behavior of the electricity consumption according to claim 2, wherein the data cleaning is carried out on the sorted data, the data cleaning comprises the steps of carrying out statistical judgment on each 15-minute data point, and removing the data which are higher than 95% of the daily data.
4. The method for detecting abnormal behavior of electricity consumption according to claim 2, wherein the clustering algorithm is used for clustering the characteristic data to obtain an energy consumption mode, and the method comprises the following steps:
clustering calculation is carried out on the characteristic data by adopting a clustering algorithm to obtain a plurality of clustering categories;
calculating the 15-minute-by-15-minute data corresponding to the clustering category by using a boxplot to obtain an upper bound and a lower bound of the 15-minute-by-15-minute energy consumption data;
and taking the upper and lower intervals as the energy utilization modes of the clustering categories.
5. The power consumption behavior abnormality detection method according to claim 1, characterized by further comprising calculating the similarity of the actual power consumption and each cluster using a cosine similarity algorithm, and using the power consumption pattern for the pair of clusters with the largest similarity as the match.
6. The method for detecting abnormality in electric behavior according to claim 4, wherein the step of determining abnormality in electric behavior by matching actually generated energy consumption data with the obtained energy consumption pattern includes:
comparing the actual energy consumption with the lower limit of the energy consumption mode, judging whether the energy consumption condition is started, and if the actual energy consumption of the whole day is lower than the lower limit of the energy consumption mode, judging that the whole day is not started abnormally;
comparing the actual energy consumption with the upper bound of the energy consumption mode, and judging the abnormal height of the electricity consumption stage; if the actual energy utilization at a plurality of time points in the electricity utilization time period exceeds the upper limit of the energy utilization mode, judging that the energy utilization is abnormally high;
comparing the actual energy consumption with the lower bound of the energy consumption mode, and judging the abnormal low of the electricity consumption stage; if the actual energy consumption at a plurality of time points in the electricity consumption time period is lower than the lower limit of the energy consumption mode, judging that the energy consumption is abnormally low;
comparing the actual energy consumption with the lower bound of the energy consumption mode, and judging the abnormal energy consumption in the store closing stage; if the store closing stage actually uses the energy, a plurality of time points exist, and the energy is higher than the upper bound of the energy using mode. It is determined that the energy consumption for the time period of the store closing is abnormal.
7. An electricity usage behavior anomaly detection system, comprising:
the acquisition module is used for acquiring historical annual energy data and sorting the historical annual energy data according to a preset time period;
the characteristic extraction module is used for carrying out data cleaning on the sorted data and carrying out characteristic extraction on the cleaned data to obtain characteristic data; the characteristic data comprises extreme difference, standard deviation, daily total energy consumption and average energy consumption in a preset time period of the energy consumption data;
the clustering module is used for clustering and calculating the characteristic data by adopting a clustering algorithm to obtain an energy consumption mode; each energy utilization mode corresponds to a clustering result, and the feature data of the same cluster is the same energy utilization mode;
and the abnormity judgment module is used for matching the actually generated energy utilization data with the obtained energy utilization mode to judge the abnormity of the electricity utilization behavior.
8. The system for detecting abnormality in electric behavior according to claim 7, further comprising: and the data cleaning module is used for cleaning the data of the arrangement data, including performing statistical judgment on each 15-minute data point and removing the data of the day which is higher than the distribution of 95% quantiles.
9. A computer-readable storage medium, on which a computer program is stored which, when executed by a processor, causes the processor to process the method steps of any of the preceding claims 1-6.
10. An electronic device, comprising:
a processor; and the number of the first and second groups,
a memory arranged to store computer executable instructions that, when executed, cause the processor to perform the method of any one of claims 1-6.
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