Resident self-use charging pile electrical load identification method and system
Through multiple preprocessing and identification steps, the problem of identifying the power load of charging piles for residents' self-use has been solved, and the accurate identification of the power load of charging piles for residents' self-use has been achieved, preventing safety risks and promoting the healthy growth of the number of users.
Patent Information
- Application Number
- CN202510869998.7
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-06-26
- Publication Date
- 2025-10-17
AI Technical Summary
Existing technologies are unable to effectively identify the actual load of residents' self-used charging piles, making it difficult to detect and correct users' illegal electricity usage, posing a safety hazard and affecting normal registration.
Through multiple preprocessing and identification steps, including data collection, the first to third preprocessing and identification, the normal or abnormal electricity load of residents' self-used charging piles is screened out by using mean, variance and threshold comparison.
It has achieved accurate identification of the electricity load of residents' self-used charging piles, prevented safety risks, and promoted the healthy growth of the number of users.
Smart Images
Figure CN120804970A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the field of intelligent identification of power systems, and in particular to a method and system for identifying the power load of residential self-use charging piles. Background Art
[0002] In recent years, new energy vehicle sales have reached 40.9% of total vehicle sales, and the number of residential charging stations equipped with meters has also increased significantly. However, in practice, some users of residential charging stations are connecting other loads, such as household appliances, to the dedicated meters to circumvent tiered residential electricity prices or other high electricity prices. However, this practice poses significant safety risks and also affects the normal registration of residential charging stations.
[0003] Currently, there is no effective method to identify the actual load of users of residential charging piles for their own use, and the illegal electricity usage behavior of such users is difficult to detect and correct. Summary of the Invention
[0004] One of the purposes of the present invention is to provide a method for identifying the electricity load of residential charging piles for self-use, so as to accurately identify the actual load of users of residential charging piles for self-use, prevent possible safety risks, and promote the healthy growth of the number of users of residential charging piles for self-use.
[0005] A second object of the present invention is to provide a system for implementing the method for identifying the power load of residential self-use charging piles.
[0006] The present invention provides a method for identifying the electricity load of a residential self-use charging pile, comprising the following steps:
[0007] S1. Obtain daily power point data of residents' self-use charging piles;
[0008] S2. Perform a first preprocessing on the daily power point data of the residential self-use charging pile obtained in step S1 to obtain the first non-zero power point data, and then perform a first identification based on the first non-zero power point data. If the identification is normal, the identification is completed, otherwise the identification is abnormal and proceeds to the next step;
[0009] S3. The daily power point data of the residential self-use charging pile identified in step S2 is preprocessed for the second time to obtain the second non-zero power point data, and then based on the second non-zero power point data, a second identification is performed. If the identification is normal, the identification is completed, otherwise the identification is abnormal and proceeds to the next step;
[0010] S4. Perform a third preprocessing on the daily power point data of the residential self-use charging pile identified as abnormal in step S3 to obtain third non-zero power point data, and then perform a third identification based on the third non-zero power point data to finally complete the identification of the power load of the residential self-use charging pile.
[0011] In step S1, the data is obtained by continuous interval sampling, and the daily power point data of the resident self-use charging pile obtained is continuous interval preset time power point data.
[0012] In actual application, 15-minute time interval is used for continuous sampling, and the total power point data of a single charging pile per day is 96.
[0013] In step S2, the first preprocessing is specifically deleting the zero power data point in the daily power point data of the resident self-use charging pile obtained in step S1 to obtain first non-zero power point data.
[0014] Step S2 is specifically:
[0015] Based on the daily power point data of the resident self-use charging pile, the zero power data point is deleted to obtain the first non-zero power point data.
[0016] The first mean and the first variance of the first non-zero power point data are calculated; and the first mean and the first variance obtained are compared with a preset threshold value to obtain a first identification result.
[0017] The first identification specifically includes:
[0018] According to the relationship between the first variance and the first preset threshold value, if the first variance is less than the first preset threshold value, it is judged that the self-use charging pile load is a basically normal load, otherwise it is judged that the resident self-use charging pile is an abnormal load used by connecting other electrical loads.
[0019] For the self-use charging pile judged as a basically normal load, if the first mean is within a preset threshold range, it is judged as a normal load, otherwise it is judged that the resident self-use charging pile is an abnormal load used by multiple charging pile loads at the same time.
[0020] In step S3, the second preprocessing is specifically: for the daily power point data of the resident self-use charging pile identified as abnormal in step S2, the zero power data point and the power data point in the power drop process are deleted to obtain second non-zero power point data.
[0021] The judgment method of the power data point in the power drop process is:
[0022] For the charging pile B i Data point in daily power point data Start judging from m=2 and set If And Less than threshold value β, judge data point As the power data point in the power drop process of the charging pile B i ; wherein, As the power data point in the power drop process of the charging pile B iThe mth data point in chronological order in the daily power point data;
[0023] If the data point Charging pile B i The power data point during the power reduction process is the previous data point of the data point. Make the following judgment: If and is less than the threshold β, then the data point Also determined to be charging pile B i Power data points during power reduction.
[0024] Step S3 is specifically as follows:
[0025] Based on the daily power point data of the residential self-use charging pile identified as abnormal in step S2, the zero power data point and the power data point in the power reduction process are deleted to obtain the second non-zero power point data;
[0026] The second mean and the second variance of the second non-zero power point data are calculated; and the obtained second mean and the second variance are compared with a second preset threshold to obtain a second recognition result.
[0027] The second identification specifically includes:
[0028] Based on the relationship between the second variance and the second preset threshold, if the second variance is less than the second preset threshold, the load of the self-use charging pile is determined to be a basically normal load; otherwise, the resident's self-use charging pile is determined to be an abnormal load used in conjunction with other power loads;
[0029] For the self-use charging piles judged to be basically normal loads, if the second mean value is within the preset threshold range, it is judged to be a normal load; otherwise, it is judged that the resident's self-use charging pile is an abnormal load with multiple charging piles used at the same time.
[0030] In step S4, the third preprocessing is specifically as follows: for the daily power point data of the residential self-use charging pile identified as abnormal in step S3, the power data points in the power reduction process are deleted to obtain the third non-zero power point data.
[0031] Step S4 is specifically as follows:
[0032] Based on the daily power point data of the residential self-use charging pile identified as abnormal in step S3, the power data points in the power reduction process are deleted to obtain third non-zero power point data;
[0033] Based on the third non-zero power point data, obtaining the number of continuous non-zero data segments and the number of discrete non-zero data points;
[0034] According to the relationship between the obtained continuous non-zero data segment quantity and the discrete non-zero data point quantity and the third preset threshold, a third identification result is obtained, and the resident self-use charging pile electricity load identification is completed.
[0035] The identification method of the continuous non-zero data segment is that if the charging pile B i In the daily power point data, in the daily power point data arranged in time sequence, if two or more continuous data points are non-zero data, it is determined that the continuous non-zero data is a continuous non-zero data segment.
[0036] The set composed of the continuous non-zero data segment is a non-zero data set.
[0037] The identification method of the discrete non-zero data point is that if the charging pile B i In the daily power point data, in the daily power point data arranged in time sequence, if the data points before and after a non-zero data point are zero power data points, it is determined that the non-zero data point is a discrete non-zero data point; if the non-zero data point is the first data point in the daily power point data, only the adjacent data point after it needs to be a zero power data point, then it is determined that the non-zero data point is a discrete non-zero data point; if the non-zero data point is the last data point in the daily power point data, only the adjacent data point before it needs to be a zero power data point, then it is determined that the non-zero data point is a discrete non-zero data point. i i In the daily power point data, in the daily power point data arranged in time sequence, if the data points before and after a non-zero data point are zero power data points, it is determined that the non-zero data point is a discrete non-zero data point; if the non-zero data point is the first data point in the daily power point data, only the adjacent data point after it needs to be a zero power data point, then it is determined that the non-zero data point is a discrete non-zero data point; if the non-zero data point is the last data point in the daily power point data, only the adjacent data point before it needs to be a zero power data point, then it is determined that the non-zero data point is a discrete non-zero data point.
[0038] The third identification is specifically:
[0039] In the third non-zero power point data, the number of continuous non-zero data segments is g i , the number of discrete non-zero data points is j i , if the value of g i / (g i +j i ) is less than a threshold value γ, it is determined that the resident self-use charging pile is an abnormal load used by other electricity loads; if the value of g i / (g i +j i ) is greater than or equal to the threshold value γ, the mean and variance of each continuous non-zero data segment in the non-zero data set are calculated, if all the variances obtained are less than a third preset threshold, it is determined that the resident self-use charging pile is an abnormal load used by multiple charging pile loads at the same time, otherwise it is determined that the resident self-use charging pile is an abnormal load used by other electricity loads.
[0040] The application also provides a system for realizing the resident self-use charging pile electricity load identification method, comprising a data acquisition module, a first identification module, a second identification module, and a third identification module.
[0041] The data acquisition module acquires daily power point data of the resident self-use charging pile and uploads the data to the first identification module.
[0042] The first identification module performs first preprocessing on the daily power point data of the resident self-use charging pile according to the received data, obtains first non-zero power point data, and then performs first identification based on the first non-zero power point data.
[0043] The second identification module performs second preprocessing on the daily power point data of the resident self-use charging pile identified as abnormal by the first identification module according to the received data, obtains second non-zero power point data, and then performs second identification based on the second non-zero power point data.
[0044] The third identification module performs third preprocessing on the daily power point data of the resident self-use charging pile identified as abnormal by the second identification module according to the received data, obtains third non-zero power point data, and then performs third identification based on the third non-zero power point data, thereby finally completing the resident self-use charging pile load identification.
[0045] The present application discloses a kind of resident self-use charging pile load identification method and system thereof, can accurately identify the actual load of resident self-use charging pile user, prevent possible security risk, promote the healthy growth of resident self-use charging pile user quantity. BRIEF DESCRIPTION OF DRAWINGS
[0046] Figure 1 It is the flowchart of the method of the present application;
[0047] Figure 2 It is the structural schematic diagram of the system of the present application. DETAILED DESCRIPTION
[0048] The present application provides a kind of resident self-use charging pile load identification method, its flowchart is as shown in Figure 1 As shown, the following steps are included:
[0049] S1. obtaining daily power point data of resident self-use charging pile, specifically:
[0050] Continuous interval sampling is used to obtain data, and the obtained daily power point data of resident self-use charging pile is continuous interval power point data with a preset time.
[0051] In practical application, 15 minutes of time interval is used for continuous sampling, and the total power point data of each charging pile per day is 96.
[0052] S2. The first non-zero power point data is obtained by performing the first preprocessing on the daily power point data of the resident self-use charging pile obtained in step S1, and then the first identification is performed based on the first non-zero power point data. If the identification is normal, the identification is completed, otherwise, the identification is abnormal and the next step is entered;
[0053] The first preprocessing in step S2 is to delete the zero power data points in the daily power point data of the resident self-use charging pile obtained in step S1 to obtain the first non-zero power point data.
[0054] Step S2 specifically comprises:
[0055] Based on the daily power point data of the resident self-use charging pile, the zero power data points are deleted to obtain the first non-zero power point data.
[0056] The first mean and the first variance of the first non-zero power point data are calculated; and the first identification result is obtained by comparing the obtained first mean and first variance with a preset threshold.
[0057] The first identification specifically comprises:
[0058] According to the relationship between the first variance and the first preset threshold, if the first variance is less than the first preset threshold, it is judged that the self-use charging pile load is a basically normal load, otherwise it is judged that the resident self-use charging pile is an abnormal load used by other electrical load.
[0059] For the self-use charging pile judged as a basically normal load, if the first mean is within the preset threshold range, it is judged as a normal load, otherwise it is judged that the resident self-use charging pile is an abnormal load used by multiple charging pile loads.
[0060] S3. The second non-zero power point data is obtained by performing the second preprocessing on the daily power point data of the resident self-use charging pile identified as abnormal in step S2, and then the second identification is performed based on the second non-zero power point data. If the identification is normal, the identification is completed, otherwise, the identification is abnormal and the next step is entered;
[0061] In step S3, the second preprocessing specifically comprises: for the daily power point data of the resident self-use charging pile identified as abnormal in step S2, deleting the zero power data points and the power data points in the power drop process to obtain the second non-zero power point data;
[0062] The judgment method of the power data points in the power drop process is:
[0063] For charging pile B i Data points in daily power point data Start judging from m=2 and set If And If the data point is smaller than the threshold value β, the data point is determined to be a charging pile B a charging pile B i a power data point in a power drop process; wherein, a charging pile B i an mth data point in chronological order in the daily power point data;
[0064] If the data point is determined to be a charging pile B a charging pile B i If the data point is a power data point in a power drop process, a previous data point of the data point is determined as follows: if and the data point is smaller than the threshold value β, the data point is also determined to be a charging pile B a charging pile B i a power data point in a power drop process.
[0065] Step S3 is specifically:
[0066] Based on the daily power point data of the abnormal resident self-use charging pile identified in step S2, the zero power data point and the power data point in the power drop process are deleted to obtain second non-zero power point data;
[0067] The second mean and the second variance of the second non-zero power point data are calculated; and according to the obtained second mean and second variance, the second preset threshold value is compared to obtain a second identification result.
[0068] The second identification specifically includes:
[0069] According to the relationship between the second variance and the second preset threshold value, if the second variance is smaller than the second preset threshold value, the self-use charging pile load is determined to be a basically normal load, otherwise the resident self-use charging pile is determined to be an abnormal load used by other electrical load;
[0070] The self-use charging pile determined to be a basically normal load is judged, if the second mean is within a preset threshold value range, it is determined to be a normal load, otherwise the resident self-use charging pile is determined to be an abnormal load used by multiple charging pile loads.
[0071] S4. The daily power point data of the resident self-use charging pile identified as abnormal in step S3 is preprocessed for a third time to obtain third non-zero power point data, and based on the third non-zero power point data, a third identification is performed to finally complete the resident self-use charging pile electrical load identification.
[0072] In step S4, the third preprocessing is specifically: for the daily power point data of the resident self-use charging pile identified as abnormal in step S3, the power data point in the power drop process is deleted to obtain third non-zero power point data.
[0073] Step S4 is specifically as follows:
[0074] Based on the daily power point data of the residential self-use charging pile identified as abnormal in step S3, the power data points in the power reduction process are deleted to obtain third non-zero power point data;
[0075] Based on the third non-zero power point data, obtaining the number of continuous non-zero data segments and the number of discrete non-zero data points;
[0076] A third identification result is obtained based on the relationship between the number of continuous non-zero data segments and the number of discrete non-zero data points obtained and the third preset threshold, thereby completing the identification of the electricity load of the residents' self-use charging piles.
[0077] The method for identifying the continuous non-zero data segments is as follows: if charging pile B i In the daily power point data, if two or more consecutive data points are all non-zero data in the daily power point data arranged in time sequence, the consecutive non-zero data are determined to be a consecutive non-zero data segment.
[0078] The set of continuous non-zero data segments is a non-zero data set.
[0079] The method for identifying discrete non-zero data points is as follows: if charging pile B i In the daily power point data, if the data points before and after a non-zero data point are all zero power data points, then the non-zero data point is determined to be a discrete non-zero data point; if the non-zero data point is charging pile B i The first data point in the daily power point data only needs the adjacent data points to be zero power data points, and then the non-zero data point is determined to be a discrete non-zero data point; if the non-zero data point is charging pile B i For the last data point in the daily power point data, it is only necessary that the preceding adjacent data point is a zero power data point in order to determine that the non-zero data point is a discrete non-zero data point.
[0080] The third identification is specifically:
[0081] Assume that the number of consecutive non-zero data segments in the third non-zero power point data is g i , the number of discrete non-zero data points is j i , if g i / (g i +j i ) is less than the threshold γ, then the resident's self-use charging pile is determined to be an abnormal load used in conjunction with other power loads; if g i / (g i +j iIf the value of the first difference is greater than or equal to a threshold value γ, the average and variance of each continuous non-zero data segment in the non-zero data set are calculated, and if all the variances obtained are less than a third preset threshold value, the resident self-use charging pile is determined to be an abnormal load simultaneously used by multiple charging pile loads, otherwise, the resident self-use charging pile is determined to be an abnormal load used by connecting other electrical loads.
[0082] The application further provides a system for realizing the resident self-use charging pile electrical load identification method, a structural schematic diagram of which is shown in the figure. Figure 2 The system comprises a data acquisition module, a first identification module, a second identification module and a third identification module.
[0083] The data acquisition module acquires daily power point data of the resident self-use charging pile and uploads the data to the first identification module.
[0084] The first identification module performs first preprocessing on the daily power point data of the resident self-use charging pile according to the received data to obtain first non-zero power point data, and then performs first identification based on the first non-zero power point data, and if the identification is normal, the identification is completed, otherwise, the data is uploaded to the second identification module.
[0085] The second identification module performs second preprocessing on the daily power point data of the resident self-use charging pile identified as abnormal by the first identification module according to the received data to obtain second non-zero power point data, and then performs second identification based on the second non-zero power point data, and if the identification is normal, the identification is completed, otherwise, the data is uploaded to the third identification module.
[0086] The third identification module performs third preprocessing on the daily power point data of the resident self-use charging pile identified as abnormal by the second identification module according to the received data to obtain third non-zero power point data, and then performs third identification based on the third non-zero power point data, and finally completes the resident self-use charging pile electrical load identification.
[0087] The method of the application is further described below in combination with an embodiment.
[0088] Taking a certain city company as an example, there are 11710 resident self-use charging pile users, and the total power data of one day is taken, and the total power point data is 11710*96=1124160.
[0089] Non-zero power data screening: the proportion of non-zero data is generally not more than 10%, and after non-zero judgment is performed on 1124160 data, the non-zero data after screening is about 112416.
[0090] First identification of self-use charging pile load: The variance and mean of the non-zero power data of 11710 charging piles were calculated respectively. Before the effective control of residential self-use charging pile electricity load, the abnormal users with variance exceeding the threshold were 706 households, accounting for 6.03%.
[0091] Normal charging pile load judgment of first identification of load: 11004 normal users, accounting for 93.97%, through the average of 11004 non-zero data, the mean within the preset threshold range is normal load, and the rest is abnormal load of multiple charging pile load usage.
[0092] Second identification of self-use charging pile load: The total power data of 706 households identified as abnormal in the first identification was screened, and the variance was calculated again after removing the interference data. The total data amount was about 704x96=67584, and the second identification of abnormal users was 285 households.
[0093] Normal charging pile load judgment of second identification of load: 416 normal users, accounting for 98.8%, through the average of 421 non-zero data, the mean within the preset threshold range is normal load, and the rest is abnormal load of multiple charging pile load usage.
[0094] Third identification of self-use charging pile load: The total power data of 290 households identified as abnormal in the second identification was screened, and the total data amount was about 290x96=27840. After identifying the continuous non-zero data segment and discrete non-zero data points, the variance and mean of each continuous data segment were calculated, and the electricity load was judged by the proportion of continuous non-zero data segment and variance. Among them, 182 abnormal users were connected to other electricity loads, and the accuracy rate of on-site verification of abnormal users was more than 95%.
Claims
1. A method for identifying the electricity load of a residential self-use charging pile, characterized in that: The following steps are involved: S1. Obtain daily power point data of residents' self-use charging piles; S2. Perform a first preprocessing on the daily power point data of the residential self-use charging pile obtained in step S1 to obtain the first non-zero power point data, and then perform a first identification based on the first non-zero power point data. If the identification is normal, the identification is completed, otherwise the identification is abnormal and proceeds to the next step; S3. The daily power point data of the residential self-use charging pile identified in step S2 is preprocessed for the second time to obtain the second non-zero power point data, and then based on the second non-zero power point data, a second identification is performed. If the identification is normal, the identification is completed, otherwise the identification is abnormal and proceeds to the next step; S4. Perform a third preprocessing on the daily power point data of the residential self-use charging pile identified as abnormal in step S3 to obtain third non-zero power point data, and then perform a third identification based on the third non-zero power point data to finally complete the identification of the power load of the residential self-use charging pile.
2. The method for identifying the electricity load of a residential self-use charging pile according to claim 1, characterized in that: In step S1, data is acquired by continuous interval sampling, and the obtained daily power point data of the residents' self-use charging pile is the power point data of continuous preset intervals.
3. The method for identifying the electricity load of a residential self-use charging pile according to claim 1, characterized in that: Step S2 is specifically as follows: Based on the daily power point data of the residential self-use charging pile, the zero power data point is deleted to obtain the first non-zero power point data; Calculating a first mean and a first variance of the first non-zero power point data; Comparing the obtained first mean and first variance with a preset threshold value to obtain a first recognition result; The first identification specifically includes: Based on the relationship between the first variance and the first preset threshold, if the first variance is less than the first preset threshold, the load of the self-use charging pile is determined to be a basically normal load; otherwise, the resident's self-use charging pile is determined to be an abnormal load used in conjunction with other power loads; For the self-use charging piles judged to be basically normal loads, if the first mean value is within the preset threshold range, it is judged to be a normal load; otherwise, it is judged that the resident's self-use charging pile is an abnormal load with multiple charging piles used at the same time.
4. The method for identifying the electricity load of a residential self-use charging pile according to claim 1, characterized in that: Step S3 specifically comprises: based on the daily power point data of the residential self-use charging pile identified as abnormal in step S2, deleting the zero power data point and the power data point in the power reduction process to obtain the second non-zero power point data; The second mean and the second variance of the second non-zero power point data are calculated; and the obtained second mean and the second variance are compared with a second preset threshold to obtain a second recognition result.
5. The method for identifying the electricity load of a residential self-use charging pile according to claim 4 is characterized in that: The method for determining the power data point during the power reduction process is as follows: For charging pile B i Data points in daily power point data Start judging from m=2 and set like and is less than the threshold β, then the data point is judged Charging pile B i Power data points during power reduction; where, Charging pile B i The mth data point in chronological order in the daily power point data; If the data point Charging pile B i The power data point during the power reduction process is the previous data point of the data point. Make the following judgment: If and is less than the threshold β, then the data point Also determined to be charging pile B i Power data points during power reduction.
6. The method for identifying the electricity load of a residential self-use charging pile according to claim 5, characterized in that: The second identification specifically includes: Based on the relationship between the second variance and the second preset threshold, if the second variance is less than the second preset threshold, the load of the self-use charging pile is determined to be a basically normal load; otherwise, the resident's self-use charging pile is determined to be an abnormal load used in conjunction with other power loads; For the self-use charging piles judged to be basically normal loads, if the second mean value is within the preset threshold range, it is judged to be a normal load; otherwise, it is judged that the resident's self-use charging pile is an abnormal load with multiple charging piles used at the same time.
7. The method for identifying the electricity load of a residential charging pile according to claim 1, characterized in that: Step S4 is specifically as follows: Based on the daily power point data of the residential self-use charging pile identified as abnormal in step S3, the power data points in the power reduction process are deleted to obtain third non-zero power point data; Based on the third non-zero power point data, obtaining the number of continuous non-zero data segments and the number of discrete non-zero data points; A third identification result is obtained based on the relationship between the number of continuous non-zero data segments and the number of discrete non-zero data points obtained and the third preset threshold, thereby completing the identification of the electricity load of the residents' self-use charging piles.
8. The method for identifying the electricity load of a residential self-use charging pile according to claim 7, characterized in that: The method for identifying the continuous non-zero data segments is as follows: if charging pile B i In the daily power point data, if two or more consecutive data points are all non-zero data in the daily power point data arranged in time sequence, the consecutive non-zero data are determined to be continuous non-zero data segments; and the set of the continuous non-zero data segments is a non-zero data set; The method for identifying discrete non-zero data points is as follows: if charging pile B i In the daily power point data, if the data points before and after a non-zero data point are all zero-power data points, the non-zero data point is determined to be a discrete non-zero data point. If the non-zero data point is charging pile B i For the first data point in the daily power point data, it is only necessary that the adjacent data points thereafter are zero power data points in order to determine that the non-zero data point is a discrete non-zero data point; If the non-zero data point is charging pile B i For the last data point in the daily power point data, it is only necessary that the preceding adjacent data point is a zero power data point in order to determine that the non-zero data point is a discrete non-zero data point.
9. The method for identifying the electricity load of a residential charging pile according to claim 8, characterized in that: The third identification is specifically: Assume that the number of consecutive non-zero data segments in the third non-zero power point data is g i , the number of discrete non-zero data points is j i , if g i / (g i +j i ) is less than the threshold γ, then the resident's self-use charging pile is determined to be an abnormal load used in conjunction with other power loads; if g i / (g i +j i ) is greater than or equal to the threshold value γ, the mean and variance of each continuous non-zero data segment in the non-zero data set are calculated. If all the obtained variances are less than the third preset threshold, it is determined that the resident's self-use charging pile is an abnormal load used by multiple charging piles at the same time; otherwise, it is determined that the resident's self-use charging pile is an abnormal load used in conjunction with other power loads.
10. A system for implementing the method for identifying the electricity load of a residential self-use charging pile according to any one of claims 1 to 9, characterized in that: It includes a data acquisition module, a first recognition module, a second recognition module, and a third recognition module; The data acquisition module obtains the daily power point data of residents' self-use charging piles and uploads the data to the first identification module; The first recognition module performs a first preprocessing on the daily power point data of the residents' self-use charging pile based on the received data to obtain the first non-zero power point data, and then performs a first recognition based on the first non-zero power point data. If the recognition is normal, the recognition is completed; otherwise, the data is uploaded to the second recognition module; The second recognition module performs a second preprocessing on the daily power point data of the residential self-use charging pile identified as abnormal by the first recognition module based on the received data to obtain second non-zero power point data. Then, a second recognition is performed based on the second non-zero power point data. If the identification is normal, the recognition is completed; otherwise, the data is uploaded to the third recognition module; Based on the received data, the third identification module performs a third preprocessing on the daily power point data of the residents' self-use charging piles that are identified as abnormal by the second identification module to obtain third non-zero power point data, and then performs a third identification based on the third non-zero power point data to finally complete the identification of the power load of the residents' self-use charging piles.