CN114297186A - Power consumption data preprocessing method and system based on deviation coefficient - Google Patents
Power consumption data preprocessing method and system based on deviation coefficient Download PDFInfo
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Abstract
The invention discloses a deviation coefficient-based electricity consumption data preprocessing method and system. According to the invention, the electricity utilization data is corrected according to the deviation condition of the electricity utilization data, the interference of abnormal factors on the electricity utilization data is reduced, the influence of false abnormal data is eliminated, and more continuous and reliable reference data is provided for monitoring the electricity utilization data.
Description
Technical Field
The invention relates to the technical field of data processing. In particular to a method and a system for preprocessing power consumption data based on a deviation coefficient.
Background
With the use of smart meters and the development of industry intellectualization, it is an industry trend to realize user electricity consumption monitoring through the internet of things. The remote monitoring of the power utilization condition of the user is realized through the Internet of things, the abnormal power utilization condition of the user needs to be identified and judged, so that the historical power utilization data of the user needs to be analyzed and processed, and the processed data is used as the basis for prejudging the running condition of the power grid. In the actual operation process of the power grid, some influence factors exist, which cause the power consumption data to deviate from the actual value, such as line loss increase caused by more line connectors and loss caused by larger voltage fluctuation, and the like, and the influence factors can cause some good data to be represented as abnormal data, so that the abnormal data is increased, the reference data required in data analysis is reduced, the data analysis accuracy is influenced, and the prejudgment of the operation condition of the power grid is further influenced.
Disclosure of Invention
Therefore, the technical problem to be solved by the present invention is to provide a power consumption data preprocessing method and system based on deviation coefficients, which corrects the power consumption data according to the deviation condition of the power consumption data, reduces the interference of abnormal factors to the power consumption data, eliminates false abnormal data, and provides continuous and reliable reference data for monitoring the power consumption data.
In order to solve the technical problems, the invention provides the following technical scheme:
a power utilization data preprocessing method based on deviation coefficients comprises the following steps:
a) and correcting the electricity consumption data according to the electricity consumption deviation coefficient tau by using the following formula:
τ＝Qfirst stage/ΔQ
In the formula: alpha is a correction coefficient;
Qfirst stageCounting time points T for electricity consumptionnThe collected power consumption n is more than or equal to 1;
QcorrectionThe corrected power consumption;
delta Q is the average electricity consumption in unit time in the electricity data statistics period T;
b) and cleaning the corrected data.
In the electricity consumption data preprocessing method based on the deviation coefficient, in the step a), the correction coefficient α is determined by the following method:
the electricity consumption data statistics period T is divided according to the time length delta T, the overall standard deviation of each time period is calculated, the average value delta q of electricity consumption in the time period with the minimum overall standard deviation is selected as the correction reference electricity consumption, and then the correction coefficient alpha is determined according to the following formula:
α＝(Δq/ΔQ)(2Δt/T)。
in the electricity consumption data preprocessing method based on the deviation coefficient, delta T/T is greater than or equal to 0.05.
In the electricity consumption data preprocessing method based on the deviation coefficient, delta T/T is less than or equal to 0.5.
In the electricity consumption data preprocessing method based on the deviation coefficient, in the step a), before the electricity consumption data is corrected, the fluctuation of the electricity consumption data is evaluated by a fluctuation ratio ζ, wherein ζ | (Q)n+1-Qn)/QnL, n is greater than or equal to 1; when zeta is greater than or equal to 0.25 and the fluctuation ratio zeta of the electricity consumption data collected by the (n +1) th collection point is greater than or equal to 3 collection points continuously and is less than or equal to 0.1, the electricity consumption data is correctedAnd if so, correcting the electricity consumption data from the (n +1) th acquisition point to the (n + m) th acquisition point by using an independent correction coefficient, wherein m is greater than or equal to 3.
According to the power consumption data preprocessing method based on the deviation coefficient, when zeta is larger than or equal to 0.25, under the condition that the fluctuation ratio zeta of power consumption data collected by less than 3 collecting points from the (n +1) th collecting point is smaller than or equal to 0.1, the data collected by the (n +1) th collecting point is still corrected by adopting the original correction coefficient.
When zeta is greater than or equal to 0.25, the fluctuation ratio zeta of the electricity utilization data collected by more than or equal to [0.8m ] collection points from the (n +1) th collection point to the electricity utilization data collected by the (n +1) th collection point is less than or equal to 0.1, and when the electricity utilization data is corrected, the electricity utilization data from the (n +1) th collection point to the (n + m) th collection point is corrected by using an independent correction coefficient, wherein m is greater than or equal to 5.
The system for preprocessing the electricity consumption data by using the electricity consumption data preprocessing method based on the deviation coefficient comprises the following steps:
the data acquisition module is used for acquiring and grouping the electricity utilization data;
the data deviation analysis module is used for carrying out deviation analysis on the electricity utilization data;
the data fluctuation analysis module is used for carrying out fluctuation analysis on the electricity utilization data of two adjacent acquisition points;
the data correction module is used for correcting the electricity utilization data;
the data cleaning module is used for cleaning the corrected electricity utilization data;
the data collection module is respectively in communication connection with the data deviation analysis module and the data fluctuation analysis module, the data deviation analysis module and the data fluctuation analysis module are respectively in communication connection with the data correction module, and the data correction module is in communication connection with the data cleaning module.
The system further comprises a data sorting module, the data deviation analysis module and the data fluctuation analysis module are respectively in communication connection with the data sorting module, and the data sorting module is in communication connection with the data correction module.
The system further comprises a data storage module, the data sorting module is in communication connection with the data storage module, and the data storage module is in communication connection with the data correction module.
The technical scheme of the invention achieves the following beneficial technical effects:
1. according to the invention, the power utilization data is corrected according to the deviation condition of the power utilization data, the possibility that false abnormal data is cleaned is reduced, and the data for sample analysis is provided for monitoring the power utilization data of the power grid by using historical power utilization data.
2. According to the fluctuation condition of the power utilization data, different correction modes are adopted for the power utilization data, and insufficient or excessive correction of the power utilization data in a time period with large power utilization data fluctuation is avoided.
Drawings
FIG. 1 is a schematic diagram of the operation of the system for preprocessing power consumption data based on deviation coefficient according to the present invention;
FIG. 2 is a line graph of electricity consumption data before correction;
FIG. 3 is a line graph of corrected electricity usage data.
Detailed Description
As shown in fig. 1, the system for preprocessing power consumption data by using a power consumption data preprocessing method based on a deviation coefficient in the present invention includes a data acquisition module, a data deviation analysis module, a data fluctuation analysis module, a data correction module, a data cleaning module, a data sorting module and a data storage module, wherein the data acquisition module is respectively in communication connection with the data deviation analysis module and the data fluctuation analysis module, the data deviation analysis module and the data fluctuation analysis module are respectively in communication connection with the data sorting module, the data sorting module is in communication connection with the data storage module, the data storage module is in communication connection with the data correction module, and the data correction module is in communication connection with the data cleaning module.
The data acquisition module is used for acquiring and grouping the electricity utilization data; the data deviation analysis module is used for carrying out deviation analysis on the electricity utilization data; the data fluctuation analysis module is used for carrying out fluctuation analysis on the electricity utilization data of two adjacent acquisition points; the data correction module is used for correcting the electricity utilization data; and the data cleaning module is used for cleaning the corrected electricity utilization data.
In the actual power utilization process, a plurality of factors can cause deviation between the power utilization data acquired by the intelligent electric meter and the actual power utilization data, and before the power utilization data are corrected, deviation analysis needs to be performed on the power utilization data, so that correct data can be prevented from being adjusted into wrong data or wrong data can be prevented from being adjusted into data with a better spectrum. The method comprises the following steps of firstly performing deviation analysis on power consumption data, setting a deviation threshold value, wherein the deviation threshold value set in the embodiment is (0.85, 1.3), then correcting the power consumption data according to the deviation degree of the power consumption data, and finally cleaning the corrected power consumption data, namely, preprocessing historical power consumption data by using a power consumption data preprocessing system, and specifically comprises the following steps:
a) and correcting the electricity consumption data according to the electricity consumption deviation coefficient tau by using the following formula:
τ＝Qfirst stage/ΔQ
In the formula: alpha is a correction coefficient;
Qfirst stageCounting time points T for electricity consumptionnThe collected power consumption n is more than or equal to 1;
QcorrectionThe corrected power consumption;
delta Q is the average electricity consumption in unit time in the electricity data statistics period T;
b) and cleaning the corrected data.
In step a), the correction factor α is determined by:
the electricity consumption data statistics period T is divided according to the time length delta T, the overall standard deviation of each time period is calculated, the average value delta q of electricity consumption in the time period with the minimum overall standard deviation is selected as the correction reference electricity consumption, and then the correction coefficient alpha is determined according to the following formula:
α＝(Δq/ΔQ)(2Δt/T)。
the correction coefficient alpha is provided based on regular fluctuation of the electricity consumption data, namely the electricity consumption data are close in a certain time, the fluctuation range is small, and the regularity is relatively obvious, for example, the using time point and the time length of equipment with large electricity consumption have certain regularity, so that the average value delta Q of the electricity consumption in the time period with the minimum overall standard deviation is selected as the correction reference electricity consumption, and is related and deduced with the average electricity consumption delta Q in unit time in the electricity consumption data statistics period T, the time length delta T and the electricity consumption data statistics period T, and a calculation formula of the correction coefficient alpha is obtained.
In order to reduce the difficulty of preprocessing the power consumption data and improve the efficiency of preprocessing the power consumption data, in this embodiment, Δ T/T is greater than or equal to 0.05 and less than or equal to 0.5, that is, when Δ T/T is less than 0.05, the magnitude of Δ T is adjusted.
In view of the fact that power consumption data in different power consumption periods have certain differences, correction of power consumption data with large differences by the same correction coefficient is excessive, namely, the corrected data has a distortion phenomenon, so that in step a), before the power consumption data are corrected, the power consumption data volatility is evaluated by using a fluctuation ratio zeta, wherein zeta | (Q |)n+1-Qn)/QnL, n is greater than or equal to 1; when zeta is greater than or equal to 0.25 and the fluctuation ratio zeta of the electricity consumption data collected by more than or equal to 3 collection points from the (n +1) th collection point is less than or equal to 0.1, when the electricity consumption data is corrected, the electricity consumption data from the (n +1) th collection point to the (n + m) th collection point is corrected by using an independent correction coefficient, wherein m is greater than or equal to 3, and when zeta is greater than or equal to 0.25, the data collected by the (n +1) th collection point is corrected by using the original correction coefficient under the condition that the fluctuation ratio zeta of the electricity consumption data collected by less than 3 collection points from the (n +1) th collection point is less than or equal to 0.1. Since some factors may cause the power consumption data to fluctuate more frequently, when ζ is greater than or equal to 0.25, it is greater than or equal to the (n +1) th to (n + m) th collection pointsAt [0.8m]And if the fluctuation ratio zeta of the electricity utilization data collected by the collection points and the electricity utilization data collected by the (n +1) th collection point is less than or equal to 0.1, when the electricity utilization data is corrected, the electricity utilization data from the (n +1) th collection point to the (n + m) th collection point is corrected by using an independent correction coefficient, wherein m is greater than or equal to 5.
The electricity consumption data of the user A in 10/3/2020 to 11/2020 is processed by the electricity consumption data preprocessing method in the present invention, and the processing results are shown in Table 1.
TABLE 1 historical electricity usage data for UserA and its preprocessing results
| Date | Electric power consumption | Coefficient of deviation τ | Corrected electricity consumption data |
| 10 and 3 days in 2020 | 2.14 | 1.196 | 2.14 |
| 10/month/4/2020 | 1.98 | 1.106 | 1.98 |
| 10 and 5 days in 2020 | 1.72 | 0.961 | 1.72 |
| Year 2020, 10 and 6 | 1.48 | 0.823 | 1.49 |
| 10 and 7 in 2020 | 2.14 | 1.196 | 2.14 |
| Year 2020, 10 and 8 | 1.58 | 0.883 | 1.58 |
| 10 and 9 days in 2020 | 2.00 | 1.117 | 2.00 |
| 10 months and 10 days in 2020 | 1.51 | 0.844 | 1.52 |
| Year 2020, 10 and 11 | 2.10 | 1.173 | 2.10 |
| 10 and 12 months in 2020 | 1.52 | 0.849 | 1.53 |
| 10 and 13 days of 2020 | 2.10 | 1.173 | 2.10 |
| Year 2020, 10 and 14 | 1.60 | 0.894 | 1.60 |
| 10 and 15 days in 2020 | 1.75 | 0.978 | 1.75 |
| Year 2020, 10 and 16 | 2.09 | 1.168 | 2.09 |
| Year 2020, 10 and 17 | 1.85 | 1.034 | 1.85 |
| Year 2020, 10 and 18 | 1.80 | 1.006 | 1.80 |
| 10 and 19 months in 2020 | 1.73 | 0.966 | 1.73 |
| 10 and 20 days in 2020 | 1.47 | 0.821 | 1.48 |
| 21/10/2020 | 1.99 | 1.112 | 1.99 |
| Year 2020, 10 and 22 | 1.51 | 0.844 | 1.52 |
| Year 2020, 10 and 23 | 1.60 | 0.894 | 1.60 |
| 24 days 10 month in 2020 | 1.43 | 0.799 | 1.44 |
| 10 and 25 days in 2020 | 1.62 | 0.905 | 1.62 |
| 26/10/2020 | 2.18 | 1.218 | 2.18 |
| 10 and 27 days in 2020 | 1.64 | 0.916 | 1.64 |
| 10 and 28 days in 2020 | 2.00 | 1.117 | 2.00 |
| 10 and 29 months in 2020 | 2.02 | 1.128 | 2.02 |
| 10 and 30 days in 2020 | 1.73 | 0.966 | 1.73 |
| 10 and 31 days in 2020 | 1.52 | 0.849 | 1.53 |
| Year 2020, 11 and 1 | 1.76 | 0.983 | 1.76 |
| Year 2020, 11 and 2 | 1.50 | 0.838 | 1.51 |
| Year 2020, 11 and 3 | 2.25 | 1.257 | 2.25 |
| Year 2020, 11 and 4 | 1.42 | 0.793 | 1.43 |
| Year 2020, 11 and 5 | 1.45 | 0.810 | 1.46 |
| Year 2020, 11 and 6 | 1.55 | 0.866 | 1.55 |
| 11/7/2020 | 1.41 | 0.788 | 1.42 |
| Year 2020, 11 and 8 | 2.25 | 1.257 | 2.25 |
| Year 2020, 11 and 9 | 1.61 | 0.899 | 1.61 |
| Year 2020, 11 and 10 | 2.13 | 1.190 | 2.13 |
| 11/2020 | 2.06 | 1.151 | 2.06 |
| 11/month/12/2020 | 1.72 | 0.961 | 1.72 |
| Year 2020, 11 and 13 | 1.33 | 0.743 | 1.34 |
| Year 2020, 11 and 14 | 1.46 | 0.816 | 1.47 |
Where, T is 43, Δ T is 5, and Δ q is an average used amount of electricity from 10 and 15 days in 2020 to 10 and 19 days in 2020. The corrected power consumption data is made into a line graph, as shown in fig. 2 and fig. 3, the line graph of the corrected power consumption data and the line graph of the power consumption data before correction are not obvious in change, but the power consumption data deviating from the larger power consumption data after correction are close to the power consumption data, and the power consumption change trend is formed by compounding the basic change data and the change trend data, wherein the basic change data of the power consumption data refers to the power consumption data which only changes along with the length of the service time and does not change along with the change of seasons, such as illumination and other daily power consumption, and the change trend data of the power consumption data refers to the power consumption data which changes along with the change of seasons, such as air conditioning refrigeration or heating power consumption.
It should be understood that the above examples are only for clarity of illustration and are not intended to limit the embodiments. Other variations and modifications will be apparent to persons skilled in the art in light of the above description. And are neither required nor exhaustive of all embodiments. And obvious variations or modifications are possible which remain within the scope of the appended claims.
Claims (10)
1. A power utilization data preprocessing method based on deviation coefficients is characterized by comprising the following steps:
a) and correcting the electricity consumption data according to the electricity consumption deviation coefficient tau by using the following formula:
τ＝Qfirst stage/ΔQ
In the formula: alpha is a correction coefficient;
Qfirst stageCounting time points T for electricity consumptionnThe collected power consumption n is more than or equal to 1;
QcorrectionThe corrected power consumption;
delta Q is the average electricity consumption in unit time in the electricity data statistics period T;
b) and cleaning the corrected data.
2. The method for preprocessing power consumption data based on deviation coefficient as claimed in claim 1, wherein in step a), the correction coefficient α is determined by:
the electricity consumption data statistics period T is divided according to the time length delta T, the overall standard deviation of each time period is calculated, the average value delta q of electricity consumption in the time period with the minimum overall standard deviation is selected as the correction reference electricity consumption, and then the correction coefficient alpha is determined according to the following formula:
α＝(Δq/ΔQ)(2Δt/T)。
3. the method for preprocessing power consumption data based on coefficient of deviation as claimed in claim 2, wherein at/T is greater than or equal to 0.05.
4. The method for preprocessing power consumption data based on coefficient of deviation as claimed in claim 3, wherein at/T is less than or equal to 0.5.
5. The method for preprocessing power consumption data based on deviation factor as claimed in any of claims 1 to 4, wherein in step a), before correcting the power consumption data, the power consumption data fluctuation is evaluated by a fluctuation ratio ζ, wherein ζ | (Q |)n+1-Qn)/QnL, n is greater than or equal to 1; when zeta is greater than or equal to 0.25 and the fluctuation ratio zeta of the electricity consumption data collected by the collection points greater than or equal to 3 from the (n +1) th collection point is less than or equal to 0.1, when the electricity consumption data is corrected, the electricity consumption data from the (n +1) th collection point to the (n + m) th collection point is corrected by using an independent correction coefficient, wherein m is greater than or equal to 3.
6. The method for preprocessing power consumption data based on coefficient of deviation as claimed in claim 5, wherein when ζ is greater than or equal to 0.25, in case that the power consumption data fluctuation ratio ζ collected from less than 3 collection points from the (n +1) th collection point is less than or equal to 0.1, the data collected from the (n +1) th collection point is still corrected by using the original correction coefficient.
7. The method for preprocessing power consumption data based on coefficient of deviation according to claim 6, wherein when ζ is greater than or equal to 0.25, a fluctuation ratio ζ of power consumption data collected at [0.8m ] or more from the (n +1) th to (n + m) th collection points is less than or equal to 0.1, and power consumption data collected at the (n +1) th collection point is corrected using an independent correction coefficient when the power consumption data is corrected, wherein m is greater than or equal to 5.
8. The system for preprocessing the electricity consumption data by using the method for preprocessing the electricity consumption data based on the deviation coefficient as claimed in any one of claims 1 to 7, is characterized by comprising the following steps:
the data acquisition module is used for acquiring and grouping the electricity utilization data;
the data deviation analysis module is used for carrying out deviation analysis on the electricity utilization data;
the data fluctuation analysis module is used for carrying out fluctuation analysis on the electricity utilization data of two adjacent acquisition points;
the data correction module is used for correcting the electricity utilization data;
the data cleaning module is used for cleaning the corrected electricity utilization data;
the data collection module is respectively in communication connection with the data deviation analysis module and the data fluctuation analysis module, the data deviation analysis module and the data fluctuation analysis module are respectively in communication connection with the data correction module, and the data correction module is in communication connection with the data cleaning module.
9. The system of claim 8, further comprising a data grooming module, wherein the data deviation analysis module and the data fluctuation analysis module are each communicatively coupled to the data grooming module, and wherein the data grooming module is communicatively coupled to the data modification module.
10. The system of claim 9, further comprising a data storage module, the data collation module being communicatively coupled to the data storage module, the data storage module being communicatively coupled to the data modification module.
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Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106327359A (en) * | 2016-09-22 | 2017-01-11 | 国网上海市电力公司 | Electricity consumption mode analysis-based meter centralized reading data anomaly judgment method |
| CN106505635A (en) * | 2016-09-20 | 2017-03-15 | 北京恒泰实达科技股份有限公司 | Abandon the minimum active power dispatch model of wind and scheduling system |
| CN106503880A (en) * | 2016-09-22 | 2017-03-15 | 国网上海市电力公司 | A kind of INTELLIGENT IDENTIFICATION method of low-voltage collecting meter reading system zero power user |
| CN107292482A (en) * | 2017-04-28 | 2017-10-24 | 国网浙江省电力公司 | Electricity charge method of adjustment based on load fluctuation |
| CN107292428A (en) * | 2017-06-07 | 2017-10-24 | 国网浙江省电力公司物资分公司 | A kind of distribution Power Material procurement demand forecasting system |
| CN108647275A (en) * | 2018-04-28 | 2018-10-12 | 国网北京市电力公司 | The recognition methods of isolated island detecting state and device, storage medium, processor |
| CN108695806A (en) * | 2018-08-24 | 2018-10-23 | 四川大学 | Embedded heating material is from ice-melt conducting wire anti-icing control method online |
| CN108882872A (en) * | 2016-04-15 | 2018-11-23 | 欧姆龙株式会社 | Biont information analytical equipment, Biont information analysis system, program and Biont information analysis method |
| CN108898251A (en) * | 2018-06-29 | 2018-11-27 | 上海电力学院 | Consider the marine wind electric field power forecasting method of meteorological similitude and power swing |
| CN110611333A (en) * | 2019-11-11 | 2019-12-24 | 燕山大学 | Island microgrid distributed coordination control method considering communication problem |
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108882872A (en) * | 2016-04-15 | 2018-11-23 | 欧姆龙株式会社 | Biont information analytical equipment, Biont information analysis system, program and Biont information analysis method |
| CN106505635A (en) * | 2016-09-20 | 2017-03-15 | 北京恒泰实达科技股份有限公司 | Abandon the minimum active power dispatch model of wind and scheduling system |
| CN106327359A (en) * | 2016-09-22 | 2017-01-11 | 国网上海市电力公司 | Electricity consumption mode analysis-based meter centralized reading data anomaly judgment method |
| CN106503880A (en) * | 2016-09-22 | 2017-03-15 | 国网上海市电力公司 | A kind of INTELLIGENT IDENTIFICATION method of low-voltage collecting meter reading system zero power user |
| CN107292482A (en) * | 2017-04-28 | 2017-10-24 | 国网浙江省电力公司 | Electricity charge method of adjustment based on load fluctuation |
| CN107292428A (en) * | 2017-06-07 | 2017-10-24 | 国网浙江省电力公司物资分公司 | A kind of distribution Power Material procurement demand forecasting system |
| CN108647275A (en) * | 2018-04-28 | 2018-10-12 | 国网北京市电力公司 | The recognition methods of isolated island detecting state and device, storage medium, processor |
| CN108898251A (en) * | 2018-06-29 | 2018-11-27 | 上海电力学院 | Consider the marine wind electric field power forecasting method of meteorological similitude and power swing |
| CN108695806A (en) * | 2018-08-24 | 2018-10-23 | 四川大学 | Embedded heating material is from ice-melt conducting wire anti-icing control method online |
| CN110611333A (en) * | 2019-11-11 | 2019-12-24 | 燕山大学 | Island microgrid distributed coordination control method considering communication problem |
Non-Patent Citations (2)
| Title |
|---|
| 胡峰 等: "基于分时段多模型的短期电价预测", 华东电力, vol. 36, no. 06, 24 June 2008 (2008-06-24), pages 20 - 23 * |
| 覃泓皓 等: "考虑季节特性的改进小波包-RBF用电量预测方法", 电力科学与技术学报, vol. 31, no. 02, 28 June 2016 (2016-06-28), pages 37 - 44 * |
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