Method and system for correcting daily-cleared monthly accumulated electric quantity

Abstract

The invention provides a correction method and system for daily-clear monthly-settled electric quantity, and the method comprises the steps: obtaining electric quantity data collected in each metering device in real time or at regular intervals, and carrying out the preprocessing of the collected electric quantity data; comparing the preprocessed data electric quantity data in the preset time period with historical data in the same period, and determining the volatility between the two data; and when the volatility in a certain preset time period exceeds a preset volatility threshold value and the absolute value of the volatility exceeds the preset volatility threshold value, judging that the electric quantity data in the preset time period is extreme value data, marking the extreme value data as abnormal, and correcting the extreme value data according to a preset abnormality processing rule. The method can effectively solve the problem caused by electric quantity deviation, can improve the operation efficiency and market competitiveness of power supply enterprises, and has important practical significance and application value.

Description

Technical Field

[0001] The present invention relates to the technical field of power system automation, and in particular to a correction method and system for daily and monthly electricity consumption. Background Art

[0002] With the deepening reform of the power market and the rapid development of the power spot market, in order to ensure the fairness and accuracy of transactions, the amount of electricity generated daily needs to be accurately recorded and settled at the end of the month. However, in actual operations, due to various factors (such as equipment failure, network transmission, etc.), there may be differences between the daily settlement data and the final monthly settlement results. In addition, the existence of outliers will also affect the fairness of settlement.

[0003] With the increasing maturity and development of the power market, market-based transactions have become an important trading model in the power industry. Under this model, the accurate calculation and settlement of daily and monthly electricity consumption is particularly important, which is directly related to the economic benefits of power supply companies and fair transactions in the market. However, in actual operations, due to the influence of various factors, such as power grid fluctuations, metering errors, data transmission delays, etc., there are certain deviations in daily and monthly electricity consumption, which brings many challenges to the operation and management of power supply companies. Summary of the invention

[0004] The purpose of the present invention is to propose a method and system for correcting daily and monthly electricity consumption, so as to effectively solve the technical problems caused by electricity consumption deviation.

[0005] On the one hand, a method for correcting daily and monthly electricity consumption is provided, comprising:

[0006] Obtaining the electricity data collected in real time or periodically from each metering device, and preprocessing the collected electricity data;

[0007] Compare the pre-processed data power data within the preset time period with the historical data for the same period to determine the volatility between the two;

[0008] When the volatility within a preset time period exceeds the preset volatility threshold and its absolute value exceeds the preset volatility threshold, the electricity data within the preset time period is determined to be extreme value data, and these extreme value data are marked as abnormal, and the extreme value data are corrected according to the preset abnormality processing rules.

[0009] Preferably, the electricity data is hourly electricity data collected by a metering device, wherein a plurality of metering points are preset in the metering device, and the electricity data of each metering point is recorded according to a preset event node.

[0010] Preferably, the preprocessing of the collected power data includes detecting the collected power data according to a preset data standard, deleting the data if it is determined to be obviously erroneous data; and checking and correcting the timestamp of the power data to retain the correct time series of the power data.

[0011] Preferably, the preprocessing of the collected power data further includes traversing the power data of each hour, and if the power value of the power data of any hour is less than zero, marking these power values ​​as abnormal, and recording the specific time point and location;

[0012] Set all negative power values ​​marked as abnormal to zero.

[0013] Preferably, the method further includes extracting historical electricity data corresponding to the metering point on the same date in the past year from a database as historical data for the same period.

[0014] Preferably, it also includes, when it is determined that the power data within the preset time period is extreme value data, recording the specific time point and location corresponding to the extreme value data marked as abnormal;

[0015] Set all negative power values ​​marked as abnormal to zero.

[0016] Preferably, the method further includes aggregating all negative electric quantities and extreme electric quantities marked as abnormal into an abnormal data log, and generating a corresponding abnormal data report;

[0017] The abnormal data report is sent to the corresponding terminal in a preset manner.

[0018] Preferably, the method further comprises obtaining a monthly frozen electricity amount for the entire month from a metering device, wherein the monthly frozen electricity amount is the total electricity amount recorded by the metering device at the end of the month;

[0019] Compare the monthly frozen electricity with the cumulative sum of the corresponding peak, flat and valley electricity of the previous month. If the two are consistent, they will be used as the official monthly settlement data. If the two are inconsistent, compare the peak, flat and valley electricity data of the daily time nodes with the monthly frozen electricity on a daily basis, calculate the electricity difference for each day, and check whether there are data anomalies on specific dates or time periods.

[0020] Preferably, the method further comprises: taking the monthly frozen electricity as a reference value, calculating the difference between the monthly frozen electricity and the accumulated data of the peak, flat and valley electricity data at the previous daily time node;

[0021] Divide the total difference by the corresponding time value to get the increased or decreased electricity value corresponding to each hourly node. Update the peak, flat and valley electricity data of the daily time nodes of the previous month according to the increased or decreased electricity value corresponding to each hourly node to generate new monthly settlement data.

[0022] On the other hand, a correction system for daily and monthly electricity is also provided to implement the correction method for daily and monthly electricity, including:

[0023] A data acquisition module is used to acquire the electricity data collected in real time or periodically from each metering device and to pre-process the collected electricity data;

[0024] The data processing module is used to compare the pre-processed data power data within a preset time period with the historical data of the same period to determine the volatility between the two;

[0025] When the volatility within a preset time period exceeds the preset volatility threshold and its absolute value exceeds the preset volatility threshold, the electricity data within the preset time period is determined to be extreme value data, and these extreme value data are marked as abnormal, and the extreme value data are corrected according to the preset abnormality processing rules.

[0026] In summary, the implementation of the embodiments of the present invention has the following beneficial effects:

[0027] The correction method and system for daily and monthly electricity volume provided by the present invention conduct in-depth mining of transaction data, identify key factors affecting electricity volume deviation, use these key factors to build a scientific correction model, and continuously adjust the model parameters through an iterative optimization algorithm to improve the accuracy and reliability of the correction, and apply the corrected electricity volume data to monthly settlement; it can not only effectively solve the problems caused by electricity volume deviation, but also improve the operating efficiency and market competitiveness of power supply enterprises, and has important practical significance and application value. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, without paying creative labor, other drawings obtained based on these drawings still belong to the scope of the present invention.

[0029] Figure 1 The figure is a schematic diagram of the main process of a method for correcting daily and monthly electricity consumption in an embodiment of the present invention. DETAILED DESCRIPTION

[0030] In order to make the objectives, technical solutions and advantages of the present invention more clear, the present invention will be further described in detail below with reference to the accompanying drawings.

[0031] like Figure 1 FIG. 1 is a schematic diagram of an embodiment of a method for correcting daily and monthly electricity provided by the present invention. In this embodiment, the method includes the following steps:

[0032] Step S1, obtain the electricity data collected in real time or regularly in each metering device, and pre-process the collected electricity data; it is understandable that first, all basic data related to meter reading code, archives, electricity, electricity price, and electricity fee calculation in the region are extracted from the integrated power grid marketing system database. The marketing system can be regarded as a database containing all regional metering equipment. This system has a direct docking interface with the marketing system, so all the basic data information of the required metering electricity can be obtained from the database. Collect hourly electricity data from each metering device (such as a smart meter) in real time or regularly. The data of each metering point is recorded hourly, with a total of 24 data points a day. The data type includes electricity data for three time periods: peak, flat, and valley.

[0033] In one embodiment, the electricity data is hourly electricity data collected by a metering device, wherein a plurality of metering points are preset in the metering device, and the electricity data of each metering point is recorded according to a preset event node.

[0034] In one embodiment, the preprocessing of the collected electricity data includes detecting the collected electricity data according to a preset data standard, deleting the data if it is determined to be obviously erroneous, and checking and correcting the timestamp of the electricity data to retain the correct time series of the electricity data. Understandably, preprocessing includes data cleaning: removing obviously erroneous data, such as non-numeric data, null values, etc. Format unification: ensuring that all data uses the same format and unit (such as kilowatt-hour). Timestamp verification: checking and correcting the timestamp to ensure that the time series of the data is correct.

[0035] Traverse the electricity data for each hour. If the electricity value of any hour's electricity data is less than zero, mark these electricity values ​​as abnormal and record the specific time and location; set all negative electricity values ​​marked as abnormal to zero. Understandably, identify negative electricity: traverse the data for each hour and find any electricity value less than zero. Mark and record: Mark these negative electricity values ​​as abnormal and record the specific time and location. Zeroing: Set all marked negative electricity values ​​to zero to prevent them from affecting subsequent calculations.

[0036] Step S2, compare the pre-processed data power data within the preset time period with the historical data of the same period to determine the volatility between the two; understandably, extract the historical data of the same date of the metering point from the database, usually taking the data of the most recent year as a reference. For the data of the current hour, calculate its volatility with the historical data of the same period.

[0037] In one embodiment, historical electricity consumption data corresponding to the metering point on the same date in the past year is extracted from a database as historical data for the same period.

[0038] It should be noted that the calculation methods of volatility mainly include simple volatility, historical volatility and implied volatility. The following is the detailed calculation method of each method:

[0039] Simple volatility, calculation formula: Volatility = Standard Deviation / Average Price

[0040] Note: Simple volatility is based on historical price data and measures the volatility of prices by calculating the standard deviation of prices. Standard deviation is used to measure the degree of dispersion of data, while average price refers to the average price over a period of time. This method is relatively simple, but it only considers historical data and may have certain limitations.

[0041] Historical volatility, calculated by the formula: Volatility = standard deviation / average logarithmic return or volatility = logarithmic return standard deviation × √ number of days × √ (annual trading days)

[0042] Note: Historical volatility is also calculated based on historical price data, but it measures the volatility of prices by calculating the standard deviation of the logarithmic return of prices over a period of time. Logarithmic return refers to the logarithmic change of prices. Historical volatility is more accurate than simple volatility, but it also relies on historical data and may not accurately reflect future volatility.

[0043] Implied volatility, calculation method: calculated using option pricing model

[0044] Note: Implied volatility is not calculated directly based on historical price data, but is inferred from option pricing models (such as the Black-Scholes model). It reflects the market's expectations of future volatility.

[0045] In addition, there are some concepts and methods related to volatility calculation, such as annualized volatility and weighted moving average volatility. Annualized volatility is an indicator that converts simple volatility into an annualized one, while weighted moving average volatility takes into account the weights of price fluctuations at different time points. In general, there are many ways to calculate volatility, and the choice of method depends on the specific application scenario and data situation. In practical applications, it is necessary to select an appropriate calculation method based on the actual situation and combine it with other analysis tools for comprehensive judgment.

[0046] In one embodiment, when it is determined that the power data within a preset time period is extreme value data, the specific time point and position corresponding to the extreme value data marked as abnormal are recorded; all negative power values ​​marked as abnormal are set to zero. All marked extreme value data are set to zero to prevent them from affecting subsequent calculations.

[0047] Step S3, when the volatility within a preset time period exceeds the preset volatility threshold and its absolute value exceeds the preset volatility threshold, the power data within the preset time period is determined to be extreme value data, and these extreme value data are marked as abnormal, and the extreme value data is corrected according to the preset abnormality handling rules. It is understandable that a reasonable threshold value X (for example, 300%) is set according to business needs and actual conditions. This threshold value is used to determine whether the current data belongs to an extreme value. If the data volatility of a certain hour exceeds 300%, and the absolute value exceeds the threshold value X preset by the system, the data is considered to be an extreme value.

[0048] In one embodiment, all negative and extreme electric quantities marked as abnormal are summarized into an abnormal data log, and a corresponding abnormal data report is generated; the abnormal data report is sent to the corresponding terminal in a preset manner. Understandably, all intercepted negative and extreme electric quantities are summarized into an abnormal data log; the abnormal data report is sent to relevant managers and technical support teams via email, SMS or other communication methods. Professionals manually review the abnormal data to confirm the accuracy of the data processing. If it is found that some data is misjudged as abnormal, these data can be manually restored. The verified data is updated to the database for use in subsequent steps.

[0049] During the operation of the system, data quality is continuously monitored to detect new abnormalities in a timely manner. Once abnormal data is found, the system automatically triggers an alarm and notifies relevant personnel to handle it. System verification can effectively intercept and process negative and extreme power, ensuring that the data used in subsequent steps has high accuracy and reliability. This not only helps to improve the accuracy of settlement, but also enhances the stability and credibility of the system.

[0050] One embodiment also includes a specific process of comparing and leveling the electricity. In the monthly leveling and correction fitting method for the monthly settlement of electricity in market-based transactions, electricity comparison is a key step to ensure data consistency and accuracy. This process includes two main stages: first, comparing the monthly frozen electricity data of the metering device with the peak, flat and valley electricity data of the 24-hour nodes every day of the previous month; second, deciding whether leveling and fitting is needed based on the comparison results.

[0051] Specifically, the monthly frozen electricity for the entire month is obtained from the metering device, and the monthly frozen electricity is the total electricity recorded by the metering device at the end of the month; the monthly frozen electricity is compared with the sum of the peak, flat and valley electricity corresponding to the previous month. If the two are consistent, it is used as the formal monthly settlement data; if the two are inconsistent, the peak, flat and valley electricity data of the daily time nodes are compared with the monthly frozen electricity on a daily basis, and the electricity difference of each day is calculated to check whether there is data anomaly on a specific date or time period. Understandably, obtain the monthly frozen electricity: obtain the frozen electricity data for the entire month from the metering device. This is the total electricity automatically recorded by the metering device at the end of the month. Obtain daily time-sharing electricity data: collect electricity data for the peak, flat and valley periods every hour of every day last month (a total of 720 time points). These data are usually stored in a database and have been system-verified.

[0052] At the same time, to ensure that all data is valid and remove any outliers or erroneous data, convert all power data into the same unit (such as kilowatt-hour) and ensure the consistency of date and time stamps. Add up the peak, flat and valley power data of the nodes for 24 hours every day in the last month to get a total power value.

[0053] Compare the monthly frozen electricity with the sum of the peak, flat and valley electricity of the 24-hour nodes every day of the previous month. If the two are exactly the same, no further processing is required and they are directly used as the official monthly settlement data. If there are differences, proceed to the next step for detailed comparison and adjustment. For inconsistencies, compare the peak, flat and valley electricity data of the 24-hour nodes every day with the monthly frozen electricity on a daily basis. Calculate the difference in electricity for each day and record it. Check whether there are data anomalies on specific dates or time periods.

[0054] In one embodiment, the monthly frozen electricity is used as a reference value to calculate the difference between the monthly frozen electricity and the accumulated data of the peak, flat and valley electricity data at the last daily time node; the total difference is divided by the corresponding time value to obtain the increased or decreased electricity value corresponding to each hourly node, and the peak, flat and valley electricity data of the daily time nodes of the previous month are updated according to the increased or decreased electricity value corresponding to each hourly node to generate new monthly settlement data. Understandably, the monthly frozen electricity is used as a reference value to calculate the difference between the monthly frozen electricity and the accumulated data of the peak, flat and valley electricity at the 24 hourly nodes of the previous month; the difference is evenly distributed to each hourly node of the previous month. Specifically: divide the total difference by 720 (i.e., 30 days × 24 hours / day) to obtain the electricity value that should be increased or decreased at each hourly node. Add or subtract the average difference to the peak, flat and valley electricity of each hourly node to ensure that the final total amount is consistent with the monthly frozen electricity.

[0055] Generate new daily time-of-use electricity data: Based on the above average difference results, update the peak, flat and valley electricity data of the 24-hour nodes every day of the previous month. Re-accumulate the adjusted daily time-of-use electricity data to generate new monthly settlement data.

[0056] Compare the updated monthly settlement data with the monthly frozen electricity again to ensure that the two are completely consistent. Record the log information of the entire comparison process. Notify the comparison results to the relevant management personnel and technical support team. It can effectively realize the comparison and correction of electricity data, ensure the accuracy and consistency of the final settlement data, and enhance the reliability and stability of the system.

[0057] The embodiment of the present invention further provides a correction system for daily and monthly electricity consumption, which is used to implement the correction method for daily and monthly electricity consumption, including:

[0058] A data acquisition module is used to acquire the electricity data collected in real time or periodically from each metering device and to pre-process the collected electricity data;

[0059] The data processing module is used to compare the pre-processed data power data within a preset time period with the historical data of the same period to determine the volatility between the two;

[0060] When the volatility within a preset time period exceeds the preset volatility threshold and its absolute value exceeds the preset volatility threshold, the electricity data within the preset time period is determined to be extreme value data, and these extreme value data are marked as abnormal, and the extreme value data are corrected according to the preset abnormality processing rules.

[0061] It should be noted that the system described in the above embodiment corresponds to the method described in the above embodiment. Therefore, the undetailed parts of the system described in the above embodiment can be obtained by referring to the contents of the method described in the above embodiment, and will not be repeated here.

[0062] In summary, the implementation of the embodiments of the present invention has the following beneficial effects:

[0063] The correction method and system for daily and monthly electricity volume provided by the present invention conduct in-depth mining of transaction data, identify key factors affecting electricity volume deviation, use these key factors to build a scientific correction model, and continuously adjust the model parameters through an iterative optimization algorithm to improve the accuracy and reliability of the correction, and apply the corrected electricity volume data to monthly settlement; it can not only effectively solve the problems caused by electricity volume deviation, but also improve the operating efficiency and market competitiveness of power supply enterprises, and has important practical significance and application value.

[0064] The above disclosure is only the preferred embodiment of the present invention, which certainly cannot be used to limit the scope of the present invention. Therefore, equivalent changes made according to the claims of the present invention are still within the scope of the present invention.

Claims

1. A method for correcting daily and monthly electricity consumption, characterized in that: include: Obtaining the electricity data collected in real time or periodically from each metering device, and preprocessing the collected electricity data; Compare the pre-processed data power data within the preset time period with the historical data for the same period to determine the volatility between the two; When the volatility within a preset time period exceeds the preset volatility threshold and its absolute value exceeds the preset volatility threshold, the electricity data within the preset time period is determined to be extreme value data, and these extreme value data are marked as abnormal, and the extreme value data are corrected according to the preset abnormality processing rules.

2. The method according to claim 1, characterized in that The electricity data is hourly electricity data collected by a metering device, wherein a plurality of metering points are preset in the metering device, and the electricity data of each metering point is recorded according to a preset event node.

3. The method according to claim 2, characterized in that The preprocessing of the collected power data includes: detecting the collected power data according to a preset data standard, deleting the data if it is determined to be obviously erroneous; and checking and correcting the timestamp of the power data to retain the correct time series of the power data.

4. The method according to claim 3, characterized in that The preprocessing of the collected power data also includes traversing the power data of each hour, and if the power value of the power data of any hour is less than zero, marking these power values ​​as abnormal, and recording the specific time point and location; Set all negative power values ​​marked as abnormal to zero.

5. The method according to claim 4, characterized in that It also includes extracting historical electricity consumption data corresponding to the metering point on the same date in the past year from the database as historical data for the same period.

6. The method according to claim 5, characterized in that It also includes, when it is determined that the power data within the preset time period is extreme value data, recording the specific time point and location corresponding to the extreme value data marked as abnormal; Set all negative power values ​​marked as abnormal to zero.

7. The method according to claim 6, characterized in that It also includes aggregating all negative power and extreme power marked as abnormal into an abnormal data log, and generating a corresponding abnormal data report; The abnormal data report is sent to the corresponding terminal in a preset manner.

8. The method according to claim 7, characterized in that The method further includes obtaining a monthly frozen electricity amount for the entire month from the metering device, wherein the monthly frozen electricity amount is the total electricity amount recorded by the metering device at the end of the month; Compare the monthly frozen electricity with the accumulated sum of the peak, flat and valley electricity corresponding to the previous month. If the two are consistent, they will be used as the official monthly settlement data. If the two are inconsistent, compare the peak, flat and valley electricity data at daily time nodes with the monthly frozen electricity, calculate the electricity difference for each day, and check whether there are data anomalies on specific dates or time periods.

9. The method according to claim 8, characterized in that It also includes, taking the monthly frozen electricity as a benchmark value, calculating the difference between the monthly frozen electricity and the accumulated data of the peak, flat and valley electricity data at the previous daily time node; Divide the total difference by the corresponding time value to get the increased or decreased electricity value corresponding to each hourly node. Update the peak, flat and valley electricity data of the daily time nodes of the previous month according to the increased or decreased electricity value corresponding to each hourly node to generate new monthly settlement data.

10. A calibration system for daily and monthly electricity consumption, used to implement the method according to any one of claims 1 to 9, characterized in that: include: A data acquisition module is used to acquire the electricity data collected in real time or periodically from each metering device and to pre-process the collected electricity data; The data processing module is used to compare the pre-processed data power data within a preset time period with the historical data of the same period to determine the volatility between the two; When the volatility within a preset time period exceeds the preset volatility threshold and its absolute value exceeds the preset volatility threshold, the electricity data within the preset time period is determined to be extreme value data, and these extreme value data are marked as abnormal, and the extreme value data are corrected according to the preset abnormality processing rules.

Images