Returned and supplemented electric quantity calculation method and device considering power consumption characteristics of industrial users, and storage medium

Abstract

The invention discloses a calculation method and device considering power consumption characteristics of industrial users and a storage medium, and belongs to the technical field of electric energy metering, and the calculation method comprises the steps: obtaining a daily power consumption time sequence of a complete year based on power consumption data information; carrying out feature mining and working condition recognition by adopting a clustering analysis method based on image recognition, and generating a date-working condition mapping database; retrieving a working condition label corresponding to specific fault time in the fault time period in the date-working condition mapping database, and identifying a working condition judgment result corresponding to the specific fault time; analyzing the time sequence characteristics of the fault time period in the production cycle, and dynamically matching the total power consumption data of the corresponding time sequence in the latest working condition cycle of the same type before the fault occurs in combination with the working condition judgment result; and subtracting the total electricity consumption data of the corresponding time sequence from the total electricity consumption metered by the electricity meter to obtain the returned and supplemented electricity during the fault period. The method is suitable for different scenes, and can achieve the precise electric quantity return and compensation under the working condition of the electric power measurement fault of the industrial user.

Description

Technical Field

[0001] This invention relates to a method, device, and storage medium for calculating refund and compensation electricity consumption that takes into account the electricity consumption characteristics of industrial users, and belongs to the field of electricity metering technology. Background Technology

[0002] Electricity metering data serves as the core basis for trade settlement between power generators and consumers in power system operations, and its accuracy directly affects the economic interests of both parties. However, factors such as aging power system equipment and human error can lead to problems like wiring errors, transformer malfunctions, and secondary circuit failures in metering devices, resulting in errors in user electricity consumption measurement. In this context, how to scientifically and accurately calculate electricity refunds and compensations to safeguard the legitimate rights and interests of both power generators and consumers is a key issue that electricity metering personnel must address.

[0003] Among various types of electricity users, industrial users' electricity metering devices consist of multiple parts, including electricity meters, voltage transformers, current transformers, and secondary circuits. The wiring is relatively complex, and the risk of failure is high. Furthermore, due to the large electricity loads of industrial users, metering errors often lead to significant economic losses. Therefore, the accuracy and reliability of their metering devices have always been a key focus for electricity metering personnel, and the optimization of related electricity reimbursement methods has important engineering practical significance.

[0004] Currently, electricity refund / compensation calculations mainly employ a manual calculation method based on correction coefficients. This method has the following technical limitations: First, its theoretical model is built upon ideal assumptions of average power factor and three-phase balanced load, resulting in significant calculation deviations under actual operating conditions, severely impacting the method's applicability. Second, in metering fault scenarios such as current loss or meter malfunction, the correction coefficient method becomes inapplicable, failing to provide an effective solution for calculating refund / compensation. Furthermore, this method involves complex computational steps, making manual calculation prone to errors, thus compromising the reliability and accuracy of the refund / compensation calculation results. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to overcome the defects of the prior art and provide a method, device and storage medium for calculating the power refund and compensation that takes into account the power consumption characteristics of industrial users. It is applicable to different scenarios and can realize accurate power refund and compensation under the fault conditions of power metering of industrial users.

[0006] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is as follows:

[0007] In a first aspect, the present invention provides a method for calculating the refund / compensation electricity consumption that takes into account the electricity consumption characteristics of industrial users, comprising the following steps:

[0008] Obtain the user's electricity consumption data from the previous year, and obtain the complete daily electricity consumption time series for the entire year based on the electricity consumption data;

[0009] A clustering analysis method based on image recognition is used to perform feature mining and operating condition identification on the daily electricity consumption time series, generating an electricity consumption feature database with operating condition labels, namely a date-operating condition mapping database.

[0010] Obtain the user's fault time period, retrieve the corresponding operating condition tag in the date-operating condition mapping database for the specific fault time within the fault time period, and identify the operating condition judgment result corresponding to the specific fault time.

[0011] Analyze the timing characteristics of the fault period in the production cycle, and then combine the operating condition judgment results to dynamically match the total power consumption data of the corresponding timing in the most recent operating condition cycle of the same type before the fault occurred.

[0012] Obtain the total electricity consumption measured by the electricity meter during the fault period, and subtract the total electricity consumption data of the corresponding time period from the total electricity consumption measured by the electricity meter to obtain the refund and compensation electricity during the fault period.

[0013] The clustering analysis method includes the K-means algorithm.

[0014] The date-operating condition mapping database categorizes operating conditions into off-peak and peak seasons.

[0015] The total electricity consumption data corresponding to the time sequence includes the electricity consumption during the same period of the previous normal off-season, which is the same length as the off-season fault period, and the electricity consumption during the same period of the previous normal peak season, which is the same length as the peak season fault period.

[0016] The total electricity consumption measured by the meter includes the total electricity consumption during the off-season and the total electricity consumption during the peak season, as measured by the meter during the fault period.

[0017] The formula for calculating the refund / refund amount during the fault period is as follows: (This involves subtracting the total electricity consumption data for the corresponding time period from the total electricity consumption measured by the meter.)

[0018]

[0019] in, The amount of electricity refunded or replenished during the fault period. To match the electricity consumption during the same period of the previous normal off-season, which is of equal length to the off-season fault period. To match the electricity consumption during the same period of the previous normal peak season, which is of equal length to the peak season outage period. This represents the total off-season electricity consumption as measured by the meter during the period of the power outage. This represents the total peak season electricity consumption measured by the meter during the fault period.

[0020] After obtaining the date-operation condition mapping database, the operations and maintenance personnel manually adjust the production operation condition parameters in the date-operation condition mapping database according to the actual situation.

[0021] The analysis of the timing characteristics of the fault time period in the production cycle specifically involves dividing the fault time period into multiple production working condition periods according to the working condition information in the date-working condition mapping database, and calculating the different production working condition periods separately.

[0022] Secondly, the present invention provides a power consumption calculation device that considers the power consumption characteristics of industrial users, comprising:

[0023] The daily electricity consumption time series establishment module is used to obtain the user's electricity consumption data information from the previous year and obtain the complete annual daily electricity consumption time series based on the electricity consumption data information.

[0024] The date-operating condition mapping database construction module is used to perform feature mining and operating condition identification on the daily electricity consumption time series using image recognition-based clustering analysis methods, and generate an electricity consumption feature database with operating condition labels, namely the date-operating condition mapping database.

[0025] The operating condition determination module is used to obtain the user's fault time period, retrieve the operating condition tag corresponding to the specific fault time in the date-operating condition mapping database within the fault time period, and identify the operating condition determination result corresponding to the specific fault time.

[0026] The reference cycle matching module is used to analyze the timing characteristics of the fault period in the production cycle, and then, combined with the working condition judgment results, dynamically match the total power consumption data of the corresponding timing in the most recent working condition cycle of the same type before the fault occurred.

[0027] The power consumption calculation module is used to obtain the total power consumption measured by the electricity meter during the fault period. It calculates the difference between the total power consumption data of the corresponding time sequence and the total power consumption measured by the electricity meter to obtain the power consumption to be refunded or compensated during the fault period.

[0028] Secondly, the present invention provides a computer-readable storage medium having a computer program / instruction stored thereon, which, when executed by a processor, implements the aforementioned calculation method considering the electricity consumption characteristics of industrial users.

[0029] The beneficial effects of this invention are as follows: This invention provides a calculation method, device, and storage medium that considers the electricity consumption characteristics of industrial users. Firstly, it employs an identification method based on daily electricity consumption time series to accurately identify the user's peak and off-peak operating conditions. Secondly, through a dynamic time-series matching mechanism, it achieves intelligent selection of the same-period electricity benchmark value, ensuring the spatiotemporal consistency of the reference data. For cross-cycle fault scenarios, it innovatively proposes a segmented calculation method for different operating conditions, using a dual benchmark value system for differentiated processing, effectively eliminating calculation deviations caused by a single benchmark value. Furthermore, this invention, through non-intrusive data analysis, achieves intelligent, accurate, and automated calculation of refund and compensation electricity consumption without affecting the user's normal production. The calculation reliability is significantly improved compared to traditional methods. Moreover, compared to the traditional correction coefficient method, this invention is applicable to different scenarios, overcoming the application limitations of traditional methods in typical fault scenarios such as current loss and meter stoppage, achieving full operating condition coverage, providing scientific and reliable technical support for electricity marketing and settlement, and possessing significant engineering application value and market promotion prospects. Attached Figure Description

[0030] Figure 1 This is a flowchart illustrating a method for calculating electricity refunds and subsidies that takes into account the electricity consumption characteristics of industrial users, according to the present invention. Detailed Implementation

[0031] The present invention will be further described below with reference to the accompanying drawings. The following embodiments are only used to illustrate the technical solution of the present invention more clearly, and should not be used to limit the scope of protection of the present invention.

[0032] Example 1

[0033] like Figure 1 As shown, this invention discloses a method for calculating electricity refunds and subsidies that takes into account the electricity consumption characteristics of industrial users, including the following steps:

[0034] Step 1: Obtain the user's electricity consumption data for the previous year, and obtain the daily electricity consumption time series for the complete year based on the electricity consumption data.

[0035] Step two involves using image recognition-based clustering analysis to perform feature mining and operating condition identification on the daily electricity consumption time series, automatically dividing it into two typical production condition categories: off-peak and peak seasons. This generates an electricity consumption feature database with operating condition labels (off-peak and peak seasons), i.e., a date-operating condition mapping database. In this embodiment, the K-means algorithm is preferred as the clustering analysis method. To improve the algorithm's adaptability, the method also provides a manual correction interface, allowing maintenance personnel to manually adjust and optimize production condition parameters according to actual conditions.

[0036] Step three involves employing intelligent production condition discrimination technology based on time-series features. This involves acquiring the user's fault time period, retrieving the corresponding condition tag in the date-condition mapping database for the specific fault time within that period, and identifying the condition judgment result corresponding to the specific fault time, thereby achieving accurate identification of the production condition during the fault period.

[0037] Step four involves adopting an adaptive synchronous power consumption benchmark selection mechanism based on user production conditions. The timing characteristics of the fault period within the production cycle are analyzed, and combined with the operating condition determination results (off-season / peak season), the total power consumption data for the corresponding time period in the most recent cycle of the same type of operating condition (off-season / peak season) before the fault occurs is dynamically matched and used as the synchronous power consumption benchmark reference value. The total power consumption data for the corresponding time period includes the synchronous power consumption of the previous normal off-season (equal in length to the off-season fault period) and the synchronous power consumption of the previous normal peak season (equal in length to the peak season fault period).

[0038] Step 5: Obtain the total electricity consumption measured by the electricity meter for the corresponding time period of the fault. Subtract the total electricity consumption data for the corresponding time period from the total electricity consumption measured by the electricity meter to obtain the refund / refund amount for the fault period. The total electricity consumption measured by the electricity meter includes the total off-season electricity consumption measured by the electricity meter during the fault period and the total peak-season electricity consumption measured by the electricity meter during the fault period.

[0039] A segmented calculation strategy is adopted for the refund / refund electricity volume. Considering that the duration of the fault may span multiple production periods, the refund / refund electricity volume for off-peak and peak seasons is calculated separately. The calculation formula for the refund / refund electricity volume during the fault period is as follows:

[0040]

[0041] in, The amount of electricity refunded or replenished during the fault period. To match the electricity consumption during the same period of the previous normal off-season, which is of equal length to the off-season fault period. To match the electricity consumption during the same period of the previous normal peak season, which is of equal length to the peak season outage period. This represents the total off-season electricity consumption as measured by the meter during the period of the power outage. This represents the total peak season electricity consumption measured by the meter during the fault period.

[0042] This invention first employs a daily electricity consumption time series-based identification method to accurately identify users' peak and off-peak operating conditions. Secondly, through a dynamic time-series matching mechanism, it achieves intelligent selection of the same-period electricity consumption benchmark value, ensuring the spatiotemporal consistency of the reference data. For cross-cycle fault scenarios, it innovatively proposes a segmented calculation method for different operating conditions, using a dual benchmark value system for differentiated processing, effectively eliminating calculation deviations caused by a single benchmark value. Furthermore, this invention utilizes non-intrusive data analysis methods to achieve intelligent, accurate, and automated calculation of refund and compensation electricity consumption without affecting users' normal production. The calculation reliability is significantly improved compared to traditional methods. Moreover, compared to the traditional correction coefficient method, this invention is applicable to different scenarios, overcoming the application limitations of traditional methods in typical fault scenarios such as current loss and meter stoppage, achieving full operating condition coverage. It provides scientific and reliable technical support for electricity marketing and settlement, possessing significant engineering application value and market promotion prospects.

[0043] Example 2

[0044] This embodiment discloses a specific application based on embodiment 1, including the following steps:

[0045] Step 1: Obtain the user's fault time and electricity consumption data. The meter fault time is from 2024 / 08 / 25 to 2024 / 09 / 15.

[0046] Step two: Obtain the user's daily electricity consumption time series for 2023, and use the K-means clustering analysis method to cluster the daily electricity consumption data for this year, resulting in the following date-operating condition mapping database:

[0047] Table 1 Date-Condition Mapping Database

[0048]

[0049] Step 3: According to the table above, the period from August 25th to August 31st is the peak season, and the period from September 1st to September 15th is the off-season.

[0050] Step 4: Select the user's total electricity consumption from July 25th to July 31st as the peak season electricity consumption. =18592kWh; the total electricity consumption during the period from June 1st to June 15th was selected as the off-season electricity consumption. =26295 kWh.

[0051] Step 5: Total peak season electricity consumption measured by the meter during the fault period: =270 kWh, the total off-season electricity consumption measured by the meter during the fault period: =1005 kWh, refund and replenishment power: ∆W=18592-270+26295-1005=43972(kWh).

[0052] Example 3

[0053] This embodiment discloses a power consumption calculation device that considers the power consumption characteristics of industrial users, including:

[0054] The daily electricity consumption time series establishment module is used to obtain the user's electricity consumption data information from the previous year and obtain the complete annual daily electricity consumption time series based on the electricity consumption data information.

[0055] The date-operating condition mapping database construction module is used to perform feature mining and operating condition identification on the daily electricity consumption time series using image recognition-based clustering analysis methods, and generate an electricity consumption feature database with operating condition labels, namely the date-operating condition mapping database.

[0056] The operating condition determination module is used to obtain the user's fault time period, retrieve the operating condition tag corresponding to the specific fault time in the date-operating condition mapping database within the fault time period, and identify the operating condition determination result corresponding to the specific fault time.

[0057] The reference cycle matching module is used to analyze the timing characteristics of the fault period in the production cycle, and then, combined with the working condition judgment results, dynamically match the total power consumption data of the corresponding timing in the most recent working condition cycle of the same type before the fault occurred.

[0058] The power consumption calculation module is used to obtain the total power consumption measured by the electricity meter during the fault period. It calculates the difference between the total power consumption data of the corresponding time sequence and the total power consumption measured by the electricity meter to obtain the power consumption to be refunded or compensated during the fault period.

[0059] Example 4

[0060] This embodiment discloses a computer-readable storage medium storing a computer program / instruction. When the computer program / instruction is executed by a processor, it implements the method for calculating the electricity consumption of industrial users that takes into account the electricity consumption characteristics of industrial users in Embodiment 1 or 2.

[0061] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0062] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0063] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0064] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A calculation method considering the electricity consumption characteristics of industrial users, characterized in that: Includes the following steps: Obtain the user's electricity consumption data from the previous year, and obtain the complete daily electricity consumption time series for the entire year based on the electricity consumption data; A clustering analysis method based on image recognition is used to perform feature mining and operating condition identification on the daily electricity consumption time series, generating an electricity consumption feature database with operating condition labels, namely a date-operating condition mapping database. Obtain the user's fault time period, retrieve the corresponding operating condition tag in the date-operating condition mapping database for the specific fault time within the fault time period, and identify the operating condition judgment result corresponding to the specific fault time. Analyze the timing characteristics of the fault period in the production cycle, and then combine the operating condition judgment results to dynamically match the total power consumption data of the corresponding timing in the most recent operating condition cycle of the same type before the fault occurred. Obtain the total electricity consumption measured by the electricity meter during the fault period, and subtract the total electricity consumption data of the corresponding time period from the total electricity consumption measured by the electricity meter to obtain the refund and compensation electricity during the fault period.

2. The calculation method considering the electricity consumption characteristics of industrial users according to claim 1, characterized in that: The clustering analysis method includes the K-means algorithm.

3. The calculation method considering the electricity consumption characteristics of industrial users according to claim 1, characterized in that: The date-operating condition mapping database categorizes operating conditions into off-peak and peak seasons.

4. The calculation method considering the electricity consumption characteristics of industrial users according to claim 3, characterized in that: The total electricity consumption data corresponding to the time sequence includes the electricity consumption during the same period of the previous normal off-season, which is the same length as the off-season fault period, and the electricity consumption during the same period of the previous normal peak season, which is the same length as the peak season fault period.

5. The calculation method considering the electricity consumption characteristics of industrial users according to claim 4, characterized in that: The total electricity consumption measured by the meter includes the total electricity consumption during the off-season and the total electricity consumption during the peak season, as measured by the meter during the fault period.

6. The calculation method considering the electricity consumption characteristics of industrial users according to claim 5, characterized in that: The formula for calculating the refund / refund amount during the fault period is as follows: (This involves subtracting the total electricity consumption data for the corresponding time period from the total electricity consumption measured by the meter.) in, The amount of electricity refunded or replenished during the fault period. To match the electricity consumption during the same period of the previous normal off-season, which is of equal length to the off-season fault period. To match the electricity consumption during the same period of the previous normal peak season, which is of equal length to the peak season outage period. This represents the total off-season electricity consumption as measured by the meter during the period of the power outage. This represents the total peak season electricity consumption measured by the meter during the fault period.

7. The calculation method considering the electricity consumption characteristics of industrial users according to claim 1, characterized in that: After obtaining the date-operation condition mapping database, the operations and maintenance personnel manually adjust the production operation condition parameters in the date-operation condition mapping database according to the actual situation.

8. The calculation method considering the electricity consumption characteristics of industrial users according to claim 1, characterized in that: The analysis of the timing characteristics of the fault time period in the production cycle specifically involves dividing the fault time period into multiple production working condition periods according to the working condition information in the date-working condition mapping database, and calculating the different production working condition periods separately.

9. A calculation device considering the electricity consumption characteristics of industrial users, characterized in that: include: The daily electricity consumption time series establishment module is used to obtain the user's electricity consumption data information from the previous year and obtain the complete annual daily electricity consumption time series based on the electricity consumption data information. The date-operating condition mapping database construction module is used to perform feature mining and operating condition identification on the daily electricity consumption time series using image recognition-based clustering analysis methods, and generate an electricity consumption feature database with operating condition labels, namely the date-operating condition mapping database. The operating condition determination module is used to obtain the user's fault time period, retrieve the operating condition tag corresponding to the specific fault time in the date-operating condition mapping database within the fault time period, and identify the operating condition determination result corresponding to the specific fault time. The reference cycle matching module is used to analyze the timing characteristics of the fault period in the production cycle, and then, combined with the working condition judgment results, dynamically match the total power consumption data of the corresponding timing in the most recent working condition cycle of the same type before the fault occurred. The power consumption calculation module is used to obtain the total power consumption measured by the electricity meter during the fault period. It calculates the difference between the total power consumption data of the corresponding time sequence and the total power consumption measured by the electricity meter to obtain the power consumption to be refunded or compensated during the fault period.

10. A computer-readable storage medium having a computer program / instructions stored thereon, characterized in that: When the computer program / instruction is executed by the processor, it implements the calculation method for considering the electricity consumption characteristics of industrial users as described in any one of claims 1-8.

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