A Method and Device for Verifying Settlement Data of a Distributed Photovoltaic Power Station

Abstract

The present disclosure relates to the field of energy technologies, and provides a method and device for verifying settlement data of a distributed photovoltaic power station. The method includes: obtaining predicted settlement data according to the self-generated power and the predicted difference rate obtained through a difference rate prediction model; obtaining an actual difference rate based on the predicted settlement data and the obtained actual settlement data, and determining whether the actual difference rate is greater than a first preset threshold; if the actual difference rate is greater than the first preset threshold, obtaining a power generation difference rate according to the actual settlement power and the predicted power generation obtained through a power generation prediction model; if the power generation difference rate is greater than a second preset threshold, performing data verification with the settlement party based on the actual settlement data. The present disclosure can achieve the verification of settlement data, thereby ensuring the accuracy of the settlement data of the distributed photovoltaic power station.

Description

Technical Field

[0001] The present disclosure relates to the field of energy technologies, and in particular, to a method and device for verifying settlement data of a distributed photovoltaic power station. Background Art

[0002] At present, the utilization of clean energy has received increasing attention. Due to the ability to make full use of solar energy, photovoltaic power generation technology has gained more and more market favor in the field of energy technologies. Photovoltaic power stations mainly include large-scale centralized photovoltaic power stations and distributed photovoltaic power stations. Among them, a distributed photovoltaic power station system that directly converts solar energy into electrical energy can be connected to the public power grid and supply power to nearby users together with the public power grid.

[0003] In order to encourage the popularization and operation of distributed photovoltaic power stations, it is very important to provide subsidies for distributed photovoltaic power stations connected to the public power grid for power supply. At present, the subsidy is mainly carried out in the form of monthly settlement, and the settlement data is obtained based on the power generation of the distributed photovoltaic power station. Therefore, the accurate calculation of the power generation of the distributed photovoltaic power station is particularly important. However, in the prior art, the settlement data of the distributed photovoltaic power station cannot be verified, so the accuracy of the settlement data cannot be known. Summary of the Invention

[0004] In view of this, embodiments of the present disclosure provide a method, device, electronic device and computer-readable storage medium for verifying settlement data of a distributed photovoltaic power station, so as to solve the problem that the settlement data of the distributed photovoltaic power station cannot be verified in the prior art, and thus the accuracy of the settlement data cannot be known.

[0005] In a first aspect of the embodiments of the present disclosure, a method for verifying settlement data of a distributed photovoltaic power station is provided, including:

[0006] Obtaining predicted settlement data according to the self-generated power and the predicted difference rate obtained by the difference rate prediction model;

[0007] Based on the predicted settlement data and the obtained actual settlement data, obtaining an actual difference rate, and determining whether the actual difference rate is greater than a first preset threshold;

[0008] If the actual difference rate is greater than the first preset threshold, obtaining a power generation difference rate according to the actual settlement power and the predicted power generation obtained by the power generation prediction model;

[0009] If the power generation difference rate is greater than a second preset threshold, verifying data with the settlement party based on the actual settlement data.

[0010] In a second aspect of the embodiments of the present disclosure, a device for verifying settlement data of a distributed photovoltaic power station is provided, including:

[0011] A predicted settlement data acquisition module, configured to acquire predicted settlement data according to the self-generated power and the predicted difference rate obtained by a difference rate prediction model;

[0012] An actual difference rate acquisition module, configured to acquire an actual difference rate based on the predicted settlement data and the acquired actual settlement data, and determine whether the actual difference rate is greater than a first preset threshold;

[0013] A power generation difference rate acquisition module, configured to, if the actual difference rate is greater than the first preset threshold, acquire a power generation difference rate according to the actual settlement power and the predicted power generation obtained by a power generation prediction model;

[0014] A data verification module, configured to, if the power generation difference rate is greater than a second preset threshold, perform data verification with a settlement party based on the actual settlement data.

[0015] In a third aspect of the embodiments of the present disclosure, there is provided an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the above method are implemented.

[0016] In a fourth aspect of the embodiments of the present disclosure, there is provided a computer-readable storage medium storing a computer program, and when the computer program is executed by a processor, the steps of the above method are implemented.

[0017] The beneficial effects of the embodiments of the present disclosure compared with the prior art at least include: The embodiments of the present disclosure first acquire predicted settlement data according to the self-generated power and the predicted difference rate obtained by a difference rate prediction model, and initially determine the rationality of the settlement data by determining whether the actual difference rate is within a reasonable range. When it is not within the reasonable range, then according to the actual settlement power and the predicted power generation obtained by a power generation prediction model, a power generation difference rate is acquired, and the rationality of the settlement data is determined again by whether the power generation difference rate is within a reasonable range, so as to accurately obtain the accuracy of the settlement data and realize the verification of the settlement data. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions in the embodiments of the present disclosure, the following will briefly introduce the drawings required for use in the embodiments or the description of the prior art. Obviously, the following drawings are only some embodiments of the present disclosure. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0019] Figure 1 It is a schematic diagram of the application scenario of the embodiments of the present disclosure;

[0020] Figure 2It is a schematic flowchart of a method for verifying settlement data of a distributed photovoltaic power station provided by an embodiment of the present disclosure;

[0021] Figure 3 It is a schematic flowchart of a specific embodiment of a method for verifying settlement data of a distributed photovoltaic power station provided by an embodiment of the present disclosure;

[0022] Figure 4 It is a schematic flowchart of a specific embodiment of another method for verifying settlement data of a distributed photovoltaic power station provided by an embodiment of the present disclosure;

[0023] Figure 5 It is a schematic diagram of a device for verifying settlement data of a distributed photovoltaic power station provided by an embodiment of the present disclosure;

[0024] Figure 6 It is a schematic structural diagram of an electronic device provided by an embodiment of the present disclosure. Detailed implementation manners

[0025] In the following description, specific details such as specific system architectures and technologies are presented for the purpose of illustration rather than limitation, so as to thoroughly understand the embodiments of the present disclosure. However, those skilled in the art should clearly understand that the present disclosure can also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to avoid unnecessary details from interfering with the description of the present disclosure.

[0026] Hereinafter, a method and a device for verifying settlement data of a distributed photovoltaic power station according to an embodiment of the present disclosure will be described in detail with reference to the accompanying drawings.

[0027] During the operation of a distributed photovoltaic power station, subsidies play a very important role in the promotion of distributed photovoltaic power stations, enabling photovoltaic power station users who supply electricity to have higher enthusiasm for participation, thereby encouraging more distributed photovoltaic power stations to be connected to the public grid. Currently, subsidies are mainly distributed in a monthly settlement manner. A grid settlement meter is set on the path where the photovoltaic power station is connected to the public grid. This grid settlement meter is used to measure the actual settlement electricity quantity for subsidy settlement on a monthly basis. The settlement party obtains the actual settlement subsidy based on the actual settlement electricity quantity measured by this grid settlement meter and the actual revenue unit price. During the process of obtaining the actual settlement subsidy, distributed photovoltaic power station users cannot obtain the power generation quantity of the distributed photovoltaic power station, thus unable to verify the actual settlement electricity quantity, and further unable to verify the actual settlement subsidy, and the accuracy of the settlement data cannot be obtained.

[0028] To verify the accuracy of the settlement data, an embodiment of the present disclosure proposes a brand-new method for verifying the settlement data of a distributed photovoltaic power station. To obtain the power generation of the distributed photovoltaic power station, in the embodiment of the present disclosure, a self-measuring electric meter is installed in parallel with the grid settlement meter on the path where the photovoltaic power station is connected to the public grid, and the self-measuring electric meter is used to measure the power data of the photovoltaic power station in real time (denoted as self-measured power generation). Figure 1 It is a schematic diagram of the application scenario of the embodiment of the present disclosure. In this scenario, the terminal device 10 may be included. The terminal device 10 may be hardware or software. When the terminal device 10 is hardware, it may be various electronic devices with a display screen and supporting data processing, including but not limited to smart phones, tablet computers, laptop portable computers, and desktop computers, etc.; when the terminal device 10 is software, it may be installed in the above-mentioned electronic devices. The terminal device 10 may be implemented as multiple software or software modules, or may be implemented as a single software or software module, and the embodiment of the present disclosure does not limit this. Of course, the embodiment of the present invention may also be applied to a server.

[0029] Specifically, in the Figure 1 shown application scenario, the self-measured power generation measured by the self-measuring electric meter, the actual settlement power measured by the grid settlement meter, and the actual settlement data provided by the settlement party are input into the terminal device 10. A pre-constructed difference rate prediction model and a power generation prediction model are set in the terminal device 10. After the terminal device 10 obtains the data, it obtains the predicted settlement data based on the self-measured power generation and the predicted difference rate obtained through the difference rate prediction model, and compares the obtained predicted settlement data with the actual settlement data to confirm whether the difference between the two is within a reasonable range; if it is not within a reasonable range, then further compare the actual settlement power with the predicted power generation obtained through the power generation prediction model to confirm whether the difference between the two is within a reasonable range; if it is still not within a reasonable range, it means that the accuracy of the actual settlement data is in doubt and it is necessary to further verify the data with the settlement party to ensure the accuracy of the settlement data. It should be understood that the actual settlement data in the embodiment of the present disclosure may be actual settlement subsidies or actual settlement power. Since the actual settlement subsidies are calculated based on the actual settlement power and the actual revenue unit price, the verification of the actual settlement subsidies is also the verification of the actual settlement power.

[0030] It should be noted that the specific type, quantity, and combination of the terminal device 10 can be adjusted according to the actual needs of the application scenario, and the embodiment of the present disclosure does not limit this.

[0031] It should be noted that the above application scenario is only shown for the convenience of understanding the present disclosure, and the embodiments of the present disclosure are not limited in this regard. On the contrary, the embodiments of the present disclosure can be applied to any applicable scenario.

[0032] Figure 2 It is a schematic flowchart of a method for verifying settlement data of a distributed photovoltaic power station provided by an embodiment of the present disclosure. Figure 2 The method for verifying settlement data of the Figure 1 distributed photovoltaic power station can be executed by the Figure 2 terminal device 10. As

[0033] shown, the method for verifying settlement data of the distributed photovoltaic power station includes the following steps:

[0034] During the operation of the distributed photovoltaic power station, the self - measured electricity meter can measure the electricity generation of the photovoltaic power station in real time, so as to obtain the self - measured electricity generation of the photovoltaic power station in each settlement period. Since the self - measured electricity generation is usually not the same as the actual settlement electricity measured by the grid settlement meter, and the actual settlement electricity in each settlement period and the actual settlement data based on the actual settlement electricity can only be obtained until the settlement, therefore, in order to accurately predict the actual settlement data, the embodiment of the present disclosure constructs a difference rate prediction model based on the historical self - measured electricity generation and the historical actual settlement electricity, so as to predict the prediction difference rate between the self - measured electricity generation and the actual settlement electricity in the current settlement period through the difference rate prediction model, and thus the predicted settlement electricity can be obtained based on the self - measured electricity generation and the prediction difference rate of the current period to obtain the predicted settlement data. The predicted settlement data can be the predicted settlement electricity (predicted settlement electricity=(prediction difference rate + 1)*self - measured electricity generation), or it can be the predicted settlement subsidy. When the predicted settlement data is the predicted settlement subsidy, it can be obtained according to the obtained predicted settlement electricity combined with the revenue unit price (predicted settlement subsidy = predicted settlement electricity * revenue unit price).

[0035] In this embodiment, the difference rate prediction model can be obtained in the following way: obtain the actual settlement electricity and the self - measured electricity generation of each settlement period recorded historically; determine the electricity generation difference rate according to the actual settlement electricity and the self - measured electricity generation of each settlement period, where the electricity generation difference rate=(self - measured electricity generation - actual settlement electricity) / actual settlement electricity; train the difference rate prediction model based on the actual settlement electricity of each settlement period, the self - measured electricity generation of each settlement period and the electricity generation difference rate to obtain the trained difference rate prediction model. Among them, when obtaining the electricity generation difference rate, a verification table can be established, and the verification table includes the data acquisition time, the self - measured electricity generation, the actual settlement electricity and the electricity generation difference rate calculated according to the self - measured electricity generation and the actual settlement electricity, so as to establish the reference data for model training. Usually, the verification table is designed in a reasonable form, which can clearly display the verification data and the calculation results, and is convenient for quickly obtaining the required key data.

[0036] It is understandable that during the operation of a distributed photovoltaic power station, as the operation time increases and the settlement cycle increases, the actual settlement power, self-generated power, and power generation difference rate data will also increase. By continuously training the difference rate prediction model with the increasing data, the difference rate prediction model can be continuously optimized to improve the prediction accuracy of the difference rate prediction model.

[0037] S202, based on the predicted settlement data and the obtained actual settlement data, obtain the actual difference rate and determine whether the actual difference rate is greater than the first preset threshold.

[0038] In this embodiment, the actual difference rate = (predicted settlement data - actual settlement data) / actual settlement data, where the actual settlement data and the predicted settlement data are of the same type of data. For example, both the predicted settlement data and the actual settlement data can be power data, that is, the predicted settlement data is the predicted settlement power, and the actual settlement data is the actual settlement power. At this time, the obtained actual difference rate is the difference rate between the predicted settlement power and the actual settlement power. Another example is that both the predicted settlement data and the actual settlement data can be subsidy data, that is, the predicted settlement data is the predicted settlement subsidy (the product of the predicted settlement power and the revenue unit price), and the actual settlement data is the actual settlement subsidy (the product of the actual settlement power and the actual revenue unit price). At this time, the obtained actual difference rate is the difference rate between the predicted settlement subsidy and the actual settlement subsidy.

[0039] When the predicted settlement data is subsidy data, in order to obtain the subsidy data, the revenue unit price and the actual revenue unit price need to be obtained. First, based on the data platform, the Common Information Model (CIM) platform, and the algorithm platform (providing algorithm models), a subsidy database is constructed according to the revenue unit price of distributed photovoltaic power station subsidies in each region. The coverage range can be set as needed. For example, it can be within the scope of the whole country, within the scope of provinces and cities, or within a smaller scope. There is no limit here. Then, when calculating the predicted settlement data, the location information of the distributed photovoltaic power station can be determined first, and then the corresponding revenue unit price can be selected based on the region where the distributed photovoltaic power station is located. It is understandable that the revenue unit prices in each region may vary greatly, and at the same time, the revenue unit price in the same region may also be adjusted over time. Therefore, the revenue unit prices in each region in the subsidy database can also be updated, either automatically or by staff updating the corresponding data according to the updated information. When the actual settlement data is subsidy data, the actual revenue unit price can be calculated through the actual settlement subsidy provided by the settlement party and the actual settlement power measured by the grid settlement form. Considering that the subsidy database may be updated in a timely manner, there may be a difference between the revenue unit price selected from the subsidy database and the actual revenue unit price.

[0040] After obtaining the actual difference rate, it is necessary to determine whether the actual difference rate is greater than the first preset threshold. The first preset threshold is the threshold of the actual difference rate, which characterizes whether the actual difference rate meets the preset requirements. If the actual difference rate is less than or equal to the first preset threshold, it means that the difference between the predicted settlement data and the actual settlement data is within a reasonable range. At this time, the settlement data verification process ends, and step S201 is returned to wait for the next settlement data verification according to the settlement cycle.

[0041] S203. If the actual difference rate is greater than the first preset threshold, then based on the actual settlement power consumption and the predicted power generation obtained through the power generation prediction model, obtain the power generation difference rate.

[0042] When the actual difference rate is greater than the first preset threshold, it means that the difference between the predicted settlement data and the actual settlement data is not within a reasonable range. At this time, it is necessary to continue the verification of the settlement data.

[0043] When both the actual settlement data and the predicted settlement data are subsidy data, considering that there may be a difference between the revenue unit price selected from the subsidy database and the actual revenue unit price, before obtaining the power generation difference rate, it is necessary to determine the actual revenue unit price according to the actual settlement subsidy and the actual settlement power consumption measured by the grid settlement table, and judge whether the actual revenue unit price is consistent with the revenue unit price. If the actual revenue unit price is not consistent with the revenue unit price, it means that the above actual difference rate may not be within a reasonable range due to the inconsistency of the revenue unit price. At this time, it is necessary to update the revenue unit price to the actual revenue unit price and return to step S201 to update the revenue unit price in the subsidy database while verifying the settlement data. If the actual revenue unit price is consistent with the revenue unit price, it means that the above actual difference rate is not within a reasonable range not due to the inconsistency of the revenue unit price. At this time, perform the step of obtaining the power generation difference rate according to the actual settlement power consumption and the predicted power generation obtained through the settlement power generation prediction model.

[0044] The power generation prediction model is trained according to the historically recorded actual settlement power consumption, so as to accurately predict the power consumption for actual settlement in each settlement cycle. In this embodiment, the power generation prediction model can be obtained in the following way: obtain the actual settlement power consumption of each settlement cycle in the historical record; based on the settlement cycle and the actual settlement power consumption corresponding to each settlement cycle, train the power generation prediction model to obtain the trained power generation prediction model. It can be understood that during the operation of the distributed photovoltaic power station, as the operation time increases and the settlement cycle increases, the actual settlement power consumption data will also increase. By continuously training the power generation prediction model with the increasing data, the power generation prediction model can be continuously optimized to improve the prediction accuracy of the power generation prediction model.

[0045] After obtaining the actual settlement power consumption through the grid settlement meter and the predicted power generation through the power generation prediction model, the power generation difference rate can be calculated. The power generation difference rate = (predicted power generation - actual settlement power consumption) / actual settlement power consumption, which ensures the gap between the predicted power generation and the actual settlement power consumption. The smaller the value of the power generation difference rate, the closer the predicted power generation is to the actual settlement power consumption. The threshold of the power generation difference rate (i.e., the second preset threshold) can be set as needed, for example, it can be 0.01 - 0.05, specifically it can be 0.01, 0.02, 0.03, 0.04, 0.05, etc.

[0046] After obtaining the power generation difference rate, it is necessary to further confirm whether the power generation difference rate is within a reasonable range. Therefore, it is necessary to judge the relationship between the power generation difference rate and the second preset threshold.

[0047] S204, if the power generation difference rate is greater than the second preset threshold, data verification is performed with the settlement party based on the actual settlement data.

[0048] If the power generation difference rate is greater than the second preset threshold, it means that the power generation difference rate is not within a reasonable range, and further means that the actual settlement power consumption related to the power generation difference rate may be unreasonable. Therefore, data verification is performed with the settlement party based on the actual settlement data. When the actual settlement data is the actual settlement subsidy, the difference between the actual settlement subsidy and the settlement subsidy calculated according to the self-generated power and the revenue unit price can be verified with the settlement party to finally obtain the verified settlement data.

[0049] If the power generation difference rate is less than or equal to the second preset threshold, it means that the power generation difference rate is within a reasonable range, and further means that the actual settlement power consumption related to the power generation difference rate is reasonable, and also means that the actual settlement data provided by the settlement party is reasonable. At this time, the settlement data verification ends. Since there is a deviation in the predicted difference rate obtained through the previously obtained difference rate prediction model, it is necessary to optimize the difference rate prediction model according to the actual difference rate to update the difference rate prediction model and improve the accuracy of the difference rate prediction model; at the same time, check the measurement accuracy of the self-purchased electricity meter and adjust the measurement accuracy of the self-purchased electricity meter based on the inspection results to ensure the measurement accuracy of the self-purchased electricity meter.

[0050] The beneficial effects of the settlement data verification method for the distributed photovoltaic power station provided by the embodiments of the present disclosure at least include:

[0051] (1) In the embodiments of the present disclosure, first, according to the self-generated power and the predicted difference rate obtained through the difference rate prediction model, predicted settlement data is obtained, and the rationality of the settlement data is initially determined by determining whether the actual difference rate is within a reasonable range. When it is not within the reasonable range, the power generation difference rate is obtained based on the actual settlement power and the predicted power generation obtained through the power generation prediction model, and the rationality of the settlement data is determined again by whether the power generation difference rate is within the reasonable range. The accuracy of the settlement data can be accurately obtained, the verification of the settlement data can be realized, and at the same time, the settlement data can be predicted, so as to know the expected subsidy income.

[0052] (2) In the embodiments of the present disclosure, by constructing a subsidy database, the revenue unit price of the subsidy can be quickly obtained. During the verification process of the settlement data, the data in the subsidy database can be updated by comparing the revenue unit price and the actual revenue unit price, which can not only improve the accuracy of the settlement data verification but also improve the accuracy of the subsidy database.

[0053] (3) In the embodiments of the present disclosure, the difference rate prediction model is trained with the continuously enriched actual settlement power, self-generated power, and power generation difference rate data, so that the difference rate prediction model can be continuously optimized and the prediction accuracy of the difference rate prediction model can be improved.

[0054] (4) In the embodiments of the present disclosure, the power generation prediction model is trained with the continuously enriched actual settlement power data, so that the power generation prediction model can be continuously optimized and the prediction accuracy of the power generation prediction model can be improved.

[0055] Several specific embodiments of the settlement data verification method for a distributed photovoltaic power station are given below. It should be understood that the following embodiments are only used to illustrate the settlement data verification method for a distributed photovoltaic power station and do not limit the protection scope.

[0056] Figure 3 It is a schematic flow chart of a specific embodiment of the settlement data verification method for a distributed photovoltaic power station provided by the embodiments of the present disclosure. In this embodiment, the predicted settlement data includes predicted settlement power, and the actual settlement data includes actual settlement power. As Figure 3 shown, it includes the following steps:

[0057] S301, based on the settlement period, obtain the self-generated power in the current settlement period and the actual settlement power measured by the grid settlement form;

[0058] S302, obtain the predicted difference rate in the current settlement period through the difference rate prediction model;

[0059] S303, determine the predicted settlement power according to the self-generated power and the predicted difference rate;

[0060] S304. Obtain the actual difference rate based on the predicted settlement power consumption and the actual settlement power consumption;

[0061] S305. Determine whether the actual difference rate is greater than the first preset threshold;

[0062] If the actual difference rate is not greater than the first preset threshold, then:

[0063] S306. End the verification of the current settlement data and return to step S301;

[0064] If the actual difference rate is greater than the first preset threshold, then:

[0065] S307. Obtain the power generation difference rate based on the actual settlement power consumption and the predicted power generation obtained through the power generation prediction model;

[0066] S308. Determine whether the power generation difference rate is greater than the second preset threshold;

[0067] If the power generation difference rate is not greater than the second preset threshold, then:

[0068] S309. End the verification of the current settlement data, optimize the difference rate prediction model based on the actual difference rate to update the difference rate prediction model;

[0069] S310. Check the measurement accuracy of the self - collected electricity meter and adjust the measurement accuracy of the self - collected electricity meter based on the inspection result.

[0070] If the power generation difference rate is greater than the second preset threshold, then:

[0071] S311. Conduct data verification with the settlement party based on the actual settlement data.

[0072] Figure 4 It is a flowchart of another method for verifying settlement data of a distributed photovoltaic power station provided by an embodiment of the present disclosure. In this embodiment, the predicted settlement data includes predicted settlement subsidies, and the actual settlement data includes actual settlement subsidies. As Figure 4 shown, it includes the following steps:

[0073] S401. Based on the settlement period, obtain the self - generated power in the current settlement period, the actual settlement power consumption measured by the grid settlement meter, and the actual settlement subsidy provided by the settlement party;

[0074] S402. Obtain the predicted difference rate of the current settlement period through the difference rate prediction model;

[0075] S403. Determine the predicted settlement power consumption according to the self - generated power and the predicted difference rate;

[0076] S404. Obtain the predicted settlement subsidy according to the predicted settlement power consumption and the revenue unit price in the subsidy database;

[0077] S405. Obtain the actual difference rate based on the predicted settlement subsidy and the actual settlement subsidy.

[0078] S406. Determine whether the actual difference rate is greater than the first preset threshold.

[0079] If the actual difference rate is not greater than the first preset threshold, then:

[0080] S407. End the verification of the current settlement data and return to step S401.

[0081] If the actual difference rate is greater than the first preset threshold, then:

[0082] S408. Determine the actual revenue unit price based on the actual settlement subsidy and the actual settlement power consumption measured by the grid settlement form.

[0083] S409. Determine whether the actual revenue unit price is consistent with the revenue unit price.

[0084] If the actual revenue unit price is not consistent with the revenue unit price, then:

[0085] S410. Update the revenue unit price to the actual revenue unit price and return to step S404.

[0086] If the actual revenue unit price is consistent with the revenue unit price, then:

[0087] S411. Obtain the power generation difference rate based on the actual settlement power consumption and the predicted power generation obtained through the settlement power generation prediction model.

[0088] S412. Determine whether the power generation difference rate is greater than the second preset threshold.

[0089] If the power generation difference rate is not greater than the second preset threshold, then:

[0090] S413. End the verification of the current settlement data, optimize the difference rate prediction model based on the actual difference rate to update the difference rate prediction model.

[0091] S414. Check the measurement accuracy of the self - installed electricity meter and adjust the measurement accuracy of the self - installed electricity meter based on the inspection result.

[0092] If the power generation difference rate is greater than the second preset threshold, then:

[0093] S415. Conduct data verification with the settlement party based on the actual settlement data.

[0094] All of the above optional technical solutions can be combined arbitrarily to form optional embodiments of the present application, which will not be elaborated one by one here. It should be understood that the sequence numbers of the steps in the above embodiments do not indicate the order of execution, and the execution order of each process should be determined according to its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present disclosure.

[0095] The following is an embodiment of the device of the present disclosure, which can be used to execute the method embodiment of the present disclosure. For details not disclosed in the device embodiment of the present disclosure, please refer to the method embodiment of the present disclosure.

[0096] Figure 5 It is a schematic diagram of a device for verifying settlement data of a distributed photovoltaic power station provided by an embodiment of the present disclosure. As Figure 5 shown, the device for verifying settlement data of the distributed photovoltaic power station includes:

[0097] A predicted settlement data acquisition module 501, configured to acquire predicted settlement data according to the self-generated power and the predicted difference rate obtained through a difference rate prediction model;

[0098] An actual difference rate acquisition module 502, configured to acquire an actual difference rate based on the predicted settlement data and the acquired actual settlement data, and determine whether the actual difference rate is greater than a first preset threshold;

[0099] A power generation difference rate acquisition module 503, configured to, if the actual difference rate is greater than the first preset threshold, acquire a power generation difference rate according to the actual settlement power and the predicted power generation obtained through a power generation prediction model;

[0100] A data verification module 504, configured to, if the power generation difference rate is greater than a second preset threshold, perform data verification with the settlement party based on the actual settlement data.

[0101] In some embodiments, the device for verifying settlement data of the distributed photovoltaic power station further includes:

[0102] A verification end module 505, configured to end the verification of the current settlement data if the actual difference rate is not greater than the first preset threshold.

[0103] In some embodiments, the device for verifying settlement data of the distributed photovoltaic power station further includes:

[0104] A model optimization module 506, configured to, if the power generation difference rate is not greater than the second preset threshold, optimize the difference rate prediction model based on the actual difference rate to update the difference rate prediction model.

[0105] Figure 6 It is a schematic diagram of an electronic device 6 provided by an embodiment of the present disclosure. As Figure 6As shown, the electronic device 6 of this embodiment includes: a processor 601, a memory 602, and a computer program 603 stored in the memory 602 and executable on the processor 601. When the processor 601 executes the computer program 603, the steps in the above-mentioned method embodiments are implemented. Alternatively, when the processor 601 executes the computer program 603, the functions of each module / unit in the above-mentioned device embodiments are implemented.

[0106] Exemplarily, the computer program 603 can be divided into one or more modules / units. The one or more modules / units are stored in the memory 602 and executed by the processor 601 to complete the present disclosure. The one or more modules / units can be a series of computer program instruction segments capable of performing specific functions, and the instruction segments are used to describe the execution process of the computer program 603 in the electronic device 6.

[0107] The electronic device 6 can be a desktop computer, a notebook, a palm computer, a cloud server, and other electronic devices. The electronic device 6 can include, but is not limited to, the processor 601 and the memory 602. Those skilled in the art can understand that Figure 6 merely examples of the electronic device 6, which do not constitute a limitation on the electronic device 6, may include more or fewer components than shown in the figure, or combine certain components, or different components. For example, the electronic device may also include input / output devices, network access devices, buses, etc.

[0108] The processor 601 can be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc.

[0109] The memory 602 may be an internal storage unit of the electronic device 6, for example, the hard disk or memory of the electronic device 6. The memory 602 may also be an external storage device of the electronic device 6, for example, a plug-in hard disk equipped on the electronic device 6, a Smart Media Card (SMC), a Secure Digital (SD) card, a Flash Card, etc. Further, the memory 602 may also include both the internal storage unit and the external storage device of the electronic device 6. The memory 602 is used to store computer programs and other programs and data required by the electronic device. The memory 602 may also be used to temporarily store the data that has been output or will be output.

[0110] Those skilled in the art can clearly understand that, for the convenience and conciseness of description, only the above-mentioned division of each functional unit and module is used as an example for illustration. In actual applications, the above-mentioned functions can be allocated to different functional units and modules according to needs, that is, the internal structure of the device is divided into different functional units or modules to complete all or part of the functions described above. Each functional unit and module in the embodiment can be integrated into a processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of a software functional unit. In addition, the specific names of each functional unit and module are only for the convenience of mutual distinction and do not limit the protection scope of the present application. The specific working process of the units and modules in the above system can refer to the corresponding process in the foregoing method embodiment and will not be elaborated herein.

[0111] In the above embodiments, the descriptions of the respective embodiments have their own emphases. For the parts not detailed or recorded in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0112] Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, or by a combination of computer software and electronic hardware. Whether these functions are executed in hardware or software depends on the specific application and design constraints of the technical solution. A professional technician can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present disclosure.

[0113] In the embodiments provided by the present disclosure, it should be understood that the disclosed apparatus / electronic device and method can be implemented in other ways. For example, the apparatus / electronic device embodiments described above are merely illustrative. For example, the division of modules or units is only a logical function division. In actual implementation, there may be other division methods. Multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections between each other can be through some interfaces. The indirect couplings or communication connections of the apparatus or unit can be in electrical, mechanical or other forms.

[0114] The units described as separate components may or may not be physically separated. The components displayed as units may or may not be physical units, that is, they can be located in one place, or can be distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0115] In addition, in each embodiment of the present disclosure, each functional unit can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit. The above integrated units can be implemented in the form of hardware or in the form of software functional units.

[0116] If the integrated module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, to implement all or part of the processes in the above method embodiments of the present disclosure, it can also be completed by a computer program instructing relevant hardware. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, the steps of the above method embodiments can be implemented. The computer program can include computer program code, and the computer program code can be in the form of source code, object code, executable file or some intermediate form, etc. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal, and software distribution medium, etc. It should be noted that the content included in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the computer-readable medium does not include electrical carrier signals and telecommunication signals.

[0117] The above embodiments are only used to illustrate the technical solutions of the present disclosure, rather than to limit them; although the present disclosure has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the various embodiments of the present disclosure, and should all be included within the protection scope of the present disclosure.

Claims

1. A method for verifying settlement data of a distributed photovoltaic power station, characterized in that, it includes: Obtaining predicted settlement data according to the self-generated power and the predicted difference rate obtained through the difference rate prediction model; Based on the predicted settlement data and the obtained actual settlement data, obtaining the actual difference rate, and determining whether the actual difference rate is greater than a first preset threshold; If the actual difference rate is greater than the first preset threshold, obtaining the power generation difference rate according to the actual settlement power and the predicted power generation obtained through the power generation prediction model; If the power generation difference rate is greater than a second preset threshold, performing data verification with the settlement party based on the actual settlement data; If the power generation difference rate is not greater than the second preset threshold, optimizing the difference rate prediction model based on the actual difference rate to update the difference rate prediction model.

2. The method according to claim 1, characterized in that, the predicted settlement data includes predicted settlement power, and the actual settlement data includes actual settlement power.

3. The method according to claim 1, characterized in that, the predicted settlement data includes predicted settlement subsidies, and the actual settlement data includes actual settlement subsidies; The step of obtaining predicted settlement data according to the self-generated power and the predicted difference rate obtained through the difference rate prediction model includes: Based on the settlement period, obtaining the self-generated power of the current settlement period; Obtaining the predicted difference rate of the current settlement period through the difference rate prediction model; Determining the predicted settlement power according to the self-generated power and the predicted difference rate; Obtaining the predicted settlement subsidies according to the predicted settlement power and the revenue unit price.

4. The method according to claim 3, characterized in that, The step of obtaining the actual difference rate based on the predicted settlement data and the obtained actual settlement data includes: Based on the settlement period, obtaining the actual settlement power measured by the grid settlement table of the current settlement period; Obtaining the actual settlement subsidies according to the actual settlement power and the revenue unit price; Obtaining the actual difference rate according to the actual settlement subsidies and the predicted settlement subsidies.

5. The method according to claim 3, characterized in that, the actual settlement subsidies are directly obtained by the settlement party; before the step of obtaining the power generation difference rate according to the actual settlement power and the predicted power generation obtained through the settlement power generation prediction model, it further includes: If the actual difference rate is greater than the first preset threshold, determining the actual revenue unit price according to the actual settlement subsidies and the actual settlement power measured by the grid settlement table; Judging whether the actual revenue unit price is consistent with the revenue unit price; If the actual revenue unit price is not consistent with the revenue unit price, updating the revenue unit price to the actual revenue unit price, and returning to the step of obtaining predicted settlement data according to the self-generated power and the predicted difference rate obtained through the difference rate prediction model; If the actual revenue unit price is consistent with the revenue unit price, performing the step of obtaining the power generation difference rate according to the actual settlement power and the predicted power generation obtained through the settlement power generation prediction model.

6. The method according to any one of claims 3 to 5, characterized in that, The process of obtaining the revenue unit price includes: Determine the location information of the distributed photovoltaic power station; Based on the location information of the distributed photovoltaic power station, obtain the revenue unit price that matches the location information from the subsidy database.

7. According to the method described in claim 1, wherein, after the step of determining whether the actual difference rate is greater than the first preset threshold, it further includes: If the actual difference rate is not greater than the first preset threshold, end the verification of the current settlement data, and return to the step of obtaining the predicted settlement data based on the self-generated power and the predicted difference rate obtained through the difference rate prediction model.

8. According to the method described in claim 7, wherein, after the step of optimizing the difference rate prediction model based on the actual difference rate to update the difference rate prediction model, it further includes: Check the measurement accuracy of the self-generated electricity meter, and adjust the measurement accuracy of the self-generated electricity meter based on the inspection result.

9. According to the method described in claim 1, wherein, the training process of the difference rate prediction model includes: Obtain the actual settlement electricity and the self-generated power for each settlement period in the historical record; Determine the power generation difference rate according to the actual settlement electricity and the self-generated power for each settlement period; Based on the actual settlement electricity for each settlement period, the self-generated power for each settlement period, and the power generation difference rate, train the difference rate prediction model to obtain a trained difference rate prediction model; and / or, the training process of the power generation prediction model includes: Obtain the actual settlement electricity for each settlement period in the historical record; Based on the settlement period and the actual settlement electricity corresponding to each settlement period, train the power generation prediction model to obtain a trained power generation prediction model.

10. A device for verifying settlement data of a distributed photovoltaic power station, wherein, it includes: A predicted settlement data acquisition module, configured to obtain predicted settlement data according to the self-generated power and the predicted difference rate obtained through the difference rate prediction model; An actual difference rate acquisition module, configured to obtain the actual difference rate based on the predicted settlement data and the obtained actual settlement data, and determine whether the actual difference rate is greater than the first preset threshold; A power generation difference rate acquisition module, configured to, if the actual difference rate is greater than the first preset threshold, obtain the power generation difference rate according to the actual settlement electricity and the predicted power generation obtained through the power generation prediction model; A data verification module, configured to, if the power generation difference rate is greater than the second preset threshold, perform data verification with the settlement party based on the actual settlement data; If the power generation difference rate is not greater than the second preset threshold, optimize the difference rate prediction model based on the actual difference rate to update the difference rate prediction model.

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