Power prediction evaluation method and apparatus, storage medium, and program product

Abstract

The present disclosure relates to a power prediction evaluation method and device, a storage medium and a program product. The method comprises the following steps: acquiring power prediction data provided by an energy field station within a preset time period, and unit operation power data of the energy field station within the preset time period; determining a first accuracy rate of current power prediction of the energy field station according to the unit operation power data and the power prediction data; acquiring evaluation standard data generated based on a preset file; and determining evaluation result data according to the first accuracy rate and the evaluation standard data. The evaluation standard data generated based on the preset file can be effectively acquired, and it is determined whether the first accuracy rate of the current power prediction meets the evaluation standard in the preset file according to the evaluation standard data corresponding to the preset file, which can effectively improve the intelligent degree of power prediction evaluation, thereby facilitating the improvement of the work efficiency of evaluating the accuracy rate of the current power prediction according to the preset file.

Description

Technical Field

[0001] This disclosure relates to the field of data processing, and more specifically, to a power prediction and evaluation method, apparatus, storage medium, and program product. Background Technology

[0002] In recent years, with the continuous and rapid development of new energy sources, the installed capacity of new energy power generation has shown a rapid increasing trend. Because new energy power generation depends on natural climate conditions, its intermittency and volatility have a significant impact on the safety and stability of the power grid, leading to high wind and solar curtailment rates in some areas. The contradiction between new energy curtailment and power grid operation safety has become increasingly prominent. To standardize the grid connection operation management and power ancillary service management of the power system, regional power regulatory agencies, based on the actual situation of the local power system and the needs of power market construction, frequently formulate management regulations to reduce new energy curtailment and increase its absorption capacity. These regulations have different requirements for different regions and different seasons, and are updated periodically. However, current power forecasting and assessment methods have low automation levels and cannot reliably assess according to the requirements of the latest management regulations, which is detrimental to improving the efficiency of power forecasting and assessment and cannot meet current power forecasting and assessment needs. Summary of the Invention

[0003] To achieve the above objectives, the first aspect of this disclosure provides a power prediction and evaluation method, the method comprising: Obtain power prediction data for a preset time period after the current time provided by the energy power station, as well as the unit operating power data of the energy power station within the preset time period; The first accuracy rate of the current power prediction of the energy station is determined based on the unit operating power data and the power prediction data. Obtain evaluation standard data generated based on preset files; The evaluation result data is determined based on the first accuracy rate and the evaluation standard data. The evaluation result data is used to characterize whether the accuracy of the current power prediction of the energy station meets the evaluation standard in the preset file.

[0004] In some embodiments, obtaining evaluation criterion data generated based on a preset file includes: If it is determined that there is a preset file to be updated, an evaluation standard update prompt window is displayed. The evaluation standard update prompt window is used to prompt whether to update the evaluation standard data according to the preset file. In response to receiving an update request triggered by the evaluation standard update prompt window, the evaluation standard data corresponding to the preset file is obtained by calling the preset standard recognition model; If it is determined that there is no preset file to be updated, the evaluation standard data generated within the historical time period is obtained.

[0005] In some embodiments, the unit operating power data includes unit operating power sampled multiple times within the preset time period, and the power prediction data includes power prediction values ​​within the preset time period. Determining the first accuracy rate of the current power prediction for the energy station based on the unit operating power data and the power prediction data includes: For each sampled unit operating power, the power difference between the unit operating power and the predicted power value is determined; Determine the target number of times that the power difference is less than or equal to a preset difference threshold in multiple samples within the preset time period; Obtain the total number of samples within the preset time period; The ratio of the target number of samples to the total number of samples is taken as the first accuracy rate.

[0006] In some embodiments, the evaluation result data includes first identification information and second identification information, the evaluation standard data includes a target accuracy rate, and the step of determining the evaluation result data based on the first accuracy rate and the evaluation standard data includes: If the first accuracy rate is less than the target accuracy rate, the evaluation result data is determined to be the first identification information; If the first accuracy rate is greater than or equal to the target accuracy rate, the evaluation result data is determined to be the second identification information.

[0007] In some embodiments, the method further includes: If it is determined that the evaluation result data of the energy station includes the second identification information, the unit operation status data of the energy station is obtained; Determine unit fault information based on the unit operating status data; The system generates and displays a first prompt message based on the unit fault information, which is used to prompt the user to repair the fault point.

[0008] In some embodiments, the power prediction data is obtained by predicting using a preset power prediction model, and the method further includes: After displaying the first prompt information, if it is determined that a fault repair completion instruction triggered by the user has been received, the second accuracy rate predicted within the target time period after the repair completion time is determined; If it is determined that the second accuracy rate is less than a preset accuracy rate threshold, a second prompt message is generated. This second prompt message is used to remind the user that the power prediction model is a model that needs to be updated.

[0009] In some embodiments, the method further includes: Obtain the target difference between the target accuracy and the first accuracy; An accuracy improvement index value is generated based on the target difference, and the accuracy improvement index value is positively correlated with the target difference; The accuracy improvement index value is displayed through a preset window.

[0010] A second aspect of this disclosure provides a power prediction and evaluation apparatus, the apparatus comprising: The first acquisition module is configured to acquire power prediction data for a preset time period after the current time provided by the energy power station, and unit operating power data of the energy power station within the preset time period. The first determining module is configured to determine a first accuracy rate of the current power prediction of the energy station based on the unit operating power data and the power prediction data. The second acquisition module is configured to acquire evaluation standard data generated based on a preset file; The second determining module is configured to determine evaluation result data based on the first accuracy rate and the evaluation standard data, wherein the evaluation result data is used to characterize whether the accuracy rate of the current power prediction of the energy station meets the evaluation standard in the preset file.

[0011] In some embodiments, the second acquisition module is configured to: If it is determined that there is a preset file to be updated, an evaluation standard update prompt window is displayed. The evaluation standard update prompt window is used to prompt whether to update the evaluation standard data according to the preset file. In response to receiving an update request triggered by the evaluation standard update prompt window, the evaluation standard data corresponding to the preset file is obtained by calling the preset standard recognition model; If it is determined that there is no preset file to be updated, the evaluation standard data generated within the historical time period is obtained.

[0012] In some embodiments, the unit operating power data includes unit operating power sampled multiple times within the preset time period, and the power prediction data includes power prediction values ​​within the preset time period. The first determining module is configured to: For each sampled unit operating power, the power difference between the unit operating power and the predicted power value is determined; Determine the target number of times that the power difference is less than or equal to a preset difference threshold in multiple samples within the preset time period; Obtain the total number of samples within the preset time period; The ratio of the target number of samples to the total number of samples is taken as the first accuracy rate.

[0013] In some embodiments, the evaluation result data includes first identification information and second identification information, the evaluation criterion data includes target accuracy, and the second determining module is configured to: If the first accuracy rate is less than the target accuracy rate, the evaluation result data is determined to be the first identification information; If the first accuracy rate is greater than or equal to the target accuracy rate, the evaluation result data is determined to be the second identification information.

[0014] In some embodiments, the apparatus further includes: The third acquisition module is configured to acquire the unit operating status data of the energy station when it is determined that the evaluation result data of the energy station includes the second identification information; The third determining module is configured to determine unit fault information based on the unit operating status data. The first prompt module is configured to generate and display a first prompt message based on the unit fault information. The first prompt message is used to prompt the user to repair the fault point.

[0015] In some embodiments, the power prediction data is obtained by predicting using a preset power prediction model, and the device further includes: The fourth determination module is configured to, after displaying the first prompt information, if it is determined that a fault repair completion instruction triggered by the user has been received, determine the second accuracy rate predicted within a target time period after the repair completion time; The second prompt module is configured to generate a second prompt message if it is determined that the second accuracy rate is less than a preset accuracy rate threshold. The second prompt message is used to prompt the user that the power prediction model is a model that needs to be updated.

[0016] In some embodiments, the apparatus further includes: The fourth acquisition module is configured to acquire the target difference between the target accuracy and the first accuracy. The fifth determining module is configured to generate an accuracy improvement index value based on the target difference, wherein the accuracy improvement index value is positively correlated with the accuracy difference. The third prompt module is configured to display the accuracy improvement index value through a preset window.

[0017] A third aspect of this disclosure provides a non-transitory computer-readable storage medium having a computer program stored thereon that, when executed by a processor, implements the steps of the method described in the first aspect above.

[0018] This disclosure provides a fourth aspect of a computer program product, including a computer program that, when executed by a processor, implements the steps of the method described in the first aspect above.

[0019] The fifth aspect of this disclosure provides an electronic device, comprising: A memory on which computer programs are stored; A processor for executing the computer program in the memory to implement the steps of the method described in the first aspect above.

[0020] The above technical solution can effectively obtain evaluation standard data generated based on a preset file, and determine whether the first accuracy of the current power prediction meets the evaluation standard in the preset file based on the evaluation standard data corresponding to the preset file. This can effectively improve the intelligence level of power prediction evaluation, thereby improving the work efficiency of evaluating the accuracy of the current power prediction based on the preset file.

[0021] Other features and advantages of this disclosure will be described in detail in the following detailed description section. Attached Figure Description

[0022] The accompanying drawings are provided to further illustrate the present disclosure and form part of the specification. They are used together with the following detailed description to explain the present disclosure, but do not constitute a limitation thereof. In the drawings: Figure 1 This is a flowchart illustrating a power prediction and evaluation method according to an exemplary embodiment of this disclosure; Figure 2 Based on this disclosure Figure 1 The illustrated embodiment presents a flowchart of a power prediction and evaluation method; Figure 3 Based on this disclosure Figure 1 The illustrated embodiment shows a flowchart of another power prediction and evaluation method; Figure 4 Based on this disclosure Figure 1 The illustrated embodiment shows a flowchart of another power prediction and evaluation method; Figure 5 Based on this disclosure Figure 1 The illustrated embodiment shows a flowchart of another power prediction and evaluation method; Figure 6 This is a block diagram of a power prediction and evaluation apparatus provided in an exemplary embodiment of this disclosure; Figure 7 This is a block diagram illustrating an electronic device according to an exemplary embodiment; Figure 8 This is a block diagram illustrating another electronic device according to an exemplary embodiment. Detailed Implementation

[0023] The specific embodiments of this disclosure will be described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are for illustration and explanation only and are not intended to limit this disclosure.

[0024] It should be noted that all actions involving the acquisition of signals, information, or data in this disclosure are carried out in compliance with the relevant data protection laws and policies of the country where the location is situated, and with authorization from the owner of the relevant device.

[0025] Figure 1 This is a flowchart illustrating a power prediction and evaluation method according to an exemplary embodiment of this disclosure, such as... Figure 1 As shown, the method may include: Step 101: Obtain power prediction data for a preset time period after the current time provided by the energy power station, as well as the unit operating power data of the energy power station within the preset time period.

[0026] The power prediction data can be at least one of the following: short-term power prediction, ultra-short-term power prediction, medium-short-term power prediction, medium-term power prediction, and long-term power prediction.

[0027] For example, when the power forecast data is a very short-term power forecast, the preset time period can be 0-4 hours in the future (extended to 6 hours in some scenarios); when the power forecast data is a short-term power forecast, the preset time period can be 15 minutes to 24 hours in the future (commonly within 1 day); when the power forecast data is a medium-short term power forecast, the preset time period can be 24 hours to 72 hours in the future; when the power forecast data is a medium term power forecast, the preset time period can be 1 week to 1 month in the future; and when the power forecast data is a long term power forecast, the preset time period can be more than 1 month in the future.

[0028] Step 102: Determine the first accuracy rate of the current power prediction of the energy station based on the unit operating power data and the power prediction data.

[0029] This step can be done through... Figure 2 The method shown is implemented as follows: Figure 2 Based on this disclosure Figure 1 The illustrated embodiment presents a flowchart of a power prediction and evaluation method, as shown below. Figure 2 As shown, step 102 includes: S11, for each sampled unit operating power, determine the power difference between the unit operating power and the predicted power value.

[0030] For example, if the operating power of the unit in the t-th sampling is... The predicted power value is This power difference can be expressed as .

[0031] S12, determine the target number of samplings in which the power difference is less than or equal to a preset difference threshold in multiple samplings within the preset time period.

[0032] For example, the preset difference threshold is P, and the target number is to satisfy... The number of times P is sampled can be represented as n, for example.

[0033] S13, obtain the total number of samples within the preset time period.

[0034] S14, the ratio of the target sampling number to the total number of samples is taken as the first accuracy.

[0035] For example, if the total number of samples is N, then the first accuracy can be expressed as: .

[0036] Through the above steps S11 to S13, the first accuracy rate of the current power prediction of the energy station can be effectively determined.

[0037] Step 103: Obtain evaluation standard data generated based on a preset file.

[0038] The preset document can be a management specification document issued by the energy regulatory authority, and may include at least one of the following: text materials, digital images, and audio files.

[0039] The implementation method of this step can be as follows: Figure 3 ( Figure 3 Based on this disclosure Figure 1 The flowchart of another power prediction and evaluation method shown in the embodiment illustrates that includes: S21, if it is determined that there is a preset file to be updated, an evaluation standard update prompt window is displayed. The evaluation standard update prompt window is used to prompt whether to update the evaluation standard data according to the preset file.

[0040] The evaluation standard update prompt window may include an "Agree to update" button and a "Do not update" button. Users can trigger an update request by clicking the "Agree to update" button and trigger an instruction to temporarily suspend the update by clicking the "Do not update" button.

[0041] S22, in response to receiving an update request triggered by the evaluation standard update prompt window, the evaluation standard data corresponding to the preset file is obtained by calling the preset standard recognition model.

[0042] Specifically, when calling the preset standard recognition model to obtain the evaluation standard data corresponding to the preset file, the preset file can be input into the preset standard recognition model to obtain the evaluation standard data output by the preset standard recognition model.

[0043] It should be noted that the preset standard recognition pattern can be a pre-trained neural network model. The training process of the model can be as follows: obtaining a training dataset, which includes multiple sample files, each of which is labeled with evaluation standard sample data; using the training dataset to train the preset initial neural network model to obtain the preset standard recognition pattern.

[0044] S23, if it is determined that there is no preset file to be updated, obtain the evaluation standard data generated within the historical time period.

[0045] Through S21 to S23 above, evaluation standard data can be automatically and efficiently generated based on preset files, providing reliable data basis for evaluation based on the evaluation standards specified in the preset files.

[0046] Step 104: Determine the evaluation result data based on the first accuracy rate and the evaluation standard data. The evaluation result data is used to characterize whether the accuracy of the current power prediction of the energy station meets the evaluation standard in the preset file.

[0047] Specifically, if the first accuracy rate is less than the target accuracy rate, the evaluation result data is determined to be first identification information; if the first accuracy rate is greater than or equal to the target accuracy rate, the evaluation result data is determined to be second identification information.

[0048] For example, the first identification information may be characters, images, or codes used to represent whether the assessment meets the standard. The second identification information may be characters, images, or codes used to represent whether the assessment fails to meet the standard. For example, "OK" may be used to indicate that the assessment meets the standard, and "ERROR" may be used to indicate that the assessment fails to meet the standard; binary code "01" may be used to indicate that the assessment meets the standard, and "10" may be used to indicate that the assessment fails to meet the standard.

[0049] The above technical solution can effectively acquire evaluation standard data generated based on a preset file, and determine whether the first accuracy of the current power prediction meets the evaluation standard in the preset file based on the evaluation standard data corresponding to the preset file. This can effectively improve the intelligence level of power prediction evaluation, thereby improving the work efficiency of evaluating the accuracy of the current power prediction based on the preset file.

[0050] Figure 4 Based on this disclosure Figure 1 The illustrated embodiment presents a flowchart of another power prediction and evaluation method, in which... Figure 1 After determining the evaluation result data based on the first accuracy rate and the evaluation standard data in step 104, the method further includes: Step 105: If it is determined that the evaluation result data of the energy station includes the second identification information, obtain the unit operation status data of the energy station.

[0051] The second identification information is a character, image, or code used to characterize non-compliance assessments. The unit's operating status information includes operating information under fault conditions and operating information under non-fault conditions. This operating information can include multi-dimensional parameters such as mechanical, electrical, thermal, control, and status signals. Mechanical parameters can include speed, vibration, displacement, torque, pressure, flow rate, and noise; electrical parameters can include voltage, current, power, power factor, frequency, and insulation resistance, used to reflect the operating efficiency and safety of the electrical system; thermal parameters can include temperature, temperature difference, and heat, used to assess equipment heat dissipation and thermal efficiency; control and status signals can include switching quantities (such as equipment start / stop status, protection action signals), analog quantities (such as regulating valve opening, pitch angle), and control commands (such as power setpoints).

[0052] Step 106: Determine the unit fault information based on the unit operating status data.

[0053] In this step, the operating information that belongs to the fault state in the unit's operating status data can be used as the fault information of the unit.

[0054] Step 107: Generate and display a first prompt message based on the unit fault information. The first prompt message is used to prompt the user to repair the fault point.

[0055] The first notification message may include information about the unit's malfunction and text prompts for maintenance.

[0056] Steps 105 to 107 above can promptly and effectively prompt users to repair the fault points, thereby effectively improving the efficiency of fault handling.

[0057] Optionally, after step 107, the method may further include: if it is determined that a fault repair completion instruction triggered by the user has been received, determining a second accuracy rate of the prediction within a target time period after the repair completion time; if it is determined that the second accuracy rate is less than a preset accuracy threshold, generating a second prompt message, which is used to prompt the user that the power prediction model is a model to be updated.

[0058] Figure 5 Based on this disclosure Figure 1 The illustrated embodiment shows a flowchart of another power prediction and evaluation method, as follows: Figure 5As shown, the method may further include: Step 108: Obtain the target difference between the target accuracy and the first accuracy.

[0059] Step 109: Generate an accuracy improvement index value based on the target difference, wherein the accuracy improvement index value is positively correlated with the target difference.

[0060] Step 110: Display the accuracy improvement index value through a preset window.

[0061] In cases involving multiple energy power stations, the identifiers of these energy power stations can be sorted according to the accuracy improvement index value, and the sorting can be displayed through a preset window to enable managers to quickly obtain the overall situation of power prediction and assessment within the region.

[0062] The above technical solution, by obtaining the target difference between the target accuracy and the first accuracy, generating an accuracy improvement index value based on the target difference, and displaying the accuracy improvement index value through a preset window, can effectively improve the automation level of obtaining the accuracy improvement index value.

[0063] Figure 6 This is a block diagram of a power prediction and evaluation apparatus provided in an exemplary embodiment of the present disclosure. The apparatus 600 may include: The first acquisition module 601 is configured to acquire power prediction data for a preset time period after the current time provided by the energy station, and unit operating power data of the energy station within the preset time period. The first determining module 602 is configured to determine the first accuracy of the current power prediction of the energy station based on the unit operating power data and the power prediction data. The second acquisition module 603 is configured to acquire evaluation standard data generated based on a preset file; The second determining module 604 is configured to determine evaluation result data based on the first accuracy rate and the evaluation standard data, wherein the evaluation result data is used to characterize whether the accuracy rate of the current power prediction of the energy station meets the evaluation standard in the preset file.

[0064] The above technical solution can effectively acquire evaluation standard data generated based on a preset file, and determine whether the first accuracy of the current power prediction meets the evaluation standard in the preset file based on the evaluation standard data corresponding to the preset file. This can effectively improve the intelligence level of power prediction evaluation, thereby improving the work efficiency of evaluating the accuracy of the current power prediction based on the preset file.

[0065] In some embodiments, the second acquisition module 603 is configured to: If it is determined that there is a preset file to be updated, an evaluation standard update prompt window is displayed. The evaluation standard update prompt window is used to prompt whether to update the evaluation standard data according to the preset file. In response to receiving an update request triggered by the evaluation standard update prompt window, the evaluation standard data corresponding to the preset file is obtained by calling the preset standard recognition model; If it is determined that there is no preset file to be updated, the evaluation standard data generated within the historical time period is obtained.

[0066] In some embodiments, the unit operating power data includes the unit operating power sampled multiple times within the preset time period, and the power prediction data includes the power prediction value within the preset time period. The first determining module 602 is configured to: For each sampled unit operating power, the power difference between the unit operating power and the predicted power value is determined; Determine the target number of times that the power difference is less than or equal to a preset difference threshold in multiple samples within the preset time period; Obtain the total number of samples within the preset time period; The ratio of the target number of samples to the total number of samples is taken as the first accuracy rate.

[0067] In some embodiments, the evaluation result data includes first identification information and second identification information, the evaluation standard data includes target accuracy, and the second determining module 604 is configured to: If the first accuracy rate is less than the target accuracy rate, the evaluation result data is determined to be the first identification information; If the first accuracy rate is greater than or equal to the target accuracy rate, the evaluation result data is determined to be the second identification information.

[0068] In some embodiments, the apparatus further includes: The third acquisition module is configured to acquire the unit operating status data of the energy station when it is determined that the evaluation result data of the energy station includes the second identification information; The third determining module is configured to determine unit fault information based on the unit operating status data. The first prompt module is configured to generate and display first prompt information based on the unit fault information. The first prompt information includes the reason why the first accuracy of power prediction is not up to standard, which is used to prompt the user to repair the fault point.

[0069] In some embodiments, the power prediction data is obtained by predicting using a preset power prediction model, and the device further includes: The fourth determination module is configured to, after displaying the first prompt information, if it is determined that a fault repair completion instruction triggered by the user has been received, determine the second accuracy rate predicted within a target time period after the repair completion time; The second prompt module is configured to generate a second prompt message if it is determined that the second accuracy rate is less than a preset accuracy rate threshold. The second prompt message is used to prompt the user that the power prediction model is a model that needs to be updated.

[0070] In some embodiments, the apparatus further includes: The fourth acquisition module is configured to acquire the target difference between the target accuracy and the first accuracy. The fifth determining module is configured to generate an accuracy improvement index value based on the target difference, wherein the accuracy improvement index value is positively correlated with the accuracy difference. The third prompt module is configured to display the accuracy improvement index value through a preset window.

[0071] The above technical solution, by obtaining the target difference between the target accuracy and the first accuracy, generating an accuracy improvement index value based on the target difference, and displaying the accuracy improvement index value through a preset window, can effectively improve the automation level of obtaining the accuracy improvement index value.

[0072] Regarding the apparatus in the above embodiments, the specific manner in which each module performs its operation has been described in detail in the embodiments related to the method, and will not be elaborated upon here.

[0073] Figure 7 This is a block diagram illustrating an electronic device according to an exemplary embodiment. Figure 7 As shown, the electronic device 700 may include a processor 701 and a memory 702. The electronic device 700 may also include one or more of a multimedia component 703, an input / output (I / O) interface 704, and a communication component 705.

[0074] The processor 701 controls the overall operation of the electronic device 700 to complete all or part of the steps in the power prediction and evaluation method described above. The memory 702 stores various types of data to support the operation of the electronic device 700. This data may include, for example, instructions for any application or method operating on the electronic device 700, and application-related data such as contact data, sent and received messages, pictures, audio, video, etc. The memory 702 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read-Only Memory (EPROM), Programmable Read-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The multimedia component 703 may include a screen and audio components. The screen may be, for example, a touchscreen, and the audio component is used to output and / or input audio signals. For example, the audio component may include a microphone for receiving external audio signals. The received audio signals may be further stored in memory 702 or transmitted via communication component 705. The audio component also includes at least one speaker for outputting audio signals. I / O interface 704 provides an interface between processor 701 and other interface modules, such as a keyboard, mouse, buttons, etc. These buttons may be virtual or physical buttons. Communication component 705 is used for wired or wireless communication between the electronic device 700 and other devices. Wireless communication, such as Wi-Fi, Bluetooth, Near Field Communication (NFC), 2G, 3G, 4G, NB-IoT, eMTC, or other 5G technologies, or combinations thereof, is not limited here. Therefore, the corresponding communication component 705 may include: a Wi-Fi module, a Bluetooth module, an NFC module, etc.

[0075] In an exemplary embodiment, the electronic device 700 may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the power prediction and evaluation method described above.

[0076] In another exemplary embodiment, a computer-readable storage medium including program instructions is also provided, which, when executed by a processor, implement the steps of the power prediction and evaluation method described above. For example, the computer-readable storage medium may be the memory 702 including program instructions described above, which may be executed by the processor 701 of the electronic device 700 to complete the power prediction and evaluation method described above.

[0077] Figure 8 This is a block diagram illustrating another electronic device according to an exemplary embodiment. For example, electronic device 1900 may be provided as a server. (Refer to...) Figure 8 The electronic device 1900 includes a processor 1922, which may be one or more, and a memory 1932 for storing computer programs executable by the processor 1922. The computer program stored in the memory 1932 may include one or more modules, each corresponding to a set of instructions. Furthermore, the processor 1922 may be configured to execute the computer program to perform the power prediction and evaluation method described above.

[0078] Additionally, the electronic device 1900 may also include a power supply component 1926 and a communication component 1950. The power supply component 1926 can be configured to perform power management of the electronic device 1900, and the communication component 1950 can be configured to enable communication of the electronic device 1900, such as wired or wireless communication. Furthermore, the electronic device 1900 may also include an input / output (I / O) interface 1958. The electronic device 1900 can operate on an operating system stored in memory 1932.

[0079] In another exemplary embodiment, a computer-readable storage medium including program instructions is also provided, which, when executed by a processor, implement the steps of the power prediction and evaluation method described above. For example, the non-transitory computer-readable storage medium may be the memory 1932 including the program instructions described above, which may be executed by the processor 1922 of the electronic device 1900 to complete the power prediction and evaluation method described above.

[0080] In another exemplary embodiment, a computer program product is also provided, the computer program product comprising a computer program executable by a programmable device, the computer program having a code portion for performing the power prediction and evaluation method described above when executed by the programmable device.

[0081] The preferred embodiments of this disclosure have been described in detail above with reference to the accompanying drawings. However, this disclosure is not limited to the specific details of the above embodiments. Within the scope of the technical concept of this disclosure, various simple modifications can be made to the technical solutions of this disclosure, and these simple modifications all fall within the protection scope of this disclosure.

[0082] It should also be noted that the various specific technical features described in the above specific embodiments can be combined in any suitable manner without contradiction. In order to avoid unnecessary repetition, this disclosure will not describe the various possible combinations separately.

[0083] Furthermore, various different embodiments of this disclosure can be combined in any way, as long as they do not violate the spirit of this disclosure, they should also be regarded as the content disclosed in this disclosure.

Claims

1. A power prediction and evaluation method, characterized in that, The method includes: Obtain power prediction data for a preset time period after the current time provided by the energy power station, as well as the unit operating power data of the energy power station within the preset time period; The first accuracy rate of the current power prediction of the energy station is determined based on the unit operating power data and the power prediction data. Obtain evaluation standard data generated based on preset files; The evaluation result data is determined based on the first accuracy rate and the evaluation standard data. The evaluation result data is used to characterize whether the accuracy of the current power prediction of the energy station meets the evaluation standard in the preset file.

2. The power prediction and evaluation method according to claim 1, characterized in that, The acquisition of evaluation standard data generated based on a preset file includes: If it is determined that there is a preset file to be updated, an evaluation standard update prompt window is displayed. The evaluation standard update prompt window is used to prompt whether to update the evaluation standard data according to the preset file. In response to receiving an update request triggered by the evaluation standard update prompt window, the evaluation standard data corresponding to the preset file is obtained by calling the preset standard recognition model; If it is determined that there is no preset file to be updated, the evaluation standard data generated within the historical time period is obtained.

3. The power prediction and evaluation method according to claim 1, characterized in that, The unit operating power data includes the unit operating power sampled multiple times within the preset time period, and the power prediction data includes the power prediction value within the preset time period. Determining the first accuracy rate of the current power prediction for the energy station based on the unit operating power data and the power prediction data includes: For each sampled unit operating power, the power difference between the unit operating power and the predicted power value is determined; Determine the target number of times that the power difference is less than or equal to a preset difference threshold in multiple samples within the preset time period; Obtain the total number of samples within the preset time period; The ratio of the target number of samples to the total number of samples is taken as the first accuracy rate.

4. The power prediction and evaluation method according to claim 1, characterized in that, The evaluation result data includes first identification information and second identification information, and the evaluation standard data includes a target accuracy rate. Determining the evaluation result data based on the first accuracy rate and the evaluation standard data includes: If the first accuracy rate is less than the target accuracy rate, the evaluation result data is determined to be the first identification information; If the first accuracy rate is greater than or equal to the target accuracy rate, the evaluation result data is determined to be the second identification information.

5. The power prediction and evaluation method according to claim 4, characterized in that, The method further includes: If it is determined that the evaluation result data of the energy station includes the second identification information, the unit operation status data of the energy station is obtained; Determine unit fault information based on the unit operating status data; The system generates and displays a first prompt message based on the unit fault information. This first prompt message is used to prompt the user to repair the fault point.

6. The power prediction and evaluation method according to claim 5, characterized in that, The power prediction data is obtained by predicting through a preset power prediction model, and the method further includes: After displaying the first prompt information, if it is determined that a fault repair completion instruction triggered by the user has been received, the second accuracy rate predicted within the target time period after the repair completion time is determined; If it is determined that the second accuracy rate is less than a preset accuracy rate threshold, a second prompt message is generated. This second prompt message is used to remind the user that the power prediction model is a model that needs to be updated.

7. The power prediction and evaluation method according to any one of claims 1-6, characterized in that, The method further includes: Obtain the target difference between the target accuracy and the first accuracy; An accuracy improvement index value is generated based on the target difference, and the accuracy improvement index value is positively correlated with the target difference; The accuracy improvement index value is displayed through a preset window.

8. A power prediction and evaluation device, characterized in that, The device includes: The first acquisition module is configured to acquire power prediction data for a preset time period after the current time provided by the energy power station, and unit operating power data of the energy power station within the preset time period. The first determining module is configured to determine a first accuracy rate of the current power prediction of the energy station based on the unit operating power data and the power prediction data. The second acquisition module is configured to acquire evaluation standard data generated based on a preset file; The second determining module is configured to determine evaluation result data based on the first accuracy rate and the evaluation standard data, wherein the evaluation result data is used to characterize whether the accuracy rate of the current power prediction of the energy station meets the evaluation standard in the preset file.

9. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the steps of the method described in any one of claims 1-7.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1-7.

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