A long-term power prediction method, system, device and medium for a photovoltaic power station

Abstract

The application provides a kind of photovoltaic power station medium and long term electric quantity prediction method, system, equipment and medium, comprising the following steps: step 1, constructs weather forecast model using the historical average climate data obtained;Step 2, the weather forecast model is corrected using climate factor, to obtain the weather forecast model after correction;Step 3, the weather of the target photovoltaic station area is predicted using the weather forecast model after correction, to obtain weather forecast data;Step 4, the weather forecast data obtained by prediction is used as the input of the constructed medium and long term electric quantity prediction model, to obtain the electric quantity prediction result corresponding to the target photovoltaic station;The application can improve the prediction accuracy of medium and long term electric quantity.

Description

Technical Field

[0001] This invention belongs to the field of photovoltaic power generation, specifically relating to a method, system, equipment, and medium for predicting medium- and long-term power generation in photovoltaic power plants. Background Technology

[0002] In the context of electricity trading, optimizing medium- and long-term electricity forecasting will help power plants to rationally declare medium- and long-term trading volumes and reduce revenue losses caused by positive and negative electricity volume deviations.

[0003] In existing technologies, the methods for predicting new energy power or electricity volume are mainly focused on the short and ultra-short term. However, due to the large time scale of medium and long term predictions, the low accuracy of weather forecasts, the limited sample of power generation data recorded in the early stage, and the significant difference between power generation prediction and short-term power prediction, short-term power prediction technology cannot be used for medium and long term power prediction, thus making it impossible to predict the medium and long term power generation of new energy. Summary of the Invention

[0004] The purpose of this invention is to provide a method, system, equipment and medium for predicting medium- and long-term power generation in photovoltaic power plants, which solves the problem of low prediction accuracy in existing medium- and long-term power generation prediction methods.

[0005] To achieve the above objectives, the technical solution adopted by the present invention is as follows: This invention provides a method for predicting medium- and long-term power generation in photovoltaic power plants, comprising the following steps: Step 1: Construct a meteorological forecasting model using the acquired historical average climate data; Step 2: Use climate factors to correct the meteorological forecast model to obtain the corrected meteorological forecast model; Step 3: Use the modified meteorological forecasting model to forecast the weather in the area where the target photovoltaic power station is located, and obtain meteorological forecast data; Step 4: Use the predicted meteorological data as input to the constructed medium- and long-term power generation prediction model to obtain the power generation prediction results corresponding to the target photovoltaic power station.

[0006] Preferably, in step 1, historical average climatological data is obtained, specifically through the following method: Based on years of ERA5 reanalysis data, the multi-year climatological average irradiance of the target photovoltaic power station area is calculated. For a target photovoltaic power station that provides historical measured data, the historical measured data is used to perform statistical regression correction on the multi-year climatological average irradiance corresponding to the target photovoltaic power station to obtain the corrected multi-year climatological average irradiance corresponding to the target photovoltaic power station. The corrected multi-year climatological average irradiance is then used as the historical average climatological data. For target photovoltaic power plants that do not provide historical measured data, the multi-year climatological average irradiance corresponding to the target photovoltaic power plant shall be used as the historical climatological average data.

[0007] Preferably, the method further includes correcting the power prediction result obtained in step 4 using the predicted power curtailment period and degree to obtain the final power prediction result for the target photovoltaic power station.

[0008] Preferably, the predicted power rationing period and degree are obtained by: Obtain historical operating data of the target photovoltaic power station; Semi-supervised learning and active learning methods are used to identify abnormal data in the obtained historical operation data to obtain the corresponding power curtailment anomaly data of the target photovoltaic power station. Based on the operating characteristics of the target photovoltaic power plant and the absorption characteristics of surrounding nodes, and combined with the obtained power curtailment anomaly data, the power curtailment period and degree of power curtailment within the set future time period of the target photovoltaic power plant can be predicted.

[0009] Preferably, in step 1, historical average climatological data is obtained, specifically through the following method: Based on years of ERA5 reanalysis data, the multi-year climatological average irradiance of the target photovoltaic power station area is calculated. For a target photovoltaic power station that provides historical measured data, the historical measured data is used to perform statistical regression correction on the multi-year climatological average irradiance corresponding to the target photovoltaic power station to obtain the corrected multi-year climatological average irradiance corresponding to the target photovoltaic power station. The corrected multi-year climatological average irradiance is then used as the historical average climatological data. For target photovoltaic power stations that do not provide historical measured data, the multi-year climatological average irradiance corresponding to the target photovoltaic power station shall be used as the historical average climatological data. The method also includes correcting the power prediction results obtained in step 4 by using the predicted power curtailment period and degree to obtain the final power prediction results for the target photovoltaic power station.

[0010] Preferably, in step 1, historical average climatological data is obtained, specifically through the following method: Based on years of ERA5 reanalysis data, the multi-year climatological average irradiance of the target photovoltaic power station area is calculated. For a target photovoltaic power station that provides historical measured data, the historical measured data is used to perform statistical regression correction on the multi-year climatological average irradiance corresponding to the target photovoltaic power station to obtain the corrected multi-year climatological average irradiance corresponding to the target photovoltaic power station. The corrected multi-year climatological average irradiance is then used as the historical average climatological data. For target photovoltaic power stations that do not provide historical measured data, the multi-year climatological average irradiance corresponding to the target photovoltaic power station shall be used as the historical average climatological data. The method also includes correcting the power prediction results obtained in step 4 by using the predicted power curtailment period and power curtailment level to obtain the final power prediction results of the target photovoltaic power station. The predicted power rationing period and severity are obtained using the following method: Obtain historical operating data of the target photovoltaic power station; Semi-supervised learning and active learning methods are used to identify abnormal data in the obtained historical operation data to obtain the corresponding power curtailment anomaly data of the target photovoltaic power station. Based on the operating characteristics of the target photovoltaic power plant and the absorption characteristics of surrounding nodes, and combined with the obtained power curtailment anomaly data, the power curtailment period and degree of power curtailment within the set future time period of the target photovoltaic power plant can be predicted.

[0011] A medium- to long-term power generation forecasting system for photovoltaic power plants, comprising: The meteorological forecasting model building unit is used to build meteorological forecasting models using the acquired historical average climate data. The model correction unit is used to correct the meteorological forecast model using climate factors to obtain the corrected meteorological forecast model. The meteorological forecasting unit is used to forecast the weather in the area where the target photovoltaic power station is located using the modified meteorological forecasting model, and obtain meteorological forecast data. The power prediction unit is used to take the predicted meteorological forecast data as input to the constructed medium- and long-term power prediction model to obtain the power prediction results corresponding to the target photovoltaic power station.

[0012] A computer device, comprising: A processor is used to execute computer programs; A computer-readable storage medium storing a computer program that, when executed by the processor, performs the method.

[0013] A computer-readable storage medium storing a computer program that, when executed by a processor, implements the method.

[0014] A computer program product comprising a computer program that, when executed by a processor, implements the method.

[0015] Compared with the prior art, the beneficial effects of the present invention are: This invention provides a method for predicting medium- and long-term electricity generation in photovoltaic power plants. By modifying the meteorological prediction model with climate factors, the accuracy of meteorological prediction is improved, thereby effectively optimizing the capture of abnormal changes in future weather patterns and improving the accuracy of medium- and long-term electricity generation prediction. Detailed Implementation

[0016] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this application with unnecessary detail.

[0017] It should be understood that, when used in this application specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or a collection thereof.

[0018] It should also be understood that the term “and / or” as used in this application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0019] As used in this application specification and the appended claims, the term "if" may be interpreted, depending on the context, as "when," "once," "in response to determination," or "in response to detection." Similarly, the phrase "if determined" or "if detected [the described condition or event]" may be interpreted, depending on the context, as meaning "once determined," "in response to determination," "once detected [the described condition or event]," or "in response to detection [the described condition or event]."

[0020] Furthermore, in the description of this application and the appended claims, the terms "first," "second," "third," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0021] References to "one embodiment" or "some embodiments" as described in this specification mean that one or more embodiments of this application include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0022] Example 1 This embodiment provides a method for predicting medium- and long-term power generation in photovoltaic power plants, including the following steps: Step 1: Establish historical average climatological data Weather forecasting primarily starts from the global climate system affecting each photovoltaic (PV) site. Based on ERA5 reanalysis data and combined with historical measured data from the target PV sites, it predicts the irradiance of the target PV sites within a specified future time period, obtaining historical average climatological data. Specifically: S1 calculates the multi-year climatological average irradiance of the target photovoltaic power station area based on years of ERA5 reanalysis data.

[0023] S2, For a target photovoltaic power station that provides historical measured data, use the historical measured data to perform statistical regression correction on the multi-year climatological average irradiance corresponding to the target photovoltaic power station to obtain the corrected multi-year climatological average irradiance corresponding to the target photovoltaic power station, and use the obtained corrected multi-year climatological average irradiance as the historical average climatological data. For target photovoltaic power plants that do not provide historical measured data, the multi-year climatological average irradiance corresponding to the target photovoltaic power plant shall be used as the historical climatological average data.

[0024] Step 2: Construction of a meteorological prediction model based on climate factors.

[0025] 1) Analysis of the impact of multiple climate factors on radiation Climate change assessments play a crucial role in analyzing the evolution and impacts of regional climate resources and meteorological disasters. As the butterfly effect theory suggests, "a butterfly flapping its wings in the Amazon rainforest of South America can, two weeks later, cause a tornado in Texas." In the complex models of meteorological systems, errors can grow exponentially, meaning that even a small error can have enormous consequences over time. Therefore, the longer the forecast period, the more important it is to consider the impacts of climate processes.

[0026] Therefore, when considering the impact of changes in meteorological elements in other regions globally on their own region, the concept of "climate factors" is used to distinguish different types of climate change processes. Before introducing related technologies for climate factor research, the quantitative impact of different types of climate factors on the meteorology of Northwest my country was investigated. The results showed that five types of climate factors—the Arctic polar vortex, the subtropical high, the Indo-Pacific warm pool, the tropical atmospheric oscillation, and the Antarctic Oscillation—have a significant impact on meteorological changes in Northwest my country. Therefore, it is also necessary to include the consideration of climate change processes brought about by climate factors in the medium- and long-term forecasting process of this project.

[0027] 2) Use the historical average climate data obtained in step 2 to construct a meteorological prediction model.

[0028] 3) Obtain the main climate factors affecting the meteorological anomalies in the area where the target photovoltaic power station is located, and use the obtained climate factors to correct the meteorological prediction model to obtain the corrected meteorological prediction model.

[0029] 4) Use the modified meteorological forecasting model to predict the weather in the area where the target photovoltaic power station is located, and obtain meteorological forecast data.

[0030] Step 3: Use the predicted meteorological data as input to the constructed medium- and long-term power generation prediction model to obtain the power generation prediction results corresponding to the target photovoltaic power station.

[0031] Step 4: Predict the time period and degree of power curtailment for the target photovoltaic power plant, and use the predicted power curtailment time period and degree of power curtailment to correct the power prediction results to obtain the final power prediction results for the target photovoltaic power plant.

[0032] Obtain historical operating data of the target photovoltaic power station; Semi-supervised learning and active learning methods are used to identify abnormal data in the obtained historical operation data to obtain the corresponding power curtailment anomaly data of the target photovoltaic power station. Based on the operating characteristics of the target photovoltaic power plant and the absorption characteristics of surrounding nodes, and combined with the obtained power curtailment anomaly data, the power curtailment period and degree of power curtailment within the set future time period of the target photovoltaic power plant can be predicted.

[0033] Example 2 This embodiment provides a medium- to long-term power forecasting system for photovoltaic power plants, comprising: The meteorological forecasting model building unit is used to build meteorological forecasting models using the acquired historical average climate data. The model correction unit is used to correct the meteorological forecast model using climate factors to obtain the corrected meteorological forecast model. The meteorological forecasting unit is used to forecast the weather in the area where the target photovoltaic power station is located using the modified meteorological forecasting model, and obtain meteorological forecast data. The power prediction unit is used to take the predicted meteorological forecast data as input to the constructed medium- and long-term power prediction model to obtain the power prediction results corresponding to the target photovoltaic power station.

[0034] Example 3 This embodiment 3 provides a computer device, including: a memory for storing a computer program; and a processor for executing the computer program to implement the steps of a computer method.

[0035] When the processor executes the computer program, it implements the steps of the above-described computer method.

[0036] For example, a method for predicting the medium- and long-term power generation of a photovoltaic power plant includes the following steps: Step 1: Construct a meteorological forecasting model using the acquired historical average climate data; Step 2: Use climate factors to correct the meteorological forecast model to obtain the corrected meteorological forecast model; Step 3: Use the modified meteorological forecasting model to forecast the weather in the area where the target photovoltaic power station is located, and obtain meteorological forecast data; Step 4: Use the predicted meteorological data as input to the constructed medium- and long-term power generation prediction model to obtain the power generation prediction results corresponding to the target photovoltaic power station.

[0037] Alternatively, when the processor executes the computer program, it implements the functions of each module in the above system, such as: The meteorological forecasting model building unit is used to build meteorological forecasting models using the acquired historical average climate data. The model correction unit is used to correct the meteorological forecast model using climate factors to obtain the corrected meteorological forecast model. The meteorological forecasting unit is used to forecast the weather in the area where the target photovoltaic power station is located using the modified meteorological forecasting model, and obtain meteorological forecast data. The power prediction unit is used to take the predicted meteorological forecast data as input to the constructed medium- and long-term power prediction model to obtain the power prediction results corresponding to the target photovoltaic power station.

[0038] The computer device may be a desktop computer, laptop, handheld computer, or cloud server, etc. The computer device may include, but is not limited to, a processor and memory. Those skilled in the art will understand that the above are examples of computer devices and do not constitute a limitation on the computer device; it may include more components than described above, or combine certain components, or different components. For example, the computer device may also include input / output devices, network access devices, buses, etc.

[0039] The processor 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. A general-purpose processor can be a microprocessor, or any conventional processor, etc. The processor is the control center of the computer device, connecting various parts of the computer device through various interfaces and lines.

[0040] The memory can be used to store the computer program and / or module, and the processor implements various functions of the computer device by running or executing the computer program and / or module stored in the memory, and by calling the data stored in the memory.

[0041] The memory may primarily include a program storage area and a data storage area. The program storage area may store the operating system and at least one application program required for a function (such as sound playback, image playback, etc.); the data storage area may store data created based on the use of the mobile phone (such as audio data, phonebook, etc.). Furthermore, the memory may include high-speed random access memory, and may also include non-volatile memory, such as hard disks, RAM, plug-in hard disks, SmartMediaCards (SMC), Secure Digital (SD) cards, FlashCards, at least one disk storage device, flash memory device, or other volatile solid-state storage devices.

[0042] Example 4 This embodiment 4 also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the method described.

[0043] For example, a method for predicting the medium- and long-term power generation of a photovoltaic power plant includes the following steps: Step 1: Construct a meteorological forecasting model using the acquired historical average climate data; Step 2: Use climate factors to correct the meteorological forecast model to obtain the corrected meteorological forecast model; Step 3: Use the modified meteorological forecasting model to forecast the weather in the area where the target photovoltaic power station is located, and obtain meteorological forecast data; Step 4: Use the predicted meteorological data as input to the constructed medium- and long-term power generation prediction model to obtain the power generation prediction results corresponding to the target photovoltaic power station.

[0044] If the modules / units integrated in the computer system are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium.

[0045] Based on this understanding, all or part of the processes in the above-described method can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the above-described computer method. The computer program includes computer program code, which can be in the form of source code, object code, executable file, or a preset intermediate form, etc.

[0046] The computer-readable storage medium may include: any entity or device capable of carrying the computer program code, recording media, USB flash drive, portable hard drive, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal, and software distribution medium, etc.

[0047] It should be noted that the content contained in the computer-readable storage medium may 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 storage medium does not include electrical carrier signals and telecommunication signals.

[0048] Example 5 This embodiment 5 provides a computer product, which includes a computer program stored in a computer-readable storage medium. The processor of the computer device reads the computer program from the computer-readable storage medium and executes the computer program, enabling the computer device to perform the method in embodiment 1. Specifically: A method for predicting medium- and long-term power generation in photovoltaic power plants includes the following steps: Step 1: Construct a meteorological forecasting model using the acquired historical average climate data; Step 2: Use climate factors to correct the meteorological forecast model to obtain the corrected meteorological forecast model; Step 3: Use the modified meteorological forecasting model to forecast the weather in the area where the target photovoltaic power station is located, and obtain meteorological forecast data; Step 4: Use the predicted meteorological data as input to the constructed medium- and long-term power generation prediction model to obtain the power generation prediction results corresponding to the target photovoltaic power station.

[0049] It should be noted that those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods.

[0050] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A method for predicting medium- and long-term power generation in photovoltaic power plants, characterized in that, Includes the following steps: Step 1: Construct a meteorological forecasting model using the acquired historical average climate data; Step 2: Use climate factors to correct the meteorological forecast model to obtain the corrected meteorological forecast model; Step 3: Use the modified meteorological forecasting model to forecast the weather in the area where the target photovoltaic power station is located, and obtain meteorological forecast data; Step 4: Use the predicted meteorological data as input to the constructed medium- and long-term power generation prediction model to obtain the power generation prediction results corresponding to the target photovoltaic power station.

2. The method for medium- and long-term power generation forecasting of a photovoltaic power plant according to claim 1, characterized in that, In step 1, historical average climatological data is obtained, specifically through the following method: Based on years of ERA5 reanalysis data, the multi-year climatological average irradiance of the target photovoltaic power station area is calculated. For a target photovoltaic power station that provides historical measured data, the historical measured data is used to perform statistical regression correction on the multi-year climatological average irradiance corresponding to the target photovoltaic power station to obtain the corrected multi-year climatological average irradiance corresponding to the target photovoltaic power station. The corrected multi-year climatological average irradiance is then used as the historical average climatological data. For target photovoltaic power plants that do not provide historical measured data, the multi-year climatological average irradiance corresponding to the target photovoltaic power plant shall be used as the historical climatological average data.

3. The method for predicting medium- and long-term power generation in a photovoltaic power plant according to claim 1, characterized in that, The method also includes correcting the power prediction results obtained in step 4 by using the predicted power curtailment period and degree to obtain the final power prediction results for the target photovoltaic power station.

4. The method for predicting medium- and long-term power generation in a photovoltaic power plant according to claim 3, characterized in that, The predicted power rationing period and severity are obtained using the following method: Obtain historical operating data of the target photovoltaic power station; Semi-supervised learning and active learning methods are used to identify abnormal data in the obtained historical operation data to obtain the corresponding power curtailment anomaly data of the target photovoltaic power station. Based on the operating characteristics of the target photovoltaic power plant and the absorption characteristics of surrounding nodes, and combined with the obtained power curtailment anomaly data, the power curtailment period and degree of power curtailment within the set future time period of the target photovoltaic power plant can be predicted.

5. The method for medium- and long-term power generation forecasting of a photovoltaic power plant according to claim 1, characterized in that, In step 1, historical average climatological data is obtained, specifically through the following method: Based on years of ERA5 reanalysis data, the multi-year climatological average irradiance of the target photovoltaic power station area is calculated. For a target photovoltaic power station that provides historical measured data, the historical measured data is used to perform statistical regression correction on the multi-year climatological average irradiance corresponding to the target photovoltaic power station to obtain the corrected multi-year climatological average irradiance corresponding to the target photovoltaic power station. The corrected multi-year climatological average irradiance is then used as the historical average climatological data. For target photovoltaic power stations that do not provide historical measured data, the multi-year climatological average irradiance corresponding to the target photovoltaic power station shall be used as the historical average climatological data. The method also includes correcting the power prediction results obtained in step 4 by using the predicted power curtailment period and degree to obtain the final power prediction results for the target photovoltaic power station.

6. The method for medium- and long-term power generation forecasting of a photovoltaic power plant according to claim 1, characterized in that, In step 1, historical average climatological data is obtained, specifically through the following method: Based on years of ERA5 reanalysis data, the multi-year climatological average irradiance of the target photovoltaic power station area is calculated. For a target photovoltaic power station that provides historical measured data, the historical measured data is used to perform statistical regression correction on the multi-year climatological average irradiance corresponding to the target photovoltaic power station to obtain the corrected multi-year climatological average irradiance corresponding to the target photovoltaic power station. The corrected multi-year climatological average irradiance is then used as the historical average climatological data. For target photovoltaic power stations that do not provide historical measured data, the multi-year climatological average irradiance corresponding to the target photovoltaic power station shall be used as the historical average climatological data. The method also includes correcting the power prediction results obtained in step 4 by using the predicted power curtailment period and power curtailment level to obtain the final power prediction results of the target photovoltaic power station. The predicted power rationing period and severity are obtained using the following method: Obtain historical operating data of the target photovoltaic power station; Semi-supervised learning and active learning methods are used to identify abnormal data in the obtained historical operation data to obtain the corresponding power curtailment anomaly data of the target photovoltaic power station. Based on the operating characteristics of the target photovoltaic power plant and the absorption characteristics of surrounding nodes, and combined with the obtained power curtailment anomaly data, the power curtailment period and degree of power curtailment within the set future time period of the target photovoltaic power plant can be predicted.

7. A medium- to long-term power generation forecasting system for photovoltaic power plants, characterized in that, include: The meteorological forecasting model building unit is used to build meteorological forecasting models using the acquired historical average climate data. The model correction unit is used to correct the meteorological forecast model using climate factors to obtain the corrected meteorological forecast model. The meteorological forecasting unit is used to forecast the weather in the area where the target photovoltaic power station is located using the modified meteorological forecasting model, and obtain meteorological forecast data. The power prediction unit is used to take the predicted meteorological forecast data as input to the constructed medium- and long-term power prediction model to obtain the power prediction results corresponding to the target photovoltaic power station.

8. A computer device, characterized in that, include: A processor is used to execute computer programs; A computer-readable storage medium storing a computer program that, when executed by the processor, performs the method as described in any one of claims 1-6.

9. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the method as described in any one of claims 1-6.

10. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the method as described in any one of claims 1-6.