Method for predicting power generation capacity of photovoltaic power station and computing equipment

Abstract

The invention provides a method for predicting the power generation capacity of a photovoltaic power station and computing equipment, and is applied to the technical field of new energy prediction. The method comprises the following steps: establishing a station library, and collecting historical irradiance data of a predetermined number of stations around; the predetermined number of stations are determined by calculating the distances between all stations and the target photovoltaic power station; the historical irradiance data of the predetermined number of stations are processed, the historical irradiance data of the target photovoltaic power station are calculated based on double fitting, the first fitting in the double fitting is used for describing the fluctuation of the irradiance, and the second fitting in the double fitting is used for calculating the irradiance of the missing point. According to the method, the prediction accuracy can be improved, and the problem of data leakage can be effectively avoided.

Description

Technical Field

[0001] The present invention relates to the technical field of new energy prediction, and in particular to a method and a computing device for predicting the power generation capacity of a photovoltaic power station. Background Art

[0002] In the process of developing new energy forecasting business, the historical irradiance of power plants is often used. However, there is no accumulated historical data for newly built power plants, so the irradiance of adjacent sites is usually used instead.

[0003] In solar power generation forecasting, historical irradiance data is crucial for establishing accurate power generation forecasting models, because solar radiation is the basic input for solar power generation, and its intensity and duration directly affect power generation. However, newly built solar power stations often lack sufficient historical data, which poses a challenge to the construction of forecasting models, and directly using data from neighboring sites may lead to large errors in forecast results. Although sites with similar geographical locations may experience similar weather conditions, factors such as terrain, obstructions, and atmospheric conditions may lead to significant differences in irradiance.

[0004] Therefore, a method for predicting the power generation capacity of a photovoltaic power station is needed to more accurately estimate the irradiance data without historical irradiance data. Summary of the invention

[0005] The present invention aims to provide a method and a computing device for predicting the power generation capacity of a photovoltaic power station, and solves the problem of large errors in directly using the irradiance of adjacent stations instead for newly built photovoltaic power stations without historical irradiance.

[0006] According to one aspect of the present invention, there is provided a method for predicting power generation capacity of a photovoltaic power station, comprising:

[0007] Establish a station database to collect historical irradiance data of a predetermined number of surrounding stations;

[0008] The predetermined number of stations is determined by calculating the distances between all stations and the target photovoltaic power station;

[0009] The historical irradiance data of the predetermined number of stations are processed, and the historical irradiance data of the target photovoltaic power station is calculated based on double fitting, wherein the first fitting in the double fitting is used to describe the fluctuation of the irradiance, and the second fitting in the double fitting is used to calculate the irradiance of the missing point.

[0010] According to some embodiments, calculating the distances between all stations and the target photovoltaic power station includes:

[0011] Query the longitude and latitude of all stations in the station database;

[0012] Get the latitude and longitude of the target photovoltaic power station;

[0013] Calculate the distance between all stations in the station library and the target photovoltaic power station. The distance formula is:

[0014]

[0015] in, are the latitudes of the site and the target PV power station, γ a , γ b are the longitudes of the site and the target PV power station respectively.

[0016] According to some embodiments, the predetermined number of stations comprises:

[0017] Sort the distances between all stations in the station database and the target photovoltaic power station in ascending order, and select the top N stations;

[0018] Check whether the historical irradiance data of N stations meet the requirements. If not, eliminate the unsatisfactory stations and fill in the gaps from the N+1th station in sequence until the historical irradiance data of N stations meet the requirements.

[0019] According to some embodiments, the predetermined number of stations is determined by calculating the distances between all stations and the target photovoltaic power station, including:

[0020] Sort the distances between all stations in the station database and the target photovoltaic power station in ascending order, and select the top N stations;

[0021] Check whether the historical irradiance data of N stations meet the requirements. If not, eliminate the unsatisfactory stations and fill in the gaps from the N+1th station in sequence until the historical irradiance data of N stations meet the requirements.

[0022] According to some embodiments, data processing is performed on the historical irradiance data of the predetermined number of stations, including:

[0023] Read historical irradiance data in a fixed time window to ensure uniform distribution of the time series;

[0024] Characterization processing is performed to calculate the maximum irradiance value of the day, and the irradiance value at each time point within the day is divided by the maximum irradiance value of the day to obtain a proportional series, which reflects the trend of irradiance changing over time.

[0025] According to some embodiments, processing the historical irradiance data of the predetermined number of stations further includes:

[0026] The average value of adjacent data points is calculated. If the deviation between the current point and the average value of the adjacent data points is greater than a set value, the data is judged to be abnormal and the data of the current point is discarded.

[0027] According to some embodiments, the first fitting in the dual fitting comprises:

[0028] A quadratic polynomial-based curve fitting is used to establish a relationship between the time and the ratio series, and all data points with a ratio of 0 are removed from the ratio series.

[0029] According to some embodiments, the second fitting in the dual fitting comprises:

[0030] Based on the linear fitting of the first-order polynomial, the relationship between the irradiance and the ratio at the existing time point is established through the linear fitting based on the first-order polynomial.

[0031] According to some embodiments, calculating historical irradiance data of a target photovoltaic power station based on double fitting includes:

[0032] For the points with missing data, the time is substituted into the quadratic polynomial, and the irradiance value at the same time point in adjacent days within the time window is checked. If the irradiance value at the same time point in the adjacent days is also 0, the irradiance of the point is replaced by 0. Otherwise, the proportion of the missing point is substituted into the linear polynomial to obtain the irradiance of the missing point.

[0033] According to some embodiments, the formula for calculating the historical irradiance of the target photovoltaic power station is:

[0034]

[0035] Where I represents the historical irradiance of the target PV power station, I i represents the historical irradiance of the i-th station, d i Indicates the distance between the i-th station and the target PV power station.

[0036] According to another aspect of the present invention, a computer program product is provided, comprising a computer program, wherein the computer program implements any of the above methods when executed by a processor.

[0037] According to another aspect of the present invention, there is provided a computing device, comprising:

[0038] Processor; and

[0039] A memory stores a computer program, and when the computer program is executed by the processor, the processor is caused to perform any of the aforementioned methods.

[0040] According to an exemplary embodiment of the present invention, by collecting historical irradiance data of a predetermined number of stations and fitting the historical irradiance data with quadratic polynomials and linear polynomials, the historical irradiance of the target photovoltaic power station can be more accurately reflected. The curve fitting based on the quadratic polynomial is combined with the linear fitting based on the linear polynomial to form a two-stage prediction model. The quadratic polynomial fitting can capture the nonlinear change trend in the historical irradiance data, and the linear polynomial fitting simplifies the data structure, making it easier to use for real-time prediction and analysis.

[0041] According to some embodiments, the method of the present invention processes the historical irradiance data of a predetermined number of surrounding stations through a series of detailed data processing, and finally establishes a polynomial relationship with time, instead of directly using the original data of the historical irradiance data of the surrounding stations. This not only takes into account the characteristics of the target photovoltaic power station itself and the relationship between power generation and time, but also avoids directly exposing data details, thereby reducing the risk of data leakage of historical irradiance data of other stations.

[0042] It is to be understood that the foregoing general description and the following detailed description are exemplary only and are not restrictive of the invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for describing the embodiments are briefly introduced below.

[0044] Figure 1 A flow chart of a method for predicting power generation capacity of a photovoltaic power plant according to an example embodiment is shown.

[0045] Figure 2 A flow chart for determining a predetermined number of stations according to an example embodiment is shown.

[0046] Figure 3 A block diagram of a computing device according to an example embodiment of the present invention is shown. DETAILED DESCRIPTION

[0047] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in many forms and should not be construed as limited to the embodiments set forth herein; rather, these embodiments are provided so that the present invention will be comprehensive and complete and fully convey the concepts of the example embodiments to those skilled in the art. The same reference numerals in the figures represent the same or similar parts, and thus their repeated description will be omitted.

[0048] In addition, the described features, structures or characteristics may be combined in one or more embodiments in any suitable manner. In the following description, many specific details are provided to provide a full understanding of the embodiments of the present invention. However, those skilled in the art will appreciate that the technical solution of the present invention can be practiced without one or more of the specific details, or other methods, components, devices, steps, etc. may be adopted. In other cases, known methods, devices, implementations or operations are not shown or described in detail to avoid blurring various aspects of the present invention.

[0049] The block diagrams shown in the accompanying drawings are merely functional entities and do not necessarily correspond to physically independent entities. That is, these functional entities may be implemented in software form, or in one or more hardware modules or integrated circuits, or in different networks and / or processor devices and / or microcontroller devices.

[0050] The flowcharts shown in the accompanying drawings are only exemplary and do not necessarily include all the contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps can be decomposed, and some operations / steps can be combined or partially combined, so the actual execution order may change according to actual conditions.

[0051] It should be understood that although the terms first, second, third, etc. may be used herein to describe various components, these components should not be limited by these terms. These terms are used to distinguish one component from another component. Therefore, the first component discussed below can be referred to as the second component without departing from the teachings of the present inventive concept. As used herein, the term "and / or" includes any one of the associated listed items and all combinations of one or more.

[0052] The user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in the present invention are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with the relevant laws, regulations and standards of relevant countries and regions, and provide corresponding operation entrances for users to choose to authorize or refuse.

[0053] Those skilled in the art will appreciate that the drawings are merely schematic diagrams of example embodiments, and the modules or processes in the drawings are not necessarily necessary for implementing the present invention, and therefore cannot be used to limit the protection scope of the present invention.

[0054] For newly built photovoltaic power stations without historical irradiance, directly using the historical irradiance data of adjacent stations, on the one hand, because the irradiance of the two places is not exactly the same, the data of the adjacent stations may cause large errors in the prediction results; on the other hand, due to the different property rights of the two places and different data ownership, directly using the data of other property owners will lead to data leakage. The present invention proposes a method for predicting the power generation capacity of a photovoltaic power station, which solves the above problems by calculating the irradiance of a specified location based on the irradiance of the adjacent area.

[0055] Exemplary embodiments of the present invention are described below with reference to the accompanying drawings.

[0056] Figure 1 A flow chart of a method for predicting power generation capacity of a photovoltaic power plant according to an example embodiment is shown.

[0057] This method can effectively utilize the historical data of surrounding stations and provide reliable historical irradiance prediction for newly built photovoltaic power stations.

[0058] In S101, a station database is established to collect historical irradiance data of a predetermined number of surrounding stations.

[0059] According to the example embodiment, a database is created for collecting and storing historical irradiance data of surrounding stations. The database also includes the latitude and longitude coordinates of each station, which facilitates the subsequent rapid retrieval and determination of a predetermined number of stations. The predetermined number of stations is determined by calculating the distances between all stations and the target photovoltaic power station. (See the flowchart for determining the predetermined number of stations. Figure 2 . )

[0060] Calculating the distance between all stations and the target photovoltaic power station includes querying the longitude and latitude of all stations in the station database, extracting the geographical coordinates of each station from the established station database, including latitude (Φ) and longitude (γ), obtaining the longitude and latitude of the target photovoltaic power station from the geographical coordinates of the target photovoltaic power station, and calculating the distance between the station and the target photovoltaic power station. The distance formula is:

[0061]

[0062] in, are the latitudes of the site and the target PV power station, γ a , γ b are the longitudes of the site and the target PV power station respectively; 111.12 is an approximate value obtained by converting the angle into distance by dividing the average value of the earth's equatorial circumference by 360 degrees.

[0063] In S103, historical irradiance data of a target photovoltaic power station is calculated based on double fitting using historical irradiance data of a predetermined number of stations.

[0064] According to an example embodiment, the historical irradiance data of the predetermined number of stations are further processed to ensure data quality, so as to better calculate the historical irradiance data of the target photovoltaic power station based on double fitting.

[0065] According to some embodiments, the historical irradiance data of the station that meets the conditions is cleaned, and the average value of the adjacent data points is calculated, that is, for each pair of consecutive time points, the average value of their irradiance values ​​is calculated. The deviation of each data point from the average value of the adjacent points before and after is compared. If the deviation between the current point and the average value of the adjacent points is greater than the set value, the data is judged to be abnormal and the data of the point is eliminated.

[0066] According to the exemplary embodiment, deviation = |(current point value - average value) / average value|*100%, the set value is (5+number of minutes apart)%, if the deviation percentage is greater than (5+number of minutes apart)%, the current data point is considered abnormal. Wherein, "number of minutes apart" refers to the time interval (in minutes) between the current point and the adjacent point, taking into account the effect of the time interval on the allowable deviation, as the time interval increases, the allowable deviation also increases accordingly.

[0067] The data processing of the historical irradiance data of the predetermined number of stations also includes reading historical data, reading the historical irradiance data of a fixed time window, and ensuring the uniform distribution of the time series. According to some embodiments, the fixed time window takes 15 days as an example, and the uniform distribution of the time series is ensured, that is, there is a data point at a certain time interval, and the time is converted into an integer according to its position in a day, which is convenient for data processing and comparison, for example, a day is divided into 24 integers (representing 24 hours), and the daily irradiance of all points is selected from the predetermined stations.

[0068] The historical irradiance data of the predetermined number of stations are characterized and the maximum daily irradiance value I is calculated. max :

[0069] I max =MAX(I1,I2,I3……)

[0070] Among them, I1, I2, and I3 are the irradiances at each time point;

[0071] The irradiance value at each time point in the day is divided by the maximum irradiance value of the day to obtain a ratio series, where δ is the ratio at each time point, and the ratio represents fluctuation:

[0072]

[0073] Among them, δ1, δ2, and δ3 represent the percentage of the irradiance value at each point to the maximum irradiance of the day.

[0074] According to an example embodiment, historical irradiance data of a target photovoltaic power station is calculated based on double fitting, wherein a first fitting in the double fitting is used to describe the fluctuation of irradiance, and the first fitting includes curve fitting based on a quadratic polynomial, removing all data points with a proportion of 0 from a proportion series, and performing a least squares polynomial fitting on the time and proportion series to obtain a quadratic polynomial function:

[0075] y=ax 2 +bx+c

[0076] Among them, y represents the magnification result, and x represents the time point position.

[0077] Use the fitted quadratic polynomial function to predict known data points and compare the difference between the predicted value and the actual proportional series to evaluate the accuracy of the model. Quadratic polynomial fitting can reveal the potential curve trend in the data, which is very important for predicting future behavior or periodic patterns. The fitted function can also help identify outliers in the original data and remove data points that deviate significantly from the function's predicted value.

[0078] For the points with missing data, the time is substituted into the quadratic polynomial, and the irradiance value at the same time point in adjacent days within the time window is checked. If the irradiance value at the same time point in the adjacent days is also 0, the irradiance of the point is replaced by 0, otherwise it is replaced by the calculated result.

[0079] According to an example embodiment, the second fitting in the double fitting is used to calculate the irradiance of the missing point, and the second fitting includes a linear fitting based on a first-order polynomial, and a model simplification is performed on the basis of the first fitting, and a linear fitting based on a first-order polynomial is performed on the irradiance and proportion series of the existing time points to obtain a first-order function:

[0080] y=ax+b

[0081] Among them, y represents the irradiance result and x represents the magnification.

[0082] Calculate the irradiance of the missing point, substitute the proportion of the missing point into the first-order polynomial to obtain the irradiance of the missing point. If the maximum irradiance value of the date where the missing point is located is unknown, the average maximum irradiance value of the adjacent dates can be used as a substitute.

[0083] Calculate the irradiance of the target photovoltaic power station according to the formula:

[0084]

[0085] Where I represents the historical irradiance of the target PV power station, I i represents the historical irradiance of the i-th station, d iIndicates the distance between the i-th station and the target PV power station.

[0086] This example embodiment establishes the relationship between the irradiance and the ratio at the existing time point through the linear fitting based on the first-order polynomial. The linear fitting based on the first-order polynomial not only simplifies the analysis of the data set, but also provides a practical tool for irradiance prediction of photovoltaic power plants.

[0087] The method for predicting the power generation capacity of a photovoltaic power station of the present invention processes the historical irradiance data of surrounding stations, eliminates abnormal values, improves the accuracy and consistency of the data, reduces the impact of noise on the prediction model, and further effectively improves the data quality used for the prediction of the historical irradiance of the photovoltaic power station, thereby improving the accuracy and stability of the prediction. The clean data can more accurately reflect the actual irradiance variation law, making the prediction model more reliable.

[0088] The present invention combines the curve fitting based on quadratic polynomials with the linear fitting based on linear polynomials, which improves the flexibility and accuracy of the prediction, can better adapt to the complex pattern of irradiance changing over time, and maintains the simplicity of the model. In the process of two fittings, the model not only uses direct observation data, but also makes full use of the inherent structure of the data and the law of time series, which makes the prediction results more reliable.

[0089] Figure 2 A flow chart for determining a predetermined number of stations according to an example embodiment is shown.

[0090] In S201, the distances between the stations and the target photovoltaic power station are sorted in ascending order, and the top N stations are selected.

[0091] According to an example embodiment, all stations are sorted in ascending order of distance based on the calculated distances between the stations and the target photovoltaic power station, and the top N stations are selected from the sorted list as preliminary candidates.

[0092] In S203, it is detected whether the historical irradiance data of N stations meet the requirements.

[0093] According to some embodiments, it is checked whether the amount of historical irradiance data of the N initially selected sites meets the requirements and whether the altitude difference is less than 500 meters. If so, proceed to S207; otherwise, proceed to S205.

[0094] In S205, stations that do not meet the requirements are eliminated, and the eliminated stations are filled in order from the N+1th place.

[0095] According to an example embodiment, if it is found during the detection process that the data quality of some stations does not meet the requirements, these stations are eliminated from the preliminary candidate list, and the stations from the N+1th place in the distance list are selected in sequence to supplement them. The historical irradiance data of the newly supplemented stations are also checked until the historical irradiance data of the N stations meet the requirements.

[0096] In S207, a predetermined number of stations is determined.

[0097] After completing the quality inspection of the historical irradiance data, N stations that meet the requirements are finally determined as the predetermined number of stations for calculating the historical irradiance data of the target photovoltaic power station.

[0098] This embodiment can ensure that the stations used for analyzing and predicting the historical irradiance data of the target photovoltaic power station are geographically adjacent and have reliable data quality by detecting the historical irradiance data of the stations, thereby improving the accuracy and reliability of the prediction model.

[0099] The present invention uses the information of surrounding stations for prediction and uses the fitted model to predict the irradiance of the target photovoltaic power station, rather than directly using the real data of the neighboring stations. This not only respects the ownership and privacy of the data, but also avoids the risk of data leakage. And by converting the predicted irradiance data into a prediction of the power generation of the photovoltaic power station, it can better plan power production and scheduling, optimize the operating efficiency of the power station, reduce operating costs, and improve economic benefits.

[0100] Figure 3 A block diagram of a computing device according to an example embodiment of the present invention is shown.

[0101] like Figure 3 As shown, computing device 30 includes processor 12 and memory 14. Computing device 30 may also include bus 22, network interface 16, and I / O interface 18. Processor 12, memory 14, network interface 16, and I / O interface 18 may communicate with each other via bus 22.

[0102] The processor 12 may include one or more general-purpose CPUs (Central Processing Units, processors), microprocessors, or application-specific integrated circuits, etc., for executing relevant program instructions.

[0103] The memory 14 may include a machine system readable medium in the form of a volatile memory, such as a random access memory (RAM), a read-only memory (ROM) and / or a cache memory. The memory 14 is used to store one or more programs including instructions and data. The processor 12 can read the instructions stored in the memory 14 to execute the above-mentioned method according to the embodiment of the present invention.

[0104] The computing device 30 may also communicate with one or more networks via the network interface 16. The network interface 16 may be a wireless network interface.

[0105] The bus 22 may include an address bus, a data bus, a control bus, etc. The bus 22 provides a path for exchanging information between components.

[0106] It should be noted that, in the specific implementation process, the computing device 30 may also include other components necessary for normal operation. In addition, those skilled in the art may understand that the above device may only include components necessary for implementing the embodiments of this specification, and need not include all components shown in the figure.

[0107] The present invention also provides a computer-readable storage medium having a computer program stored thereon, which implements the steps of the above method when executed by a processor. The computer-readable storage medium may include, but is not limited to, any type of disk, including a floppy disk, an optical disk, a DVD, a CD-ROM, a microdrive, and a magneto-optical disk, a ROM, a RAM, an EPROM, an EEPROM, a DRAM, a VRAM, a flash memory device, a magnetic card or an optical card, a nanosystem (including a molecular memory IC), a network storage device, a cloud storage device, or any type of medium or device suitable for storing instructions and / or data.

[0108] An embodiment of the present invention also provides a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to cause a computer to execute part or all of the steps of any method recorded in the above method embodiments.

[0109] Those skilled in the art can clearly understand that the technical solution of the present invention can be implemented with the help of software and / or hardware. "Unit" and "module" in this specification refer to software and / or hardware that can independently complete or cooperate with other components to complete specific functions, where the hardware can be, for example, a field programmable gate array, an integrated circuit, etc.

[0110] It should be noted that, for the above-mentioned method embodiments, for the sake of simplicity, they are all described as a series of action combinations, but those skilled in the art should know that the present invention is not limited by the described action sequence, because according to the present invention, certain steps can be performed in other sequences or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the present invention.

[0111] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0112] In the several embodiments provided by the present invention, it should be understood that the disclosed devices can be implemented in other ways. For example, the device embodiments described above are only schematic, such as the division of units, which is only a logical function division. There may be other division methods in actual implementation, such as 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 mutual coupling or direct coupling or communication connection shown or discussed can be through some service interfaces, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0113] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0114] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0115] If the integrated 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 memory. Based on this understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art or the whole or part of the technical solution can be embodied in the form of a software product, which is stored in a memory and includes several instructions for a computer device (which can be a personal computer, server or network device, etc.) to perform all or part of the steps of the methods of various embodiments of the present invention.

[0116] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0117] The exemplary embodiments of the present invention are specifically shown and described above. It should be understood that the present invention is not limited to the detailed structures, configurations or implementations described herein; on the contrary, the present invention is intended to cover various modifications and equivalent configurations included in the spirit and scope of the appended clauses.

[0118] Those skilled in the art can clearly understand that the technical solution of the present invention can be implemented with the help of software and / or hardware. "Unit" and "module" in this specification refer to software and / or hardware that can independently complete or cooperate with other components to complete specific functions, where the hardware can be, for example, a field programmable gate array, an integrated circuit, etc.

[0119] It should be noted that, for the above-mentioned method embodiments, for the sake of simplicity, they are all described as a series of action combinations, but those skilled in the art should know that the present invention is not limited by the described action sequence, because according to the present invention, certain steps can be performed in other sequences or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the present invention.

[0120] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0121] In the several embodiments provided by the present invention, it should be understood that the disclosed devices can be implemented in other ways. For example, the device embodiments described above are only schematic, such as the division of units, which is only a logical function division. There may be other division methods in actual implementation, such as 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 mutual coupling or direct coupling or communication connection shown or discussed can be through some service interfaces, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0122] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0123] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0124] If the integrated 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 memory. Based on this understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art or the whole or part of the technical solution can be embodied in the form of a software product, which is stored in a memory and includes several instructions for a computer device (which can be a personal computer, server or network device, etc.) to perform all or part of the steps of the methods of various embodiments of the present invention.

[0125] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0126] The exemplary embodiments of the present invention are specifically shown and described above. It should be understood that the present invention is not limited to the detailed structures, configurations or implementations described herein; on the contrary, the present invention is intended to cover various modifications and equivalent configurations included in the spirit and scope of the appended clauses.

Claims

1. A method for predicting the power generation capacity of a photovoltaic power station, characterized in that: include: Establish a station database to collect historical irradiance data of a predetermined number of surrounding stations; The predetermined number of stations is determined by calculating the distances between all stations and the target photovoltaic power station; The historical irradiance data of the predetermined number of stations are processed, and the historical irradiance data of the target photovoltaic power station is calculated based on double fitting, wherein the first fitting in the double fitting is used to describe the fluctuation of the irradiance, and the second fitting in the double fitting is used to calculate the irradiance of the missing point.

2. The method according to claim 1, characterized in that The calculation of the distances between all stations and the target photovoltaic power station includes: Query the longitude and latitude of all stations in the station database; Get the latitude and longitude of the target photovoltaic power station; Calculate the distance between all stations in the station library and the target photovoltaic power station. The distance formula is: in, are the latitudes of the site and the target PV power station, γ a , γ b are the longitudes of the site and the target PV power station respectively.

3. The method according to claim 1, characterized in that The predetermined number of stations is determined by calculating the distances between all stations and the target photovoltaic power station, including: Sort the distances between all stations in the station database and the target photovoltaic power station in ascending order, and select the top N stations; Check whether the historical irradiance data of N stations meet the requirements. If not, eliminate the unsatisfactory stations and fill in the gaps from the N+1th station in sequence until the historical irradiance data of N stations meet the requirements.

4. The method according to claim 1, characterized in that: Processing the historical irradiance data of the predetermined number of stations includes: Read historical irradiance data in a fixed time window to ensure uniform distribution of the time series; Characterization processing is performed to calculate the maximum irradiance value of the day, and the irradiance value at each time point within the day is divided by the maximum irradiance value of the day to obtain a proportional series, which reflects the trend of irradiance changing over time.

5. The method according to claim 4, characterized in that Processing the historical irradiance data of the predetermined number of stations also includes: The average value of adjacent data points is calculated. If the deviation between the current point and the average value of the adjacent data points is greater than a set value, the data is judged to be abnormal and the data of the current point is discarded.

6. The method according to claim 1, characterized in that The first fit in the dual fit includes: The quadratic polynomial-based curve fitting establishes a relationship between the time and the ratio series, and removes all data points with a ratio of 0 from the ratio series.

7. The method according to claim 6, characterized in that The second fit in the dual fit includes: The linear fitting based on the first-order polynomial establishes the relationship between the irradiance and the ratio at the existing time point.

8. The method according to claim 7, characterized in that The historical irradiance data of the target photovoltaic power station is calculated based on double fitting, including: For the points with missing data, the time is substituted into the quadratic polynomial, and the irradiance value at the same time point in adjacent days within the time window is checked. If the irradiance value at the same time point in the adjacent days is also 0, the irradiance of the point is replaced by 0. Otherwise, the proportion of the missing point is substituted into the linear polynomial to obtain the irradiance of the missing point.

9. The method according to claim 1, characterized in that: The formula for calculating the historical irradiance of the target photovoltaic power station is: Where I represents the historical irradiance of the target PV power station, I i represents the historical irradiance of the i-th station, d i Indicates the distance between the i-th station and the target PV power station.

10. A computing device, characterized in that include: processor; as well as A memory storing a computer program, which, when executed by the processor, enables the processor to perform the method according to any one of claims 1 to 9.

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