A method for identifying a factor and a characteristic quantity affecting photovoltaic power generation and a related device

Abstract

The application discloses a kind of identification method and related device of the factor and characteristic quantity of influence photovoltaic power generation, comprising: the operation parameters of photovoltaic power station are arranged to form photovoltaic power station dataset;After error detection and outlier rejection to photovoltaic power station dataset, according to photovoltaic power station periphery meteorological station, satellite remote sensing, radar and reanalysis data are interpolated to obtain multi-source data;Determine typical weather process type and select the meteorological factor of influence photovoltaic power generation power;According to weather process type and meteorological factor, based on multi-source data, extract meteorological factor observation data and photovoltaic power generation power data under different weather types as sample data set of photovoltaic power generation output analysis;To calculate the correlation coefficient of each meteorological factor and photovoltaic power generation power under different weather types, and then determine the key meteorological factor and characteristic quantity of influence photovoltaic power generation power.The problem that the prior art does not consider enough comprehensive meteorological factor to influence photovoltaic power generation power is solved.

Description

Technical Field

[0001] This application relates to the field of photovoltaic power generation technology, and in particular to a method and related apparatus for obtaining factors and characteristic quantities that affect photovoltaic power generation. Background Technology

[0002] Solar energy, as a renewable and clean energy source, has promising application prospects, and the photovoltaic (PV) industry, which utilizes solar power generation, is developing rapidly. However, PV power generation is affected by various meteorological factors, exhibiting significant fluctuations and intermittency, which in turn significantly impacts the stability and security of the traditional power grid connected to it. Therefore, quantitatively identifying the key meteorological factors affecting PV power generation and their characteristic quantities is particularly important, as it can provide crucial foundational support for energy meteorological services such as PV power plant site selection, solar energy resource assessment, daily operation and maintenance, and PV power generation forecasting.

[0003] Existing methods for identifying key meteorological factors and their characteristics affecting photovoltaic (PV) power generation are mostly based on meteorological elements observed at PV power plants, such as solar radiation, temperature, humidity, wind, and air pressure, along with PV power generation data. These methods utilize correlation coefficients between the characteristics of each meteorological factor and PV power generation to identify key meteorological factors, determining the magnitude and degree of influence of these factors based on the magnitude and ranking of the correlation coefficients. However, these methods primarily rely on observational data from PV power plants, resulting in a limited range of data sources and limitations in data quality control and the selection of meteorological factors. Furthermore, they do not comprehensively consider all meteorological factors affecting PV power generation. Summary of the Invention

[0004] This application provides a method and related apparatus for identifying factors and characteristic quantities that affect photovoltaic power generation, in order to solve the problem that the existing technology does not fully consider the meteorological factors affecting photovoltaic power generation.

[0005] In view of this, the first aspect of this application provides a method for identifying factors and characteristic quantities affecting photovoltaic power generation, the method comprising:

[0006] The operating parameters of the photovoltaic power stations in the area to be identified are collected and organized to form a photovoltaic power station dataset;

[0007] After performing data error detection and outlier removal on the photovoltaic power station dataset, interpolation was performed based on meteorological stations, satellite remote sensing, radar, and reanalysis data around the photovoltaic power station to obtain multi-source data.

[0008] Determine the typical weather process types for the area to be identified, and select meteorological factors that affect photovoltaic power generation.

[0009] Based on the weather process type and meteorological factors, meteorological factor observation data and photovoltaic power generation data under different weather types are extracted from the multi-source data to serve as a sample dataset for photovoltaic power generation output analysis.

[0010] Based on the sample dataset, the correlation coefficients between various meteorological factors and photovoltaic power generation under different weather types are calculated. The correlation coefficients are then sorted and analyzed to determine the key meteorological factors and characteristic quantities affecting photovoltaic power generation.

[0011] Optionally, the step of summarizing and organizing the operating parameters of the photovoltaic power stations in the area to be identified to form a photovoltaic power station dataset specifically includes:

[0012] The meteorological elements, radiation data, power generation observation data, location coordinates, and installed capacity of the photovoltaic power stations in the area to be identified are collected and organized to form a photovoltaic power station dataset.

[0013] Optionally, the step of calculating the correlation coefficients between various meteorological factors and photovoltaic power generation under different weather types based on the sample dataset specifically includes:

[0014] Based on the Pearson correlation coefficient calculation formula, the correlation coefficients between various meteorological factors and photovoltaic power generation under different weather types are calculated according to the sample dataset.

[0015] The formula for calculating the Pearson correlation coefficient is as follows:

[0016]

[0017] Among them, Corr j Let be the correlation coefficient between the j-th meteorological element and photovoltaic power generation. Let be the average value of the j-th meteorological element data over the analysis period. This represents the average value of photovoltaic power generation data over the analysis period.

[0018] Optionally, the meteorological factors include: solar radiation, temperature, humidity, wind, air pressure, PM2.5 concentration, PM10 concentration, visibility, and total cloud cover.

[0019] A second aspect of this application provides a system for identifying factors and characteristic quantities affecting photovoltaic power generation, the system comprising:

[0020] The generation unit is used to summarize and organize the operating parameters of the photovoltaic power station in the area to be identified, and form a photovoltaic power station dataset.

[0021] The data processing unit is used to perform data error detection and outlier removal on the photovoltaic power station dataset, and then interpolate and supplement it based on meteorological stations, satellite remote sensing, radar and reanalysis data around the photovoltaic power station to obtain multi-source data.

[0022] Selecting units is used to determine the typical weather process types of the area to be identified and to select meteorological factors that affect photovoltaic power generation.

[0023] The extraction unit is used to extract meteorological factor observation data and photovoltaic power generation data under different weather types based on the weather process type and the meteorological factors, and use them as a sample dataset for photovoltaic power generation output analysis.

[0024] The calculation unit is used to calculate the correlation coefficients between various meteorological factors and photovoltaic power generation under different weather types based on the sample dataset, sort and analyze the correlation coefficients, and determine the key meteorological factors and characteristic quantities affecting photovoltaic power generation.

[0025] Optionally, the generating unit is specifically used for:

[0026] The meteorological elements, radiation data, power generation observation data, location coordinates, and installed capacity of the photovoltaic power stations in the area to be identified are compiled and organized to form a photovoltaic power station dataset.

[0027] Optionally, the computing unit is specifically used for:

[0028] Based on the Pearson correlation coefficient calculation formula, the correlation coefficients between various meteorological factors and photovoltaic power generation under different weather types are calculated according to the sample dataset.

[0029] The correlation coefficients were sorted and analyzed to determine the key meteorological factors and characteristic quantities affecting photovoltaic power generation.

[0030] The formula for calculating the Pearson correlation coefficient is as follows:

[0031]

[0032] Among them, Corr j Let be the correlation coefficient between the j-th meteorological element and photovoltaic power generation. Let be the average value of the j-th meteorological element data over the analysis period. This represents the average value of photovoltaic power generation data over the analysis period.

[0033] Optionally, the meteorological factors include: solar radiation, temperature, humidity, wind, air pressure, PM2.5 concentration, PM10 concentration, visibility, and total cloud cover.

[0034] A third aspect of this application provides a device for identifying factors and characteristic quantities affecting photovoltaic power generation, the device comprising a processor and a memory:

[0035] The memory is used to store program code and transmit the program code to the processor;

[0036] The processor is configured to execute, according to the instructions in the program code, the steps of the method for identifying factors and characteristic quantities affecting photovoltaic power generation as described in the first aspect above.

[0037] The fourth aspect of this application provides a computer-readable storage medium for storing program code for executing the method for identifying factors and characteristic quantities affecting photovoltaic power generation as described in the first aspect above.

[0038] As can be seen from the above technical solutions, this application has the following advantages:

[0039] This application provides a method for identifying factors and characteristic quantities affecting photovoltaic power generation, including: summarizing and organizing the operating parameters of photovoltaic power stations in the area to be identified to form a photovoltaic power station dataset; after performing data error detection and outlier removal on the photovoltaic power station dataset, interpolating and supplementing it based on meteorological stations, satellite remote sensing, radar, and reanalysis data around the photovoltaic power station to obtain multi-source data; determining the typical weather process types of the area to be identified and selecting meteorological factors affecting photovoltaic power generation; based on the weather process type and meteorological factors, extracting meteorological factor observation data and photovoltaic power generation data under different weather types from the multi-source data as a sample dataset for photovoltaic power generation output analysis; calculating the correlation coefficients between each meteorological factor and photovoltaic power generation under different weather types based on the sample dataset, ranking and analyzing the correlation coefficients to determine the key meteorological factors and characteristic quantities affecting photovoltaic power generation.

[0040] Compared with existing technologies, this application employs multi-source data for quality control and interpolation of photovoltaic power plant observation data, resulting in a higher quality and more complete time series for the analysis dataset. Furthermore, it adds meteorological factors such as PM2.5 concentration, PM10 concentration, visibility, and total cloud cover to characterize the impact of aerosols and cloud cover on photovoltaic power generation, thus considering a more comprehensive range of meteorological factors and better capturing key meteorological influencing factors under various weather types. This application develops existing methods for identifying key meteorological factors and their characteristics in photovoltaic power generation, which rely on single data sources and do not consider the influence of environmental meteorological factors, into a method that utilizes multi-source data and comprehensively selects meteorological factors. This solves the problem of insufficient comprehensive consideration of meteorological factors affecting photovoltaic power generation in existing technologies. Attached Figure Description

[0041] Figure 1 This is a flowchart illustrating an embodiment of a method for identifying factors and characteristic quantities affecting photovoltaic power generation provided in this application.

[0042] Figure 2This is a schematic diagram of an embodiment of a system for identifying factors and characteristics affecting photovoltaic power generation, provided in this application. Detailed Implementation

[0043] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present application.

[0044] Please see Figure 1 The present application provides a method for identifying factors and characteristic quantities affecting photovoltaic power generation, including:

[0045] Step 101: Summarize and organize the operating parameters of the photovoltaic power stations in the area to be identified to form a photovoltaic power station dataset;

[0046] It should be noted that the meteorological elements, radiation and power generation observation data of the photovoltaic power stations in the area to be identified, as well as the location coordinates, installed capacity and other parameter data, are collected and organized to form a photovoltaic power station dataset for the area to be identified.

[0047] Step 102: After performing data error detection and outlier removal on the photovoltaic power station dataset, interpolation is performed based on meteorological stations, satellite remote sensing, radar, and reanalysis data around the photovoltaic power station to obtain multi-source data;

[0048] It should be noted that, by combining multi-source data such as surrounding meteorological stations, satellite remote sensing, radar, reanalysis, and numerical model forecasts, the quality control of the photovoltaic power station dataset is carried out. Data error detection and outlier removal are performed through methods such as climate extreme value checks, background field difference checks, and checks based on photovoltaic power curves. Furthermore, for the default data in the quality-controlled photovoltaic power station dataset, interpolation will be performed using multi-source data such as meteorological stations around the photovoltaic power station, satellite remote sensing, radar, and reanalysis to achieve a more complete time-series dataset, thus obtaining multi-source data.

[0049] Step 103: Determine the typical weather process types of the area to be identified and select meteorological factors that affect photovoltaic power generation.

[0050] It should be noted that the weather processes for the area to be identified were selected through comprehensive analysis of multi-source data, including sunny, cloudy, and overcast weather types. Furthermore, a relatively comprehensive and objective selection of meteorological factors affecting photovoltaic power generation was made, including solar radiation, temperature, humidity, wind, air pressure, PM2.5 concentration, PM10 concentration, visibility, and total cloud cover.

[0051] Step 104: Based on the weather process type and meteorological factors, extract meteorological factor observation data and photovoltaic power generation data under different weather types from multi-source data to serve as a sample dataset for photovoltaic power generation output analysis;

[0052] It should be noted that, based on the typical weather processes and meteorological factors affecting photovoltaic power generation selected in step 103, and the multi-source data obtained in steps 101-102, meteorological factor observation data and photovoltaic power generation data under different weather types are extracted as sample datasets for photovoltaic power generation output analysis.

[0053] Step 105: Calculate the correlation coefficients between various meteorological factors and photovoltaic power generation under different weather types based on the sample dataset, sort and analyze the correlation coefficients, and determine the key meteorological factors and characteristic quantities affecting photovoltaic power generation.

[0054] First, based on the Pearson correlation coefficient calculation formula, the correlation coefficients between various meteorological factors and photovoltaic power generation under different weather types are calculated according to the sample dataset.

[0055] The formula for calculating the Pearson correlation coefficient is as follows:

[0056]

[0057] Among them, Corr j Let be the correlation coefficient between the j-th meteorological element and photovoltaic power generation. Let be the average value of the j-th meteorological element data over the analysis period. This represents the average value of photovoltaic power generation data over the analysis period.

[0058] Next, the correlation coefficients were sorted and analyzed to determine the key meteorological factors and characteristic quantities affecting photovoltaic power generation.

[0059] This embodiment provides a method for identifying factors and characteristic quantities affecting photovoltaic (PV) power generation, based on multi-source data for quality control and interpolation of PV power plant observation data. It utilizes multi-source data such as satellite remote sensing, radar, and reanalysis to detect errors and interpolate PV power plant observation data, obtaining a high-quality, relatively complete time-series dataset to ensure the accuracy and rationality of the identification results of key meteorological elements and their characteristic quantities affecting PV power generation. Furthermore, it adds meteorological factors characterizing aerosols and cloud cover to the existing method. Specifically, it extracts observational data characterizing aerosols and cloud cover in the area where the PV power plant is located from multi-source data, for example, adding meteorological factors such as PM2.5 concentration, PM10 concentration, visibility, and total cloud cover, but not limited to these factors. These meteorological factors are added to the existing list of meteorological factors affecting PV power generation, and correlation coefficient analysis and identification of key meteorological factors and their characteristic quantities are conducted.

[0060] The above is a method for identifying factors and characteristic quantities affecting photovoltaic power generation provided in the embodiments of this application. The following is a system for identifying factors and characteristic quantities affecting photovoltaic power generation provided in the embodiments of this application.

[0061] Please see Figure 2 This application provides a system for identifying factors and characteristic quantities affecting photovoltaic power generation, comprising:

[0062] The generation unit 201 is used to summarize and organize the operating parameters of the photovoltaic power station in the area to be identified, and form a photovoltaic power station dataset.

[0063] Data processing unit 202 is used to perform data error detection and outlier removal on the photovoltaic power station dataset, and then interpolate and supplement it based on meteorological stations, satellite remote sensing, radar and reanalysis data around the photovoltaic power station to obtain multi-source data;

[0064] Unit 203 is selected to determine the typical weather process types of the area to be identified and to select meteorological factors that affect photovoltaic power generation.

[0065] Extraction unit 204 is used to extract meteorological factor observation data and photovoltaic power generation data under different weather types based on multi-source data according to weather process type and meteorological factors, as a sample dataset for photovoltaic power generation output analysis;

[0066] The calculation unit 205 is used to calculate the correlation coefficients between various meteorological factors and photovoltaic power generation under different weather types based on the sample dataset, sort and analyze the correlation coefficients, and determine the key meteorological factors and characteristic quantities that affect photovoltaic power generation.

[0067] Furthermore, this application embodiment also provides a device for identifying factors and characteristic quantities affecting photovoltaic power generation, the device including a processor and a memory:

[0068] The memory is used to store program code and transmit the program code to the processor;

[0069] The processor is used to execute the steps of the method for identifying factors and characteristic quantities affecting photovoltaic power generation as described in the above method embodiments, according to the instructions in the program code.

[0070] Furthermore, this application embodiment also provides a computer-readable storage medium for storing program code for executing the methods described in the above method embodiments.

[0071] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working process of the system and unit described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0072] The terms "first," "second," "third," "fourth," etc., used in this application's specification and the aforementioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented, for example, in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0073] It should be understood that in this application, "at least one (item)" means one or more, and "more than" means two or more. "And / or" is used to describe the relationship between related objects, indicating that three relationships can exist. For example, "A and / or B" can represent three cases: only A exists, only B exists, and both A and B exist simultaneously, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one (item) of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one (item) of a, b, or c can represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, and c can be single or multiple.

[0074] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection between apparatuses or units through some interfaces, and may be electrical, mechanical, or other forms.

[0075] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0076] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0077] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes: USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, optical disks, and other media capable of storing program code.

[0078] 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.

Claims

1. A method for identifying factors and characteristic quantities affecting photovoltaic power generation, characterized in that, include: The operating parameters of the photovoltaic power stations in the area to be identified are collected and organized to form a photovoltaic power station dataset; After performing data error detection and outlier removal on the photovoltaic power station dataset, interpolation was performed based on meteorological stations, satellite remote sensing, radar, and reanalysis data around the photovoltaic power station to obtain multi-source data. Determine the typical weather process types for the area to be identified, and select meteorological factors that affect photovoltaic power generation. Based on the weather process type and meteorological factors, meteorological factor observation data and photovoltaic power generation data under different weather types are extracted from the multi-source data to serve as a sample dataset for photovoltaic power generation output analysis. Based on the sample dataset, the correlation coefficients between various meteorological factors and photovoltaic power generation under different weather types are calculated. The correlation coefficients are then sorted and analyzed to determine the key meteorological factors and characteristic quantities affecting photovoltaic power generation.

2. The method for identifying factors and characteristic quantities affecting photovoltaic power generation according to claim 1, characterized in that, The process of summarizing and organizing the operating parameters of the photovoltaic power stations in the area to be identified to form a photovoltaic power station dataset specifically includes: The meteorological elements, radiation data, power generation observation data, location coordinates, and installed capacity of the photovoltaic power stations in the area to be identified are compiled and organized to form a photovoltaic power station dataset.

3. The method for identifying factors and characteristic quantities affecting photovoltaic power generation according to claim 1, characterized in that, The calculation of the correlation coefficients between various meteorological factors and photovoltaic power generation under different weather types based on the sample dataset specifically includes: Based on the Pearson correlation coefficient calculation formula, the correlation coefficients between various meteorological factors and photovoltaic power generation under different weather types are calculated according to the sample dataset. The formula for calculating the Pearson correlation coefficient is as follows: Among them, Corr j Let be the correlation coefficient between the j-th meteorological element and photovoltaic power generation. Let be the average value of the j-th meteorological element data over the analysis period. This represents the average value of photovoltaic power generation data over the analysis period.

4. The method for identifying factors and characteristic quantities affecting photovoltaic power generation according to claim 1, characterized in that, The meteorological factors include: solar radiation, temperature, humidity, wind, air pressure, PM2.5 concentration, PM10 concentration, visibility, and total cloud cover.

5. A system for identifying factors and characteristic quantities affecting photovoltaic power generation, characterized in that, include: The generation unit is used to summarize and organize the operating parameters of the photovoltaic power station in the area to be identified, and form a photovoltaic power station dataset. The data processing unit is used to perform data error detection and outlier removal on the photovoltaic power station dataset, and then interpolate and supplement it based on meteorological stations, satellite remote sensing, radar and reanalysis data around the photovoltaic power station to obtain multi-source data. Selecting units is used to determine the typical weather process types of the area to be identified and to select meteorological factors that affect photovoltaic power generation. The extraction unit is used to extract meteorological factor observation data and photovoltaic power generation data under different weather types based on the weather process type and the meteorological factors, and use them as a sample dataset for photovoltaic power generation output analysis. The calculation unit is used to calculate the correlation coefficients between various meteorological factors and photovoltaic power generation under different weather types based on the sample dataset, sort and analyze the correlation coefficients, and determine the key meteorological factors and characteristic quantities affecting photovoltaic power generation.

6. The identification system for factors and characteristic quantities affecting photovoltaic power generation according to claim 5, characterized in that, The generation unit is specifically used for: The meteorological elements, radiation data, power generation observation data, location coordinates, and installed capacity of the photovoltaic power stations in the area to be identified are compiled and organized to form a photovoltaic power station dataset.

7. The identification system for factors and characteristic quantities affecting photovoltaic power generation according to claim 5, characterized in that, The computing unit is specifically used for: Based on the Pearson correlation coefficient calculation formula, the correlation coefficients between various meteorological factors and photovoltaic power generation under different weather types are calculated according to the sample dataset. The correlation coefficients were sorted and analyzed to determine the key meteorological factors and characteristic quantities affecting photovoltaic power generation. The formula for calculating the Pearson correlation coefficient is as follows: Among them, Corr j Let be the correlation coefficient between the j-th meteorological element and photovoltaic power generation. Let be the average value of the j-th meteorological element data over the analysis period. This represents the average value of photovoltaic power generation data over the analysis period.

8. The identification system for factors and characteristic quantities affecting photovoltaic power generation according to claim 5, characterized in that, The meteorological factors include: solar radiation, temperature, humidity, wind, air pressure, PM2.5 concentration, PM10 concentration, visibility, and total cloud cover.

9. A device for identifying factors and characteristic quantities affecting photovoltaic power generation, characterized in that, The device includes a processor and a memory: The memory is used to store program code and transmit the program code to the processor; The processor is used to execute the method for identifying factors and characteristic quantities affecting photovoltaic power generation as described in any one of claims 1-4, according to the instructions in the program code.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium is used to store program code for executing the method for identifying factors and characteristic quantities affecting photovoltaic power generation as described in any one of claims 1-4.

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