Photovoltaic power generation efficiency optimization method based on multi-meteorological-element nonlinear coupling model

Abstract

The invention discloses a photovoltaic power generation efficiency optimization method based on a multi-meteorological-element nonlinear coupling model, and belongs to the technical field of photovoltaic power generation, and the method comprises the steps: obtaining the meteorological data of a key meteorological parameter which has an influence on the photovoltaic power generation efficiency in a specified historical time period; constructing correction models of the corresponding key meteorological parameters for the photovoltaic power generation efficiency one by one; constructing a photovoltaic power generation efficiency optimization model, and inverting the influence coefficient of each key meteorological parameter through the actual value of the photovoltaic power generation efficiency; and calculating the meteorological parameter anomaly degree of each key meteorological parameter, and dynamically allocating the influence coefficient of the corresponding key meteorological parameter in the photovoltaic power generation efficiency optimization model when the numerical value of any key meteorological parameter is higher than the corresponding meteorological parameter anomaly degree, thereby correcting the constructed photovoltaic power generation efficiency optimization model. Therefore, according to the method, the nonlinear cooperative influence of multiple meteorological elements on the photovoltaic power generation efficiency is considered, and the precision of photovoltaic power generation efficiency estimation is improved.

Description

Technical Field

[0001] The invention relates to a photovoltaic power generation efficiency optimization method based on a multi-meteorological element nonlinear coupling model, and belongs to the technical field of photovoltaic power generation. Background Art

[0002] Photovoltaic power generation efficiency is the ratio of solar panels or photovoltaic modules to convert light energy into electrical energy. It is an important indicator to measure the performance of photovoltaic power stations. With the continuous growth of global demand for clean energy, improving photovoltaic power generation efficiency has become an important direction of technological innovation in the photovoltaic industry. Photovoltaic output has obvious volatility, and the demand for photovoltaic power generation forecasts is also increasing. At present, the calculation of photovoltaic power generation efficiency mainly depends on the monitoring and analysis of the actual operating data of photovoltaic modules and systems. However, in actual operation, in addition to hardware equipment, photovoltaic power generation efficiency is also affected by various meteorological factors such as temperature and wind speed. Therefore, taking into account the impact of meteorological factors on photovoltaic power generation efficiency on the existing basis can further improve the prediction accuracy of photovoltaic power generation.

[0003] In the existing technology, the efficiency of photovoltaic power generation is mostly determined by the hardware itself. When calculating the photovoltaic power generation, the photovoltaic power generation efficiency is recorded as a fixed value. The traditional photovoltaic efficiency correction method only considers the deployment environment of the photovoltaic components, that is, the elevation angle, component temperature and other parameters. However, it ignores the impact of meteorological factors on power generation efficiency. Moreover, the photovoltaic power generation efficiency is not linearly affected by meteorological factors. The use of fixed weight coefficients cannot accurately describe the changes in power generation efficiency, resulting in inaccurate calculation of photovoltaic power generation efficiency, which in turn reduces the prediction of photovoltaic power generation. Summary of the invention

[0004] In view of the above analysis, the present invention aims to provide a photovoltaic power generation efficiency optimization method based on nonlinear coordination of multiple meteorological elements to solve the problem of low accuracy in existing photovoltaic power generation efficiency calculations.

[0005] In order to achieve the above technical objectives, the present invention will adopt the following technical solutions:

[0006] A photovoltaic power generation efficiency optimization method based on a nonlinear coupling model of multiple meteorological elements comprises the following steps:

[0007] Determine the key meteorological parameters that affect photovoltaic power generation efficiency and obtain meteorological data of the corresponding key meteorological parameters within a specified historical period;

[0008] According to the meteorological data of each key meteorological parameter in a specified historical period, a correction model of the corresponding key meteorological parameter for photovoltaic power generation efficiency is constructed one by one;

[0009] Based on the correction model of each key meteorological parameter relative to the photovoltaic power generation efficiency, a photovoltaic power generation efficiency optimization model is constructed, and the influence coefficient of each key meteorological parameter is inverted through the actual value of photovoltaic power generation efficiency;

[0010] The meteorological parameter anomaly of each key meteorological parameter is calculated, and when the value of any key meteorological parameter is higher than the corresponding meteorological parameter anomaly, the influence coefficient of the corresponding key meteorological parameter in the photovoltaic power generation efficiency optimization model is dynamically allocated, so as to correct the constructed photovoltaic power generation efficiency optimization model.

[0011] Preferably, the constructed photovoltaic power generation efficiency optimization model is expressed by the following formula:

[0012] );

[0013] Where: Represents the calculated value of photovoltaic power generation efficiency; Indicates the power generation efficiency of photovoltaic modules under standard test conditions; is the influence coefficient of different key meteorological parameters, is the correction model of photovoltaic power generation efficiency under different key meteorological parameters; i represents the total number of key meteorological parameters.

[0014] Preferably, the meteorological parameter anomaly of each key meteorological parameter is specifically calculated by the following formula:

[0015] ;

[0016] The corrected photovoltaic power generation efficiency optimization model is expressed as:

[0017] );

[0018] ;

[0019] Where: is the influence coefficient of different key meteorological parameters; To consider the influence coefficients of different key meteorological parameters for the anomaly of meteorological parameters; is the anomaly degree of meteorological parameters; is the corresponding meteorological parameter value, is the corresponding abnormal value of meteorological parameters, is the sensitivity coefficient of the corresponding meteorological parameter, Indicates the correction value of photovoltaic power generation efficiency.

[0020] Preferably, the meteorological parameter abnormal value is the threshold value determined by threshold regression; sensitivity coefficient It is determined by the correlation coefficient ratio of each parameter and power generation.

[0021] Preferably, the sensitivity coefficients of different key meteorological parameters Specifically calculated by the following formula:

[0022] ;

[0023] Where: Represents the correlation coefficient between different key meteorological parameters and PV power generation; Represents the total number of correlation coefficients between the corresponding key meteorological parameters and PV power generation;

[0024] The correlation coefficients of different key meteorological parameters and photovoltaic power generation are calculated by the following formula:

[0025] ;

[0026] Where: Indicates the value of any key meteorological parameter; Indicates the total number of corresponding key meteorological parameter values; express Key meteorological parameters The average value of represents the photovoltaic power generation, express Photovoltaic power generation The average value of .

[0027] Preferably, the key meteorological parameters that have an impact on photovoltaic power generation efficiency are selected as temperature, humidity and wind speed.

[0028] Preferably, the correction model of temperature for photovoltaic power generation efficiency is expressed by the following formula:

[0029] ;

[0030] Where: represents the photovoltaic power generation efficiency loss caused by temperature; t is the air temperature, is the air temperature under standard test conditions.

[0031] Preferably, the correction model of humidity for photovoltaic power generation efficiency is expressed by the following formula:

[0032] ;

[0033] Where: Represents the photovoltaic power generation efficiency loss caused by humidity; rh is humidity, is the attenuation coefficient of humidity on photovoltaic power generation efficiency.

[0034] Preferably, the correction model of wind speed for photovoltaic power generation efficiency is expressed by the following formula:

[0035] ;

[0036] Where: represents the photovoltaic power generation efficiency loss caused by wind speed; v is the wind speed, is the suppression coefficient of wind speed on photovoltaic power generation efficiency.

[0037] Another technical object of the present invention is to provide an electronic device, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the computer program runs and executes the above-mentioned photovoltaic power generation efficiency optimization method based on the nonlinear coupling model of multiple meteorological elements.

[0038] Based on the above technical objectives, the present invention has the following advantages over the prior art:

[0039] The photovoltaic power generation efficiency optimization method described in the present invention constructs a model based on the impact of multiple meteorological factors on photovoltaic power generation efficiency, and introduces abnormality information of different key meteorological parameters to dynamically correct the model, thereby taking into account the nonlinear synergistic impact of multiple meteorological factors on photovoltaic power generation efficiency. At the same time, the model has low requirements on the amount of data for modeling, and takes into account the correction of abnormal coefficients under extreme weather conditions, and makes real-time dynamic adjustments to model parameters, further improving the accuracy of photovoltaic power generation efficiency estimation. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 It is a flow chart of the photovoltaic power generation efficiency optimization method based on nonlinear coordination of multiple meteorological elements according to the present invention;

[0041] Figure 2 This is a result display diagram of an application example of the present invention. DETAILED DESCRIPTION

[0042] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments. The following description of at least one exemplary embodiment is actually only illustrative and is by no means any limitation to the present invention and its application or use. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention. Unless otherwise specified, the relative arrangement, expressions and numerical values ​​of the components and steps described in these embodiments do not limit the scope of the present invention. The techniques, methods and equipment known to ordinary technicians in the relevant fields may not be discussed in detail, but in appropriate cases, the techniques, methods and equipment should be regarded as part of the specification. In all examples shown and discussed here, any specific value should be interpreted as merely exemplary, rather than as a limitation. Therefore, other examples of the exemplary embodiments may have different values.

[0043] Example 1

[0044] like Figure 1 As shown, the photovoltaic power generation efficiency optimization method based on the nonlinear coupling model of multiple meteorological elements described in this embodiment specifically includes the following steps:

[0045] Step 1: Determine the key meteorological parameters that affect the efficiency of photovoltaic power generation, and obtain the meteorological data of the corresponding key meteorological parameters within a specified historical period.

[0046] In this step, the key meteorological parameters that have a greater impact on photovoltaic power generation efficiency include temperature, humidity and wind speed.

[0047] The meteorological data of the corresponding key meteorological parameters obtained within the specified historical period can be the meteorological observation data (from the meteorological data automatic monitoring station near the photovoltaic station) or the forecast field data (from the regional numerical model forecast results) of the key meteorological parameters within the specified historical period.

[0048] When there are missing, missed or abnormal values ​​in the meteorological data collected from the automatic station near the photovoltaic site, the mean or median of the mobile phone meteorological data at the nearby time is used to fill the missing values.

[0049] Step 2: Based on the meteorological data of each key meteorological parameter in a specified historical period, a correction model of the corresponding key meteorological parameter for photovoltaic power generation efficiency is constructed one by one.

[0050] In view of the fact that three key meteorological parameters were selected in step 1: temperature, humidity and wind speed; therefore, this step will construct different equations for the impact of these three key meteorological parameters on photovoltaic power generation efficiency, in preparation for building a photovoltaic power generation efficiency optimization model in step 3. Specifically:

[0051] The temperature correction model for photovoltaic power generation efficiency is recorded as the temperature correction model and is expressed by the following formula:

[0052] ;

[0053] Where: represents the photovoltaic power generation efficiency loss caused by temperature; t is the air temperature, It is the air temperature under standard test conditions, generally 25℃.

[0054] The correction model of humidity for photovoltaic power generation efficiency is recorded as humidity correction model and is expressed by the following formula:

[0055] ;

[0056] Where: Represents the photovoltaic power generation efficiency loss caused by humidity; rh is humidity, It is the attenuation coefficient of humidity on photovoltaic power generation efficiency, which is determined by the type of photovoltaic module itself.

[0057] The correction model of wind speed for photovoltaic power generation efficiency is recorded as wind speed correction model, which is expressed by the following formula:

[0058] ;

[0059] Where: represents the photovoltaic power generation efficiency loss caused by wind speed; v is the wind speed, is the suppression coefficient of wind speed on photovoltaic power generation efficiency, which is determined by the type of photovoltaic module itself.

[0060] Step 3: Construct a photovoltaic power generation efficiency optimization model based on the correction model of each key meteorological parameter relative to photovoltaic power generation efficiency, and invert the influence coefficient of each key meteorological parameter through the actual value of photovoltaic power generation efficiency.

[0061] Specifically, the constructed photovoltaic power generation efficiency optimization model is expressed by the following formula:

[0062] );

[0063] Where: Represents the calculated value of photovoltaic power generation efficiency; Indicates the power generation efficiency of photovoltaic modules under standard test conditions (STC); is the influence coefficient of different key meteorological parameters, is the correction model of photovoltaic power generation efficiency under different key meteorological parameters; i represents the total number of key meteorological parameters.

[0064] It can be seen that the calculated value of photovoltaic power generation efficiency obtained by the present invention is It is obtained by synergistically correcting the power generation efficiency of photovoltaic modules under standard test conditions through various key meteorological parameters, and has a high degree of authenticity.

[0065] In this embodiment, since the key meteorological parameters are selected as temperature, humidity and wind speed, the constructed photovoltaic power generation efficiency optimization model is expressed by the following formula:

[0066]

[0067] ;

[0068] Where: Indicates the influence coefficient of temperature; A correction model representing the effect of temperature on photovoltaic power generation efficiency; Indicates the influence coefficient of wind speed; A correction model representing the effect of wind speed on photovoltaic power generation efficiency; It represents the influence coefficient of humidity; A correction model representing the effect of humidity on photovoltaic power generation efficiency.

[0069] In addition, in this step, in order to invert the influence coefficients of each key meteorological parameter, it is specifically achieved by the following method:

[0070] Step 3.1, calculate the actual value of photovoltaic power generation efficiency:

[0071] ;

[0072] ;

[0073] ;

[0074] in, is the actual value of photovoltaic power generation efficiency, P is the actual power generation of the photovoltaic power station on that day, and P t is the theoretical daily power generation, a is the installed capacity of the photovoltaic power station, h is the theoretical full-power hours, and S is the solar radiation, which is 1000W / m 2 is the standard solar radiation.

[0075] Step 3.2: Use the least squares method to find the influence coefficients of different key meteorological parameters In the selection coefficient, the influence of temperature is generally considered to have the greatest weight. The specific calculation formula is:

[0076] ;

[0077] in, is the influence coefficient of different key meteorological parameters, X is the matrix of actual values ​​(specifically, part or all of the meteorological data of each key meteorological parameter in a specified historical period), and Y' is the actual power generation efficiency matrix at the corresponding moment.

[0078] Step 4: Calculate the meteorological parameter anomaly of each key meteorological parameter, and when the value of any key meteorological parameter is higher than the corresponding meteorological parameter anomaly, dynamically allocate the influence coefficient of the corresponding key meteorological parameter in the photovoltaic power generation efficiency optimization model, so as to correct the constructed photovoltaic power generation efficiency optimization model.

[0079] In actual application, it is found that the impact of various key meteorological parameters on photovoltaic power generation efficiency does not change linearly, so it is necessary to calculate the degree of abnormality of different key meteorological parameters and redistribute the weights for moments with large deviations to fit the actual power changes. Specifically:

[0080] The meteorological parameter anomaly of each key meteorological parameter is calculated by the following formula:

[0081] ;

[0082] The corrected photovoltaic power generation efficiency optimization model is expressed as:

[0083] );

[0084] ;

[0085] Where: is the influence coefficient of different key meteorological parameters; To consider the influence coefficients of different key meteorological parameters for the anomaly of meteorological parameters; is the anomaly degree of meteorological parameters; is the corresponding meteorological parameter value, is the corresponding abnormal value of meteorological parameters, is the sensitivity coefficient of the corresponding meteorological parameter.

[0086] In this embodiment, considering that the key meteorological parameters are selected as temperature, humidity and wind speed, the meteorological parameter anomaly of different key meteorological parameters is calculated, specifically including the meteorological parameter anomaly of temperature, the meteorological parameter anomaly of humidity and the meteorological parameter anomaly of wind speed.

[0087] Abnormal values ​​of meteorological parameters That is, the threshold value determined by threshold regression. The specific formula is:

[0088]

[0089] Among them, y is the explained variable, x i is the threshold variable, c i is the threshold to be estimated, x1,…,x m Here, the explained variable is the photovoltaic power generation, the threshold variable is the corresponding meteorological parameter, and the control variable is other meteorological parameters.

[0090] The threshold is estimated based on the principle of minimizing the residual sum of squares. The specific steps are:

[0091] For the threshold value c to be estimated i Sort the possible values ​​of and generate a set of candidate values ​​after excluding extreme values;

[0092] Perform interval regression on each candidate value and fit the linear regression equation to obtain the corresponding parameter estimates , , …, And parameter estimates ;

[0093]

[0094]

[0095] Calculate the residual sum of squares:

[0096]

[0097] After traversing all candidate values, the candidate value that minimizes the RSS is selected as the threshold value c to be estimated. i .

[0098] Sensitivity coefficients of different key meteorological parameters It is determined by the correlation coefficient ratio of each key meteorological parameter and photovoltaic power generation, and is calculated by the following formula:

[0099] ;

[0100] Where: Represents the correlation coefficient between different key meteorological parameters and PV power generation; The total number of correlation coefficients representing the corresponding key meteorological parameters and PV power generation;

[0101] The correlation coefficients of different key meteorological parameters and photovoltaic power generation are calculated by the following formula:

[0102] ;

[0103] Where: Indicates the value of any key meteorological parameter; Indicates the total number of corresponding key meteorological parameter values; express Key meteorological parameters The average value of represents the photovoltaic power generation, express Photovoltaic power generation The average value of .

[0104] In this embodiment, since the key meteorological parameters are selected as temperature, humidity and wind speed, when calculating the correlation coefficient between temperature and photovoltaic power generation, the key meteorological parameter values ​​are This refers to the temperature observation value or temperature forecast value within the specified historical period obtained in step 1. When calculating the correlation coefficient between humidity and photovoltaic power generation, the key meteorological parameter value It refers to the humidity observation value or humidity forecast value within the specified historical period obtained in step 1. When calculating the correlation coefficient between wind speed and photovoltaic power generation, the key meteorological parameter value It refers to the wind speed observation value or wind speed forecast value within the specified historical period obtained in step one.

[0105] It can be seen that when only considering the three key meteorological parameters of temperature, humidity and wind speed, the corrected photovoltaic power generation efficiency optimization model is:

[0106] .

[0107] Where: The temperature influence coefficient representing the anomaly of the meteorological parameter taking into account the temperature; represents the wind speed influence coefficient considering the anomaly of the meteorological parameter of wind speed; The humidity influence coefficient represents the degree of abnormality of meteorological parameters taking humidity into account.

[0108] Example 2

[0109] The present invention also provides a storage medium, and when the program stored in the storage medium is run, the program executes the above-mentioned photovoltaic power generation efficiency optimization method based on nonlinear coordination of multiple meteorological elements.

[0110] Example 3

[0111] The present invention also provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the above-mentioned photovoltaic power generation efficiency optimization method based on nonlinear coordination of multiple meteorological elements through the operation of the computer program.

[0112] The serial numbers of the above embodiments of the present invention are only for description and do not represent the advantages or disadvantages of the embodiments.

[0113] In the above embodiments of the present invention, 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.

[0114] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only schematic. For example, the division of the units can be a logical function division. There may be other division methods in actual implementation. For example, 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 interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0115] 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 units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0116] 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.

[0117] 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 storage medium. Based on this understanding, the technical solution of the present invention, 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. The computer software product is stored in a storage medium, including 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 method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, disk or optical disk, etc. Various media that can store program codes.

[0118] Application Examples

[0119] The power generation efficiency of a photovoltaic station from March 16 to 26, 2018 was verified. The black solid line is the power generation efficiency of the photovoltaic modules of the photovoltaic power station, which is a constant value of 0.8. The blue solid line is the actual power generation efficiency of the photovoltaic station from March 16 to 26. The red dotted line is the power generation efficiency calculated by the meteorological elements of the corresponding date after the algorithm is optimized. It can be seen that it fits the actual curve trend, which can better describe the changes in power generation efficiency under different weather conditions and provide a scientific reference for power generation estimation, power dispatching and other situations.

[0120] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the above embodiments do not limit the present invention in any form, and any technical solution obtained by equivalent replacement or equivalent transformation falls within the protection scope of the present invention.

Claims

1. A photovoltaic power generation efficiency optimization method based on a nonlinear coupling model of multiple meteorological elements, characterized in that: The steps include: Determine the key meteorological parameters that affect photovoltaic power generation efficiency and obtain meteorological data of the corresponding key meteorological parameters within a specified historical period; According to the meteorological data of each key meteorological parameter in a specified historical period, a correction model of the corresponding key meteorological parameter for photovoltaic power generation efficiency is constructed one by one; Based on the correction model of each key meteorological parameter relative to the photovoltaic power generation efficiency, a photovoltaic power generation efficiency optimization model is constructed, and the influence coefficient of each key meteorological parameter is inverted through the actual value of photovoltaic power generation efficiency; The meteorological parameter anomaly of each key meteorological parameter is calculated, and when the value of any key meteorological parameter is higher than the corresponding meteorological parameter anomaly, the influence coefficient of the corresponding key meteorological parameter in the photovoltaic power generation efficiency optimization model is dynamically allocated, so as to correct the constructed photovoltaic power generation efficiency optimization model.

2. The photovoltaic power generation efficiency optimization method based on the nonlinear coupling model of multiple meteorological elements according to claim 1 is characterized in that: The constructed photovoltaic power generation efficiency optimization model is expressed by the following formula: )； Where: Represents the calculated value of photovoltaic power generation efficiency; Indicates the power generation efficiency of photovoltaic modules under standard test conditions; is the influence coefficient of different key meteorological parameters, is the correction model of photovoltaic power generation efficiency under different key meteorological parameters; i represents the total number of key meteorological parameters.

3. The photovoltaic power generation efficiency optimization method based on the nonlinear coupling model of multiple meteorological elements according to claim 2 is characterized in that: The meteorological parameter anomaly of each key meteorological parameter is calculated by the following formula: ； The corrected photovoltaic power generation efficiency optimization model is expressed as: )； ； Where: is the influence coefficient of different key meteorological parameters; To consider the influence coefficients of different key meteorological parameters for the anomaly of meteorological parameters; is the anomaly degree of meteorological parameters; is the corresponding meteorological parameter value, is the corresponding abnormal value of meteorological parameters, is the sensitivity coefficient of the corresponding meteorological parameter, Indicates the correction value of photovoltaic power generation efficiency.

4. The photovoltaic power generation efficiency optimization method based on the multi-meteorological element nonlinear coupling model according to claim 3 is characterized in that: Abnormal values ​​of meteorological parameters is the threshold value determined by threshold regression; Sensitivity coefficient It is determined by the correlation coefficient ratio of each parameter and power generation.

5. The photovoltaic power generation efficiency optimization method based on the nonlinear coupling model of multiple meteorological elements according to claim 4 is characterized in that: Sensitivity coefficients of different key meteorological parameters Specifically calculated by the following formula: ； Where: Represents the correlation coefficient between different key meteorological parameters and PV power generation; Represents the total number of correlation coefficients between the corresponding key meteorological parameters and PV power generation; The correlation coefficients of different key meteorological parameters and photovoltaic power generation are calculated by the following formula: ； Where: Indicates the value of any key meteorological parameter; Indicates the total number of corresponding key meteorological parameter values; express Key meteorological parameters The average value of represents the photovoltaic power generation, express Photovoltaic power generation The average value of .

6. The photovoltaic power generation efficiency optimization method based on the nonlinear coupling model of multiple meteorological elements according to claim 4 is characterized in that: The key meteorological parameters that have an impact on photovoltaic power generation efficiency are selected as temperature, humidity and wind speed.

7. The photovoltaic power generation efficiency optimization method based on the multi-meteorological element nonlinear coupling model according to claim 5 is characterized in that: The correction model of temperature for photovoltaic power generation efficiency is expressed by the following formula: ； Where: represents the photovoltaic power generation efficiency loss caused by temperature; t is the air temperature, is the air temperature under standard test conditions.

8. The photovoltaic power generation efficiency optimization method based on the nonlinear coupling model of multiple meteorological elements according to claim 5 is characterized in that: The correction model of humidity for photovoltaic power generation efficiency is expressed by the following formula: ； Where: Represents the photovoltaic power generation efficiency loss caused by humidity; rh is humidity, is the attenuation coefficient of humidity on photovoltaic power generation efficiency.

9. The photovoltaic power generation efficiency optimization method based on the nonlinear coupling model of multiple meteorological elements according to claim 5 is characterized in that: The correction model of wind speed for photovoltaic power generation efficiency is expressed by the following formula: ； Where: represents the photovoltaic power generation efficiency loss caused by wind speed; v is the wind speed, is the suppression coefficient of wind speed on photovoltaic power generation efficiency.

10. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: The computer program runs to execute the photovoltaic power generation efficiency optimization method based on a nonlinear coupling model of multiple meteorological elements as described in any one of claims 1 to 9.

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