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

Abstract

The present invention discloses a method for optimizing the photovoltaic power generation efficiency based on a non-linear coupling model of multiple meteorological elements, belonging to the technical field of photovoltaic power generation, including: obtaining meteorological data of key meteorological parameters that affect the photovoltaic power generation efficiency within a specified historical period; constructing a corresponding correction model for each key meteorological parameter with respect to the photovoltaic power generation efficiency; constructing an optimization model for the photovoltaic power generation efficiency, and inversely calculating the influence coefficients of each key meteorological parameter through the actual value of the photovoltaic power generation efficiency; calculating the meteorological parameter abnormality degree of each key meteorological parameter, and when the value of any key meteorological parameter is higher than the corresponding meteorological parameter abnormality degree, dynamically allocating the influence coefficient of the corresponding key meteorological parameter in the optimization model of the photovoltaic power generation efficiency, so as to correct the constructed optimization model of the photovoltaic power generation efficiency. It can be seen that the present invention considers the non-linear collaborative influence of multiple meteorological elements on the photovoltaic power generation efficiency and improves the accuracy of the estimation of the photovoltaic power generation efficiency.

Description

Technical Field

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

[0002] The photovoltaic power generation efficiency is the ratio of the light energy converted into electrical energy by a solar panel or a photovoltaic module, and is an important indicator for measuring the performance of a photovoltaic power station. With the continuous growth of the global demand for clean energy, improving the photovoltaic power generation efficiency has become an important direction for technological innovation in the photovoltaic industry. The photovoltaic output has obvious volatility, and the demand for photovoltaic power generation prediction is also increasing continuously. At present, the calculation of the photovoltaic power generation efficiency mainly relies on the monitoring and analysis of the actual operation data of the photovoltaic module and the system. However, in actual operation, in addition to the hardware equipment, the photovoltaic power generation efficiency is also affected by various meteorological elements such as temperature and wind speed. Therefore, considering the influence of meteorological factors on the photovoltaic power generation efficiency on the existing basis can further improve the prediction accuracy of the photovoltaic power generation amount.

[0003] In the prior art, the photovoltaic power generation efficiency is mostly determined by the hardware itself. When calculating the photovoltaic power generation amount, 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 module, that is, parameters such as the elevation angle and the module temperature. However, it ignores the influence of the change of meteorological elements on the power generation efficiency, and the influence of meteorological elements on the photovoltaic power generation efficiency is not linear. Using a fixed weight coefficient cannot accurately describe the change of the power generation efficiency, resulting in inaccurate calculation of the photovoltaic power generation efficiency, and further reducing the prediction of the photovoltaic power generation amount. Summary of the Invention

[0004] In view of the above analysis, the present invention aims to provide a method for optimizing the photovoltaic power generation efficiency based on the non-linear cooperation of multi-meteorological elements to solve the problem of low accuracy in calculating the existing photovoltaic power generation efficiency.

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

[0006] A method for optimizing the photovoltaic power generation efficiency based on a multi-meteorological element non-linear coupling model, comprising the following steps:

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

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

[0009] Construct an optimization model for photovoltaic power generation efficiency based on the correction model of each key meteorological parameter relative to the photovoltaic power generation efficiency, and invert the influence coefficients of each key meteorological parameter through the actual value of the photovoltaic power generation efficiency;

[0010] Calculate the meteorological parameter anomaly degree of each key meteorological parameter, and when the value of any key meteorological parameter is higher than the corresponding meteorological parameter anomaly degree, 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.

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

[0012] );

[0013] In the formula: Represents the calculated value of the photovoltaic power generation efficiency; Represents the power generation efficiency of the photovoltaic module under standard test conditions; Are the influence coefficients of different key meteorological parameters, Are the correction models of different key meteorological parameters on the photovoltaic power generation efficiency; i represents the total number of key meteorological parameters.

[0014] Preferably, the meteorological parameter anomaly degree 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] In the formula: Are the influence coefficients of different key meteorological parameters; Are the influence coefficients of different key meteorological parameters considering the meteorological parameter anomaly degree; Is the meteorological parameter anomaly degree; Is the corresponding meteorological parameter value, Is the corresponding meteorological parameter anomaly value, Is the sensitivity coefficient of the corresponding meteorological parameter, Represents the corrected value of the photovoltaic power generation efficiency.

[0020] Preferably, the meteorological parameter anomaly value Is the threshold determined by threshold regression; the sensitivity coefficient Is determined by the proportion of the correlation coefficients of each parameter and the power generation.

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

[0022] ;

[0023] In the formula: represents the correlation coefficient between different key meteorological parameters and photovoltaic power generation; represents the total number of correlation coefficients between the corresponding key meteorological parameters and photovoltaic power generation;

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

[0025] ;

[0026] In the formula: represents any key meteorological parameter value; represents the total number of corresponding key meteorological parameter values; represents the average value of key meteorological parameter values represents the photovoltaic power generation, represents the average value of photovoltaic power generations

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

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

[0029] ;

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

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

[0032] ;

[0033] In the formula: represents the loss of photovoltaic power generation efficiency caused by humidity; rh is the humidity, is the attenuation coefficient of humidity for the photovoltaic power generation efficiency.

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

[0035] ;

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

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

[0038] Based on the above technical objects, compared with the prior art, the present invention has the following advantages:

[0039] The photovoltaic power generation efficiency optimization method described in the present invention constructs a model based on the influence of multi-meteorological elements on photovoltaic power generation efficiency, and introduces the abnormality information of different key meteorological parameters to dynamically correct the model, so as to take into account the non-linear synergistic influence of multi-meteorological elements on photovoltaic power generation efficiency. At the same time, the model has low requirements for the amount of data for modeling, and considers the correction of abnormal coefficients under extreme weather, and dynamically adjusts the model parameters in real time, further improving the accuracy of photovoltaic power generation efficiency estimation. Description of the Drawings

[0040] Figure 1 is a flowchart of the photovoltaic power generation efficiency optimization method based on multi-meteorological element non-linear synergy described in the present invention;

[0041] Figure 2 is a result display diagram of an application example of the present invention. Detailed Embodiments

[0042] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a 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 in no way constitutes a limitation on the present invention and its application or use. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention. Unless otherwise specifically stated, the relative arrangements, expressions, and numerical values of the components and steps described in these embodiments do not limit the scope of the present invention. Technologies, methods, and devices known to those of ordinary skill in the relevant art may not be discussed in detail, but where appropriate, the technologies, methods, and devices should be regarded as part of the specification. In all the examples shown and discussed here, any specific value should be construed as merely exemplary and not as a limitation. Therefore, other examples of the exemplary embodiments may have different values.

[0043] Embodiment 1

[0044] As Figure 1 shown, the method for optimizing the photovoltaic power generation efficiency based on the multi-meteorological element non-linear coupling model described in this embodiment specifically includes the following steps:

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

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

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

[0048] When there are missing measurements, missed measurements, or abnormal values in the automatic station meteorological data near the photovoltaic site, the mean or median of the mobile meteorological data at adjacent times is used to fill the missing values.

[0049] Step 2: According to the meteorological data of each key meteorological parameter during the specified historical period, construct a correction model for the corresponding key meteorological parameter for the photovoltaic power generation efficiency one by one.

[0050] Given that three key meteorological parameters are selected in Step 1: temperature, humidity, and wind speed; therefore, in this step, different equations will be constructed for the impacts of these three key meteorological parameters on the photovoltaic power generation efficiency to prepare for constructing an optimization model of photovoltaic power generation efficiency in Step 3. Specifically:

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

[0052] ;

[0053] In the formula: represents the loss of photovoltaic power generation efficiency caused by temperature; t is the air temperature, is the air temperature under standard test conditions, generally 25 °C.

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

[0055] ;

[0056] In the formula: represents the loss of photovoltaic power generation efficiency caused by humidity; rh is the humidity, 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, denoted as the wind speed correction model, is expressed by the following formula:

[0058] ;

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

[0060] Step 3: Construct an optimization model of photovoltaic power generation efficiency based on the correction models of each key meteorological parameter with respect to photovoltaic power generation efficiency, and invert the influence coefficients of each key meteorological parameter through the actual value of photovoltaic power generation efficiency.

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

[0062] );

[0063] In the formula: represents the calculated value of photovoltaic power generation efficiency; represents the power generation efficiency of the photovoltaic module under standard test conditions (STC); are the influence coefficients of different key meteorological parameters, is a correction model for different key meteorological parameters on the photovoltaic power generation efficiency; i represents the total number of key meteorological parameters.

[0064] Thus, it can be seen that the calculated value of the photovoltaic power generation efficiency obtained by the present invention is obtained by the collaborative correction of each key meteorological parameter on the power generation efficiency of the photovoltaic module under standard test conditions, and it has high authenticity.

[0065] In this embodiment, in view of the fact that the key meteorological parameters are selected as temperature, humidity and wind speed, therefore, the optimized model of photovoltaic power generation efficiency constructed is expressed by the following formula:

[0066]

[0067] ;

[0068] In the formula: represents the influence coefficient of temperature; represents the correction model of temperature on the photovoltaic power generation efficiency; represents the influence coefficient of wind speed; represents the correction model of wind speed on the photovoltaic power generation efficiency; then represents the influence coefficient of humidity; represents the correction model of humidity on the 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 realized by the following method:

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

[0071] ;

[0072] ;

[0073] ;

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

[0075] Step 3.2, use the least squares method to obtain the influence coefficients of different key meteorological parameters , in the selection of coefficients, generally, the influence weight of temperature needs to be considered the largest, and the specific calculation formula is:

[0076] ;

[0077] Among them, is the influence coefficient of different key meteorological parameters to be obtained, X is the matrix of each actual value (specifically, part or all of the meteorological data of each key meteorological parameter within the specified historical period), and Y' is the matrix of the actual power generation efficiency at the corresponding time.

[0078] Step Four: Calculate the meteorological parameter abnormality of each key meteorological parameter, and when the value of any key meteorological parameter is higher than the corresponding meteorological parameter abnormality, 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] It is found in actual application that the influence of each key meteorological parameter on the photovoltaic power generation efficiency is not linearly changing. Therefore, it is necessary to calculate the abnormality degree of different key meteorological parameters, and for the moments with large deviations, reallocate the weights to fit the actual power change situation. Specifically:

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

[0081] ;

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

[0083] );

[0084] ;

[0085] In the formula: is the influence coefficient of different key meteorological parameters; is the influence coefficient of different key meteorological parameters considering the meteorological parameter abnormality; is the meteorological parameter abnormality; is the corresponding meteorological parameter value, is the corresponding meteorological parameter abnormal value, is the sensitivity coefficient of the corresponding meteorological parameter.

[0086] In this embodiment, since the key meteorological parameters are selected as temperature, humidity and wind speed, therefore, when calculating the meteorological parameter abnormality of different key meteorological parameters, it specifically includes the meteorological parameter abnormality of temperature, the meteorological parameter abnormality of humidity and the meteorological parameter abnormality of wind speed.

[0087] Meteorological parameter abnormal value is the threshold determined by threshold regression, and the specific formula is:

[0088]

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

[0090] The threshold value is estimated according to the principle of the minimum sum of squared residuals. The specific steps are as follows:

[0091] For the threshold value c i to be estimated, sort the possible values, exclude the extreme values, and generate a set of candidate values;

[0092] Conduct interval regression for each candidate value, fit the linear regression equation, and obtain the corresponding parameter estimates 、 、…、 and the parameter estimate ;

[0093]

[0094]

[0095] Calculate the sum of squared residuals:

[0096]

[0097] After traversing all candidate values, select the candidate value that minimizes RSS as the threshold value c i .

[0098] The sensitivity coefficients of different key meteorological parameters are determined by the proportion of the correlation coefficients between each key meteorological parameter and the photovoltaic power generation, and are specifically calculated by the following formula:

[0099] ;

[0100] In the formula: represents the correlation coefficient between different key meteorological parameters and the photovoltaic power generation; represents the total number of the correlation coefficients between the corresponding key meteorological parameters and the photovoltaic power generation;

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

[0102] ;

[0103] In the formula: represents any key meteorological parameter value; represents the total number of corresponding key meteorological parameter values; represents the average value of key meteorological parameter values; represents the photovoltaic power generation, represents the average value of photovoltaic power generations.

[0104] In this embodiment, considering that the key meteorological parameters are selected as temperature, humidity, and wind speed, therefore, when calculating the correlation coefficient between temperature and photovoltaic power generation, the key meteorological parameter values refer to the temperature observation values or temperature forecast values obtained in step one within the specified historical period. When calculating the correlation coefficient between humidity and photovoltaic power generation, the key meteorological parameter values refer to the humidity observation values or humidity forecast values obtained in step one within the specified historical period. When calculating the correlation coefficient between wind speed and photovoltaic power generation, the key meteorological parameter values refer to the wind speed observation values or wind speed forecast values obtained in step one within the specified historical period.

[0105] Thus, it can be seen that in the case of only considering three key meteorological parameters of temperature, humidity, and wind speed, the optimized model of photovoltaic power generation efficiency after correction is:

[0106] .

[0107] In the formula: represents the temperature influence coefficient considering the anomaly degree of meteorological parameters of temperature; represents the wind speed influence coefficient considering the anomaly degree of meteorological parameters of wind speed; represents the humidity influence coefficient considering the anomaly degree of meteorological parameters of humidity.

[0108] Embodiment 2

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

[0110] Embodiment 3

[0111] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. The processor runs the computer program to execute the above-mentioned photovoltaic power generation efficiency optimization method based on the non-linear cooperation of multiple meteorological elements.

[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 descriptions of the respective embodiments have their own emphases. For parts not detailed in a certain embodiment, reference may be made to the relevant descriptions of other embodiments.

[0114] In several embodiments provided by the present application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are merely illustrative. For example, the division of the units can be a logical function division. In actual implementation, there may be other division methods. 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 displayed or discussed coupling or direct coupling or communication connection to each other can be through some interfaces. The indirect coupling or communication connection of units or modules can be in an electrical or other form.

[0115] The units described as separate components may or may not be physically separated. The components displayed as units may or may not be physical units, that is, they can be located in one place or distributed to multiple units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0116] In addition, in each embodiment of the present invention, the functional units can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit. The above integrated units can be implemented in the form of hardware or in the form of software functional units.

[0117] If the above 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 such an 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. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the respective embodiments of the present invention. The aforementioned storage medium includes: USB flash drives, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), mobile hard disks, magnetic disks, or optical disks and other various media that can store program codes.

[0118] Application Example

[0119] Verify the power generation efficiency of a certain photovoltaic power station from March 16th to 26th, 2018. 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 power station from March 16th to 26th. The red dashed line is the power generation efficiency calculated through the meteorological elements corresponding to the date after being optimized by this algorithm. It can be seen that it fits the actual curve trend and can better describe the change of power generation efficiency under different weather conditions, providing a scientific reference basis for power generation prediction, 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. Any technical solutions obtained by using equivalent replacement or equivalent transformation fall within the protection scope of the present invention.

Claims

1. A method for optimizing the photovoltaic power generation efficiency based on a non-linear coupling model of multiple meteorological elements, characterized in that It includes the following steps: Determine the key meteorological parameters that affect the photovoltaic power generation efficiency, and obtain the meteorological data of the corresponding key meteorological parameters within a specified historical period; According to the meteorological data of each key meteorological parameter within the specified historical period, construct a correction model for the corresponding key meteorological parameter with respect to the photovoltaic power generation efficiency one by one; Based on the correction models of each key meteorological parameter with respect to the photovoltaic power generation efficiency, construct an optimization model for the photovoltaic power generation efficiency, and invert the influence coefficients of each key meteorological parameter through the actual value of the photovoltaic power generation efficiency; Calculate the meteorological parameter abnormality of each key meteorological parameter, and when the value of any key meteorological parameter is higher than the corresponding meteorological parameter abnormality, 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; The constructed photovoltaic power generation efficiency optimization model is expressed by the following formula: )； Wherein: represents the calculated value of the photovoltaic power generation efficiency; represents the power generation efficiency of the photovoltaic module under standard test conditions; is the influence coefficient of different key meteorological parameters, is the correction model of different key meteorological parameters for the photovoltaic power generation efficiency; i represents the total number of key meteorological parameters; The meteorological parameter abnormality of each key meteorological parameter is specifically calculated by the following formula: ； The corrected photovoltaic power generation efficiency optimization model is expressed as: )； ； In the formula: is the influence coefficient of different key meteorological parameters; is the influence coefficient of different key meteorological parameters considering the anomaly degree of meteorological parameters; is the anomaly degree of meteorological parameters; is the corresponding meteorological parameter value, is the abnormal value of the corresponding meteorological parameter, is the sensitivity coefficient of the corresponding meteorological parameter, represents the correction value of the photovoltaic power generation efficiency; Abnormal value of meteorological parameter is the threshold determined by threshold regression; Sensitivity coefficient Determined by the proportion of the correlation coefficients between each parameter and the power generation.

2. The photovoltaic power generation efficiency optimization method based on the multi-meteorological element non-linear coupling model according to claim 1, wherein Sensitivity coefficients of different key meteorological parameters Specifically, it is calculated by the following formula: ； In the formula: represents the correlation coefficient between different key meteorological parameters and photovoltaic power generation; represents the total number of correlation coefficients between the corresponding key meteorological parameters and photovoltaic power generation; The correlation coefficients between different key meteorological parameters and photovoltaic power generation are all calculated by the following formula: ； Wherein: represents any key meteorological parameter value; represents the total number of corresponding key meteorological parameter values; represents the average value of key meteorological parameter values; represents the photovoltaic power generation, represents the average value of photovoltaic power generations.

3. The photovoltaic power generation efficiency optimization method based on the multi-meteorological element non-linear coupling model according to claim 1, wherein The key meteorological parameters that affect the photovoltaic power generation efficiency are selected as temperature, humidity and wind speed.

4. The photovoltaic power generation efficiency optimization method based on the multi-meteorological element non-linear coupling model according to claim 2, characterized in that The correction model of temperature with respect to the photovoltaic power generation efficiency is expressed by the following formula: ； In the formula: represents the loss of photovoltaic power generation efficiency caused by temperature; t is the air temperature, is the air temperature under standard test conditions.

5. The photovoltaic power generation efficiency optimization method based on the multi-meteorological element non-linear coupling model according to claim 2, wherein, The correction model of humidity with respect to the photovoltaic power generation efficiency is expressed by the following formula: ； Wherein: represents the loss of photovoltaic power generation efficiency caused by humidity; rh is the humidity, is the attenuation coefficient of humidity on the photovoltaic power generation efficiency.

6. The photovoltaic power generation efficiency optimization method based on the multi-meteorological element nonlinear coupling model according to claim 2, characterized in that The correction model of wind speed with respect to the photovoltaic power generation efficiency is expressed by the following formula: ； In the formula: represents the loss of photovoltaic power generation efficiency caused by wind speed; v is the wind speed, is the inhibition coefficient of wind speed on photovoltaic power generation efficiency.

7. An electronic device, comprising a memory, a processor, and a computer program stored on the memory and running on the processor, characterized in that, This computer program runs and executes the photovoltaic power generation efficiency optimization method based on the multi-meteorological element non-linear coupling model described in any one of claims 1 to 6.

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