An atmospheric visibility and photovoltaic power forecasting method, device and storage medium

Abstract

The present invention discloses a method, device and storage medium for atmospheric visibility and photovoltaic power prediction, belonging to the fields of optoelectronic new energy, new generation information technology, and atmospheric environment. The method includes: constructing an aerosol cluster model with fractal geometric characteristics based on the diffusion-limited aggregation algorithm; calculating the fractal dimension of the aerosol cluster model using the box-counting method; calculating the optical equivalent radius of the aerosol cluster model with fractal geometric characteristics according to the fractal dimension; coupling the optical equivalent radius into a mesoscale air quality model to calculate the atmospheric extinction coefficient considering the aerosol process; calculating the atmospheric visibility and photovoltaic power prediction results through the atmospheric extinction coefficient considering the aerosol process; the present invention improves the physical simulation degree and prediction simulation accuracy of the atmospheric model for the optical characteristics of atmospheric aerosols, thereby significantly improving the simulation results of the mesoscale air quality model for atmospheric visibility and photovoltaic power.

Description

Technical Field

[0001] The present invention relates to a method, device, and storage medium for forecasting atmospheric visibility and photovoltaic power, belonging to the fields of optoelectronic new energy, new generation information technology, and atmospheric environment. Background Art

[0002] The extinction characteristics of aerosols have an important impact on the near-surface solar radiation flux and further on the photovoltaic power generation. However, the accurate forecasting of these variables remains a difficult problem in meteorological and atmospheric environment numerical forecasting, and there is still a large room for improvement in forecasting accuracy and business demand.

[0003] Microscopic observation studies have shown that most aerosol particles exist in complex cluster structures in the atmosphere. However, in current most numerical models, the simulation of the optical properties of atmospheric aerosols is mainly based on the spherical particle hypothesis or simple geometric models. Although these methods are computationally simple, they ignore the complex morphology of aerosol particles in the real atmosphere and do not fully discuss the growth mechanism forming such complex morphology. Therefore, the mass concentration in their simulation results does not match the simulated optical properties. So, there are often situations where the forecasting result of the mass concentration of atmospheric aerosols by the model is acceptable, but there are large errors in the forecasting results of their corresponding atmospheric optical properties. Furthermore, it will affect the forecasting effect of atmospheric visibility and photovoltaic power. Summary of the Invention

[0004] The purpose of the present invention is to overcome the deficiencies in the prior art and provide a method, device, and storage medium for forecasting atmospheric visibility and photovoltaic power, which improves the physical simulation degree and forecasting simulation accuracy of the atmospheric model for the optical properties of atmospheric aerosols, thus significantly improving the simulation results of the mesoscale air quality model for atmospheric visibility and photovoltaic power, and being able to better predict the power generation capacity of photovoltaic power stations.

[0005] To achieve the above purpose, the present invention is implemented by adopting the following technical solutions:

[0006] In the first aspect, the present invention provides a method for forecasting atmospheric visibility and photovoltaic power, including:

[0007] Based on the diffusion-limited aggregation algorithm, construct an aerosol cluster model with fractal geometric characteristics;

[0008] Use the box-counting method to calculate the fractal dimension of the aerosol cluster model;

[0009] According to the fractal dimension, calculate the optical equivalent radius of the aerosol cluster model with fractal geometric characteristics;

[0010] Couple the optical equivalent radius into the mesoscale air quality model to calculate the atmospheric extinction coefficient considering the aerosol process;

[0011] The atmospheric visibility and photovoltaic power prediction results are calculated by considering the atmospheric extinction coefficient of the aerosol process.

[0012] Furthermore, based on the diffusion-limited aggregation algorithm, an aerosol cluster model with fractal geometric characteristics is constructed, including:

[0013] a) Initialize a three-dimensional space and place an initial monomer particle at the center position;

[0014] b) Randomly release new particles around the initial monomer particle to make them perform Brownian motion;

[0015] c) If the new particle moves to a position adjacent to the existing cluster, it adheres to form a complex;

[0016] d) Repeat steps b)-c) until the number of cluster particles reaches a preset value, generating an aerosol cluster model with a non-spherical fractal structure.

[0017] Furthermore, the box-counting method is used to calculate the fractal dimension of the aerosol cluster model, including:

[0018] The space where the aerosol cluster model is located is divided into grids with multiple different spatial measures;

[0019] In each division, count the number of grids occupied by the aerosol cluster model and the total number of grids;

[0020] Perform logarithmic fitting on the number of grids and the total number of grids under multiple different measures, and the slope obtained from the fitting is the fractal dimension of the aerosol cluster model.

[0021] Furthermore, the calculation formula for the fractal dimension is as follows:

[0022] ;

[0023] where is the number of grids covering the aerosol cluster, is the grid side length, is the fractal dimension of the aerosol cluster model obtained.

[0024] Furthermore, according to the fractal dimension, the optical equivalent radius of the aerosol cluster model with fractal geometric characteristics is calculated, and the formula is as follows:

[0025] ;

[0026] where, is the optical equivalent radius of the spherical particle, is the optical equivalent radius of the fractal particle, is the fractal factor, is the monomer radius, and is the fractal dimension of the aerosol cluster;

[0027] Among them, the calculation formula of

[0028] is as follows:

[0029] Among them, is the ratio constant, and is the number of monomer particles contained in the aerosol cluster;

[0030] is as follows:

[0031] Among them, is the aerodynamic equivalent radius in the spherical cluster model, and the calculation formula is as follows:

[0032]

[0033] Furthermore, by coupling the optical equivalent radius into the mesoscale air quality model, the atmospheric extinction coefficient considering the aerosol process is calculated, and the formula is as follows:

[0034] is as follows:

[0035] Among them, is the atmospheric extinction coefficient, is the aerosol number concentration related to the th particle size interval, is the extinction efficiency, is the size parameter of the particle size, is the number of intervals divided for the particle sizes of all aerosols in the atmosphere according to the optical characteristics of different particle sizes of aerosols in the atmospheric model.

[0036] Furthermore, the atmospheric visibility is calculated through the atmospheric extinction coefficient considering the aerosol process, and the formula is as follows:

[0037] is as follows:

[0038] Among them is the atmospheric visibility, is a constant that varies with atmospheric conditions.

[0039] Furthermore, the calculation method of the photovoltaic power includes:

[0040] Calculating the aerosol optical depth through the atmospheric extinction coefficient , and the formula is as follows:

[0041] ​;

[0042] In the formula, is the height of the top of the atmosphere, is the height of each atmospheric layer;

[0043] The short-wave radiation at the surface is calculated through the aerosol optical depth , and the formula is as follows:

[0044] ;

[0045] where is the short-wave radiation at the surface, is the solar constant, is the solar zenith angle, is the surface albedo; is the natural constant;

[0046] The photovoltaic power is calculated through the short-wave radiation at the surface, and the formula is as follows:

[0047] ;

[0048] where is the photovoltaic power, is the efficiency of the photovoltaic system, is the area of the photovoltaic module.

[0049] In a second aspect, the present invention provides an atmospheric visibility and photovoltaic power prediction device, including:

[0050] A model construction module for constructing an aerosol cluster model with fractal geometric characteristics based on the diffusion-limited aggregation algorithm;

[0051] A first calculation module for calculating the fractal dimension of the aerosol cluster model using the box-counting method;

[0052] A second calculation module for calculating the optical equivalent radius of the aerosol cluster model with fractal geometric characteristics according to the fractal dimension;

[0053] A third calculation module for coupling the optical equivalent radius into a mesoscale air quality model to calculate the atmospheric extinction coefficient considering the aerosol process;

[0054] A prediction module for calculating the atmospheric visibility and photovoltaic power prediction results through the atmospheric extinction coefficient considering the aerosol process.

[0055] In a third aspect, the present invention provides a computer-readable storage medium, on which a computer program is stored, and when the program is executed by a processor, the steps of the method described in any one of the foregoing are implemented.

[0056] Compared with the prior art, the beneficial effects achieved by the present invention:

[0057] The present invention provides a method, device, and storage medium for predicting atmospheric visibility and photovoltaic power. Based on clarifying the growth mechanism of aerosol clusters, by introducing a fractal aerosol cluster model, the physical simulation degree of the optical properties of atmospheric aerosols and the prediction simulation accuracy are improved, thus significantly improving the simulation results of mesoscale atmospheric models for atmospheric visibility and photovoltaic power generation; this method can better predict the power generation capacity of photovoltaic power plants; it has important application value in new energy resource and safety assessment, environmental management, weather forecasting, and regional climate research. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 is a flowchart of a method for predicting atmospheric visibility and photovoltaic power provided by an embodiment of the present invention;

[0059] Figure 2 is a schematic diagram of the relationship between the optical equivalent radius and the aerodynamic equivalent radius of an atmospheric aerosol cluster provided by an embodiment of the present invention, and the relationship with the number of monomers forming the aerosol cluster and the fractal dimension of the aerosol cluster;

[0060] Figure 3 is a spatial structure effect diagram of a fractal aerosol cluster simulated according to the DLA algorithm provided by an embodiment of the present invention;

[0061] Figure 4 is a schematic diagram of a method for calculating the spatial fractal dimension of fractal aerosols with different numbers of monomers using the box-counting method provided by an embodiment of the invention;

[0062] Figure 5 is a time series diagram of the simulation and observation of atmospheric visibility in a simulation case provided by an embodiment of the invention;

[0063] Figure 6 is a time series diagram of the simulation and observed values of the surface short-wave radiation flux at a certain site in the same event provided by an embodiment of the invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0064] The present invention will be further described below with reference to the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solutions of the present invention and should not be used to limit the protection scope of the present invention.

[0065] Embodiment 1. This embodiment introduces a method for predicting atmospheric visibility and photovoltaic power, including:

[0066] Based on the diffusion-limited aggregation algorithm, construct an aerosol cluster model with fractal geometric characteristics;

[0067] Use the box-counting method to calculate the fractal dimension of the aerosol cluster model;

[0068] Calculate the optical equivalent radius of the aerosol cluster model for fractal geometric features based on the fractal dimension;

[0069] Couple the optical equivalent radius into the mesoscale air quality model to calculate the atmospheric extinction coefficient considering the aerosol process;

[0070] Calculate the atmospheric visibility and photovoltaic power prediction results through the atmospheric extinction coefficient considering the aerosol process.

[0071] As Figure 1 shown, the method for predicting atmospheric visibility and photovoltaic power provided in this embodiment specifically involves the following steps in its application process:

[0072] 1. Propose a particle mathematical model of fractal geometry: Based on the observed shape characteristics of aerosol clusters under a microscope, propose an assumption about the fractal characteristics of aerosol particles; starting from this assumption, discuss the relationships between the aerodynamic equivalent radius and optical equivalent radius of fractal aerosols and the fractal dimension, amount of substance, and other mathematical and physical characteristics of aerosol clusters, and make a quantitative comparison with the results obtained based on the spherical aerosol simulation assumption in the traditional air quality model to prove that the fractal characteristics of aerosol clusters will lead to stronger atmospheric optical characteristics. In the spherical cluster model in previous numerical atmospheric models, the formula for its aerodynamic equivalent radius is:

[0073] (1);

[0074] where is the aerodynamic equivalent radius in the spherical cluster model, is the monomer radius, is the number of monomer particles contained in the aerosol cluster.

[0075] At the same time, the optical equivalent radius in the spherical cluster model is:

[0076] (2);

[0077] where is the optical equivalent radius of the spherical particle, is the ratio constant. It can be found that the ratio between the two is:

[0078] (3);

[0079] where is the ratio of the optical equivalent radius to the aerodynamic radius of the spherical particle, which is a fixed value and has nothing to do with the amount of substance of the aerosol particle, that is, for the aerosol of the spherical cluster, the larger its optical equivalent radius, the larger its aerodynamic equivalent radius, and the two are linearly correlated.

[0080] However, recent microscopic observations have shown that most atmospheric aerosol clusters caused by anthropogenic emissions often have complex shapes. On the one hand, they are very different from the spherical assumption in traditional models. On the other hand, from the shape, they are considered to have geometric self-similarity at different scales, that is, fractal characteristics. Therefore, we need to establish a fractal model of atmospheric aerosol clusters. For this fractal aerosol cluster model, its aerodynamic equivalent radius should be:

[0081] (4);

[0082] where is the aerodynamic equivalent radius of the fractal aerosol cluster model, is the monomer radius, is the number of monomer particles that make up the aerosol cluster, is the fractal dimension of the aerosol cluster, is the fractal factor, and its empirical formula is:

[0083] (5);

[0084] Furthermore, the optical equivalent radius of the fractal aerosol cluster model is:

[0085] (6);

[0086] The ratio is:

[0087] (7);

[0088] where is the ratio of the optical equivalent radius to the aerodynamic radius of the fractal aerosol cluster, which is very different from spherical particles and varies with the fractal dimension and the number of monomer particles. See Figure 2 .

[0089] 2. Generate a fractal aerosol spatial model: The Diffusion Limited Aggregation (DLA) algorithm was used to simulate the random motion and coalescence of aerosol monomer particles in three-dimensional space, and then the physical process of growing aerosol clusters. The shape of the aerosol clusters obtained from this simulation process roughly coincides with that in microscopic observations, having a complex and irregular spherical structure. See Figure 3 , and in Figure 3 , the number of monomers N that make up the cluster is 50, 100, 300, and 1000 respectively.

[0090] The specific steps are as follows:

[0091] a) First, divide a space of a fixed size as the growth space for the particles. Place a stationary monomer particle at the center of the space as the starting point for cluster growth;

[0092] b) Randomly release new monomer particles within a spherical region centered on this particle, making them move randomly in any direction and within a certain speed range;

[0093] c) If it moves to a region more than a certain distance away from the previous particle, we consider it to escape to infinity, remove this particle and re - enter step b); while if the new particle moves to the grid coordinates adjacent to the former, there is a certain probability that the two can adhere and form a complex;

[0094] d) Repeat the process of b) - c) until the number of aggregated particles reaches a pre - specified number, and finally a spatial aerosol model with a complex and irregular shape will be formed.

[0095] 3. Calculation of the fractal dimension of the aerosol model: Use the box - counting method to calculate the fractal dimension of the space where the aerosol cluster model generated above is located. The box - counting method first conducts grid divisions of the space with multiple different spatial measures. In each division, count the number of grids occupied by the aerosol cluster and the total number of grids, and then perform logarithmic fitting on these two sequences under multiple different measures. The slope obtained from the fitting is the fractal dimension of the aerosol cluster. Through this step, the physical mechanism of aerosol growth and the fractal characteristics of the aerosol cluster generated under this mechanism are further verified. (See Figure 4 ). The specific steps are as follows:

[0096] a) Conduct grid divisions of the three - dimensional space where the generated aerosol cluster model is located with multiple different spatial measures, that is, in each division, divide this three - dimensional space into three - dimensional cubic grids of the same size;

[0097] b) After each spatial grid division, count the number of grids occupied by the aerosol cluster and the total number of grids (or the spatial measure value of the grid, that is, the side length );

[0098] c) For different spatial divisions based on different measures, a set of sequences of and can be obtained. Take the logarithm of both and perform fitting. The slope is the dimension of the aerosol cluster model. The specific calculation formula is:

[0099] (8);

[0100] where is the number of grids covering the aerosol cluster, is the grid side length, is the fractal dimension of the obtained aerosol cluster model;

[0101] d) Repeat the above steps to calculate grids of different scales, and finally obtain the fractal dimension of the aerosol model, which is used to characterize the complex geometric structure of the aerosol cluster.

[0102] 4. Parameterization of the aerosol optical equivalent radius: For the aerosol cluster model with fractal geometric characteristics, a new theoretical calculation of the optical equivalent radius and the aerodynamic radius was carried out. The theoretical results are significantly different from the above two equivalent radii based on the assumption of spherical aerosol clusters. Among them, the relationship between the optical equivalent radius of the aerosol cluster model with fractal geometric characteristics and the optical equivalent radius based on the spherical aerosol cluster model assumption is:

[0103] (9);

[0104] where is the optical equivalent radius of the spherical particle, is the optical equivalent radius of the fractal particle, is the fractal factor, see formula (5), is the fractal dimension of the aerosol cluster, obtained from formula (8).

[0105] 5. Simulation calculation based on the mesoscale model: Incorporate formula (9) into the physical parameterization scheme in the numerical model, couple it into the mesoscale atmospheric numerical model, simulate the aerosol mass concentration through mesoscale numerical simulation of a specific area, obtain the corresponding aerosol particle size distribution, calculate the optical equivalent radius under its fractal characteristics through formula (9), and then obtain the two standard output simulation results of the atmospheric aerosol extinction coefficient and surface shortwave radiation in common mesoscale models.

[0106] 6. Calculate the atmospheric visibility based on the simulation results of the extinction coefficient: Based on the simulated extinction coefficient data results, use the atmospheric visibility calculation formula to analyze the spatial distribution and time series variation of the atmospheric visibility. The calculation formula of the extinction coefficient in the atmospheric model is as follows:

[0107] (10);

[0108] where, is the atmospheric extinction coefficient, is the aerosol number concentration (number per unit volume) related to the th particle size interval, is the extinction efficiency, is the optical equivalent radius of the aerosol, is a dimensional parameter. In the atmospheric mode, it is the number of intervals divided for the particle sizes of all aerosols in the atmosphere according to the optical characteristics of aerosols with different particle sizes.

[0109] Based on the simulated aerosol atmospheric extinction coefficient above, use the atmospheric visibility calculation formula to calculate the atmospheric visibility:

[0110] (11);

[0111] where is the atmospheric visibility, one of the key forecast results obtained by this technology, is a constant that varies with atmospheric conditions and is usually set to 3.912 in the study. is the atmospheric extinction coefficient obtained in the previous step of the mode, see formula (10).

[0112] 7. Calculate the photovoltaic power based on the simulated results of surface shortwave radiation: Based on the simulated surface shortwave radiation data results, obtain the photovoltaic power forecast result through the photovoltaic power calculation formula. First, calculate the aerosol optical depth through the extinction coefficient The aerosol optical depth is the integral in the vertical direction, is the height of the top of the atmosphere, is the height of each atmospheric layer, and the formula is:

[0113] (12);

[0114] Further calculate the surface shortwave radiation, and the formula is:

[0115] (13);

[0116] where is the solar constant, generally 1361 W / m², is the solar zenith angle, is the surface albedo.

[0117] Based on formulas (12) and (13), the photovoltaic power can be calculated, and the formula is:

[0118] (14);

[0119] where is the photovoltaic power, is the efficiency of the photovoltaic system, usually between 0.15 and 0.22, depending on the type and conditions of the photovoltaic modules, is the area of the photovoltaic module.

[0120] Through the above method, the present invention can accurately predict the variation characteristics of atmospheric visibility and photovoltaic power, which has important scientific significance and practical application value.

[0121] As Figure 5 shown, the time series of the observed value of atmospheric visibility at a certain site is compared with two groups of comparative simulation values. It can be seen that the simulation result after adding the fractal aerosol cluster model described in the present invention is closer to the observation.

[0122] As Figure 6 shown, the time series of the observed value of the surface shortwave radiation flux at a certain site is compared with two groups of comparative simulation values. It can be seen that after adding the fractal aerosol cluster model described in the present invention, the simulated result of the ground solar shortwave radiation flux is closer to the observation, especially for the period with a lower daytime peak radiation flux, and the improvement effect of the present technology is more obvious. Figure 6 The scatter points in [figure] represent the observed values, and the dashed line represents the original model as the control group.

[0123] Embodiment 2. This embodiment provides an atmospheric visibility and photovoltaic power forecasting device, including:

[0124] A model construction module for constructing an aerosol cluster model with fractal geometric characteristics based on the diffusion-limited aggregation algorithm;

[0125] A first calculation module for calculating the fractal dimension of the aerosol cluster model using the box-counting method;

[0126] A second calculation module for calculating the optical equivalent radius of the aerosol cluster model with fractal geometric characteristics according to the fractal dimension;

[0127] A third calculation module for coupling the optical equivalent radius into the mesoscale air quality model to calculate the atmospheric extinction coefficient considering the aerosol process;

[0128] A forecasting module for calculating the forecasting results of atmospheric visibility and photovoltaic power through the atmospheric extinction coefficient considering the aerosol process.

[0129] For the specific function implementation of the above modules, refer to the relevant content in the method of Embodiment 1, which will not be elaborated here.

[0130] Embodiment 3. This embodiment provides a computer-readable storage medium, on which a computer program is stored. When the program is executed by a processor, it implements the steps of any one of the methods described in Embodiment 1.

[0131] The above are only the preferred embodiments of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the technical principle of the present invention, several improvements and modifications can be made, and these improvements and modifications should also be regarded as the protection scope of the present invention.

[0132] Those skilled in the art should understand that the embodiments of the present disclosure can be provided as methods, systems, or computer program products. Therefore, the present disclosure can take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present disclosure can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0133] The present disclosure is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to the embodiments of the present disclosure. It should be understood that each flow and / or block in the flowchart and / or block diagram, as well as the combination of flows and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing devices generate means for implementing the specified functions in Figure 1 one or more of the processes or multiple processes and / or blocks Figure 1 one or more of the blocks or multiple blocks.

[0134] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory generate a manufactured article including instruction means that implement the specified functions in Figure 1 one or more of the processes or multiple processes and / or blocks Figure 1 one or more of the blocks or multiple blocks.

[0135] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operation steps are performed on the computer or other programmable device to generate a computer-implemented process, so that the instructions executed on the computer or other programmable device provide steps for implementing the specified functions in Figure 1 one or more of the processes or multiple processes and / or blocks Figure 1 one or more of the blocks or multiple blocks.

[0136] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present disclosure rather than to limit the scope of its protection. Although the present disclosure has been described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that after reading the present disclosure, various changes, modifications or equivalent substitutions can still be made to the specific implementation manners of the invention. However, these changes, modifications or equivalent substitutions are all within the scope of protection of the claims pending for publication.

Claims

1. A method for forecasting atmospheric visibility and photovoltaic power, characterized in that: include: Based on the diffusion-limited agglomeration algorithm, an aerosol cluster model with fractal geometric characteristics is constructed; The fractal dimension of the aerosol cluster model was calculated using the box counting method; According to the fractal dimension, the optical equivalent radius of the aerosol cluster model with fractal geometric characteristics is calculated; The optical equivalent radius is coupled into the mesoscale air mass model, and the atmospheric extinction coefficient taking into account the aerosol process is calculated; the formula is as follows: ； in, is the optical equivalent radius of a spherical particle, is the optical equivalent radius of the fractal particle, is the fractal factor, is the monomer radius, is the fractal dimension of the aerosol cluster; in, The calculation formula is as follows: ； in, is the ratio constant, is the number of monomer particles contained in the aerosol cluster; ； in, is the aerodynamic equivalent radius in the spherical cluster model, and the calculation formula is as follows: ； By considering the atmospheric extinction coefficient of the aerosol process, the atmospheric visibility and photovoltaic power forecast results are calculated.

2. The atmospheric visibility and photovoltaic power forecasting method according to claim 1, characterized in that: The aerosol cluster model with fractal geometry characteristics is constructed based on the diffusion-limited agglomeration algorithm, including: a) Initialize the three-dimensional space and place the initial monomer particle at the center; b) Randomly release new particles around the initial monomer particles to make them perform Brownian motion; c) If the new particle moves to a position adjacent to an existing cluster, it adheres to form a complex; d) Repeat steps b)-c) until the number of cluster particles reaches a preset value, thereby generating an aerosol cluster model with a non-spherical fractal structure.

3. The atmospheric visibility and photovoltaic power forecasting method according to claim 1, characterized in that: The box counting method is used to calculate the fractal dimension of the aerosol cluster model, including: The space where the aerosol cluster model is located is divided into grids with multiple different spatial measurements; In each partition, the number of grids occupied by the aerosol cluster model and the total number of grids are counted; The number of grids and the total number of grids under multiple different measurements are logarithmically fitted, and the slope obtained by fitting is the fractal dimension of the aerosol cluster model.

4. The atmospheric visibility and photovoltaic power forecasting method according to claim 3, characterized in that: The calculation formula of the fractal dimension is as follows: ； in is the number of grids covering the aerosol clusters, is the grid side length, is the fractal dimension of the aerosol cluster model obtained.

5. The atmospheric visibility and photovoltaic power forecasting method according to claim 4, characterized in that: The optical equivalent radius is coupled into the mesoscale air quality model to calculate the atmospheric extinction coefficient taking into account the aerosol process. The formula is as follows: ； in, is the atmospheric extinction coefficient, It is with The aerosol number concentration associated with each particle size interval is is the extinction efficiency, is the size parameter of the particle size, It is the number of intervals divided for the particle sizes of all aerosols in the atmosphere according to the optical characteristics of aerosols of different particle sizes in the atmospheric model.

6. The atmospheric visibility and photovoltaic power forecasting method according to claim 5, characterized in that: The atmospheric visibility is calculated by taking into account the atmospheric extinction coefficient of the aerosol process, and the formula is as follows: ； in is the atmospheric visibility, is a constant that varies with atmospheric conditions.

7. The atmospheric visibility and photovoltaic power forecasting method according to claim 6, characterized in that: The photovoltaic power calculation method comprises: Calculate the aerosol optical depth through the atmospheric extinction coefficient , the formula is as follows: ； In the formula, is the height of the top of the atmosphere, for each atmospheric layer height; Aerosol Optical Depth , calculate the surface shortwave radiation, the formula is as follows: ； in is the surface shortwave radiation, is the solar constant, is the solar zenith angle, is the surface albedo; is a natural constant; The photovoltaic power is calculated through the shortwave radiation on the ground. The formula is as follows: ； in is the photovoltaic power, is the efficiency of the photovoltaic system, is the area of ​​the PV panel.

8. An atmospheric visibility and photovoltaic power forecasting device, using the atmospheric visibility and photovoltaic power forecasting method according to claim 1, characterized in that: include: Model building module, used to build aerosol cluster model with fractal geometry characteristics based on diffusion-limited agglomeration algorithm; A first calculation module is used to calculate the fractal dimension of the aerosol cluster model using a box counting method; The second calculation module is used to calculate the optical equivalent radius of the aerosol cluster model with fractal geometric characteristics according to the fractal dimension; The third calculation module couples the optical equivalent radius into the mesoscale air quality model to calculate the atmospheric extinction coefficient taking into account the aerosol process; The forecast module is used to calculate the atmospheric visibility and photovoltaic power forecast results by taking into account the atmospheric extinction coefficient of the aerosol process.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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