A photovoltaic power generation prediction method and system

Abstract

The present invention discloses a photovoltaic power generation prediction method and system, which relates to the technical field of photovoltaic power generation and includes: collecting data; predicting whether dew characteristics occur; when dew characteristics occur, predicting the occurrence time, the volume of dew and the predicted occurrence position; determining whether dust and dew mixture appears on the surface of the photovoltaic module according to the occurrence time, dust characteristics, the volume of dew, the predicted occurrence position and the inclination angle of the photovoltaic module; if it is determined that the mixture appears, predicting the occlusion area; adding the occlusion area as an input parameter to the power generation prediction model; according to the historical data, the present invention performs data modeling on the dew situation, inputs the current environmental parameters into the model, so as to predict the occlusion area after the mixture of dust and water, thereby digitalizing the influence of dew, dust and muddy water on the power generation, thereby correcting the light that the photovoltaic module is expected to receive, thereby correcting the predicted power generation, and thereby increasing the accuracy of the prediction result.

Description

Technical Field

[0001] The present invention relates to the technical field of photovoltaic power generation, and particularly relates to a photovoltaic power generation power prediction method and system. Background Art

[0002] With the increasing demand for energy, clean and renewable energy represented by solar photovoltaic power generation will be an important direction for the future development of our energy. According to the prediction of the Joint Research Centre of the European Union: solar power generation will reach 20% of the total global power generation by 2050. With the increasing proportion of solar power generation, the drawback that the power generation of photovoltaic power generation is vulnerable to weather and environmental impacts on power generation has become increasingly prominent. To ensure the stable operation of the power supply system, the prediction of solar power generation is crucial.

[0003] For example, a photovoltaic power generation power prediction method and its application based on machine learning are disclosed in the prior art. It includes, through machine learning methods, correcting the influence of weather on power generation on the basis of calculating solar radiation, and also calculating the solar radiation received by photovoltaic modules with planar and curved shapes at any time, so as to calculate the power generation of the photovoltaic modules. The photovoltaic power generation power prediction method and its application based on machine learning have the following problems. During the calculation process, the occlusion of the photovoltaic modules caused by the mixture of dust and water on the surface of the photovoltaic modules is ignored, resulting in the reduction of the radiation received by the photovoltaic modules due to occlusion being ignored, thus affecting the prediction results. Summary of the Invention

[0004] The purpose of the present invention is to provide a photovoltaic power generation power prediction method and system to solve the problem that the prediction result of power generation is inaccurate due to the influence of surface dust.

[0005] To achieve the above purpose, the present invention provides the following technical solution: A photovoltaic power generation power prediction method, comprising:

[0006] Collecting the dust characteristics, environmental temperature and humidity, and the surface temperature of the photovoltaic module on the surface of the photovoltaic module;

[0007] Predicting whether dew characteristics appear on the surface of the photovoltaic module in the current cycle according to the environmental temperature and humidity and the surface temperature of the photovoltaic module;

[0008] In response to the appearance of the dew characteristics in the current cycle, predicting the appearance time, the volume of the dew, and the predicted appearance position of the dew;

[0009] Determining whether there is a mixture of dust and dew on the surface of the photovoltaic module in the current cycle according to the appearance time, the dust characteristics, the volume of the dew, the predicted appearance position, and the inclination angle of the photovoltaic module;

[0010] If it is determined that there is a mixture of dust and dew in the current cycle, predict the area of the photovoltaic module blocked after the mixture of dust and dew;

[0011] Use the blocked area as an input parameter and add it to the model for predicting the power generation of the photovoltaic module.

[0012] As a preferred embodiment of the photovoltaic power generation prediction method, the dust characteristics include: the dust distribution on the surface of the photovoltaic module.

[0013] As a preferred embodiment of the photovoltaic power generation prediction method, the specific steps for predicting the occurrence time of the dew characteristics include:

[0014] Collect the ambient temperature and humidity and the surface temperature of the photovoltaic module when dew appears in the historical data;

[0015] Use the ambient temperature and humidity, the surface temperature of the photovoltaic module, and the corresponding occurrence time of dew in the historical data as training data to establish a prediction model;

[0016] Input the current ambient temperature and humidity and the surface temperature of the photovoltaic module, and output the occurrence time of the dew characteristics.

[0017] As a preferred embodiment of the photovoltaic power generation prediction method, the prediction of the volume of the dew and the predicted occurrence position further includes the following steps:

[0018] Collect the ambient temperature and humidity, the surface temperature of the photovoltaic module, and the corresponding volume of the dew and the predicted occurrence position when dew appears in the historical data;

[0019] Substitute the above parameters into the training model for training to establish a prediction model for the volume of the dew and the predicted occurrence position;

[0020] Input the current ambient temperature and humidity and the surface temperature of the photovoltaic module into the prediction model, and output the volume of the dew and the predicted occurrence position.

[0021] As a preferred embodiment of the photovoltaic power generation prediction method, the determination of whether there is a mixture of dust and dew on the surface of the photovoltaic module in the current cycle includes:

[0022] Determine the retention time of the dew according to the volume of the dew and the ambient temperature and humidity;

[0023] If the retention time is greater than the preset time, it is determined that there is a possibility of a mixture of dust and dew.

[0024] As a preferred embodiment of the photovoltaic power generation prediction method, the determination of whether there is a mixture of dust and dew on the surface of the photovoltaic module in the current cycle specifically includes:

[0025] If there is a closed area where the distribution density of the dew is greater than the preset density, it is determined that dew will accumulate in the closed area, and the flow path and the width of the flow path of the water flow generated after accumulation along the surface of the photovoltaic module are determined according to the distribution density, volume of the dew in the closed area, and the inclination angle of the photovoltaic module.

[0026] As a preferred embodiment of the photovoltaic power prediction method, the determining whether dust and dew are mixed on the surface of the photovoltaic module in the current cycle further includes: determining the amount of dust on the flow path according to the flow path, the width of the flow path, and the dust distribution on the surface of the photovoltaic module.

[0027] As a preferred embodiment of the photovoltaic power prediction method, the determining whether dust and dew are mixed on the surface of the photovoltaic module in the current cycle includes:

[0028] If there is no dust on the flow path, it is determined that the dust and dew will not be mixed in the current cycle;

[0029] If there is dust on the flow path, it is determined that the dust and dew will be mixed in the current cycle.

[0030] As a preferred embodiment of the photovoltaic power prediction method, the predicting the shielding area of the photovoltaic module after the dust and dew are mixed specifically includes:

[0031] Obtaining the amount of dust on the flow path; determining the shielding area of the photovoltaic module after the dust and dew are mixed according to the amount of dust on the flow path and the volume of the water flow generated after accumulation.

[0032] The embodiment of the present invention also provides a photovoltaic power prediction system, including:

[0033] An acquisition module, including: a visual detection unit for acquiring images of the surface of the photovoltaic module, a temperature detection unit for acquiring the surface temperature of the photovoltaic module, and an environment detection unit for detecting the environmental temperature and humidity;

[0034] A storage module, which is connected to the acquisition module and is used for storing historical data of the surface temperature, dew volume, dew appearance position, appearance time of the photovoltaic module, and environmental temperature and humidity;

[0035] An operation module, which is connected to the acquisition module and the storage module, is used to determine the dew volume according to the dew images acquired by the acquisition module, establish a theoretical power generation model based on the parameters of the photovoltaic module, and establish a prediction model based on the data of the acquisition module, the storage module, and the operation module to predict whether dew will appear on the surface of the photovoltaic module, and if dew appears, whether a mixture of dew and dust will appear on the surface of the photovoltaic module. If a mixture of dew and dust will appear, predict the shielding area after the mixture of dew and dust, and use the predicted shielding area as an input parameter to the model for predicting the power generation of the photovoltaic module.

[0036] Compared with the prior art, the beneficial effects of the present invention are that the present invention analyzes the dew conditions based on the collected historical data, thus performs data modeling on the dew situation according to the historical data, inputs the current environmental parameters into the model, and obtains the occurrence time, dew volume, and predicted occurrence position when dew appears in the current cycle, so as to predict the shielding area after the mixture of dust and water, thereby digitalizing the influence of dew, dust, and muddy water on the power generation, correcting the light that the photovoltaic module is expected to receive, correcting the predicted power generation, and increasing the accuracy of the prediction result. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 It is a flowchart of the method for predicting the power generation of a photovoltaic power generation system in an embodiment of the present invention;

[0038] Figure 2 It is a flowchart of the prediction of the occurrence time in an embodiment of the present invention;

[0039] Figure 3 It is a flowchart of the prediction of the volume and predicted occurrence position of dew in an embodiment of the present invention;

[0040] Figure 4 It is a structural block diagram of the photovoltaic power generation prediction system in an embodiment of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0041] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to 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. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0042] Please refer to Figure 1 As shown, it is a flowchart of a method for predicting the power generation of a photovoltaic power generation system in an embodiment of the present invention, which specifically includes the following steps:

[0043] Step S1, collect the dust characteristics, ambient temperature and humidity, and the surface temperature of the photovoltaic module on the surface of the photovoltaic module;

[0044] Step S2, predict whether dew characteristics will appear on the surface of the photovoltaic module during the current period according to the ambient temperature and humidity and the surface temperature of the photovoltaic module;

[0045] Step S3, in response to the appearance of dew characteristics during the current period, predict the appearance time of the dew characteristics, the volume of the dew, and the expected appearance position;

[0046] Step S4, determine whether dust and dew mixture appears on the surface of the photovoltaic module during the current period according to the appearance time, dust characteristics, volume of the dew, expected appearance position, and the inclination angle of the photovoltaic module;

[0047] Step S5, if it is determined that dust and dew mixture appears during the current period, predict the shading area of the photovoltaic module after the mixture of dust and dew;

[0048] Step S6, add the shading area as an input parameter to the model for predicting the power generation of the photovoltaic module.

[0049] For ease of understanding, the specific process of the existing photovoltaic power generation prediction model is supplemented in this embodiment, including:

[0050] (1) Calculate the power generation of photovoltaic modules with planar and curved surfaces at a certain moment and location; specifically:

[0051] ① Calculate the position of the sun at a certain moment based on the hour angle coordinate system. Use the solar hour angle, solar declination angle, and local latitude parameters to determine the exact position of the sun relative to the observation point.

[0052] ② Calculate the solar radiation intensity on the ground surface.

[0053] ③ For planar photovoltaic modules, the solar radiation received by the entire photovoltaic module is uniform.

[0054] For curved photovoltaic modules, the received solar radiation is non-uniform. Therefore, in order to calculate the solar radiation received by the curved photovoltaic module, the curved surface of the curved photovoltaic module is subdivided.

[0055] ④ For planar photovoltaic modules, given the angle between the entire panel and the ground plane and the orientation projected on the ground plane, and then according to the solar position calculated in ①, the angle between the sun's rays and the central normal vector of the photovoltaic module can be directly calculated when the sun's rays shine on the center point of the photovoltaic module. Then, according to the solar radiation intensity received on the ground calculated in ②, the solar radiation received by the entire photovoltaic module can be calculated.

[0056] For a curved photovoltaic module, after the curved surface is subdivided, the angle between each planar polygon region and the ground plane and the orientation projected on the ground plane can be calculated. Then, based on the solar position calculated in ①, the angle between the solar rays irradiating the center point of each planar polygon region and the normal vector of the plane center is calculated. By calculating the angle between the direction of the normal vector of the center of each planar polygon region and the solar rays and the solar radiation intensity on the ground surface calculated in ②, the solar radiation received by each planar polygon region can be calculated. Then, by adding up the solar radiation received by all planar polygon regions, the total solar radiation is obtained.

[0057] ⑤ Whenever the power generation power of the photovoltaic module at the current moment is calculated through ④, the weather data at the current moment, the type of the photovoltaic module, and the power generation efficiency of the photovoltaic module are input into the trained machine learning model. The model will output a correction value for the power generation power at the current moment, and this value can be positive or negative. Then, the power generation power calculated in ④ is added with this correction value to obtain a more accurate value of the power generation power of the photovoltaic module.

[0058] Based on the above prior art, in this embodiment, new parameters can be added in the data collection stage, namely the dew appearance time, dew amount, appearance position, and photovoltaic module temperature. Through training with historical data, the appearance position of dew is predicted. Thus, on the basis of the existing prediction model, the influence of the combination of dew and dust on the solar radiation received by the photovoltaic module is avoided, so as to correct the actual solar irradiance received by the photovoltaic module, making the power generation power output by the prediction model more in line with the actual power generation power, and further increasing the accuracy of the prediction result when predicting the power generation power.

[0059] Specifically, in this embodiment, the dust feature is the dust distribution on the surface of the photovoltaic module.

[0060] Please refer to Figure 2 shown in the flowchart of the prediction of the appearance time of the embodiment of the present invention, which specifically includes the following steps:

[0061] Step S311: Collect the environmental temperature and humidity and the surface temperature of the photovoltaic module when dew appears in the historical data;

[0062] Step S312: Use the environmental temperature and humidity, the surface temperature of the photovoltaic module, and the corresponding dew appearance time in the historical data as training data to establish a prediction model;

[0063] Step S313: Input the current environmental temperature and humidity and the photovoltaic module temperature, and output the appearance time of the dew feature.

[0064] In the above embodiments, due to the influence of factors such as dust, angle, and wind direction, the surface temperature distribution of the photovoltaic module will not be the same temperature across the entire photovoltaic module. Moreover, there is a certain relationship between the surface temperature of the photovoltaic module and the appearance of dew. Dew appears first in the areas with lower temperature and later in the areas with higher temperature. Therefore, the surface temperature in this embodiment should be the temperature distribution of each area on the surface of the photovoltaic module. The acquisition of the surface temperature of the photovoltaic module can be achieved through infrared imaging or an infrared thermometer. The detection process is prior art and will not be elaborated here.

[0065] Please refer to Figure 3 which shows the prediction flowchart of the volume and predicted appearance position of dew in the embodiment of the present invention, specifically including the following steps:

[0066] Step S321, collect the environmental temperature and humidity, the surface temperature of the photovoltaic module, and the corresponding volume and predicted appearance position of dew when dew appears in the historical data;

[0067] Step S322, substitute the above parameters into the training model for training to establish a prediction model for the volume and predicted appearance position of dew;

[0068] Step S323, input the current environmental temperature and humidity and the surface temperature of the photovoltaic module into the prediction model, and output the volume and predicted appearance position of dew.

[0069] In practice, dew needs to act with dust for a period of time before mixing. If the dew appears for too short a time and does not mix with dust, it will not affect the current prediction result. The length of the retention time is mainly related to two factors. On the one hand, the higher the environmental temperature or the smaller the volume of dew, the less time it takes for the dew to evaporate and disappear, and the shorter the retention time of the dew. On the other hand, the higher the surface temperature of the photovoltaic module, the shorter the dew stay time. A prediction model for the retention time of dew is established based on historical data, and the retention time is predicted according to the current situation.

[0070] Specifically, if there is a closed area with a distribution density greater than the preset density, it is determined that dew will aggregate, and the width is determined according to the closed area.

[0071] In the above embodiments, it can be understood that dew will appear on the photovoltaic module in the form of small water droplets. As the dew phenomenon continues to occur, the volume and distribution density of the water droplets will continuously increase. After increasing to a certain value, adjacent water droplets will merge to form a large water droplet that flows downward along the inclination angle of the photovoltaic module. When the angle and surface roughness parameters of the photovoltaic module are determined, the density and volume required for the water droplets to merge can be determined. By predicting the appearance position of the dew and the volume of a single water droplet, the position and path width of the merged water droplet can be predicted, thereby predicting the amount and duration of the influence on light reception, and then correcting the actual radiation amount received by the photovoltaic module, thereby increasing the accuracy of the prediction result.

[0072] In step S4, determining whether dust and dew mixture appears on the surface of the photovoltaic module in the current cycle includes:

[0073] Step S41, determining the retention duration of the dew according to the volume of the dew and the ambient temperature and humidity;

[0074] Step S42, if the retention duration is greater than the preset duration, it is determined that there is a possibility of dust and dew mixture occurring;

[0075] If the retention duration is less than or equal to the preset duration, it is determined that no dust and dew mixture will occur.

[0076] Of course, it can be understood that when the retention duration is long enough (i.e., when the retention duration is greater than the preset duration), it can meet the requirement that the water will not evaporate when flowing to the dust position, and there is a possibility of their mixture. If the retention duration is short (i.e., when the retention duration is less than or equal to the preset duration), the moisture will quickly evaporate and disappear, and naturally there is no possibility of their mixture. By determining the retention duration, some situations are excluded, reducing the system computing power and optimizing the working efficiency. In this embodiment, the preset duration is 20 min. In practice, the preset duration is different for different types of photovoltaic modules in different regions and can be adjusted adaptively according to the actual working conditions and application scenarios.

[0077] Further, in response to the determination result that dust and dew mixture will occur, determining whether dust and dew mixture appears on the surface of the photovoltaic module in the current cycle specifically includes:

[0078] If there is a closed area where the distribution density of the dew is greater than the preset density, it is determined that the dew will accumulate in the closed area, and the flow path and the width of the flow path of the water flow generated after accumulation along the surface of the photovoltaic module are determined according to the distribution density, volume of the dew in the closed area, and the inclination angle of the photovoltaic module.

[0079] In the above embodiments, by dividing the surface of the photovoltaic module into several closed regions, it is convenient for the precise evaluation of local features. Then, in response to the determination that dust and dew will be mixed, the system detects the dew distribution density of each closed region. If the dew density within the closed region exceeds the preset density, it is determined that dew accumulation will occur in this region. The preset density can be determined by experimental measurement or by using computational fluid dynamics (CFD) simulation to predict the behavior of water droplets. Such accumulation may form a water flow that flows along the surface of the photovoltaic module, and further, based on the dew distribution density, the total volume, and the inclination angle of the photovoltaic module, the flow path and width of the water flow after accumulation are calculated, thereby laying an accurate foundation for determining the shaded area after mixing. Specifically, the above embodiments can not only accurately predict the dust and dew mixing effect in different regions on the surface of the photovoltaic module, but also quantify the specific impact of the mixed shading on the power generation, effectively improving the accuracy of prediction and the power supply stability.

[0080] Alternatively, the preset density and the size of the closed region in this solution can be adaptively adjusted according to the climate characteristics or the installation inclination angle of the photovoltaic module to meet different environmental requirements, so as to be widely applicable to scenarios that require efficient and accurate photovoltaic power generation prediction. Especially in areas with frequent sandstorms, by identifying and analyzing the dew and dust mixing effect in advance, the adverse impact of dust on the prediction result of power generation can be reduced, thereby improving the accuracy of power prediction.

[0081] Further, determining whether dust and dew are mixed on the surface of the photovoltaic module in the current cycle further includes: determining the amount of dust on the flow path according to the flow path, the width of the flow path, and the dust distribution on the surface of the photovoltaic module.

[0082] Further, determining whether dust and dew are mixed on the surface of the photovoltaic module in the current cycle further includes:

[0083] If there is no dust on the flow path, it is determined that dust and dew will not be mixed in the current cycle;

[0084] If there is dust on the flow path, it is determined that dust and dew will be mixed in the current cycle.

[0085] To achieve the technical effect of predicting the shaded area of the photovoltaic module after the dust and dew are mixed, it further includes:

[0086] Obtaining the amount of dust on the flow path;

[0087] Determining the shaded area of the photovoltaic module after the dust and dew are mixed according to the amount of dust on the flow path and the volume of the water flow generated after accumulation.

[0088] In this embodiment, the shielding area formed by mixing the dew amount and the dust amount is fixed (there are slight errors in the implementation, which can be ignored). For the shielding area, an empirical database of the shielding area generated by mixing different amounts of the two can be established, and the corresponding shielding area can be directly obtained through the empirical database.

[0089] Specifically, when the regional environment is certain, the characteristics of the dust have slight errors in the implementation, which can be ignored. Therefore, when the volume of the dew is fixed, the dust that can be mixed is also certain. After the dew is mixed with a certain amount of dust, it does not have the ability to flow on the photovoltaic module, that is, a shielding area is formed. In the implementation, the duration of the shielding area will also affect the power generation power of the photovoltaic module. The duration is the time from the start of the formation of the shielding area to the next moment when the surface of the photovoltaic module is cleaned. The duration is input into the model to predict the power generation power.

[0090] In the above embodiment, by establishing a prediction model and combining the ratio of the dust amount to the water flow volume, the distribution and diffusion range of the dust and dew mixture on the surface of the photovoltaic module can be determined more accurately, so as to calculate the shielding area. This process makes the prediction of the shielding rate of the photovoltaic module more accurate, and can be used as the input parameter of the power prediction model in time to dynamically adjust the prediction of the power generation power, thereby improving the accuracy of the power prediction.

[0091] Optionally, the dust distribution and the surface temperature of the photovoltaic module are both obtained by arranging an infrared camera to collect images. The application scenario of this scheme is wide, especially suitable for areas with more sand and dust or more haze, effectively reducing the influence of the shielding of the photovoltaic module by the mixture of dust and dew on the surface radiation reception amount of the photovoltaic module, thereby reducing the influence of dust on the prediction result, and thus improving the accuracy of the power prediction.

[0092] Please refer to Figure 4 as shown, which is the structural block diagram of the photovoltaic power generation power prediction system in the embodiment of the present invention, including:

[0093] An acquisition module, including: a visual detection unit for acquiring the surface image of the photovoltaic module, a temperature detection unit for acquiring the surface temperature of the photovoltaic module, and an environment detection unit for detecting the environmental temperature and humidity;

[0094] A storage module, which is connected to the acquisition module and is used to store the historical data of the surface temperature, dew volume, dew appearance position, appearance time and environmental temperature and humidity of the photovoltaic module;

[0095] An operation module, which is connected to the acquisition module and the storage module, is configured to determine the dew volume according to the dew image acquired by the acquisition module, establish a theoretical power generation model based on the parameters of the photovoltaic module, and establish a prediction model based on the data of the acquisition module, the storage module, and the operation module to predict whether dew will appear on the surface of the photovoltaic module, and if dew appears, whether a mixture of dew and dust will appear on the surface of the photovoltaic module. If a mixture of dew and dust will appear, predict the shielding area after the mixture of dew and dust, and use the predicted shielding area as an input parameter to be added to the model for predicting the power generation of the photovoltaic module.

[0096] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of devices, methods, and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagram may represent a module, a program segment, or a part of code that contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than marked in the accompanying drawings. For example, two consecutive blocks shown may actually be executed substantially in parallel, and they may sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in the block diagram and / or flowchart, and the combinations of blocks in the block diagram and / or flowchart, can be implemented by a dedicated hardware-based device for performing the specified functions or operations, or can be implemented by a combination of dedicated hardware and computer instructions.

[0097] Obviously, the above embodiments of the present invention are merely examples for clearly explaining the present invention, rather than limitations on the implementation manners of the present invention. For those of ordinary skill in the art, other different forms of changes or modifications can be made based on the above description. It is not necessary and impossible to enumerate all the implementation manners here. Any modifications, equivalent replacements, and improvements made within the spirit and principle of the present invention shall be included in the protection scope of the claims of the present invention.

Claims

1. A photovoltaic power generation prediction method, characterized in that Including: Collecting the dust characteristics, environmental temperature and humidity, and the surface temperature of the photovoltaic module on the surface of the photovoltaic module; Predicting whether dew characteristics will appear on the surface of the photovoltaic module during the current period based on the environmental temperature and humidity and the surface temperature of the photovoltaic module; In response to the appearance of the dew characteristics during the current period, predicting the appearance time of the dew characteristics, the volume of the dew, and the predicted appearance position; Determining whether a mixture of dust and dew appears on the surface of the photovoltaic module during the current period based on the appearance time, the dust characteristics, the volume of the dew, the predicted appearance position, and the inclination angle of the photovoltaic module; If it is determined that a mixture of dust and dew appears during the current period, predicting the shading area of the photovoltaic module after the mixture of dust and dew; Adding the shading area as an input parameter to the model for predicting the power generation of the photovoltaic module.

2. The photovoltaic power generation prediction method according to claim 1, wherein The dust characteristics include: the dust distribution on the surface of the photovoltaic module.

3. The photovoltaic power generation prediction method according to claim 2, wherein The specific process of predicting the appearance time of the dew characteristics includes: Collecting the environmental temperature and humidity and the surface temperature of the photovoltaic module when dew appears in historical data; Using the environmental temperature and humidity, the surface temperature of the photovoltaic module, and the corresponding appearance time of the dew in the historical data as training data to establish a prediction model; Inputting the current environmental temperature and humidity and the surface temperature of the photovoltaic module, and outputting the appearance time of the dew characteristics.

4. The photovoltaic power generation prediction method according to claim 1, wherein The prediction of the volume and predicted appearance position of the dew further includes the following steps: Collecting the environmental temperature and humidity, the surface temperature of the photovoltaic module, and the corresponding volume and predicted appearance position of the dew when dew appears in historical data; Substituting the above parameters into the training model for training to establish a prediction model for the volume and predicted appearance position of the dew; Inputting the current environmental temperature and humidity and the surface temperature of the photovoltaic module into the prediction model, and outputting the volume and predicted appearance position of the dew.

5. The photovoltaic power generation prediction method according to claim 1, wherein, The determination of whether a mixture of dust and dew appears on the surface of the photovoltaic module during the current period includes: Determining the retention time of the dew based on the volume of the dew and the environmental temperature and humidity; If the retention time is greater than the preset time, it is determined that there is a possibility of a mixture of dust and dew occurring.

6. The photovoltaic power generation prediction method according to claim 5, wherein The specific determination of whether a mixture of dust and dew appears on the surface of the photovoltaic module during the current period includes: If there is a closed area where the distribution density of the dew is greater than the preset density, it is determined that the dew will accumulate in the closed area, and the flow path and the width of the flow path of the water flow generated after accumulation along the surface of the photovoltaic module are determined according to the distribution density, volume, and inclination angle of the photovoltaic module in the closed area.

7. The photovoltaic power generation prediction method according to claim 6, characterized in that The determination of whether a mixture of dust and dew appears on the surface of the photovoltaic module during the current period further includes: determining the amount of dust on the flow path according to the flow path, the width of the flow path, and the dust distribution on the surface of the photovoltaic module.

8. The photovoltaic power generation prediction method according to claim 7, characterized in that The determination of whether a mixture of dust and dew appears on the surface of the photovoltaic module during the current period includes: If there is no dust on the flow path, it is determined that the mixture of dust and dew will not occur during the current period; If there is dust on the flow path, it is determined that the mixture of dust and dew will occur during the current period.

9. The photovoltaic power generation prediction method according to claim 7, wherein The specific prediction of the shading area of the photovoltaic module after the mixture of dust and dew includes: Obtaining the amount of dust on the flow path; Determine the shielding area of the photovoltaic module after the dust and dew are mixed according to the amount of dust on the flow path and the volume of the water flow generated after aggregation.

10. A photovoltaic power generation prediction system, characterized in that, Applying the photovoltaic power generation prediction method according to any one of claims 1-9, comprising: An acquisition module, comprising: a visual detection unit for acquiring an image of the surface of the photovoltaic module, a temperature detection unit for acquiring the surface temperature of the photovoltaic module, and an environmental detection unit for detecting the environmental temperature and humidity; A storage module, connected to the acquisition module, for storing historical data of the surface temperature, dew volume, dew appearance position, appearance time of the photovoltaic module, and environmental temperature and humidity; An operation module, connected to the acquisition module and the storage module, for determining the dew volume according to the dew image acquired by the acquisition module, establishing a theoretical power generation power model according to the parameters of the photovoltaic module, and establishing a prediction model according to the data of the acquisition module, the storage module, and the operation module, predicting whether dew will appear on the surface of the photovoltaic module, and if dew appears, predicting whether a mixture of dew and dust will appear on the surface of the photovoltaic module. If a mixture of dew and dust will appear, predicting the shielding area after the mixture of dew and dust, and using the predicted shielding area as an input parameter to the model for predicting the power generation power of the photovoltaic module.

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