Methods, devices, electronic equipment, and storage media for predicting photovoltaic power generation.

Abstract

This invention discloses a method, apparatus, electronic device, and storage medium for predicting photovoltaic (PV) power generation. The method includes: acquiring multiple initial influencing factors; determining target terrain information based on the initial influencing factors; determining at least one target influencing factor based on the target terrain information; and predicting the target PV power generation based on the at least one target influencing factor, wherein the target PV power generation is the PV power generation of the target terrain. This invention solves the technical problem of low accuracy and stability in PV power generation prediction results under different terrains in the prior art.

Description

Technical Field

[0001] This invention relates to the field of automation technology, and more specifically, to a method, apparatus, electronic device, and storage medium for predicting photovoltaic power generation. Background Technology

[0002] Photovoltaic power generation is a new type of power generation that directly converts light energy into electrical energy using the photovoltaic effect at the semiconductor interface. Compared with thermal power generation, photovoltaic power generation does not produce harmful gases such as carbon dioxide, sulfur dioxide, and dust, making it a clean and renewable energy source with good environmental benefits. However, the power generation process is affected by environmental conditions such as humidity, temperature, sunlight intensity, and cloud cover.

[0003] In recent years, with the intensification of the global energy crisis, the development and utilization of new energy sources have received increasing attention. As an important form of new energy, photovoltaic (PV) power generation has also garnered increasing attention for its power generation forecasting. PV power generation forecasting methods utilize historical data, weather information, and terrain conditions to predict the power generation of PV power plants over a future period. PV power generation forecasting is of great significance for power system dispatching, PV energy storage system planning, and PV power plant development.

[0004] Photovoltaic power generation forecasting is primarily influenced by environmental conditions, including temperature, humidity, sunlight intensity, and cloud cover. In actual power generation, these environmental conditions interact, and their impact varies across different regions and times. Therefore, photovoltaic power generation forecasting methods need to consider multiple environmental factors.

[0005] Existing photovoltaic power generation forecasting methods primarily rely on establishing the correlation between meteorological parameters and power generation. This involves analyzing the correlation between meteorological parameters and power generation to build a linear model, and then predicting power generation based on the meteorological parameters. However, this method fails to consider the impact of various environmental factors on power generation, leading to inaccurate predictions.

[0006] Furthermore, photovoltaic power generation forecasting methods require a large amount of historical data and real-time meteorological information. If the data is missing or incomplete, it will affect the accuracy of the forecast results. Therefore, obtaining sufficient historical data and real-time meteorological information is a major challenge in photovoltaic power generation forecasting. Summary of the Invention

[0007] This invention provides a method, apparatus, electronic device, and storage medium for predicting photovoltaic power generation, in order to at least solve the technical problem of low accuracy and stability of photovoltaic power generation prediction results under different terrains in the prior art.

[0008] According to one embodiment of the present invention, a method for predicting photovoltaic power generation is provided, comprising: acquiring multiple initial influencing factors; determining target terrain information based on the multiple initial influencing factors; determining at least one target influencing factor based on the target terrain information; and predicting the target photovoltaic power generation based on the at least one target influencing factor, wherein the target photovoltaic power generation is the photovoltaic power generation of the target terrain.

[0009] Optionally, the method for predicting photovoltaic power generation also includes: acquiring multiple target factors and multiple environmental topographic information, wherein the multiple target factors include environmental factors and meteorological factors; and determining multiple initial influencing factors from the multiple target factors based on the multiple environmental topographic information.

[0010] Optionally, the method for predicting photovoltaic power generation further includes: obtaining multiple preset thresholds, wherein the multiple preset thresholds correspond one-to-one with multiple initial influencing factors; comparing each initial influencing factor among the multiple initial influencing factors with its corresponding preset threshold to obtain a comparison result; and determining the target terrain information based on the comparison result.

[0011] Optionally, the method for predicting photovoltaic power generation further includes: in response to the target terrain being mountainous terrain, identifying multiple mountainous influencing factors; obtaining a first topographic feature of the mountainous terrain; and determining a first influencing factor from the multiple mountainous influencing factors based on the first topographic feature.

[0012] Optionally, the method for predicting photovoltaic power generation further includes: determining a target shading coefficient based on a first influencing factor; obtaining a first target radiation intensity of the mountainous terrain; and predicting a first photovoltaic power generation based on the target shading coefficient and the first target radiation intensity, wherein the first photovoltaic power generation is the photovoltaic power generation of the mountainous terrain.

[0013] Optionally, the method for predicting photovoltaic power generation also includes: in response to the target terrain being desert terrain, identifying multiple desert influencing factors; obtaining a second terrain feature of the desert terrain; and determining a second influencing factor from the multiple desert influencing factors based on the second terrain feature.

[0014] Optionally, the method for predicting photovoltaic power generation further includes: determining a target temperature coefficient based on a second influencing factor; obtaining a second target radiation intensity of the desert terrain; and predicting a second photovoltaic power generation based on the target temperature coefficient and the second target radiation intensity, wherein the second photovoltaic power generation is the photovoltaic power generation of the desert terrain.

[0015] According to one embodiment of the present invention, a photovoltaic power generation prediction device is also provided, comprising: an acquisition module for acquiring multiple initial influencing factors; a first determination module for determining target terrain information based on the multiple initial influencing factors; a second determination module for determining at least one target influencing factor based on the target terrain information; and a prediction module for predicting the target photovoltaic power generation based on the at least one target influencing factor, wherein the target photovoltaic power generation is the photovoltaic power generation of the target terrain.

[0016] Optionally, the acquisition module includes: a first acquisition unit, used to acquire multiple target factors and multiple environmental terrain information, wherein the multiple target factors include environmental factors and meteorological factors; and a first determination unit, used to determine multiple initial influencing factors from the multiple target factors based on the multiple environmental terrain information.

[0017] Optionally, the first determining module includes: a second acquiring unit, used to acquire multiple preset thresholds, wherein the multiple preset thresholds correspond one-to-one with multiple initial influencing factors; a comparison unit, used to compare each of the multiple initial influencing factors with the corresponding preset threshold to obtain a comparison result; and a second determining unit, used to determine the target terrain information based on the comparison result.

[0018] Optionally, the second determining module includes: a third determining unit, used to determine multiple mountain influencing factors in response to the target terrain being mountainous terrain; a third acquiring unit, used to acquire a first terrain feature of the mountainous terrain; and a fourth determining unit, used to determine a first influencing factor from the multiple mountain influencing factors based on the first terrain feature.

[0019] Optionally, the second determining module further includes: a fifth determining unit, used to determine the target shading coefficient based on the first influencing factor; a fourth acquiring unit, used to acquire the first target radiation intensity of the mountainous terrain; and a first predicting unit, used to predict the first photovoltaic power generation based on the target shading coefficient and the first target radiation intensity, wherein the first photovoltaic power generation is the photovoltaic power generation of the mountainous terrain.

[0020] Optionally, the second determining module includes: a sixth determining unit, used to determine multiple desert influencing factors in response to the target terrain being desert terrain; a fifth acquiring unit, used to acquire second terrain features of the desert terrain; and a seventh determining unit, used to determine the second influencing factor from the multiple desert influencing factors based on the second terrain features.

[0021] Optionally, the second determining module further includes: an eighth determining unit, used to determine the target temperature coefficient based on the second influencing factor; a sixth acquiring unit, used to acquire the second target radiation intensity of the desert terrain; and a second predicting unit, used to predict the second photovoltaic power generation based on the target temperature coefficient and the second target radiation intensity, wherein the second photovoltaic power generation is the photovoltaic power generation of the desert terrain.

[0022] According to one embodiment of the present invention, an electronic device is also provided, including a memory and a processor, wherein the memory stores a computer program and the processor is configured to run the computer program to perform the photovoltaic power generation prediction method of any of the above claims.

[0023] According to one embodiment of the present invention, a computer-readable storage medium is also provided, wherein a computer program is stored in the computer program, wherein the computer program is configured to execute the photovoltaic power generation prediction method of any of the above claims when running.

[0024] In this embodiment of the invention, multiple initial influencing factors are acquired, and target terrain information is determined based on these initial influencing factors. This achieves the goal of determining at least one target influencing factor based on the target terrain information, and enables the prediction of target photovoltaic power generation based on at least one target influencing factor. The target photovoltaic power generation is the photovoltaic power generation of the target terrain. This solves the technical problem of low accuracy and stability of photovoltaic power generation prediction results under different terrains in the prior art. Attached Figure Description

[0025] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this application, illustrate exemplary embodiments of the invention and, together with their description, serve to explain the invention and do not constitute an undue limitation thereof. In the drawings:

[0026] Figure 1 This is a flowchart of a method for predicting photovoltaic power generation according to one embodiment of the present invention;

[0027] Figure 2 This is a structural block diagram of a photovoltaic power generation prediction device according to one embodiment of the present invention. Detailed Implementation

[0028] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0029] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such terms can be used interchangeably where appropriate so that embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0030] According to an embodiment of the present invention, an embodiment of a method for predicting photovoltaic power generation is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system containing at least a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0031] This method embodiment can also be performed in an electronic device, similar control device, or electronic device that includes a memory and a processor. Taking an electronic device as an example, the electronic device may include one or more processors and a memory for storing data. Optionally, the aforementioned electronic device may also include a communication device for communication functions and a display device. Those skilled in the art will understand that the above structural description is merely illustrative and does not limit the structure of the aforementioned electronic device. For example, the electronic device may also include more or fewer components than those described above, or have a different configuration than those described above.

[0032] A processor may include one or more processing units. For example, a processor may include a central processing unit (CPU), a graphics processing unit (GPU), a digital signal processing (DSP) chip, a microcontroller unit (MCU), a field-programmable gate array (FPGA), a neural network processing unit (NPU), a tensor processing unit (TPU), or an artificial intelligence (AI) processor. Different processing units may be independent components or integrated into one or more processors. In some instances, electronic devices may also include one or more processors.

[0033] The memory can be used to store computer programs, such as the computer program corresponding to the photovoltaic power generation prediction method in this embodiment of the invention. The processor implements the photovoltaic power generation prediction method by running the computer program stored in the memory. The memory may include high-speed random access memory and non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory may further include memory remotely located relative to the processor, and these remote memories can be connected to electronic devices via a grid. Examples of such grids include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0034] The communication device is used to receive or transmit data via a grid. Specific examples of the aforementioned grid may include a wireless grid provided by the mobile terminal's communication provider. In one example, the communication device includes a network interface controller (NIC), which can connect to other grid devices via a base station to communicate with the Internet. In another example, the communication device may be a radio frequency (RF) module used for wireless communication with the Internet. In some embodiments of this solution, the communication device is used to connect to mobile devices such as mobile phones and tablets, enabling the mobile device to send commands to the vehicle-mounted terminal.

[0035] The display device can be a touchscreen liquid crystal display (LCD) or a touch display (also referred to as a "touchscreen" or "touch screen"). This LCD allows the user to interact with the user interface of the in-vehicle terminal. In some embodiments, the in-vehicle terminal has a graphical user interface (GUI), allowing the user to interact with the GUI through finger contact and / or gestures on a touch-sensitive surface. This human-machine interaction function may include a vehicle gear shifting function. Executable instructions for performing the aforementioned human-machine interaction function are configured / stored in one or more processor-executable computer program products or readable storage media.

[0036] Figure 1 This is a flowchart of a photovoltaic power generation prediction method according to one embodiment of the present invention, such as... Figure 1 As shown, the method includes the following steps:

[0037] Step S102: Obtain multiple initial influencing factors.

[0038] Optionally, the execution subject in this embodiment is a photovoltaic power generation prediction system. It should be noted that other electronic devices and processors can also be used as the execution subject, and no further limitations are made here.

[0039] Specifically, photovoltaic (PV) power generation capacity refers to the ability of a photovoltaic system (such as a PV power plant or PV modules) to convert solar energy into electrical energy per unit time, usually expressed in watts (W), kilowatts (kW), or megawatts (MW). It reflects the current power generation capacity or operating status of the PV system. More specifically, PV power generation capacity can be categorized as follows:

[0040] Instantaneous power: Represents the power generation capacity of a photovoltaic system at a specific moment;

[0041] Average power: The average power generation of a photovoltaic system over a period of time (such as a day, a month, or a year);

[0042] Peak power (kWp): Under standard test conditions (e.g., illuminance of 1000 W / m²), 2 (Component temperature 25℃), the maximum output power of the photovoltaic module or system.

[0043] Optionally, peak power is a theoretical value used to compare and evaluate the performance of photovoltaic modules.

[0044] In the technical solution provided by step S102 of the present invention, historical photovoltaic power generation data under different terrains and corresponding external influencing factors are obtained. From the above multiple external influencing factors, the main influencing factors under different terrains are obtained, namely the initial influencing factors.

[0045] For example, the above-mentioned terrain information may include desert high-temperature terrain, water-adjacent terrain, and mountainous terrain.

[0046] Specifically, in desert high-temperature terrain, environmental temperature has a relatively high impact, while humidity has a relatively low impact. Therefore, it can be determined that solar radiation intensity and temperature are the main influencing factors, while humidity is a secondary influencing factor.

[0047] Specifically, in water-adjacent terrain, humidity has a higher impact than temperature, indicating that solar radiation intensity and humidity are the main influencing factors, while temperature is a secondary factor.

[0048] Specifically, in mountainous terrain, due to the complex topography, the influence of clouds is more severe, and satellite cloud images need to be introduced for cloud interference analysis. Therefore, solar radiation intensity and cloud interference analysis results are taken as the main influencing factors, while temperature and humidity are taken as secondary influencing factors.

[0049] Optionally, based on the different terrains mentioned above, several key influencing factors (initial influencing factors) include: solar radiation intensity, humidity, cloud cover, and temperature.

[0050] Specifically, the data acquisition steps described above utilize satellite remote sensing and ground measurement methods to obtain historical photovoltaic power generation data and external influencing factors under different terrains, thereby improving the diversity and accuracy of the data. By combining the macroscopic perspective of satellite remote sensing with the microscopic details of ground measurement, this method can more comprehensively capture various factors affecting photovoltaic power generation. Especially in complex terrains such as mountainous areas, it can more accurately assess the impact of shadows and terrain on power generation efficiency, thereby improving the adaptability and prediction accuracy of the prediction model.

[0051] Optionally, the influencing factor analysis step can employ Pearson correlation coefficient and Spearman rank correlation coefficient to analyze the correlation between external influencing factors and historical photovoltaic power generation data, quantifying the relationship between strong and weak correlations. This method can clearly identify which factors have a significant impact on photovoltaic power generation, such as irradiance, temperature, and humidity, and which factors have a smaller impact, such as wind speed and air pressure. This provides a scientific basis for constructing prediction models, is applicable to photovoltaic power generation prediction under various meteorological conditions, and improves the model's generalization ability.

[0052] Optionally, the data acquisition module in the photovoltaic power generation prediction system is used to acquire multiple initial influencing factors. This data acquisition module further includes a satellite remote sensing unit and a ground measurement unit to improve the accuracy and comprehensiveness of data acquisition. This modular design makes data acquisition more flexible, allowing the selection of the most suitable acquisition method based on different terrain and meteorological conditions. It is suitable for photovoltaic power generation monitoring in various environments, such as offshore wind farms and high-altitude power stations, improving the efficiency and quality of data acquisition.

[0053] Step S104: Determine the target terrain information based on multiple initial influencing factors.

[0054] In the technical solution provided in step S104 of the present invention, the photovoltaic power generation prediction system analyzes the multiple initial influencing factors determined above, obtains the values ​​of multiple influencing factors of the target terrain, compares them with the rated values ​​of multiple initial influencing factors, and can determine the current terrain information.

[0055] Optionally, the above terrain information may include desert high-temperature terrain, water-adjacent terrain, and mountainous terrain.

[0056] For example, if the humidity of the current terrain information is below a certain standard (such as humidity content below 60%), it can be determined that the current terrain is not located in a water-adjacent area. If the temperature is below a certain standard (such as temperature below 40℃), it can be determined that the current terrain is not located in a high-temperature area such as a desert. By reverse inference, the terrain status of the photovoltaic panel under the corresponding data can be obtained.

[0057] Step S106: Determine at least one target influencing factor based on the target terrain information.

[0058] In the technical solution provided by step S106 of the present invention, the photovoltaic power generation prediction system can determine the terrain where the photovoltaic panel is located based on the determined current terrain information, and further determine the main influencing factors of the current terrain.

[0059] Optionally, the above terrain information may include desert high-temperature terrain, water-adjacent terrain, and mountainous terrain.

[0060] For example, if the current target terrain information is a desert high-temperature terrain, then solar radiation intensity and temperature are the target influencing factors.

[0061] For example, if the current target terrain information is a water-adjacent terrain, then solar radiation intensity and humidity are the target influencing factors.

[0062] For example, if the current target terrain information is mountainous, due to the complexity of the terrain, it is severely affected by clouds. Satellite cloud images need to be introduced to analyze cloud interference. Therefore, solar radiation intensity and cloud interference analysis results are used as target influencing factors.

[0063] Optionally, the criteria for determining primary and secondary influencing factors include setting correlation coefficient thresholds and using historical data analysis results to identify which external factors have a significant impact on the prediction results. This approach can help identify key influencing factors, optimize data processing workflows, and improve prediction efficiency.

[0064] Step S108: Predict the target photovoltaic power generation based on at least one target influencing factor, wherein the target photovoltaic power generation is the photovoltaic power generation of the target terrain.

[0065] In the technical solution provided by step S108 of the present invention, after obtaining the target influencing factors of the corresponding target terrain, the photovoltaic power generation prediction system can further calculate the target photovoltaic power generation power of the photovoltaic panel under the target terrain based on at least one target influencing factor.

[0066] Specifically, the establishment of the power generation prediction logic includes: obtaining the photovoltaic power generation under different major influencing factors, establishing a data table, performing terrain analysis on the data displayed in the data table, setting standards for major and minor influencing factors, ignoring minor influencing factors, using the major influencing factors as calculation data, and calculating the photovoltaic power generation.

[0067] Optional analysis of the impact of secondary influencing factors on prediction results under specific conditions is used to illustrate under what circumstances secondary influencing factors can be ignored without affecting prediction accuracy. This method avoids data redundancy, reduces waste of computing resources, and is suitable for real-time prediction and large-scale data processing scenarios, improving the response speed and processing capacity of the prediction system.

[0068] Optionally, the least squares method can be used for model training in the power generation prediction logic step to optimize the accuracy of photovoltaic power prediction. This method can effectively reduce prediction errors, improve the stability of the prediction model, and is suitable for power prediction of large-scale photovoltaic power generation systems, providing accurate data support for electricity market trading and dispatch.

[0069] Optionally, the above methods also include strategies for integrating target prediction convolutional neural networks, such as model fusion and weighted averaging of results, to improve the stability and accuracy of prediction results. By fusing multiple prediction models, this method can comprehensively consider the influence of various factors, improve the robustness of prediction, and is suitable for photovoltaic power generation prediction under extreme weather conditions, thus providing a guarantee for the safe operation of the power system.

[0070] Steps S102 to S108 above show that, in this invention, by acquiring multiple initial influencing factors and determining the target terrain information based on these factors, the purpose of determining at least one target influencing factor based on the target terrain information is achieved. This achieves the technical effect of predicting the target photovoltaic power generation based on at least one target influencing factor, wherein the target photovoltaic power generation is the photovoltaic power generation of the target terrain. This can solve the technical problem of low accuracy and stability of photovoltaic power generation prediction results under different terrains in the prior art.

[0071] In existing technologies, predicting photovoltaic power generation requires pre-determining the topography of the photovoltaic power station and then selecting influencing factors. However, with environmental changes, some factors that would not normally affect photovoltaic power generation may become influential in different terrains (especially in mountainous areas, where valleys can easily lead to excessively high temperatures or humidity). This makes it easy for the prediction accuracy to be poor when using pre-determined topography to select influencing factors.

[0072] In contrast, this invention constructs a widely adaptable photovoltaic power generation prediction method. While ensuring accurate prediction of photovoltaic power generation, it is suitable for photovoltaic power stations under different terrains, provides support for the unified management of photovoltaic power station related data, and can also directly use data to infer terrain, providing a reliable indicator of the occurrence of anomalies.

[0073] The method described in this embodiment will now be described in further detail.

[0074] As an optional implementation, obtaining multiple initial influencing factors includes: obtaining multiple target factors and multiple environmental topographic information, wherein the multiple target factors include environmental factors and meteorological factors; and determining multiple initial influencing factors from the multiple target factors based on the multiple environmental topographic information.

[0075] In this embodiment, the photovoltaic power generation prediction system acquires initial influencing factors of multiple different terrains by following these steps: first, acquiring target factors of multiple environmental terrain information, wherein the multiple target factors include environmental factors and meteorological factors; and then, determining the main influencing factors corresponding to multiple environmental terrains from the multiple target factors based on the terrain characteristics of the multiple environmental terrain information.

[0076] For example, the above-mentioned terrain information may include desert high-temperature terrain, water-adjacent terrain, and mountainous terrain.

[0077] Specifically, in desert high-temperature terrain, environmental temperature has a relatively high impact, while humidity has a relatively low impact. Therefore, it can be determined that solar radiation intensity and temperature are the main influencing factors, while humidity is a secondary influencing factor.

[0078] Specifically, in water-adjacent terrain, humidity has a higher impact than temperature, indicating that solar radiation intensity and humidity are the main influencing factors, while temperature is a secondary factor.

[0079] Specifically, in mountainous terrain, due to the complex topography, the influence of clouds is more severe, and satellite cloud images need to be introduced for cloud interference analysis. Therefore, solar radiation intensity and cloud interference analysis results are taken as the main influencing factors, while temperature and humidity are taken as secondary influencing factors.

[0080] As an optional implementation method, determining target terrain information based on multiple initial influencing factors includes: acquiring multiple preset thresholds, wherein each preset threshold corresponds to one of the multiple initial influencing factors; comparing each initial influencing factor with its corresponding preset threshold to obtain a comparison result; and determining the target terrain information based on the comparison result.

[0081] In this embodiment, the photovoltaic power generation prediction system determines the target terrain information of the current photovoltaic panel based on the multiple main influencing factors determined above, including the following steps: obtaining preset thresholds for multiple main influencing factors, wherein the multiple preset thresholds correspond one-to-one with multiple initial influencing factors, then comparing each initial influencing factor with its corresponding preset threshold to obtain a comparison result, and further determining the target terrain information based on the comparison result.

[0082] For example, if the humidity of the current terrain information is below a certain standard (such as humidity content below 60%), it can be determined that the current terrain is not located in a water-adjacent area. If the temperature is below a certain standard (such as temperature below 40℃), it can be determined that the current terrain is not located in a high-temperature area such as a desert. By reverse inference, the terrain status of the photovoltaic panel under the corresponding data can be obtained.

[0083] Optionally, the preset thresholds for the above-mentioned major influencing factors can be determined based on the actual measured values ​​under different terrains, or they can be formulated by relevant technical personnel based on experience, without specific limitations here.

[0084] As an optional implementation, the target terrain is mountainous terrain, and determining at least one target influencing factor based on the target terrain information includes: in response to the target terrain being mountainous terrain, determining multiple mountain influencing factors; obtaining a first terrain feature of the mountainous terrain; and determining a first influencing factor from the multiple mountain influencing factors based on the first terrain feature.

[0085] In this embodiment, if the current target terrain is mountainous terrain, the photovoltaic power generation prediction system determines at least one target influencing factor based on the terrain information of the mountainous terrain, including the following steps: when the target terrain is mountainous terrain, it is necessary to further determine multiple mountain influencing factors of the mountainous terrain, then obtain the first terrain feature of the mountainous terrain, and determine the main influencing factor of the mountainous terrain from multiple mountain influencing factors based on the first terrain feature of the mountainous terrain.

[0086] For example, mountainous terrain is significantly affected by clouds due to its complex topography, so satellite cloud images are needed to analyze cloud interference. Therefore, solar radiation intensity and cloud interference analysis results are used as the main influencing data.

[0087] As an optional implementation, the method further includes: determining a target shading coefficient based on a first influencing factor; obtaining a first target radiation intensity of the mountainous terrain; and predicting a first photovoltaic power generation capacity based on the target shading coefficient and the first target radiation intensity, wherein the first photovoltaic power generation capacity is the photovoltaic power generation capacity of the mountainous terrain.

[0088] In this embodiment, the above-mentioned method for predicting photovoltaic power generation in mountainous terrain further includes: the photovoltaic power generation prediction system can determine the target shading coefficient based on the main influencing factors of mountainous terrain, obtain the first target radiation intensity of mountainous terrain, and predict the photovoltaic power generation of mountainous terrain based on the determined target shading coefficient and the first target radiation intensity.

[0089] Specifically, the cloud shading coefficient analysis step also includes determining cloud thickness, distribution, and movement speed to more accurately simulate the impact of cloud cover on photovoltaic power generation. Through detailed analysis of cloud characteristics, this method can more accurately predict the impact of cloud shading on power generation efficiency, especially under cloudy or overcast weather conditions, providing more reliable predictive data for power dispatch and helping to optimize power system operation.

[0090] For example, the cloud cover coefficient can be determined using satellite cloud images, or it can be simulated using a quadratic function:

[0091] k = 1 - aCC - bCC 2

[0092] Where CC represents cloud cover, and its value range can be determined according to the specific cloud cover classification. a and b are both empirical coefficients.

[0093] For example, considering only the influence of clouds, the formula for calculating the photovoltaic power generation in mountainous terrain is as follows:

[0094] P = k × I × η × A

[0095] Where P is the photovoltaic power generation, k is the cloud shading coefficient, which is [0,1], that is, between complete shading "0" and no shading "1", I is the solar radiation intensity, η is the conversion efficiency of the photovoltaic module, and A is the area of ​​the photovoltaic module.

[0096] As an optional implementation, the target terrain is desert terrain, and determining at least one target influencing factor based on the target terrain information includes: in response to the target terrain being desert terrain, determining multiple desert influencing factors; obtaining a second terrain feature of the desert terrain; and determining a second influencing factor from the multiple desert influencing factors based on the second terrain feature.

[0097] In this embodiment, if the current target terrain is desert terrain, the photovoltaic power generation prediction system determines at least one target influencing factor based on the terrain information of the desert terrain, including the following steps: when the target terrain is desert terrain, it is necessary to further determine multiple desert influencing factors of the desert terrain, then obtain the second terrain features of the desert terrain, and determine the main influencing factor of the desert terrain from multiple desert influencing factors based on the second terrain features of the desert terrain.

[0098] For example, in desert high-temperature terrain, the influence of environmental temperature is relatively high, while the influence of humidity is relatively low, which indicates that solar radiation intensity and temperature are the main influencing factors.

[0099] As an optional implementation, the method further includes: determining a target temperature coefficient based on a second influencing factor; obtaining a second target radiation intensity of the desert terrain; and predicting a second photovoltaic power generation capacity based on the target temperature coefficient and the second target radiation intensity, wherein the second photovoltaic power generation capacity is the photovoltaic power generation capacity of the desert terrain.

[0100] In this embodiment, the above-mentioned method for predicting photovoltaic power generation in desert terrain further includes: the photovoltaic power generation prediction system can determine the target temperature coefficient based on the main influencing factors of desert terrain, obtain the second target radiation intensity of desert terrain, and predict the photovoltaic power generation of desert terrain based on the determined target temperature coefficient and the second target radiation intensity.

[0101] For example, considering the effect of temperature, the formula for calculating the photovoltaic power generation in desert terrain is as follows:

[0102] P=k×I×η×(1+αΔT)×A

[0103] Where P is the photovoltaic power generation, I is the solar radiation intensity, η is the conversion efficiency of the photovoltaic module under standard test conditions, α is the temperature coefficient, ΔT is the difference between the ambient temperature and the temperature under standard test conditions, and A is the area of ​​the photovoltaic module.

[0104] Optional, standard test conditions refer to 1000W / m2 Solar radiation intensity, ambient temperature 25℃.

[0105] It should be noted that due to the high dust levels in the desert environment, the cleanliness of the photovoltaic panel surface can be easily affected. Therefore, it is necessary to use drone image recognition to analyze the cleanliness, so as to facilitate timely surface cleaning and ensure the accuracy of power generation prediction.

[0106] For example, by setting image acquisition frequency, applying image processing algorithms, and using cleanliness quantification standards, the accuracy and efficiency of analyzing the cleanliness of photovoltaic panel surfaces can be improved. In dusty environments such as deserts, the cleanliness of photovoltaic panel surfaces has a significant impact on power generation efficiency. Regularly inspecting and quantifying cleanliness using drones allows for timely adjustments to cleaning and maintenance plans, ensuring power generation efficiency. This approach is suitable for the daily operation and maintenance of desert power plants, significantly improving the economic benefits of the power plant.

[0107] Furthermore, if the current mountainous terrain information indicates that the terrain is adjacent to water, then solar radiation intensity and humidity can be determined as the main influencing factors of the current terrain.

[0108] For example, considering the effect of humidity, the formula for calculating photovoltaic power generation is as follows:

[0109] P = G × η × k × I × A

[0110] Where P is the photovoltaic power generation, G is the radiation loss rate, I is the solar radiation intensity, η is the conversion efficiency of the photovoltaic module, and A is the area of ​​the photovoltaic module.

[0111] Optionally, the radiation loss rate is mainly obtained by estimating the historical data of the water-adjacent topography. Specifically, the solar radiation intensity and effective solar radiation intensity, as well as the ambient humidity, are obtained. On a seasonal basis, the loss rate of solar radiation intensity to effective solar radiation intensity under the same ambient humidity in the same season is fitted to obtain the radiation loss rate under the corresponding ambient humidity in that season.

[0112] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods according to the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, or grid device, etc.) to execute the methods of the various embodiments of the present invention.

[0113] This embodiment also provides a photovoltaic power generation prediction device, which is used to implement the above embodiments and preferred embodiments, and will not be repeated as already described. As used below, the term "module" can be a combination of software and / or hardware that implements a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0114] Figure 2 This is a structural block diagram of a photovoltaic power generation prediction device 200 according to one embodiment of the present invention, as shown below. Figure 2 As shown, the device includes: an acquisition module 201, a first determination module 202, a second determination module 203, and a prediction module 204.

[0115] Module 201 is used to acquire multiple initial influencing factors;

[0116] The first determining module 202 is used to determine the target terrain information based on multiple initial influencing factors;

[0117] The second determining module 203 is used to determine at least one target influencing factor based on the target terrain information;

[0118] The prediction module 204 is used to predict the target photovoltaic power generation based on at least one target influencing factor, wherein the target photovoltaic power generation is the photovoltaic power generation of the target terrain.

[0119] Optionally, the acquisition module 201 includes: a first acquisition unit, used to acquire multiple target factors and multiple environmental terrain information, wherein the multiple target factors include environmental factors and meteorological factors; and a first determination unit, used to determine multiple initial influencing factors from the multiple target factors based on the multiple environmental terrain information.

[0120] Optionally, the first determining module 202 includes: a second acquiring unit, used to acquire multiple preset thresholds, wherein the multiple preset thresholds correspond one-to-one with multiple initial influencing factors; a comparison unit, used to compare each of the multiple initial influencing factors with the corresponding preset threshold to obtain a comparison result; and a second determining unit, used to determine the target terrain information based on the comparison result.

[0121] Optionally, the second determining module 203 includes: a third determining unit, used to determine multiple mountain influencing factors in response to the target terrain being mountainous terrain; a third acquiring unit, used to acquire a first terrain feature of the mountainous terrain; and a fourth determining unit, used to determine a first influencing factor from the multiple mountain influencing factors based on the first terrain feature.

[0122] Optionally, the second determining module 203 further includes: a fifth determining unit, used to determine the target shading coefficient based on the first influencing factor; a fourth acquiring unit, used to acquire the first target radiation intensity of the mountainous terrain; and a first predicting unit, used to predict the first photovoltaic power generation based on the target shading coefficient and the first target radiation intensity, wherein the first photovoltaic power generation is the photovoltaic power generation of the mountainous terrain.

[0123] Optionally, the second determining module 203 includes: a sixth determining unit, used to determine multiple desert influencing factors in response to the target terrain being desert terrain; a fifth acquiring unit, used to acquire a second terrain feature of the desert terrain; and a seventh determining unit, used to determine a second influencing factor from multiple desert influencing factors based on the second terrain feature.

[0124] Optionally, the second determining module 203 further includes: an eighth determining unit, used to determine the target temperature coefficient based on the second influencing factor; a sixth acquiring unit, used to acquire the second target radiation intensity of the desert terrain; and a second predicting unit, used to predict the second photovoltaic power generation based on the target temperature coefficient and the second target radiation intensity, wherein the second photovoltaic power generation is the photovoltaic power generation of the desert terrain.

[0125] Embodiments of the present invention also provide an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor is configured to run the computer program to perform the above-described method for predicting photovoltaic power generation.

[0126] Optionally, in this embodiment, the electronic device may be configured to store a computer program for performing the following steps:

[0127] Step S102: Obtain multiple initial influencing factors;

[0128] Step S104: Determine the target terrain information based on multiple initial influencing factors;

[0129] Step S106: Determine at least one target influencing factor based on the target terrain information;

[0130] Step S108: Predict the target photovoltaic power generation based on at least one target influencing factor, wherein the target photovoltaic power generation is the photovoltaic power generation of the target terrain.

[0131] Optionally, the processor may also perform the following steps when executing the program: acquiring multiple target factors and multiple environmental terrain information, wherein the multiple target factors include environmental factors and meteorological factors; and determining multiple initial influencing factors from the multiple target factors based on the multiple environmental terrain information.

[0132] Optionally, the processor may also perform the following steps when executing the program: acquiring multiple preset thresholds, wherein each preset threshold corresponds to a multiple initial influencing factor; comparing each initial influencing factor with its corresponding preset threshold to obtain a comparison result; and determining the target terrain information based on the comparison result.

[0133] Optionally, the processor may also perform the following steps when executing the program: in response to the target terrain being mountainous terrain, determine multiple mountain influencing factors; obtain a first terrain feature of the mountainous terrain; and determine a first influencing factor from the multiple mountain influencing factors based on the first terrain feature.

[0134] Optionally, the processor may also perform the following steps when executing the program: determining the target shading coefficient based on the first influencing factor; obtaining the first target radiation intensity of the mountainous terrain; and predicting the first photovoltaic power generation based on the target shading coefficient and the first target radiation intensity, wherein the first photovoltaic power generation is the photovoltaic power generation of the mountainous terrain.

[0135] Optionally, the processor may also perform the following steps when executing the program: in response to the target terrain being desert terrain, determine multiple desert influencing factors; obtain a second terrain feature of the desert terrain; and determine a second influencing factor from the multiple desert influencing factors based on the second terrain feature.

[0136] Optionally, the processor may also perform the following steps when executing the program: determining the target temperature coefficient based on the second influencing factor; obtaining the second target radiation intensity of the desert terrain; and predicting the second photovoltaic power generation based on the target temperature coefficient and the second target radiation intensity, wherein the second photovoltaic power generation is the photovoltaic power generation of the desert terrain.

[0137] Embodiments of the present invention also provide a computer-readable storage medium storing a computer program configured to run on a computer or processor to perform the above-described substation environmental analysis method.

[0138] Optionally, in this embodiment, the computer-readable storage medium may be configured to store a computer program for performing the following steps:

[0139] Step S102: Obtain multiple initial influencing factors;

[0140] Step S104: Determine the target terrain information based on multiple initial influencing factors;

[0141] Step S106: Determine at least one target influencing factor based on the target terrain information;

[0142] Step S108: Predict the target photovoltaic power generation based on at least one target influencing factor, wherein the target photovoltaic power generation is the photovoltaic power generation of the target terrain.

[0143] Optionally, the storage medium is configured to store program code for performing the following steps: acquiring multiple target factors and multiple environmental topographic information, wherein the multiple target factors include environmental factors and meteorological factors; and determining multiple initial influencing factors from the multiple target factors based on the multiple environmental topographic information.

[0144] Optionally, the storage medium is configured to store program code for performing the following steps: obtaining multiple preset thresholds, wherein the multiple preset thresholds correspond one-to-one with multiple initial influencing factors; comparing each of the multiple initial influencing factors with its corresponding preset threshold to obtain a comparison result; and determining the target terrain information based on the comparison result.

[0145] Optionally, the storage medium is configured to store program code for performing the following steps: in response to the target terrain being mountainous terrain, determining multiple mountain influencing factors; obtaining a first terrain feature of the mountainous terrain; and determining a first influencing factor from the multiple mountain influencing factors based on the first terrain feature.

[0146] Optionally, the storage medium is configured to store program code for performing the following steps: determining a target shading coefficient based on a first influencing factor; obtaining a first target radiation intensity of the mountainous terrain; and predicting a first photovoltaic power generation based on the target shading coefficient and the first target radiation intensity, wherein the first photovoltaic power generation is the photovoltaic power generation of the mountainous terrain.

[0147] Optionally, the storage medium is configured to store program code for performing the following steps: in response to the target terrain being desert terrain, determining multiple desert influencing factors; obtaining a second terrain feature of the desert terrain; and determining a second influencing factor from the multiple desert influencing factors based on the second terrain feature.

[0148] Optionally, the storage medium is configured to store program code for performing the following steps: determining a target temperature coefficient based on a second influencing factor; obtaining a second target radiation intensity of the desert terrain; and predicting a second photovoltaic power generation capacity based on the target temperature coefficient and the second target radiation intensity, wherein the second photovoltaic power generation capacity is the photovoltaic power generation capacity of the desert terrain.

[0149] Optionally, specific examples in this embodiment can refer to the examples described in the above embodiments and optional implementations, and will not be repeated here.

[0150] In the above embodiments of the present invention, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0151] In the embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units can be a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual couplings, direct couplings, or communication connections may be through some interfaces; indirect couplings or communication connections between units or modules may be electrical or other forms.

[0152] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0153] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0154] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this 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 to cause a computer device (which may be a personal computer, server, or grid device, etc.) to execute all or part of the steps of the methods of the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0155] The above are merely preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for predicting photovoltaic power generation, characterized in that, include: Multiple initial influencing factors are obtained, wherein the multiple initial influencing factors include at least humidity and temperature; The humidity among the multiple initial influencing factors is compared with the humidity content threshold to obtain the humidity comparison result; The temperature among the multiple initial influencing factors is compared with the temperature threshold to obtain the temperature comparison result; Target terrain information is determined based on the humidity comparison results and the temperature comparison results; At least one target influencing factor is determined based on the target terrain information; The target photovoltaic power generation is predicted based on the at least one target influencing factor, wherein the target photovoltaic power generation is the photovoltaic power generation of the target terrain; The target photovoltaic power generation includes a second photovoltaic power generation, and the target terrain is desert terrain. The method further includes: in response to the target terrain being desert terrain, determining multiple desert influencing factors; obtaining a second terrain feature of the desert terrain; determining a second influencing factor from the multiple desert influencing factors based on the second terrain feature; determining a target temperature coefficient based on the second influencing factor; obtaining a second target radiation intensity of the desert terrain; and predicting the second photovoltaic power generation according to a formula for calculating the photovoltaic power generation of desert terrain, wherein the second photovoltaic power generation is the photovoltaic power generation of the desert terrain, and the formula for calculating the photovoltaic power generation of desert terrain is... Where P is the second photovoltaic power generation and I is the second target radiation intensity. The conversion efficiency of photovoltaic modules under standard test conditions. The target temperature coefficient, A represents the difference between the ambient temperature and the temperature under standard test conditions, and A represents the area of ​​the photovoltaic module.

2. The method for predicting photovoltaic power generation according to claim 1, characterized in that, Obtaining the multiple initial influencing factors includes: Acquire multiple target factors and multiple environmental terrain information, wherein the multiple target factors include environmental factors and meteorological factors; The multiple initial influencing factors are determined from the multiple target factors based on the multiple environmental terrain information.

3. A method for predicting photovoltaic power generation, characterized in that, include: Multiple initial influencing factors are obtained, wherein the multiple initial influencing factors include at least humidity and temperature; The humidity among the multiple initial influencing factors is compared with the humidity content threshold to obtain the humidity comparison result; The temperature among the multiple initial influencing factors is compared with the temperature threshold to obtain the temperature comparison result; Target terrain information is determined based on the humidity comparison results and the temperature comparison results; At least one target influencing factor is determined based on the target terrain information; The target photovoltaic power generation is predicted based on the at least one target influencing factor, wherein the target photovoltaic power generation is the photovoltaic power generation of the target terrain; The target photovoltaic power generation includes a first photovoltaic power generation, and the target terrain is mountainous terrain. The method further includes: in response to the target terrain being mountainous terrain, determining multiple mountainous influencing factors; obtaining a first terrain feature of the mountainous terrain; determining a first influencing factor from the multiple mountainous influencing factors based on the first terrain feature; determining a target shading coefficient based on the first influencing factor; obtaining a first target radiation intensity of the mountainous terrain; and predicting the first photovoltaic power generation based on the target shading coefficient, the first target radiation intensity, and a calculation formula for the photovoltaic power generation of the mountainous terrain, wherein the first photovoltaic power generation is the photovoltaic power generation of the mountainous terrain, and the calculation formula for the photovoltaic power generation of the mountainous terrain is... Where P is the photovoltaic power generation, k is the cloud shading coefficient, which is [0, 1], that is, between complete shading "0" and no shading "1", I is the solar radiation intensity, η is the conversion efficiency of the photovoltaic module, and A is the area of ​​the photovoltaic module.

4. A device for predicting photovoltaic power generation, characterized in that, The device employs the photovoltaic power generation prediction method described in any one of claims 1 to 2, and the device comprises: The acquisition module is used to acquire multiple initial influencing factors; The first determining module is used to determine the target terrain information based on the multiple initial influencing factors; The second determining module is used to determine at least one target influencing factor based on the target terrain information; A prediction module is used to predict the target photovoltaic power generation based on the at least one target influencing factor, wherein the target photovoltaic power generation is the photovoltaic power generation of the target terrain; The target photovoltaic power generation includes a second photovoltaic power generation, and the target terrain is desert terrain. The device is further configured to, in response to the target terrain being desert terrain, determine multiple desert influencing factors; acquire a second terrain feature of the desert terrain; determine a second influencing factor from the multiple desert influencing factors based on the second terrain feature; determine a target temperature coefficient based on the second influencing factor; acquire a second target radiation intensity of the desert terrain; and predict the second photovoltaic power generation according to a formula for calculating the photovoltaic power generation of desert terrain, wherein the second photovoltaic power generation is the photovoltaic power generation of the desert terrain, and the formula for calculating the photovoltaic power generation of desert terrain is... Where P is the second photovoltaic power generation and I is the second target radiation intensity. The conversion efficiency of photovoltaic modules under standard test conditions. The target temperature coefficient, A represents the difference between the ambient temperature and the temperature under standard test conditions, and A represents the area of ​​the photovoltaic module.

5. An electronic device comprising a memory and a processor, characterized in that, The memory stores a computer program, and the processor is configured to run the computer program to perform the photovoltaic power generation prediction method as described in any one of claims 1 to 3.

6. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, wherein the computer program is configured to execute the photovoltaic power generation prediction method according to any one of claims 1 to 3 when run on a computer or processor.

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