Photovoltaic power generation power prediction method and device, electronic equipment and storage medium

Abstract

The invention discloses a photovoltaic power generation power prediction method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining a plurality of initial influence factors; determining target terrain information according to the plurality of initial influence factors; determining at least one target influence factor according to the target terrain information; target photovoltaic power generation power is predicted according to the at least one target influence factor, and the target photovoltaic power generation power is the photovoltaic power generation power of the target terrain. The technical problem that in the prior art, photovoltaic power generation power prediction results under different terrains are low in accuracy and stability is solved.

Description

Technical Field

[0001] The present invention relates to the field of automation technology, and in particular to a method, device, electronic equipment and storage medium for predicting photovoltaic power generation. Background Art

[0002] Photovoltaic power generation is a new type of power generation method that uses the photovoltaic effect of semiconductor interfaces to directly convert light energy into electrical energy. Compared with thermal power generation, photovoltaic power generation does not produce harmful gases such as carbon dioxide, sulfur dioxide, and dust. It is a clean renewable energy source with good environmental benefits. During the power generation process, it will be affected by environmental conditions such as humidity, temperature, light 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 more and more attention. As an important form of new energy, photovoltaic power generation has also received more and more attention in terms of power generation prediction. The photovoltaic power generation prediction method is to predict the power generation of photovoltaic power stations in the future based on historical data, weather information, terrain conditions, etc. Photovoltaic power generation prediction is of great significance for power system scheduling, photovoltaic energy storage system planning, photovoltaic power station development, etc.

[0004] Photovoltaic power generation prediction is mainly affected by environmental conditions, including temperature, humidity, light intensity, cloud cover, etc. In the actual power generation process, these environmental conditions will affect each other, and the degree of influence of these environmental conditions varies in different regions and at different times. Therefore, the photovoltaic power generation prediction method needs to consider multiple environmental factors.

[0005] The existing photovoltaic power generation prediction method mainly predicts the power generation by establishing the correlation between meteorological parameters and power generation. That is, by analyzing the correlation between meteorological parameters and power generation, a linear model between meteorological parameters and power generation is established, and then the power generation is predicted based on the meteorological parameters. However, this method does not consider the impact of various environmental factors on power generation, resulting in inaccurate prediction results.

[0006] In addition, the photovoltaic power generation prediction method requires 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 prediction results. Therefore, how to obtain sufficient historical data and real-time meteorological information is a major challenge facing photovoltaic power generation prediction. Summary of the invention

[0007] The embodiments of the present invention provide a photovoltaic power prediction method, device, electronic device and storage medium to at least solve the technical problem of low accuracy and stability of photovoltaic power 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: obtaining 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 photovoltaic power generation prediction method further includes: 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 according to the multiple environmental terrain information.

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

[0011] Optionally, the photovoltaic power generation power prediction method further includes: in response to the target terrain being a mountainous terrain, determining a plurality of mountain influencing factors; acquiring a first terrain feature of the mountainous terrain; and determining a first influencing factor from the plurality of mountain influencing factors according to the first terrain feature.

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

[0013] Optionally, the photovoltaic power generation power prediction method further includes: in response to the target terrain being a desert terrain, determining a plurality of desert influencing factors; acquiring a second terrain feature of the desert terrain; and determining a second influencing factor from the plurality of desert influencing factors according to the second terrain feature.

[0014] Optionally, the photovoltaic power generation power prediction method also 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 power based on the target temperature coefficient and the second target radiation intensity, wherein the second photovoltaic power generation power is the photovoltaic power generation power of the desert terrain.

[0015] According to one embodiment of the present invention, there is also provided a device for predicting photovoltaic power generation, 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 at least one target influencing factor, wherein the target photovoltaic power generation power is the photovoltaic power generation power 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; a first determination unit, used to determine multiple initial influencing factors from the multiple target factors according to the multiple environmental terrain information.

[0017] Optionally, the first determination module includes: a second acquisition unit, used to acquire multiple preset thresholds, wherein the multiple preset thresholds correspond one-to-one to 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 determination unit, used to determine the target terrain information based on the comparison result.

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

[0019] Optionally, the second determination module also includes: a fifth determination unit, used to determine the target shading coefficient based on the first influencing factor; a fourth acquisition unit, used to obtain the first target radiation intensity of the mountainous terrain; and a first prediction unit, used to predict the first photovoltaic power generation power based on the target shading coefficient and the first target radiation intensity, wherein the first photovoltaic power generation power is the photovoltaic power generation power of the mountainous terrain.

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

[0021] Optionally, the second determination module also includes: an eighth determination unit, used to determine the target temperature coefficient based on the second influencing factor; a sixth acquisition unit, used to obtain the second target radiation intensity of the desert terrain; and a second prediction unit, used to predict the second photovoltaic power generation power based on the target temperature coefficient and the second target radiation intensity, wherein the second photovoltaic power generation power is the photovoltaic power generation power of the desert terrain.

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

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

[0024] In the embodiment of the present invention, multiple initial influencing factors are obtained, and target terrain information is determined according to the multiple initial influencing factors, thereby achieving the purpose of determining at least one target influencing factor according to the target terrain information, and achieving the technical effect of predicting the target photovoltaic power generation power according to at least one target influencing factor, wherein the target photovoltaic power generation power is the photovoltaic power generation power of the target terrain, thereby solving the technical problem of low accuracy and stability of photovoltaic power generation power prediction results under different terrains in the prior art. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of this application. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0026] Figure 1 is a flow chart of a photovoltaic power generation prediction method according to one embodiment of the present invention;

[0027] Figure 2 4 is a structural block diagram of a photovoltaic power prediction device according to one embodiment of the present invention. DETAILED DESCRIPTION

[0028] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work 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 and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the terms used in this way can be interchangeable where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[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 of the accompanying drawings can be executed in a computer system comprising at least one set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0031] The method embodiment may also be performed in an electronic device, a similar control device or an electronic device including 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 electronic device may also include a communication device and a display device for communication functions. It will be understood by those skilled in the art that the above structural description is only illustrative and does not limit the structure of the above electronic device. For example, the electronic device may also include more or fewer components than the above structural description, or have a configuration different from the above structural description.

[0032] The processor may include one or more processing units. For example, the 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), an artificial intelligence (AI) type processor, and other processing devices. Among them, different processing units may be independent components or integrated into one or more processors. In some instances, the electronic device may also include one or more processors.

[0033] The memory can be used to store computer programs, such as storing a computer program corresponding to the photovoltaic power prediction method in the embodiment of the present invention, and the processor implements the photovoltaic power prediction method by running the computer program stored in the memory. The memory may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory may further include a memory remotely arranged relative to the processor, and these remote memories can be connected to the electronic device through a grid. Examples of the above-mentioned grid include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and a combination thereof.

[0034] The communication device is used to receive or send data via a grid. The specific examples of the above-mentioned grid may include a wireless grid provided by a communication provider of the mobile terminal. In one example, the communication device includes a grid adapter (network interface controller, NIC), which can be connected to other grid devices through a base station so as to communicate with the Internet. In one example, the communication device can be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly. In some embodiments of the present solution, the communication device is used to connect to mobile devices such as mobile phones and tablets, and instructions can be sent to the vehicle-mounted terminal through the mobile device.

[0035] The display device may be a touch screen type liquid crystal display (LCD) and a touch display (also referred to as a "touch screen" or "touch display screen"). The liquid crystal display may enable a user to interact with a user interface of the vehicle-mounted terminal. In some embodiments, the vehicle-mounted terminal has a graphical user interface (GUI), and a user may interact with the GUI by finger contact and / or gestures on a touch-sensitive surface. The human-computer interaction function here may include a vehicle gear shifting function, and executable instructions for executing the human-computer interaction function are configured / stored in a computer program product or a readable storage medium executable by one or more processors.

[0036] Figure 1 is a flow chart of a method for predicting photovoltaic power generation according to one embodiment of the present invention. Figure 1 As shown, the method comprises the following steps:

[0037] Step S102, obtaining a plurality of initial influencing factors.

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

[0039] Specifically, photovoltaic power generation refers to the ability of a photovoltaic system (such as a photovoltaic power station or photovoltaic module) to convert light 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 photovoltaic system. Specifically, photovoltaic power generation can be divided into the following types:

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

[0041] Average power: the average value of the power generated by 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 (such as light intensity 1000W / m 2 , component temperature 25℃), the maximum output power of a photovoltaic component or system.

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

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

[0045] Exemplarily, the terrain information may include desert high temperature terrain, waterfront terrain and mountain terrain.

[0046] Specifically, in the high-temperature terrain of the desert, the influence of ambient temperature accounts for a relatively high proportion, while the influence of humidity is relatively low. It can be determined that solar radiation intensity and temperature are the main influencing factors, and humidity is a secondary influencing factor.

[0047] Specifically, in water-based terrain, the influence of humidity is relatively high and the influence of temperature is relatively low. It can be determined that solar radiation intensity and humidity are the main influencing factors, and temperature is a secondary influencing factor.

[0048] Specifically, in mountainous terrain, due to the complex terrain and the serious impact of clouds, it is necessary to introduce satellite cloud images to conduct cloud interference analysis. Therefore, solar radiation intensity and cloud interference analysis results are used as the main influencing factors, and temperature and humidity are used as secondary influencing factors.

[0049] Optionally, according to the above different terrains, it can be seen that the multiple main influencing factors (initial influencing factors) include: solar radiation intensity, humidity, cloud influence and temperature.

[0050] Specifically, the above data collection step uses satellite remote sensing and ground measurement methods to obtain historical photovoltaic power generation data and external influencing factors under different terrains to improve the diversity and accuracy of the data. By combining the macroscopic perspective of satellite remote sensing and the microscopic details of ground measurement, this method can more comprehensively capture the various factors affecting photovoltaic power generation, especially in complex terrain such as mountainous areas, and 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 use the Pearson correlation coefficient and the Spearman rank correlation coefficient to perform correlation analysis on the external influencing factors and the historical photovoltaic power generation data to quantify the relationship between strong and weak correlation data. This method can clearly identify which factors have a significant impact on photovoltaic power generation, such as light intensity, temperature, humidity, etc., and which factors have a smaller impact, such as wind speed, air pressure, etc., providing a scientific basis for building a prediction model, which is applicable to photovoltaic power generation prediction under various meteorological conditions and improves the generalization ability of the model.

[0052] Optionally, the data acquisition module in the photovoltaic power prediction system is used to obtain multiple initial influencing factors, and the above 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, and can select the most appropriate acquisition method according to different terrain and meteorological conditions. It is suitable for photovoltaic power generation monitoring in various environments, such as offshore wind farms, high mountain power stations, etc., and improves the efficiency and quality of data acquisition.

[0053] Step S104: determining target terrain information according to a plurality of initial influencing factors.

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

[0055] Optionally, the terrain information may include desert high-temperature terrain, waterfront terrain and mountain terrain.

[0056] For example, if the humidity of the current terrain information is lower than a certain standard (such as the humidity content is lower than sixty), it can be judged that the current terrain is not in a water area. When the temperature is lower than a certain standard (such as the temperature is lower than 40°C), it can be judged that the current terrain is not in a high temperature area such as a desert. By reverse inference, the terrain state of the photovoltaic panel under the corresponding data can be obtained.

[0057] Step S106: determining at least one target influencing factor according to the target terrain information.

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

[0059] Optionally, the terrain information may include desert high-temperature terrain, waterfront terrain and mountain terrain.

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

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

[0062] For example, if the current target terrain information is mountainous terrain, due to the complex terrain, it is severely affected by clouds, and it is necessary to introduce satellite cloud images to perform cloud interference analysis. Therefore, the solar radiation intensity and cloud interference analysis results are used as target influencing factors.

[0063] Optionally, the criteria for determining the main and secondary influencing factors include setting a correlation coefficient threshold and historical data analysis results to clarify which external factors have a significant impact on the forecast results. This method can help identify key influencing factors, optimize data processing procedures, and improve forecasting efficiency.

[0064] Step S108, predicting the target photovoltaic power generation power according to at least one target influencing factor, wherein the target photovoltaic power generation power is the photovoltaic power generation power of the target terrain.

[0065] In the technical solution provided in the above 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 according to at least one target influencing factor.

[0066] Specifically, the establishment of power generation prediction logic includes: obtaining photovoltaic power generation under different main influencing factors, establishing a data table, wherein a terrain analysis is performed on the data displayed in the data table, and after setting the standards for main influencing factors and secondary influencing factors, the secondary influencing factors are ignored, and the main influencing factors are used as calculation data to calculate the photovoltaic power generation.

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

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

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

[0070] From the above steps S102 to S108, it can be known that in the present invention, multiple initial influencing factors are obtained, and the target terrain information is determined according to the multiple initial influencing factors, so as to achieve the purpose of determining at least one target influencing factor according to the target terrain information, and achieve the technical effect of predicting the target photovoltaic power generation power according to at least one target influencing factor, wherein the target photovoltaic power generation power is the photovoltaic power generation power of the target terrain, thereby solving the technical problem of low accuracy and stability of photovoltaic power generation power prediction results under different terrains in the prior art.

[0071] In the prior art, the prediction of photovoltaic power generation requires the pre-determination of the topography of the photovoltaic power station, and then the selection of influencing factors to predict the photovoltaic power generation. In fact, with the changes in the environment, there will still be some factors that will not affect the photovoltaic power generation in different terrains (especially in mountainous areas, it is easy to have excessive temperature or humidity due to the influence of valleys). This leads to poor prediction accuracy when the influencing factors are selected by pre-acquired terrain.

[0072] On the contrary, the present invention constructs a photovoltaic power prediction method with wide adaptability. On the basis of ensuring the accurate prediction of photovoltaic power, it is suitable for photovoltaic stations under different terrains, provides support for the unified management of photovoltaic station related data, and can also directly reverse the terrain through data, providing reliable identification for the occurrence of abnormal situations.

[0073] The above method of this embodiment is further described in detail below.

[0074] As an optional implementation, obtaining multiple initial influencing factors includes: obtaining 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 according to the multiple environmental terrain information.

[0075] In this embodiment, the photovoltaic power generation prediction system obtains the initial influencing factors of multiple different terrains, including the following steps: first, obtain target factors of multiple environmental terrain information, wherein the multiple target factors include environmental factors and meteorological factors, and then determine the main influencing factors corresponding to multiple environmental terrains from the multiple target factors according to the terrain characteristics of the multiple environmental terrain information.

[0076] Exemplarily, the terrain information may include desert high temperature terrain, waterfront terrain and mountain terrain.

[0077] Specifically, in the high-temperature terrain of the desert, the influence of ambient temperature accounts for a relatively high proportion, while the influence of humidity is relatively low. It can be determined that solar radiation intensity and temperature are the main influencing factors, and humidity is a secondary influencing factor.

[0078] Specifically, in water-based terrain, the influence of humidity is relatively high and the influence of temperature is relatively low. It can be determined that solar radiation intensity and humidity are the main influencing factors, and temperature is a secondary influencing factor.

[0079] Specifically, in mountainous terrain, due to the complex terrain and the serious impact of clouds, it is necessary to introduce satellite cloud images to conduct cloud interference analysis. Therefore, solar radiation intensity and cloud interference analysis results are used as the main influencing factors, and temperature and humidity are used as secondary influencing factors.

[0080] As an optional implementation, determining the target terrain information based on multiple initial influencing factors includes: obtaining multiple preset thresholds, wherein the multiple preset thresholds correspond one-to-one to the multiple initial influencing factors; comparing each of the multiple initial influencing factors with the 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 of multiple main influencing factors, wherein the multiple preset thresholds correspond one-to-one to multiple initial influencing factors, and 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 lower than a certain standard (such as the humidity content is lower than sixty), it can be judged that the current terrain is not in a water area. When the temperature is lower than a certain standard (such as the temperature is lower than 40°C), it can be judged that the current terrain is not in a high temperature area such as a desert. By reverse inference, the terrain state of the photovoltaic panel under the corresponding data can be obtained.

[0083] Optionally, the preset thresholds of the above-mentioned multiple main influencing factors are determined according to the values ​​actually measured under different terrains, and can also be formulated by relevant technical personnel based on experience, which is not specifically limited 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: determining multiple mountain influencing factors in response to the target terrain being mountainous terrain; obtaining a first terrain feature of the mountainous terrain; and determining a first influencing factor from multiple mountain influencing factors based on the first terrain feature.

[0085] In this embodiment, if the current target terrain is a 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 a 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, mountain terrain is severely affected by clouds due to its complex topography, and satellite cloud images need to be introduced to conduct cloud interference analysis. Therefore, solar radiation intensity and cloud interference analysis results are used as the main influencing data.

[0087] As an optional implementation, the method also 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 power based on the target shading coefficient and the first target radiation intensity, wherein the first photovoltaic power generation power is the photovoltaic power generation power of the mountainous terrain.

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

[0089] Specifically, the cloud cover coefficient analysis step also includes determining the cloud thickness, distribution and movement speed to more accurately simulate the impact of cloud cover on photovoltaic power generation. By carefully analyzing the characteristics of the cloud layer, this method can more accurately predict the impact of cloud cover on power generation efficiency, especially in cloudy or overcast weather conditions, providing more reliable forecast data for power dispatching and helping to optimize the operation of the power system.

[0090] For example, the cloud cover coefficient can be determined by satellite cloud images, or a quadratic function can be introduced for simulation and determination:

[0091] k=1-aCC-bCC 2

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

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

[0094] P=k×I×η×A

[0095] Among them, P is the photovoltaic power generation power, k is the cloud shielding coefficient, which is [0,1], that is, between complete shielding "0" and no shielding "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 a desert terrain, and determining at least one target influencing factor based on the target terrain information includes: determining multiple desert influencing factors in response to the target terrain being a desert terrain; 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 a 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 a desert terrain, it is necessary to further determine multiple desert influencing factors of the desert terrain, then obtain the second terrain feature of the desert terrain, and determine the main influencing factor of the desert terrain from the multiple desert influencing factors based on the second terrain feature of the desert terrain.

[0098] For example, the influence of ambient temperature on the high-temperature desert terrain is relatively high, while the influence of humidity is relatively low, so it can be determined that solar radiation intensity and temperature are the main influencing factors.

[0099] As an optional implementation, the method also 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 power based on the target temperature coefficient and the second target radiation intensity, wherein the second photovoltaic power generation power is the photovoltaic power generation power of the desert terrain.

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

[0101] For example, considering the influence of temperature, the calculation formula of 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 power, 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, 25℃ ambient temperature.

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

[0106] For example, the accuracy and efficiency of analyzing the cleanliness of photovoltaic panels can be improved by setting the image acquisition frequency, applying image processing algorithms and cleanliness quantification standards. In dusty environments such as deserts, the cleanliness of photovoltaic panels has a significant impact on power generation efficiency. Regular inspection and quantification of cleanliness by drones can timely adjust the cleaning and maintenance plan to ensure power generation efficiency. This is suitable for daily operation and maintenance of desert power stations, significantly improving the economic benefits of power stations.

[0107] In addition, if the current mountain terrain information is a water-adjacent terrain, it can be determined that solar radiation intensity and humidity are the main influencing factors of the current terrain.

[0108] For example, considering the influence of humidity, the calculation formula of photovoltaic power generation is as follows:

[0109] P=G×η×k×I×A

[0110] Among them, P is the photovoltaic power generation power, 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 historical data of waterfront terrain, wherein the solar radiation intensity and effective solar radiation intensity, as well as ambient humidity are obtained, and the loss rate of the corresponding solar radiation intensity to the effective solar radiation intensity under the same ambient humidity in the same season is fitted in units of seasons to obtain the radiation loss rate under the corresponding ambient humidity of the season.

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

[0113] In this embodiment, a photovoltaic power prediction device is also provided, which is used to implement the above-mentioned embodiments and preferred embodiments, and will not be repeated here. As used below, the term "module" can implement a combination of software and / or hardware of a predetermined function. Although the device described in the following embodiments is preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceivable.

[0114] Figure 2 is a structural block diagram of a photovoltaic power prediction device 200 according to one embodiment of the present invention. 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] An acquisition module 201 is used to acquire a plurality of initial influencing factors;

[0116] A first determination module 202, configured to determine target terrain information according to a plurality of initial influencing factors;

[0117] A second determination module 203, configured to determine at least one target influencing factor according to target terrain information;

[0118] The prediction module 204 is used to predict the target photovoltaic power generation power according to at least one target influencing factor, wherein the target photovoltaic power generation power is the photovoltaic power generation power 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; a first determination unit, used to determine multiple initial influencing factors from the multiple target factors according to the multiple environmental terrain information.

[0120] Optionally, the first determination module 202 includes: a second acquisition unit, used to acquire multiple preset thresholds, wherein the multiple preset thresholds correspond one-to-one to 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 determination unit, used to determine the target terrain information based on the comparison result.

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

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

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

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

[0125] An embodiment of the present invention further provides 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 execute the above-mentioned photovoltaic power generation power prediction method.

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

[0127] Step S102, obtaining a plurality of initial influencing factors;

[0128] Step S104, determining target terrain information according to a plurality of initial influencing factors;

[0129] Step S106, determining at least one target influencing factor according to the target terrain information;

[0130] Step S108, predicting the target photovoltaic power generation power according to at least one target influencing factor, wherein the target photovoltaic power generation power is the photovoltaic power generation power of the target terrain.

[0131] Optionally, when executing the program, the processor further implements the following steps: obtaining 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 according to the multiple environmental terrain information.

[0132] Optionally, when the processor executes the program, it also implements the following steps: obtaining multiple preset thresholds, wherein the multiple preset thresholds correspond one-to-one to multiple initial influencing factors; comparing each of the multiple initial influencing factors with the corresponding preset threshold to obtain a comparison result; and determining the target terrain information based on the comparison result.

[0133] Optionally, when executing the program, the processor further implements the following steps: in response to the target terrain being a mountainous terrain, determining multiple mountain influencing factors; acquiring a first terrain feature of the mountainous terrain; and determining a first influencing factor from multiple mountain influencing factors based on the first terrain feature.

[0134] Optionally, when executing the program, the processor also implements 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 power based on the target shading coefficient and the first target radiation intensity, wherein the first photovoltaic power generation power is the photovoltaic power generation power of the mountainous terrain.

[0135] Optionally, when executing the program, the processor further implements the following steps: in response to the target terrain being a desert terrain, determining multiple desert influencing factors; acquiring 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.

[0136] Optionally, when executing the program, the processor also implements 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 power based on the target temperature coefficient and the second target radiation intensity, wherein the second photovoltaic power generation power is the photovoltaic power generation power of the desert terrain.

[0137] An embodiment of the present invention further provides a computer-readable storage medium, in which a computer program is stored, wherein the computer program is configured to execute the above-mentioned substation environment analysis method when running on a computer or a processor.

[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, obtaining a plurality of initial influencing factors;

[0140] Step S104, determining target terrain information according to a plurality of initial influencing factors;

[0141] Step S106, determining at least one target influencing factor according to the target terrain information;

[0142] Step S108, predicting the target photovoltaic power generation power according to at least one target influencing factor, wherein the target photovoltaic power generation power is the photovoltaic power generation power of the target terrain.

[0143] Optionally, the storage medium is configured to store program code for executing the following steps: obtaining 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.

[0144] Optionally, the storage medium is configured to store program code for executing the following steps: obtaining multiple preset thresholds, wherein the multiple preset thresholds correspond one-to-one to multiple initial influencing factors; comparing each of the multiple initial influencing factors with the 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 executing the following steps: in response to the target terrain being a mountainous terrain, determining multiple mountain influencing factors; obtaining a first terrain feature of the mountainous terrain; and determining a first influencing factor from 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 for the mountainous terrain; and predicting a first photovoltaic power generation power based on the target shading coefficient and the first target radiation intensity, wherein the first photovoltaic power generation power is the photovoltaic power generation power for the mountainous terrain.

[0147] Optionally, the storage medium is configured to store program code for executing the following steps: in response to the target terrain being a desert terrain, determining multiple desert influencing factors; acquiring 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 power based on the target temperature coefficient and the second target radiation intensity, wherein the second photovoltaic power generation power is the photovoltaic power generation power of the desert terrain.

[0149] Optionally, the specific examples in this embodiment may refer to the examples described in the above embodiments and optional implementation modes, and this embodiment will not be described in detail here.

[0150] In the above embodiments of the present invention, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0151] In some embodiments provided in the present application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only schematic. For example, the division of units can be a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

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

[0153] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0154] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, 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, which is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server or a grid device, etc.) to perform all or part of the steps of the methods of various embodiments of the present invention. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, magnetic disk or optical disk, etc., which can store program codes.

[0155] The above are only preferred embodiments of the present invention. It should be pointed out that, for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A method for predicting photovoltaic power generation, characterized in that: include: Obtain multiple initial influencing factors; determining target terrain information according to the multiple initial influencing factors; determining at least one target influencing factor according to the target terrain information; The target photovoltaic power generation power is predicted according to the at least one target influencing factor, wherein the target photovoltaic power generation power is the photovoltaic power generation power of the target terrain.

2. The photovoltaic power generation prediction method according to claim 1, characterized in that: Acquiring 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 plurality of initial influencing factors are determined from the plurality of target factors according to the plurality of environmental terrain information.

3. The photovoltaic power generation prediction method according to claim 1, characterized in that: Determining the target terrain information according to the multiple initial influencing factors includes: Acquire a plurality of preset thresholds, wherein the plurality of preset thresholds correspond one-to-one to the plurality of initial influencing factors; Comparing each of the multiple initial influencing factors with a corresponding preset threshold to obtain a comparison result; The target terrain information is determined according to the comparison result.

4. The photovoltaic power generation prediction method according to claim 1, characterized in that: The target terrain is a mountainous terrain, and determining the at least one target influencing factor according to the target terrain information includes: In response to the target terrain being the mountainous terrain, determining a plurality of mountainous influencing factors; Acquire a first terrain feature of the mountainous terrain; A first influencing factor is determined from the plurality of mountain influencing factors according to the first terrain feature.

5. The photovoltaic power generation prediction method according to claim 4, characterized in that: The method further comprises: Determining a target occlusion coefficient according to the first influencing factor; Acquire a first target radiation intensity of the mountainous terrain; A first photovoltaic power generation power is predicted according to the target shielding coefficient and the first target radiation intensity, wherein the first photovoltaic power generation power is the photovoltaic power generation power of the mountainous terrain.

6. The photovoltaic power generation prediction method according to claim 1, characterized in that: The target terrain is a desert terrain, and determining the at least one target influencing factor according to the target terrain information includes: In response to the target terrain being the desert terrain, determining a plurality of desert influencing factors; Acquire a second terrain feature of the desert terrain; A second influencing factor is determined from the plurality of desert influencing factors according to the second terrain feature.

7. The photovoltaic power generation prediction method according to claim 6, characterized in that: The method further comprises: determining a target temperature coefficient according to the second influencing factor; Acquire a second target radiation intensity of the desert terrain; The second photovoltaic power generation power is predicted according to the target temperature coefficient and the second target radiation intensity, wherein the second photovoltaic power generation power is the photovoltaic power generation power of the desert terrain.

8. A photovoltaic power generation prediction device, characterized in that: include: An acquisition module, used for acquiring multiple initial influencing factors; A first determination module, configured to determine target terrain information according to the plurality of initial influencing factors; A second determination module, configured to determine at least one target influencing factor according to the target terrain information; The prediction module is used to predict the target photovoltaic power generation power according to the at least one target influencing factor, wherein the target photovoltaic power generation power is the photovoltaic power generation power of the target terrain.

9. 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 execute the photovoltaic power prediction method as described in any one of claims 1 to 7.

10. 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 prediction method described in any one of claims 1 to 7 when running on a computer or a processor.

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