Photovoltaic prediction method and device based on remote sensing data, medium and electronic equipment

Abstract

The invention provides a photovoltaic prediction method and device based on remote sensing data, a medium and electronic equipment, and relates to the technical field of power grids. Obtaining terrain elevation data under a preset resolution; performing terrain correction according to the terrain elevation data; after terrain correction, short-wave radiation data are predicted according to satellite remote sensing data; and obtaining photovoltaic power generation power through a prediction model according to the short-wave radiation data and the meteorological environment data. And a photovoltaic power station power prediction model is constructed, and accurate prediction of the photovoltaic power station power is realized. And accurate prediction of the photovoltaic power generation power is realized.

Description

Background Art

[0002] Photovoltaic power generation is affected by the solar radiation cycle, geographical environment and various meteorological factors. Accurate modeling and characteristic analysis is a difficult problem with multiple influencing factors. Accurate prediction of photovoltaic power generation can enable the wind, solar, water storage and charging intelligent control system to adjust the scheduling plan in time, improve the economy and stability of microgrid operation, and promote the consumption of new energy.

[0003] Satellite remote sensing has many advantages such as high temporal and spatial resolution, wide coverage, accurate and efficient data transmission, and can quantitatively obtain regional meteorological and environmental information. In the photovoltaic power generation prediction system, high-precision satellite remote sensing data is introduced to invert the accurate physical, chemical and optical parameters and change trends of atmospheric components such as clouds and aerosols. Combined with ground and sounding meteorological data, short-term meteorological environment and surface radiation prediction are carried out, which can effectively support the photovoltaic power prediction model to improve the calculation accuracy of photovoltaic power prediction under different temporal and spatial conditions. The existing radiation products have various temporal and spatial scales, and the algorithms used for inversion are also different. The radiation distribution map obtained by simply inverting satellite data will have large errors.

[0004] With the large-scale entry of new energy into the power grid, the balance of energy supply and demand between randomly fluctuating load demand and randomly fluctuating power supply requires the use of accurate prediction technology at multiple time and space scales, and requires advance planning of scheduling and operation strategies. How to achieve accurate prediction of renewable resources and accurate prediction of regional photovoltaic power is currently an urgent problem to be solved for the energy supply security of the power grid.

[0005] It should be noted that the information disclosed in the above background technology section is only used to enhance the understanding of the background of the present disclosure, and therefore may include information that does not constitute the prior art known to ordinary technicians in the field. Summary of the invention

[0006] The purpose of the present disclosure is to provide a photovoltaic prediction method, device, medium and electronic device based on remote sensing data, which at least to some extent overcome the problem of poor photovoltaic power prediction accuracy due to related technologies.

[0007] Other features and advantages of the present disclosure will become apparent from the following detailed description, or may be learned in part by the practice of the present disclosure.

[0008] According to one aspect of the present disclosure, a photovoltaic prediction method based on remote sensing data is provided, comprising:

[0009] Get terrain elevation data at a preset resolution;

[0010] performing terrain correction according to the terrain elevation data;

[0011] After terrain correction, shortwave radiation data are predicted from satellite remote sensing data;

[0012] The photovoltaic power generation power is obtained through a prediction model according to the shortwave radiation data and the meteorological environment data.

[0013] In one embodiment of the present disclosure, before the step of obtaining terrain elevation data at a preset resolution, the following steps are included:

[0014] Calculate the corresponding terrain factors based on terrain elevation data corresponding to different resolutions;

[0015] Determining terrain correction effects corresponding to terrain elevation data of different resolutions according to the terrain factors;

[0016] The preset resolution is determined according to the terrain correction effect.

[0017] In one embodiment of the present disclosure, it also includes:

[0018] The meteorological environment data are obtained by using an airborne radiation detector, wherein the meteorological environment data include: wind speed, wind direction, temperature, humidity, air pressure, cloud amount and distribution, and surface photovoltaic panel distribution and angle.

[0019] In one embodiment of the present disclosure, the prediction model adopts an improved LSTM model, and the improved LSTM model includes: an input layer, a coding layer, an LSTM layer and a fully connected layer.

[0020] In one embodiment of the present disclosure, the step of training the prediction model includes:

[0021] Acquire training samples, wherein the training samples are composed of historical radiation data, historical meteorological environment data, and historical photovoltaic power generation power;

[0022] Performing data preprocessing on the training samples to obtain preprocessed training samples;

[0023] Training the improved LSTM model using the preprocessed training samples;

[0024] The parameters of the improved LSTM model are adjusted according to the training results.

[0025] In one embodiment of the present disclosure, it also includes:

[0026] Obtain distributed meteorological environment data and distributed surface shortwave downlink radiation data of distributed photovoltaic power stations;

[0027] A distributed prediction model is constructed based on the distributed meteorological environment data and the distributed surface shortwave downlink radiation data.

[0028] In one embodiment of the present disclosure, predicting shortwave radiation data based on satellite remote sensing data after terrain correction includes:

[0029] Acquiring the satellite remote sensing data;

[0030] The surface shortwave downlink radiation information is predicted based on the satellite remote sensing data through a deep learning algorithm.

[0031] According to another aspect of the present disclosure, there is provided a photovoltaic radiation prediction device based on remote sensing data, comprising:

[0032] A data acquisition module, used to acquire terrain elevation data at a preset resolution;

[0033] A terrain correction module, used for performing terrain correction according to the terrain elevation data;

[0034] The radiation prediction module is used to predict shortwave radiation data based on satellite remote sensing data after terrain correction;

[0035] The power generation prediction module is used to obtain photovoltaic power generation power through a prediction model based on the shortwave radiation data and meteorological environment data.

[0036] According to another aspect of the present disclosure, there is provided an electronic device, including:

[0037] Processor; and

[0038] A memory, configured to store executable instructions of the processor;

[0039] The processor is configured to execute any one of the above-mentioned photovoltaic prediction methods based on remote sensing data by executing the executable instructions.

[0040] According to another aspect of the present disclosure, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the photovoltaic prediction method based on remote sensing data described in any one of the above is implemented.

[0041] A photovoltaic prediction method based on remote sensing data provided by an embodiment of the present disclosure includes: obtaining terrain elevation data at a preset resolution, reducing the impact on terrain correction by selecting the best preset resolution to ensure the best terrain correction effect in the future; performing terrain correction according to the terrain elevation data; and achieving the best terrain correction effect by using DEM data of a preset resolution. After terrain correction, shortwave radiation data is predicted based on satellite remote sensing data. Under the best terrain correction effect, accurate shortwave radiation data can be predicted in combination with satellite remote sensing data. Photovoltaic power generation power is obtained through a prediction model based on the shortwave radiation data and meteorological environment data. A photovoltaic power station power prediction model is constructed to achieve accurate prediction of photovoltaic power station power.

[0042] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] The accompanying drawings herein are incorporated into the specification and constitute a part of the specification, illustrate embodiments consistent with the present disclosure, and together with the specification are used to explain the principles of the present disclosure. Obviously, the accompanying drawings described below are only some embodiments of the present disclosure, and for ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without creative work.

[0044] Figure 1 A schematic diagram of a photovoltaic prediction method based on remote sensing data in an embodiment of the present disclosure is shown;

[0045] Figure 2 A schematic diagram of a photovoltaic prediction method based on remote sensing data in an embodiment of the present disclosure is shown;

[0046] Figure 3 A schematic diagram showing the structure of a prediction model in an embodiment of the present disclosure is shown;

[0047] Figure 4 A schematic diagram of a photovoltaic prediction device based on remote sensing data in an embodiment of the present disclosure is shown; and

[0048] Figure 5 A structural block diagram of a computer device in an embodiment of the present disclosure is shown. DETAILED DESCRIPTION

[0049] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in a variety of forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that the disclosure will be more comprehensive and complete and to fully convey the concepts of the example embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.

[0050] In addition, the accompanying drawings are only schematic illustrations of the present disclosure and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor devices and / or microcontroller devices.

[0051] Reference Figure 1 A schematic flow chart of a photovoltaic prediction method based on remote sensing data is shown. In one embodiment of the present disclosure, a photovoltaic prediction method based on remote sensing data is provided, comprising:

[0052] S101, obtaining terrain elevation data at a preset resolution;

[0053] Specifically, terrain elevation data refers to DEM (Digital Elevation Model) data. DEM (Digital Elevation Model) data of different spatial resolutions have different reflection capabilities on the real surface undulation state, resulting in scale effects in the calculated terrain parameters, which in turn affect the results of terrain correction. Therefore, it is necessary to select a suitable resolution to reduce the impact on terrain correction in order to obtain the best terrain correction effect. The preset resolution is the optimal resolution result determined after calculation and verification.

[0054] Reference Figure 2 As shown, in a specific example, before the above step S101, the following steps are included:

[0055] S201, calculating corresponding terrain factors based on terrain elevation data corresponding to different resolutions;

[0056] Specifically, the DEM data of preset resolution is used as the basic data, and the differences in terrain factors calculated by DEMs of different resolutions after DEM scale conversion are analyzed. The extraction accuracy of terrain factors is directly related to the resolution of DEM data. Terrain factors include: ground slope, profile curvature, slope aspect and other factors.

[0057] S202, determining terrain correction effects corresponding to terrain elevation data of different resolutions according to the terrain factors;

[0058] Specifically, the sensitivity of DEM resolution in terrain correction is analyzed to explore which resolution of DEM data can achieve the best terrain correction effect. Terrain correction refers to the process of transforming the radiance of all pixels to a certain reference plane through various transformations, thereby eliminating the changes in image radiance values ​​caused by terrain undulations and making the image better reflect the spectral characteristics of the objects.

[0059] S203: Determine a preset resolution according to the terrain correction effect.

[0060] Determine the optimal value of DEM resolution during terrain correction, which is the preset resolution.

[0061] S102, performing terrain correction according to the terrain elevation data;

[0062] Among them, DEM is an important input parameter in the terrain correction process. Different resolutions of DEM have obvious effects on the extracted terrain parameters, which in turn affects the results of terrain correction. The best terrain correction effect can be achieved by using DEM data with preset resolution.

[0063] S103, predicting shortwave radiation data based on satellite remote sensing data after terrain correction;

[0064] Among them, the shortwave radiation data can optionally be the surface shortwave downward radiation data. In the photovoltaic power generation prediction model, the accurate acquisition of the surface shortwave downward radiation is very important. The meteorological environment data observed on the ground provides important training samples and real-time calibration for photovoltaic power prediction. Accurate shortwave radiation data is obtained, thereby improving the subsequent photovoltaic power generation prediction results.

[0065] S104, obtaining photovoltaic power generation power through a prediction model according to the shortwave radiation data and the meteorological environment data.

[0066] Among them, the prediction model is a photovoltaic power station power prediction model, which combines meteorological environment data and shortwave radiation data to construct a photovoltaic power station power prediction model.

[0067] In this embodiment, a photovoltaic prediction method based on remote sensing data includes: obtaining terrain elevation data at a preset resolution, reducing the impact on terrain correction by selecting the best preset resolution to ensure the best terrain correction effect in the future; performing terrain correction according to the terrain elevation data; and achieving the best terrain correction effect by using DEM data of preset resolution. After terrain correction, shortwave radiation data is predicted based on satellite remote sensing data. Under the best terrain correction effect, accurate shortwave radiation data can be predicted in combination with satellite remote sensing data. Photovoltaic power generation power is obtained through a prediction model based on the shortwave radiation data and meteorological environment data. A photovoltaic power station power prediction model is constructed to achieve accurate prediction of photovoltaic power station power. Accurate prediction of photovoltaic power generation power is achieved.

[0068] In a specific example, the meteorological environment data is obtained by using an airborne radiation detector, wherein the meteorological environment data include: wind speed, wind direction, temperature, humidity, air pressure, cloud amount and distribution, and surface photovoltaic panel distribution and angle.

[0069] The atmospheric radiation detector is a real-time data monitoring station built based on the power generation principle of photovoltaic power generation. It contains the main parameters that affect the efficiency of photovoltaic power generation, such as wind speed, wind direction, temperature, humidity, air pressure, total radiation, sky cloud cover and distribution, surface photovoltaic panel distribution and angle, etc. In actual work, the system will measure the above basic data every 5 minutes.

[0070] Real-time measurement of photovoltaic power generation environmental parameters such as wind speed, wind direction, temperature, humidity, air pressure, shortwave downward radiation, and sky cloud cover.

[0071] Among them, the atmospheric radiation detector has a power failure protection function. The stored data will not be lost after the power failure. When the AC power fails, it can be automatically powered by the rechargeable battery. Equipped with a solar power supply system, the solar panels and battery packs are used together, which can be used for field operations and are suitable for year-round use in areas without electricity. The detector provides wireless transmission. The transmission methods are divided into three wireless transmission methods: short-distance wireless transmission, medium-distance wireless transmission, and long-distance wireless transmission. The communication method can be flexibly matched by the user according to their own requirements.

[0072] In a specific example, referring to Figure 3 The diagram shows the structure of the prediction model, which adopts an improved LSTM model, and the improved LSTM model includes: an input layer 301, a coding layer 302, an LSTM layer 303 and a fully connected layer 304. The training sample is input into the input layer 301, and then the long short-term memory network algorithm is used for data learning training.

[0073] In one example, the training step of the prediction model includes:

[0074] Acquire training samples, wherein the training samples are composed of historical radiation data, historical meteorological environment data, and historical photovoltaic power generation power;

[0075] Specifically, the training sample library is constructed in combination with historical data, and the training samples include historical radiation data, historical meteorological environment data, and historical photovoltaic power generation data, etc. It can also be combined with information such as the conversion coefficient of photovoltaic materials and the photoelectric conversion coefficient.

[0076] Performing data preprocessing on the training samples to obtain preprocessed training samples;

[0077] Among them, preprocessing includes normalization processing or integration processing.

[0078] Training the improved LSTM model using the preprocessed training samples;

[0079] The parameters of the improved LSTM model are adjusted according to the training results.

[0080] The prediction error is corrected and real-time calibration is achieved to achieve better prediction results. A photovoltaic power prediction model based on meteorological environment characteristics and shortwave radiation data characteristics is obtained.

[0081] In addition, a dynamic feedback correction method can be used to perform incremental learning of the model to achieve better prediction results. In one example, the photovoltaic power can be realized, and the following is also included:

[0082] Obtain distributed meteorological environment data and distributed surface shortwave downlink radiation data of distributed photovoltaic power stations;

[0083] A distributed prediction model is constructed based on the distributed meteorological environment data and the distributed surface shortwave downlink radiation data.

[0084] Specifically, based on the above characteristics of distributed photovoltaic power stations, a distributed photovoltaic power station power prediction model is constructed by combining distributed meteorological environment data, distributed surface shortwave downlink radiation data and other data. The photovoltaic power generation power of distributed photovoltaic power stations is predicted, and a more accurate prediction of distributed photovoltaic power stations is achieved.

[0085] In one example, predicting shortwave radiation data based on satellite remote sensing data after terrain correction includes:

[0086] Acquiring the satellite remote sensing data;

[0087] Collect satellite observation data through satellite equipment, such as satellite remote sensing data

[0088] The surface shortwave downlink radiation information is predicted based on the satellite remote sensing data through a deep learning algorithm.

[0089] Specifically, for example, high-temporal-resolution satellite data and deep learning methods can be used to obtain shortwave downlink radiation data of the surface over a large area and at a high temporal phase.

[0090] Reference Figure 4 As shown, in another embodiment of the present disclosure, a photovoltaic radiation prediction device based on remote sensing data is provided, comprising:

[0091] The data acquisition module 401 is used to acquire terrain elevation data at a preset resolution;

[0092] A terrain correction module 402, used for performing terrain correction according to the terrain elevation data;

[0093] The radiation prediction module 403 is used to predict shortwave radiation data based on satellite remote sensing data after terrain correction;

[0094] The power generation prediction module 404 is used to obtain photovoltaic power generation power through a prediction model according to the shortwave radiation data and meteorological environment data.

[0095] The photovoltaic radiation prediction device based on remote sensing data provided in this embodiment includes a data acquisition module 401 , a terrain correction module 402 , a radiation prediction module 403 and a power generation prediction module 404 .

[0096] In yet another embodiment of the present disclosure, an electronic device is provided, including:

[0097] Processor; and

[0098] A memory, configured to store executable instructions of the processor;

[0099] The processor is configured to execute any one of the above-mentioned photovoltaic prediction methods based on remote sensing data by executing the executable instructions.

[0100] It will be appreciated by those skilled in the art that various aspects of the present invention may be implemented as a system, method or program product. Therefore, various aspects of the present invention may be specifically implemented in the following forms, namely: a complete hardware implementation, a complete software implementation (including firmware, microcode, etc.), or a combination of hardware and software, which may be collectively referred to herein as a "circuit", "module" or "system".

[0101] Refer to the following Figure 5 The electronic device 500 according to this embodiment of the present invention is described. Figure 5 The electronic device 500 shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0102] like Figure 5As shown, the electronic device 500 is in the form of a general computing device. The components of the electronic device 500 may include but are not limited to: at least one processing unit 510, at least one storage unit 520, and a bus 530 connecting different system components (including the storage unit 520 and the processing unit 510).

[0103] The storage unit stores program codes, which can be executed by the processing unit 510, so that the processing unit 510 performs the steps according to various exemplary embodiments of the present invention described in the above “Exemplary Method” section of this specification. For example, the processing unit 510 can perform the following steps: Figure 1 As shown in .

[0104] The storage unit 520 may include a readable medium in the form of a volatile storage unit, such as a random access storage unit (RAM) 5201 and / or a cache storage unit 5202 , and may further include a read-only storage unit (ROM) 5203 .

[0105] The storage unit 520 may also include a program / utility 5204 having a set (at least one) of program modules 5205, such program modules 5205 including but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment.

[0106] Bus 530 may represent one or more of several types of bus structures, including a memory unit bus or memory unit controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.

[0107] The electronic device 500 may also communicate with one or more external devices 600 (e.g., keyboards, pointing devices, Bluetooth devices, etc.), may also communicate with one or more devices that enable a user to interact with the electronic device 600, and / or communicate with any device that enables the electronic device 500 to communicate with one or more other computing devices (e.g., routers, modems, etc.). Such communication may be performed via an input / output (I / O) interface 550. Furthermore, the electronic device 500 may also communicate with one or more networks (e.g., local area networks (LANs), wide area networks (WANs), and / or public networks, such as the Internet) via a network adapter 560. As shown, the network adapter 560 communicates with other modules of the electronic device 500 via a bus 530. It should be understood that, although not shown in the figure, other hardware and / or software modules may be used in conjunction with the electronic device 500, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0108] Through the description of the above implementation, it is easy for those skilled in the art to understand that the example implementation described here can be implemented by software, or by software combined with necessary hardware. Therefore, the technical solution according to the implementation of the present disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, including several instructions to enable a computing device (which can be a personal computer, a server, a terminal device, or a network device, etc.) to execute the method according to the implementation of the present disclosure.

[0109] In yet another embodiment of the present disclosure, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the photovoltaic prediction method based on remote sensing data described in any one of the above is implemented.

[0110] In an exemplary embodiment of the present disclosure, a computer-readable storage medium is also provided, on which a program product capable of implementing the above method of the present specification is stored. In some possible implementations, various aspects of the present invention can also be implemented in the form of a program product, which includes a program code, and when the program product is run on a terminal device, the program code is used to enable the terminal device to execute the steps according to various exemplary embodiments of the present invention described in the above "Exemplary Method" section of the present specification.

[0111] A program product for implementing the above method according to an embodiment of the present invention is described, which can adopt a portable compact disk read-only memory (CD-ROM) and include program code, and can be run on a terminal device, such as a personal computer. However, the program product of the present invention is not limited thereto, and in this document, a readable storage medium can be any tangible medium containing or storing a program, which can be used by or in combination with an instruction execution system, an apparatus or a device.

[0112] The program product may use any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples (non-exhaustive list) of readable storage media include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0113] Computer readable signal media may include data signals propagated in baseband or as part of a carrier wave, in which readable program code is carried. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Readable signal media may also be any readable medium other than a readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0114] The program code embodied on the readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wired, optical cable, RF, etc., or any suitable combination of the foregoing.

[0115] Program code for performing the operations of the present invention may be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java, C++, etc., and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user computing device, partially on the user device, as a separate software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving a remote computing device, the remote computing device may be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computing device (e.g., through the Internet using an Internet service provider).

[0116] It should be noted that, although several modules or units of the device for action execution are mentioned in the above detailed description, this division is not mandatory. In fact, according to the embodiments of the present disclosure, the features and functions of two or more modules or units described above can be embodied in one module or unit. On the contrary, the features and functions of one module or unit described above can be further divided into multiple modules or units to be embodied.

[0117] In addition, although the steps of the method in the present disclosure are described in a specific order in the drawings, this does not require or imply that the steps must be performed in this specific order, or that all the steps shown must be performed to achieve the desired results. Additionally or alternatively, some steps may be omitted, multiple steps may be combined into one step, and / or one step may be decomposed into multiple steps, etc.

[0118] Through the description of the above implementation, it is easy for those skilled in the art to understand that the example implementation described here can be implemented by software, or by software combined with necessary hardware. Therefore, the technical solution according to the implementation of the present disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, including several instructions to enable a computing device (which can be a personal computer, a server, a mobile terminal, or a network device, etc.) to execute the method according to the implementation of the present disclosure.

[0119] Those skilled in the art will readily appreciate other embodiments of the present disclosure after considering the specification and practicing the invention disclosed herein. This application is intended to cover any modification, use or adaptation of the present disclosure, which follows the general principles of the present disclosure and includes common knowledge or customary techniques in the art that are not disclosed in the present disclosure. The specification and examples are intended to be exemplary only, and the true scope and spirit of the present disclosure are indicated by the appended claims.

Claims

1. A photovoltaic prediction method based on remote sensing data, characterized in that: include: Get terrain elevation data at a preset resolution; performing terrain correction according to the terrain elevation data; After terrain correction, shortwave radiation data are predicted from satellite remote sensing data; The photovoltaic power generation power is obtained through a prediction model according to the shortwave radiation data and the meteorological environment data.

2. The photovoltaic prediction method based on multi-source remote sensing according to claim 1, characterized in that: Before the step of obtaining terrain elevation data at a preset resolution, the method includes: Calculate the corresponding terrain factors based on terrain elevation data corresponding to different resolutions; Determining terrain correction effects corresponding to terrain elevation data of different resolutions according to the terrain factors; The preset resolution is determined according to the terrain correction effect.

3. The photovoltaic prediction method based on multi-source remote sensing according to claim 1, characterized in that: Also includes: The meteorological environment data are obtained by using an airborne radiation detector, wherein the meteorological environment data include: wind speed, wind direction, temperature, humidity, air pressure, cloud amount and distribution, and surface photovoltaic panel distribution and angle.

4. The photovoltaic prediction method based on multi-source remote sensing according to claim 1, characterized in that: The prediction model adopts an improved LSTM model, which includes: an input layer, a coding layer, an LSTM layer and a fully connected layer.

5. The photovoltaic prediction method based on multi-source remote sensing according to claim 4 is characterized in that: The training step of the prediction model comprises: Acquire training samples, wherein the training samples are composed of historical radiation data, historical meteorological environment data, and historical photovoltaic power generation power; Performing data preprocessing on the training samples to obtain preprocessed training samples; Training the improved LSTM model using the preprocessed training samples; The parameters of the improved LSTM model are adjusted according to the training results.

6. The photovoltaic prediction method based on multi-source remote sensing according to claim 1, characterized in that: Also includes: Obtain distributed meteorological environment data and distributed surface shortwave downlink radiation data of distributed photovoltaic power stations; A distributed prediction model is constructed based on the distributed meteorological environment data and the distributed surface shortwave downlink radiation data.

7. The photovoltaic prediction method based on remote sensing data according to claim 1, characterized in that: The method of predicting shortwave radiation data based on satellite remote sensing data after terrain correction includes: Acquiring the satellite remote sensing data; The surface shortwave downlink radiation information is predicted based on the satellite remote sensing data through a deep learning algorithm.

8. A photovoltaic radiation prediction device based on remote sensing data, characterized in that: include: A data acquisition module, used to acquire terrain elevation data at a preset resolution; A terrain correction module, used for performing terrain correction according to the terrain elevation data; The radiation prediction module is used to predict shortwave radiation data based on satellite remote sensing data after terrain correction; The power generation prediction module is used to obtain photovoltaic power generation power through a prediction model based on the shortwave radiation data and meteorological environment data.

9. An electronic device, characterized in that: include: processor; as well as A memory, configured to store executable instructions of the processor; Wherein, the processor is configured to execute the photovoltaic prediction method based on remote sensing data as described in any one of claims 1 to 7 by executing the executable instructions.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the photovoltaic prediction method based on remote sensing data described in any one of claims 1 to 7 is implemented.

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