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

Abstract

The embodiment of the invention discloses a photovoltaic panel power generation prediction method and device, electronic equipment and a readable storage medium. The method comprises the following steps: acquiring target data; the target data is data in a future first time period, and the target data comprises illumination data, meteorological data and dust coverage information of a solar module; determining N first power generation powers corresponding to the target data from M corresponding relationships; each corresponding relation is a corresponding relation between one piece of data and one piece of generated power, and both M and N are positive integers; and determining the target generation power of the target photovoltaic panel according to the N first generation powers.

Description

Technical Field

[0001] The embodiments of the present application relate to the field of photovoltaic power generation technology, and in particular to a method, device, electronic device and readable storage medium for predicting photovoltaic panel power generation. Background Art

[0002] In the modern and future power industry, the existence of microgrids is of great significance. In the past hundred years, the transmission and distribution of electricity in various countries around the world have been mainly completed by large power grids from top to bottom. At present, the world advocates the strategic concept of energy Internet, proposing to take renewable clean energy as the main force and build a new generation of energy system in a bottom-up way. Among them, renewable clean energy is also called new energy, including wind energy, light energy, etc. In the past, due to the defects of uncontrollability, indirectness, randomness, regional dispersion and other defects of new energy, it was criticized as garbage energy. Today, the concept of microgrid (also known as: microgrid) and energy Internet has found a new applicable solution for distributed new energy. At present, vigorously developing photovoltaic power generation and wind power generation has become the main trend and direction of my country's development of new energy.

[0003] Photovoltaic power generation has a wider application range than wind power generation, especially in cities, because it is more expensive and difficult to set up wind turbines (abbreviated as: wind turbines) in cities, which will cause relatively large changes to the existing urban landscape. In cities, it is relatively easy to set up photovoltaic panels. A large number of photovoltaic panels can be laid on the roofs of factory buildings, and can even be made into wall glass of skyscrapers through certain composite materials. In this way, each building may become a distributed power generation device.

[0004] In the future power industry, if the scope of microgrids is divided by region, then a city is likely to be composed of multiple microgrids. In the near future, the large power grid will not be completely cancelled. In other words, the large power grid may still be the main source of electricity, and the microgrid is connected to the large power grid, while renewable energy in the microgrid can assist in providing electricity. In addition to cities, in remote mountainous areas, Gobi and other places, microgrids will exist in a special form (off-grid state), that is, the microgrid is not connected to the large power grid. Whether it is the case that the microgrid is connected to the large power grid or the microgrid is off-grid, in the field of control of microgrid transmission and distribution, we often need to consider the operation of the entire microgrid system. Especially in the presence of a large number of distributed new energy sources, the control of electric energy in the microgrid is particularly important.

[0005] To control the microgrid well, the first thing to solve is the prediction problem of each component within the microgrid. A typical microgrid consists of wind turbines, photovoltaics, batteries, loads and other elements. Among them, the prediction of transient power of each element is particularly important. All control strategies, including power quality control strategy, voltage and frequency stability control strategy, reasonable self-production and consumption control strategy of electric energy in the microgrid, etc., must be designed and carried out under accurate prediction of wind turbines, photovoltaics, batteries, loads, etc.

[0006] For a given area, if its historical meteorological data and historical actual photovoltaic power generation data are known, how to accurately predict the photovoltaic power generation remains to be studied and explored. The difficulty of this problem is that the photovoltaic power generation depends on the intensity and angle of sunlight, and its power generation usually has random fluctuations. The amplitude of random fluctuations also varies at different times of the day. Usually at noon when the intensity of solar radiation is high, the amplitude of random fluctuations in photovoltaic power generation is also large; while in the morning and evening when the intensity of solar radiation is low, the amplitude of random fluctuations in photovoltaic power generation is small. The existing prediction of photovoltaic power generation does not take into account the impact of random fluctuations in power generation on the prediction results, so it is usually impossible to make accurate predictions of photovoltaic power generation. Summary of the invention

[0007] The embodiments of the present application provide a method, device, electronic device and readable storage medium for predicting photovoltaic panel power generation, which can make an accurate prediction of photovoltaic power generation power.

[0008] In order to solve the above technical problems, the embodiments of the present application adopt the following technical solutions:

[0009] In a first aspect of an embodiment of the present application, a method for predicting power generation of a photovoltaic panel is provided, the method comprising: acquiring target data; the target data is data within a first time period in the future, and the target data comprises: illumination data, meteorological data and dust coverage information of solar panels; determining N first power generation powers corresponding to the target data from M corresponding relationships; each of the corresponding relationships is a corresponding relationship between a data and a power generation power, and M and N are both positive integers; determining the target power generation power of the target photovoltaic panel based on the N first power generation powers.

[0010] In a possible implementation, obtaining the target data includes: obtaining target historical data; the target historical data includes L first historical data, each of the first historical data is data within the first time period in the past; determining second data based on the average of the L first historical data; determining the target data based on the second data.

[0011] In a possible implementation, determining the target data based on the second data includes: calculating the target data based on the second data and the corresponding first power generation related factor data; wherein the first power generation related factor data includes at least one of the following: the weather conditions corresponding to the second data, the maximum weather temperature corresponding to the second data, and the minimum weather temperature corresponding to the second data.

[0012] In a possible implementation, determining the target power generation power of the target photovoltaic panel based on the N first power generation powers includes: determining third data based on the average of the N first power generation powers; calculating the target power generation power based on the third data and corresponding second power generation-related factor data; wherein the second power generation-related factor data includes at least one of the following: the weather conditions corresponding to the third data, the maximum weather temperature corresponding to the third data, and the minimum weather temperature corresponding to the third data.

[0013] The photovoltaic panel power generation prediction method provided in the embodiment of the present application can obtain target data; the target data is data within a first time period in the future, and the target data includes: illumination data, meteorological data, and dust coverage information of solar panels; N first power generation powers corresponding to the target data are determined from M corresponding relationships; each of the corresponding relationships is a corresponding relationship between a data and a power generation power, and M and N are both positive integers; the target power generation power of the target photovoltaic panel is determined according to the N first power generation powers. Since data within a time period in the future can be obtained, and at least one first power generation power corresponding to the target data can be determined from the preset M corresponding relationships, the power generation power of the target photovoltaic panel can be accurately determined directly according to the at least one first power generation power, and therefore, an accurate prediction of the photovoltaic power generation power can be made.

[0014] According to a second aspect of an embodiment of the present application, a photovoltaic panel power generation prediction device is provided, the photovoltaic panel power generation prediction device comprising: an acquisition module and a determination module; the acquisition module is used to acquire target data; the target data is data within a first time period in the future, and the target data comprises: illumination data, meteorological data and dust coverage information of solar panels; the determination module determines N first power generation powers corresponding to the target data acquired by the acquisition module from M corresponding relationships; each of the corresponding relationships is a corresponding relationship between a data and a power generation power, and M and N are both positive integers; and the target power generation power of the target photovoltaic panel is determined based on the N first power generation powers.

[0015] In a possible implementation, the acquisition module is specifically used to acquire target historical data; the target historical data includes L first historical data, each of which is data within the first time period in the past; the determination module is also used to determine the second data based on the average of the L first historical data; and determine the target data based on the second data.

[0016] In one possible implementation, the determination module is specifically used to calculate the target data based on the second data and the corresponding first power generation related factor data; wherein the first power generation related factor data includes at least one of the following: the weather conditions corresponding to the second data, the maximum weather temperature corresponding to the second data, and the minimum weather temperature corresponding to the second data.

[0017] In one possible implementation, the determination module is specifically used to determine the third data based on the average of N first power generation powers; and calculate the target power generation power based on the third data and the corresponding second power generation related factor data; wherein the second power generation related factor data includes at least one of the following: the weather conditions corresponding to the third data, the maximum weather temperature corresponding to the third data, and the minimum weather temperature corresponding to the third data.

[0018] The photovoltaic panel power generation prediction device provided in the embodiment of the present application can obtain data within a future time period and determine at least one first power generation power corresponding to the target data from the preset M corresponding relationships. Therefore, the power generation power of the target photovoltaic panel can be accurately determined directly based on the at least one first power generation power. Therefore, an accurate prediction of the photovoltaic power generation power can be made.

[0019] According to a third aspect of an embodiment of the present application, an electronic device is provided, comprising a processor, a memory, and a program or instruction stored in the memory and executable on the processor, wherein the program or instruction, when executed by the processor, implements the steps of the method described in the first aspect.

[0020] In a fourth aspect of an embodiment of the present application, a readable storage medium is provided, on which a program or instruction is stored. When the program or instruction is executed by a processor, the steps of the method described in the first aspect are implemented.

[0021] In the embodiment of the present application, target data can be obtained; the target data is data in a first time period in the future, and the target data includes: illumination data, meteorological data, and dust coverage information of solar panels; N first power generation powers corresponding to the target data are determined from M corresponding relationships; each of the corresponding relationships is a corresponding relationship between a data and a power generation power, and M and N are both positive integers; the target power generation power of the target photovoltaic panel is determined according to the N first power generation powers. Since data in a time period in the future can be obtained, and at least one first power generation power corresponding to the target data can be determined from the preset M corresponding relationships, the power generation power of the target photovoltaic panel can be accurately determined directly according to the at least one first power generation power, and therefore, an accurate prediction of the photovoltaic power generation power can be made. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 One of the flow diagrams of the photovoltaic panel power generation prediction method provided in the embodiment of the present application;

[0023] Figure 2 A second flow chart of a method for predicting photovoltaic panel power generation provided in an embodiment of the present application;

[0024] Figure 3 A third flow chart of a method for predicting photovoltaic panel power generation provided in an embodiment of the present application;

[0025] Figure 4 A fourth flow chart of a method for predicting photovoltaic panel power generation provided in an embodiment of the present application;

[0026] Figure 5 A schematic diagram of the structure of a photovoltaic panel power generation prediction device provided in an embodiment of the present application;

[0027] Figure 6 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0028] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0029] The terms "first" and "second" in the description and claims of the embodiments of the present application are used to distinguish different objects rather than to describe a specific order of the objects. For example, the first guide wheel and the second guide wheel are used to distinguish different dielectric layers rather than to describe a specific order of the dielectric layers.

[0030] In the description of the embodiments of the present application, unless otherwise specified, the meaning of "plurality" refers to two or more than two. For example, a plurality of elements refers to two elements or more than two elements.

[0031] The term "and / or" in this document is a description of the association relationship of associated objects, indicating that there can be three relationships. For example, the display panel and / or backlight can represent: the display panel exists alone, the display panel and backlight exist at the same time, and the backlight exists alone. The symbol " / " in this document indicates that the associated objects are in an or relationship, for example, input / output means input or output.

[0032] In the embodiments of the present application, words such as "exemplary" or "for example" are used to indicate examples, illustrations or descriptions. Any embodiment or design described as "exemplary" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "exemplary" or "for example" is intended to present related concepts in a specific way.

[0033] At present, in the modern and future power industry, the existence of microgrids is of great significance. In the past hundred years, the transmission and distribution of electricity in various countries in the world have been mainly completed by large power grids from top to bottom. At present, the world advocates the strategic concept of energy Internet, proposing to take renewable clean energy as the leading factor and build a new generation of energy system in a bottom-up way. Among them, renewable clean energy is also called new energy, including wind energy, light energy, etc. In the past, due to the defects of uncontrollability, indirectness, randomness, regional dispersion and other defects of new energy, it was criticized as garbage energy. Today, the concept of microgrid (also known as: microgrid) and energy Internet has found a new applicable solution for distributed new energy. At present, vigorously developing photovoltaic power generation and wind power generation has become the main trend and direction of my country's development of new energy. The scope of application of photovoltaic power generation is wider than that of wind power generation, especially in cities, because the cost and difficulty of setting up wind turbines (abbreviated as: wind turbines) in cities are relatively high, which will cause relatively large changes to the existing urban landscape. In cities, it is relatively easy to set up photovoltaic panels. A large number of photovoltaic panels can be laid on the roofs of factory buildings, and can even be made into the wall glass of skyscrapers through certain composite materials. In this way, each building may become a distributed power generation device. In the future power industry, if the scope of microgrids is divided by region, then a city is likely to be composed of multiple microgrids. In the near future, the large power grid will not be completely eliminated. In other words, the large power grid may still be the main source of electricity, and the microgrid is connected to the large power grid, while the renewable energy in the microgrid can assist in providing electricity. In addition to cities, in remote mountainous areas, Gobi and other places, microgrids will exist in a special form (off-grid state), that is, the microgrid is not connected to the large power grid. Whether it is the case that the microgrid is connected to the large power grid or the microgrid is off-grid, in the field of control of microgrid power transmission and distribution, we often need to consider the operation of the entire microgrid system. Especially in the case of a large number of distributed new energy sources, the control of electric energy in the microgrid is particularly important. To do a good job in the control of microgrids, the first thing to solve is the prediction problem of the components within the microgrid. A typical microgrid consists of wind turbines, photovoltaics, batteries, loads and other elements. Among them, the prediction of transient power of each element is particularly important. All control strategies, including power quality control strategy, voltage and frequency stability control strategy, reasonable self-production and consumption control strategy of electric energy in microgrid, etc., must be designed and carried out under accurate prediction of wind turbines, photovoltaics, batteries, loads, etc. For a given area, if its historical meteorological data and historical actual photovoltaic power generation data are known, how to accurately predict the photovoltaic power generation remains to be studied and explored. The difficulty of this problem lies in that the photovoltaic power generation depends on the intensity and angle of sunlight, and its power generation usually has random fluctuations. The amplitude of random fluctuations varies at different times of the day.Usually, at noon when the solar radiation intensity is high, the random fluctuation amplitude of photovoltaic power generation is also large; while in the morning and evening when the solar radiation intensity is low, the random fluctuation amplitude of photovoltaic power generation is small. The existing prediction of photovoltaic power generation does not consider the impact of random fluctuation of power generation on the prediction results, so it is usually impossible to make an accurate prediction of photovoltaic power generation.

[0034] However, in the embodiment of the present application, target data can be obtained; the target data is data in a first time period in the future, and the target data includes: illumination data, meteorological data, and dust coverage information of solar panels; N first power generation powers corresponding to the target data are determined from M corresponding relationships; each of the corresponding relationships is a corresponding relationship between a data and a power generation power, and M and N are both positive integers; the target power generation power of the target photovoltaic panel is determined based on the N first power generation powers. Since data in a time period in the future can be obtained, and at least one first power generation power corresponding to the target data can be determined from the preset M corresponding relationships, the power generation power of the target photovoltaic panel can be accurately determined directly based on the at least one first power generation power, and therefore, an accurate prediction of the photovoltaic power generation power can be made.

[0035] The photovoltaic panel power generation prediction method, device, electronic device and readable storage medium provided in the embodiments of the present application can be applied in the process of photovoltaic power generation.

[0036] In conjunction with the accompanying drawings, a photovoltaic panel power generation prediction method, device, electronic device and readable storage medium provided in the embodiments of the present application are described in detail through specific embodiments and their application scenarios.

[0037] Figure 1 A photovoltaic panel power generation prediction method provided by an embodiment of the present application is shown. Figure 1 As shown, the photovoltaic panel power generation prediction method provided in the embodiment of the present application may include the following steps 101 to 103.

[0038] Step 101: Obtain target data.

[0039] In the embodiment of the present application, the target data is data within a first time period in the future, and the target data includes: lighting data, meteorological data, and dust coverage information of solar panels.

[0040] In some embodiments of the present application, data within a first future time period may be determined from a plurality of pre-stored historical data.

[0041] In some embodiments of the present application, the first time period may be a time period that needs to be predicted, for example, a time period from 9 am to 11 am the day after tomorrow.

[0042] In some embodiments of the present application, the above-mentioned illumination data may include illumination intensity values.

[0043] In some embodiments of the present application, the target data may be an average value of data in a first future time period.

[0044] Optionally, in the embodiment of the present application, combined with Figure 1 ,like Figure 2 As shown, the above step 101 can be specifically implemented through the following steps 101a to 101c.

[0045] Step 101a: Obtain target historical data.

[0046] In the embodiment of the present application, the target historical data includes L first historical data, and each of the first historical data is data within the first time period in the past.

[0047] In some embodiments of the present application, data within a time period corresponding to the first time period of each day can be determined from pre-stored historical data to obtain L first historical data to obtain target historical data.

[0048] Exemplarily, the L first historical data may include: a time period from 9 am to 11 am the day before yesterday, and a time period from 9 am to 11 am yesterday.

[0049] In some embodiments of the present application, each first historical data may be an average value of data in a corresponding time period.

[0050] Step 101b: determine the second data according to the average of the L first historical data.

[0051] Step 101c: determine the target data according to the second data.

[0052] Optionally, in the embodiment of the present application, combined with Figure 2 ,like Figure 3 As shown, the above step 101c can be specifically implemented through the following step 101c1.

[0053] Step 101c1, calculating and obtaining the target data according to the second data and the corresponding first power generation related factor data.

[0054] In an embodiment of the present application, the first power generation related factor data includes at least one of the following: the weather conditions corresponding to the second data, the maximum weather temperature corresponding to the second data, and the minimum weather temperature corresponding to the second data.

[0055] In some embodiments of the present application, the second data and the corresponding first power generation related factor data may be input into a pre-trained model to obtain target data output by the model.

[0056] It should be noted that for the description of the model training method, reference can be made to the specific description in the relevant technology, and the embodiments of the present application will not be repeated here.

[0057] Step 102: Determine N first power generation powers corresponding to the target data from the M corresponding relationships.

[0058] In the embodiment of the present application, each of the corresponding relationships is a corresponding relationship between a piece of data and a generated power, and M and N are both positive integers.

[0059] In some embodiments of the present application, N data matching the target data can be determined from the M data in the M corresponding relationships, and then N power generation powers corresponding to the N data one by one can be determined as N first power generation powers.

[0060] Step 103: Determine the target power generation power of the target photovoltaic panel according to the N first power generation powers.

[0061] Optionally, in the embodiment of the present application, combined with Figure 1 ,like Figure 4 As shown, the above step 103 can be specifically implemented through the following steps 103a and 103b.

[0062] Step 103a: determine third data according to the average of the N first generated powers.

[0063] Step 103b: Calculate the target power generation according to the third data and the corresponding second power generation related factor data.

[0064] In an embodiment of the present application, the second power generation related factor data includes at least one of the following: the weather conditions corresponding to the third data, the maximum weather temperature corresponding to the third data, and the minimum weather temperature corresponding to the third data.

[0065] It should be noted that, for the description of calculating the target power generation power based on the third data and the corresponding second power generation related factor data, reference can be made to the specific description of calculating the target data based on the second data and the corresponding first power generation related factor data, and the embodiments of the present application will not be repeated here.

[0066] The embodiment of the present application provides a method for predicting photovoltaic panel power generation, which can obtain target data; the target data is data within a first time period in the future, and the target data includes: illumination data, meteorological data, and dust coverage information of solar panels; N first power generation powers corresponding to the target data are determined from M corresponding relationships; each of the corresponding relationships is a corresponding relationship between a data and a power generation power, and M and N are both positive integers; the target power generation power of the target photovoltaic panel is determined based on the N first power generation powers. Since data within a time period in the future can be obtained, and at least one first power generation power corresponding to the target data can be determined from the preset M corresponding relationships, the power generation power of the target photovoltaic panel can be accurately determined directly based on the at least one first power generation power, and therefore, an accurate prediction of the photovoltaic power generation power can be made.

[0067] Figure 5 The schematic diagram of the structure of the photovoltaic panel power generation prediction device provided in the embodiment of the present application is shown. Figure 5 As shown, the photovoltaic panel power generation prediction device 50 provided in the embodiment of the present application includes: an acquisition module 51 and a determination module 52; the acquisition module 51 is used to acquire target data; the target data is data within a first time period in the future, and the target data includes: lighting data, meteorological data and dust coverage information of solar panels; the determination module 52 determines N first power generation powers corresponding to the target data acquired by the acquisition module 51 from M corresponding relationships; each of the corresponding relationships is a corresponding relationship between a data and a power generation power, and M and N are both positive integers; and based on the N first power generation powers, the target power generation power of the target photovoltaic panel is determined.

[0068] In a possible implementation, the acquisition module 51 is specifically used to acquire target historical data; the target historical data includes L first historical data, each of which is data within the first time period in the past; the determination module 52 is also used to determine the second data based on the average of the L first historical data; and determine the target data based on the second data.

[0069] In a possible implementation, the determination module 52 is specifically used to calculate the target data based on the second data and the corresponding first power generation related factor data; wherein the first power generation related factor data includes at least one of the following: the weather conditions corresponding to the second data, the maximum weather temperature corresponding to the second data, and the minimum weather temperature corresponding to the second data.

[0070] In one possible implementation, the determination module 52 is specifically used to determine the third data based on the average of N first power generation powers; and calculate the target power generation power based on the third data and the corresponding second power generation related factor data; wherein the second power generation related factor data includes at least one of the following: the weather conditions corresponding to the third data, the maximum weather temperature corresponding to the third data, and the minimum weather temperature corresponding to the third data.

[0071] The photovoltaic panel power generation prediction device provided in the embodiment of the present application can obtain data within a future time period and determine at least one first power generation power corresponding to the target data from the preset M corresponding relationships. Therefore, the power generation power of the target photovoltaic panel can be accurately determined directly based on the at least one first power generation power. Therefore, an accurate prediction of the photovoltaic power generation power can be made.

[0072] The photovoltaic panel power generation prediction device in the embodiment of the present application can be a device, or a component, integrated circuit, or chip in a terminal. The device can be a mobile electronic device or a non-mobile electronic device. For example, the mobile electronic device can be a mobile phone, a tablet computer, a laptop computer, a PDA, an ultra-mobile personal computer (UMPC) or a netbook, etc., and the non-mobile electronic device can be a server, a personal computer (PC), etc., which is not specifically limited in the embodiment of the present application.

[0073] The photovoltaic panel power generation prediction device in the embodiment of the present application may be a device having an operating system. The operating system may be a Windows operating system, an iOS operating system, or other possible operating systems, which are not specifically limited in the embodiment of the present application.

[0074] The photovoltaic panel power generation prediction device provided in the embodiment of the present application can achieve Figures 1 to 4 To avoid repetition, the various processes implemented by the method embodiment are not described here.

[0075] Optional, such as Figure 6 As shown, an embodiment of the present application also provides an electronic device 60, including a processor 62, a memory 61, and a program or instruction stored in the memory 61 and executable on the processor 62. When the program or instruction is executed by the processor 62, each process of the above-mentioned photovoltaic panel power generation prediction method embodiment is implemented, and the same technical effect can be achieved. To avoid repetition, it will not be repeated here.

[0076] It should be noted that the electronic devices in the embodiments of the present application include the mobile electronic devices and non-mobile electronic devices mentioned above.

[0077] An embodiment of the present application also provides a readable storage medium, on which a program or instruction is stored. When the program or instruction is executed by a processor, each process of the above-mentioned photovoltaic panel power generation prediction method embodiment is implemented, and the same technical effect can be achieved. To avoid repetition, it will not be repeated here.

[0078] The processor is a processor in the electronic device described in the above embodiment. The readable storage medium includes a computer readable storage medium, such as a computer read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0079] An embodiment of the present application further provides a chip, which includes a processor and a communication interface, wherein the communication interface is coupled to the processor, and the processor is used to run programs or instructions to implement the various processes of the above-mentioned photovoltaic panel power generation prediction method embodiment, and can achieve the same technical effect. To avoid repetition, it will not be repeated here.

[0080] It should be understood that the chip mentioned in the embodiments of the present application can also be called a system-level chip, a system chip, a chip system or a system-on-chip chip, etc.

[0081] It should be noted that, in this article, the terms "comprise", "include" or any other variant thereof are intended to cover non-exclusive inclusion, so that the process, method, article or device including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise one..." do not exclude the presence of other identical elements in the process, method, article or device including the element. In addition, it should be pointed out that the scope of the methods and devices in the embodiments of the present application is not limited to performing functions in the order shown or discussed, and may also include performing functions in a substantially simultaneous manner or in reverse order according to the functions involved, for example, the described method may be performed in an order different from that described, and various steps may also be added, omitted, or combined. In addition, the features described with reference to certain examples may be combined in other examples.

[0082] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application, 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 (which can be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) to execute the methods described in each embodiment of the present application.

[0083] The embodiments of the present application are described above in conjunction with the accompanying drawings, but the present application is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the guidance of the present application, ordinary technicians in this field can also make many forms without departing from the purpose of the present application and the scope of protection of the claims, all of which are within the protection of the present application.

Claims

1. A method for predicting photovoltaic panel power generation, characterized in that: The method comprises: Acquire target data; the target data is data within a first time period in the future, and the target data includes: illumination data, meteorological data, and dust coverage information of solar panels; Determine N first power generation powers corresponding to the target data from the M corresponding relationships; each of the corresponding relationships is a corresponding relationship between a data and a power generation power, and M and N are both positive integers; The target power generation power of the target photovoltaic panel is determined according to the N first power generation powers.

2. The method according to claim 1, characterized in that: The acquiring of target data comprises: Acquire target historical data; the target historical data includes L first historical data, each of which is data within the first time period in the past; Determine second data according to the average of L first historical data; The target data is determined according to the second data.

3. The method according to claim 2, characterized in that The step of determining the target data according to the second data includes: Calculating the target data according to the second data and the corresponding first power generation related factor data; The first power generation related factor data includes at least one of the following: weather conditions corresponding to the second data, the highest weather temperature corresponding to the second data, and the lowest weather temperature corresponding to the second data.

4. The method according to claim 1, characterized in that: The step of determining a target power generation power of a target photovoltaic panel according to the N first power generation powers comprises: Determining third data according to the average values ​​of the N first generated powers; Calculating the target power generation according to the third data and the corresponding second power generation related factor data; The second power generation related factor data includes at least one of the following: the weather conditions corresponding to the third data, the highest weather temperature corresponding to the third data, and the lowest weather temperature corresponding to the third data.

5. A photovoltaic panel power generation prediction device, characterized in that: The photovoltaic panel power generation prediction device comprises: an acquisition module and a determination module; The acquisition module is used to acquire target data; the target data is data within a first time period in the future, and the target data includes: illumination data, meteorological data, and dust coverage information of solar panels; The determination module determines N first power generation powers corresponding to the target data acquired by the acquisition module from M corresponding relationships; each of the corresponding relationships is a corresponding relationship between a data and a power generation power, and M and N are both positive integers; and the target power generation power of the target photovoltaic panel is determined based on the N first power generation powers.

6. The photovoltaic panel power generation prediction device according to claim 5, characterized in that: The acquisition module is specifically used to acquire target historical data; the target historical data includes L first historical data, each of which is data within the first time period in the past; The determination module is further used to determine the second data according to the average of the L first historical data; and to determine the target data according to the second data.

7. The photovoltaic panel power generation prediction device according to claim 6, characterized in that: The determination module is specifically configured to calculate the target data according to the second data and the corresponding first power generation related factor data; The first power generation related factor data includes at least one of the following: weather conditions corresponding to the second data, the highest weather temperature corresponding to the second data, and the lowest weather temperature corresponding to the second data.

8. The photovoltaic panel power generation prediction device according to claim 5, characterized in that: The determination module is specifically configured to determine third data according to the average of the N first power generation powers; and calculate the target power generation power according to the third data and the corresponding second power generation related factor data; The second power generation related factor data includes at least one of the following: the weather conditions corresponding to the third data, the highest weather temperature corresponding to the third data, and the lowest weather temperature corresponding to the third data.

9. An electronic device, characterized in that: The method comprises a processor, a memory and a program or instruction stored in the memory and executable on the processor, wherein the program or instruction, when executed by the processor, implements the steps of the method for predicting photovoltaic panel power generation as described in any one of claims 1 to 4.

10. A readable storage medium, characterized in that: The readable storage medium stores a program or instruction, and when the program or instruction is executed by a processor, the steps of the method for predicting photovoltaic panel power generation according to any one of claims 1 to 5 are implemented.

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