CN116070728A - Photovoltaic power generation system power generation amount prediction method, device, system and medium - Google Patents
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Abstract
The invention provides a method, equipment, a system and a medium for predicting the power generation capacity of a photovoltaic power generation system, wherein the method, the equipment, the system and the medium firstly acquire an initial predicted value of a reference power station in a target period; then, acquiring a satellite cloud image of the target power generation cluster, and determining the radiation variation of each power station of the target power generation cluster in a target period according to the satellite cloud image; and finally, correcting the initial power generation data according to the radiation variation to obtain final power generation data of each power station of the target power generation cluster. The photovoltaic power prediction values of the reference power stations are used as the prediction values of all power stations in the cluster, so that the photovoltaic power prediction of a large number of power stations can be realized with smaller calculated amount, then the prediction values of all power stations are corrected according to the satellite cloud image, and the prediction accuracy of each power station is ensured.
Description
Technical Field
The invention belongs to the technical field of photovoltaic power generation, and particularly relates to a method, equipment, a system and a medium for predicting the power generation capacity of a photovoltaic power generation system.
Background
The photovoltaic power generation system is a power generation system that directly converts solar energy into electric energy using a solar cell. Because the self-power-consumption and the allowance of the distributed photovoltaic power generation system are used for surfing the internet, the randomness and the fluctuation of the output of the distributed photovoltaic power generation system greatly affect the connected power distribution network, the power generation of the distributed photovoltaic power generation system needs to be predicted, and the stable operation of the power distribution network is ensured.
Most of the current power prediction methods are directed at photovoltaic power prediction of a photovoltaic single station, when a large number of photovoltaic power stations are predicted for power generation, the single station prediction method is generally adopted to predict each power station in sequence, a large amount of calculation resources are required to be occupied, the prediction speed is low, and therefore the power generation prediction efficiency of the photovoltaic power prediction method in the prior art for the large number of photovoltaic power stations is low.
Disclosure of Invention
In view of the above, the invention provides a method, equipment, a system and a medium for predicting the power generation capacity of a photovoltaic power generation system, which aim to solve the problem that the power generation prediction efficiency of a large number of photovoltaic power stations in the prior art is low.
According to a first aspect of an embodiment of the present invention, there is provided a method for predicting an amount of power generation of a photovoltaic power generation system, the photovoltaic power generation system being divided into a plurality of power generation clusters in advance according to consistency of photovoltaic output, each power generation cluster including a plurality of power stations, the method comprising:
acquiring an initial predicted value of a reference power station in a target period; wherein the reference power plant is any one of a plurality of power plants of the target power generation cluster; the target power generation cluster is any power generation cluster; the initial predicted value is power generation data obtained by prediction according to meteorological data of a reference power station in a target period;
acquiring a satellite cloud image of a target power generation cluster, and determining the radiation variation of each power station of the target power generation cluster in a target period according to the satellite cloud image;
and correcting the initial power generation data according to the radiation variation to obtain final power generation data of each power station of the target power generation cluster.
A second aspect of the embodiment of the present invention provides a power generation amount prediction apparatus of a photovoltaic power generation system, in which the photovoltaic power generation system is divided into a plurality of power generation clusters in advance according to consistency of photovoltaic output, each power generation cluster including a plurality of power stations, the apparatus including:
the acquisition module is used for acquiring an initial predicted value of the reference power station in the target period; wherein the reference power plant is any one of a plurality of power plants of the target power generation cluster; the target power generation cluster is any power generation cluster; the initial predicted value is power generation data obtained by prediction according to meteorological data of a reference power station in a target period;
the determining module is used for acquiring a satellite cloud image of the target power generation cluster and determining the radiation variation of each power station of the target power generation cluster in a target period according to the satellite cloud image;
and the correction module is used for correcting the initial power generation data according to the radiation variation to obtain final power generation data of each power station of the target power generation cluster.
A third aspect of an embodiment of the present invention provides an electronic device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, the processor implementing the steps of the photovoltaic power generation system power generation amount prediction method of the first aspect as above when executing the computer program.
A fourth aspect of an embodiment of the invention provides a distributed photovoltaic power generation system comprising a plurality of power plants and an electronic device as in the above third aspect.
A fifth aspect of the embodiments of the present invention provides a computer-readable storage medium storing a computer program which, when executed by a processor, implements the steps of the photovoltaic power generation system power generation amount prediction method of the first aspect above.
The method, the device, the system and the medium for predicting the power generation capacity of the photovoltaic power generation system firstly acquire an initial predicted value of a reference power station in a target period; wherein the reference power plant is any one of a plurality of power plants of the target power generation cluster; the target power generation cluster is any power generation cluster; the initial predicted value is power generation data obtained by prediction according to meteorological data of a reference power station in a target period; then, acquiring a satellite cloud image of the target power generation cluster, and determining the radiation variation of each power station of the target power generation cluster in a target period according to the satellite cloud image; and finally, correcting the initial power generation data according to the radiation variation to obtain final power generation data of each power station of the target power generation cluster. The photovoltaic power prediction values of the reference power stations are used as the prediction values of all power stations in the cluster, so that the photovoltaic power prediction of a large number of power stations can be realized with smaller calculated amount, then the prediction values of all power stations are corrected according to the satellite cloud image, and the prediction accuracy of each power station is ensured.
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In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the embodiments or the description of the prior art will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is an application scenario diagram of a photovoltaic power generation system power generation amount prediction method provided by an embodiment of the invention;
FIG. 2 is a flowchart of an implementation of a photovoltaic power generation system power generation capacity prediction method provided by an embodiment of the present invention;
fig. 3 is a schematic structural diagram of a photovoltaic power generation system power generation capacity prediction device according to an embodiment of the present invention;
fig. 4 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
In the following description, for purposes of explanation and not limitation, specific details are set forth such as the particular system architecture, techniques, etc., in order to provide a thorough understanding of the embodiments of the present invention. It will be apparent, however, to one skilled in the art that the present invention may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present invention with unnecessary detail.
Fig. 1 is an application scenario diagram of a photovoltaic power generation system power generation amount prediction method provided by an embodiment of the invention. As shown in fig. 1, in some embodiments, the method for predicting the power generation capacity of the photovoltaic power generation system provided by the embodiment of the invention may be applied to the application scenario, but is not limited to the application scenario. In an embodiment of the invention, the distributed photovoltaic power generation system comprises: a plurality of power stations 11 and electronic devices 12.
The distributed photovoltaic power generation system may be divided into a plurality of power generation clusters in advance according to the consistency of the photovoltaic output, each power generation cluster includes a plurality of power stations 11, and the electronic device 12 may be a server or a terminal, and the server may be a physical server, a server cluster, a cloud server, or the like, which is not limited herein. The terminal may be a computer, a notebook, a dispatch terminal, etc., and is not limited herein.
When the power generation cluster is divided, the historical power generation data of each power generation station are firstly obtained, then the historical power generation data of each power generation station are classified according to weather types, the similarity between the historical power generation data of each power generation station under each weather type is calculated, and the power generation stations with the similarity larger than a first preset value are divided into a group, so that the power generation division result under each weather type can be obtained.
And after the division results are obtained according to weather types, the geographical positions of the power stations are obtained, and the power station division results are further divided according to the distances among the power stations, so that the plurality of power generation clusters can be obtained. Wherein the power stations within each power generation cluster have meteorological and spatial similarities and therefore necessarily have photovoltaic output consistency.
Fig. 2 is a flowchart of an implementation of a method for predicting the power generation capacity of a photovoltaic power generation system according to an embodiment of the present invention. As shown in fig. 2, in some embodiments, a photovoltaic power generation system power generation amount prediction method is applied to the electronic apparatus 12 shown in fig. 1, the method including:
s210, acquiring an initial predicted value of a reference power station in a target period; wherein the reference power plant is any one of a plurality of power plants of the target power generation cluster; the target power generation cluster is any power generation cluster; the initial predicted value is power generation data predicted from meteorological data of a reference power plant within a target period.
In the embodiment of the invention, the reference power station may be the power station with the highest sum of the photovoltaic output consistency with other power stations in the cluster, or may be the power station in the geographical position center of the cluster, or may be the power station with the highest power generation stability selected according to the historical power generation data, which is not limited herein. Because the power stations are clustered, and the consistency of the photovoltaic output of the power stations in the same cluster is higher, when the photovoltaic power is predicted, the predicted values of all the power stations in the cluster can be obtained only by predicting the reference power station, but the method is simple and convenient to calculate, and the problem of inaccurate prediction is easily caused because the power stations in the same cluster still have certain difference characteristics. Thus, the predicted values of the respective power stations need to be processed separately.
S220, acquiring a satellite cloud image of the target power generation cluster, and determining the radiation variation of each power station of the target power generation cluster in a target period according to the satellite cloud image.
In an embodiment of the present invention, the electronic device 12 may obtain a satellite cloud image from a meteorological satellite or a network platform, and predict the satellite cloud image of the target period according to the change of the satellite cloud image of each cluster in the previous period and the current satellite cloud image, so as to determine the change of the cloud image in the target period.
Because of the weather similarity of the clusters, the weather factors such as the temperature, the humidity and the like of the power stations in the same cluster are necessarily similar, because of the spatial similarity of the clusters, the longitude and the latitude of the power stations in the same cluster are similar, and therefore the illumination intensity is similar, and the main factor affecting the photovoltaic power is the change of a cloud layer, so that the change of the light radiation actually received by the power stations can be obtained by predicting the change of the cloud image in a target period.
And S230, correcting the initial power generation data according to the radiation variation to obtain final power generation data of each power station of the target power generation cluster.
In the embodiment of the invention, the radiation variation of each power station is compared with the radiation variation of the reference power station, so that the relative radiation variation of each power station and the reference power station can be obtained, and the initial power generation data of each power station is corrected, and the final power generation data is obtained.
According to the embodiment of the invention, the photovoltaic power predicted value of the reference power station is used as the predicted value of all power stations in the cluster, so that the photovoltaic power prediction of a large number of power stations can be realized with smaller calculated amount, then the predicted value of each power station is corrected according to the satellite cloud image, and the prediction accuracy of each power station is ensured.
In some embodiments, S230 may include: calculating the difference value of the radiation variation of each power station of the target power generation cluster and the radiation variation of the reference power station; determining the power generation correction amount of each power station of the target power generation cluster according to the difference value and the radiation change amount of the reference power station; and determining final power generation data of each power station of the target power generation cluster according to the initial power generation data and the power generation correction amount of each power station of the target power generation cluster.
In the embodiment of the invention, if the radiation variation of the reference power station is A 0 The radiation change quantity of other power stations in the cluster is A i Then the power generation correction amount a' = (a) i -A 0 )/A 0 。
If A i >A 0 Then A'>0, which indicates that the radiation variation amount of the power station is larger than that of the reference power station, if the power generation amount of the reference power station in the future is increased by B, (1+a'), the power generation amount of the power station in the future is increased, and if the power generation amount of the reference power station in the future is reduced, the power generation amount of the power station in the future is reduced more.
If A i <A 0 Then A'<0, which indicates that the radiation variation amount of the power station is larger than that of the reference power station, if the power generation amount of the reference power station in the future is increased by B, (1+a'), the power generation amount of the power station in the future is increased, and if the power generation amount of the reference power station in the future is reduced, the power generation amount of the power station in the future is reduced less.
In some embodiments of the present invention, in some embodiments, S220 may include: determining cloud cover coefficients of all power stations in the target power generation cluster according to the satellite cloud image of the target power generation cluster at the current moment; and determining the radiation variation of each power station of the target power generation cluster in the target period according to the cloud cover coefficient and a pre-established probability distribution model.
In some embodiments, determining the radiation variance of each power plant of the target power generation cluster within the target period from the satellite cloud map includes: according to the satellite cloud image of the target power generation cluster at the current moment, determining the cloud cover rate and the movement direction at the current moment; the cloud cover rate and the movement direction at the current moment are input into a pre-established long-period and short-period memory network model to obtain the cloud state above each power station in a target power generation cluster at the target moment; and determining the radiation variation quantity above each power station in the target power generation cluster in the target moment according to the cloud layer state above each power station.
In some embodiments, the photovoltaic power generation system power generation amount prediction method further includes: acquiring meteorological data and historical power generation data of all power stations in a historical period; according to the meteorological data and the historical power generation data, determining meteorological similarity among all power stations; determining the spatial similarity among the power stations according to the geographic parameters of the power stations; according to the meteorological similarity and the spatial similarity, determining the output consistency of each power station in each weather; and traversing all power stations, and dividing the power stations with the output consistency larger than a preset threshold value into the same power generation cluster to obtain a plurality of power generation clusters.
In the embodiment of the invention, when the weather similarity is classified, the power station with similar historical power generation data under the same weather type is specifically taken as the weather-similar power station, and the weather type can include but is not limited to at least one of the following: sunny, rainy, snowy, haze, fog, and strong wind. In the spatial similarity classification, a power station with similar historical power generation data in the same geographic environment is specifically taken as a power station with similar space, and the same geographic environment can include, but is not limited to, at least one of the following: the same altitude, the same latitude, the same longitude, the same climate zone and the same administrative zone.
In some embodiments, the photovoltaic power generation system power generation amount prediction method further includes: the method comprises the steps of obtaining the output consistency of each power station in a target power generation cluster in each weather; calculating the sum of the output consistency in the clusters in each weather of each power station; and taking the power station with the maximum sum of the output consistency as a reference power station of the target power generation cluster.
In some embodiments, the photovoltaic power generation system power generation amount prediction method further includes: acquiring meteorological data of a reference power station in a target period; wherein the meteorological data may include, but is not limited to, at least one of: irradiance, humidity, temperature, visibility; and inputting the meteorological data of the reference power station in the target period into a pre-trained convolutional neural network model to obtain an initial predicted value of the reference power station in the target period.
In summary, the beneficial effects of the invention are as follows: the photovoltaic power prediction values of the reference power stations are used as the prediction values of all power stations in the cluster, so that the photovoltaic power prediction of a large number of power stations can be realized with smaller calculated amount, then the prediction values of all power stations are corrected according to the satellite cloud image, and the prediction accuracy of each power station is ensured.
It should be understood that the sequence number of each step in the foregoing embodiment does not mean that the execution sequence of each process should be determined by the function and the internal logic, and should not limit the implementation process of the embodiment of the present invention.
Fig. 3 is a schematic structural diagram of a photovoltaic power generation system power generation capacity prediction device according to an embodiment of the present invention. As shown in fig. 3, in some embodiments, a photovoltaic power generation system power generation amount prediction apparatus includes:
the acquisition module is used for acquiring an initial predicted value of the reference power station in the target period; wherein the reference power plant is any one of a plurality of power plants of the target power generation cluster; the target power generation cluster is any power generation cluster; the initial predictive value is based on reference within a target period and the power generation data obtained by weather data prediction of the power station.
The determining module is used for acquiring the satellite cloud image of the target power generation cluster and determining the radiation variation of each power station of the target power generation cluster in the target period according to the satellite cloud image.
And the correction module is used for correcting the initial power generation data according to the radiation variation to obtain final power generation data of each power station of the target power generation cluster.
Optionally, the correction module is specifically configured to calculate a difference value between the radiation variation of each power station of the target power generation cluster and the radiation variation of the reference power station; determining the power generation correction amount of each power station of the target power generation cluster according to the difference value and the radiation change amount of the reference power station; and determining final power generation data of each power station of the target power generation cluster according to the initial power generation data and the power generation correction amount of each power station of the target power generation cluster.
Optionally, the correction module is specifically configured to determine cloud cover coefficients of each power station in the target power generation cluster according to the satellite cloud image of the target power generation cluster at the current moment; and determining the radiation variation of each power station of the target power generation cluster in the target period according to the cloud cover coefficient and a pre-established probability distribution model.
Optionally, the correction module is specifically configured to determine, according to a satellite cloud image of the target power generation cluster at the current moment, a cloud coverage rate and a movement direction at the current moment; the cloud cover rate and the movement direction at the current moment are input into a pre-established long-period and short-period memory network model to obtain the cloud state above each power station in a target power generation cluster at the target moment; and determining the radiation variation quantity above each power station in the target power generation cluster in the target moment according to the cloud layer state above each power station.
Optionally, the photovoltaic power generation system power generation capacity prediction device further includes: the dividing module is used for acquiring meteorological data and historical power generation data of all power stations in a historical period; according to the meteorological data and the historical power generation data, determining meteorological similarity among all power stations; determining the spatial similarity among the power stations according to the geographic parameters of the power stations; according to the meteorological similarity and the spatial similarity, determining the output consistency of each power station in each weather; and traversing all power stations, and dividing the power stations with the output consistency larger than a preset threshold value into the same power generation cluster to obtain a plurality of power generation clusters.
Optionally, the photovoltaic power generation system power generation capacity prediction device further includes: the selecting module is used for acquiring the output consistency of each power station in the target power generation cluster in each weather; calculating the sum of the output consistency in the clusters in each weather of each power station; and taking the power station with the maximum sum of the output consistency as a reference power station of the target power generation cluster.
Optionally, the photovoltaic power generation system power generation capacity prediction device further includes: the initial prediction module is used for acquiring meteorological data of a reference power station in a target period; wherein the meteorological data comprises at least one of: irradiance, humidity, temperature, visibility; and inputting the meteorological data of the reference power station in the target period into a pre-trained convolutional neural network model to obtain an initial predicted value of the reference power station in the target period.
The photovoltaic power generation system power generation amount prediction device provided by the embodiment can be used for executing the method embodiment, the implementation principle and the technical effect are similar, and the embodiment is not repeated here.
Fig. 4 is a schematic structural diagram of an electronic device according to an embodiment of the present invention. As shown in fig. 4, an electronic device 4 according to an embodiment of the present invention is provided, the electronic device 4 of the embodiment including: a processor 40, a memory 41 and a computer program 42 stored in the memory 41 and executable on the processor 40. The processor 40, when executing the computer program 42, implements the steps of the embodiments of the photovoltaic power generation amount prediction method of the photovoltaic power generation system, such as steps 210 to 230 shown in fig. 2. Alternatively, the processor 40, when executing the computer program 42, performs the functions of the modules/units of the system embodiments described above, such as the functions of the modules 310-330 shown in fig. 3.
By way of example, the computer program 42 may be partitioned into one or more modules/units, which are stored in the memory 41 and executed by the processor 40 to complete the present invention. One or more of the modules/units may be a series of computer program instruction segments capable of performing particular functions for describing the execution of the computer program 42 in the electronic device 4.
The electronic device 4 may be a terminal or a server, wherein the terminal may be a mobile phone, an MCU, an ECU, etc., and is not limited herein, and the server may be a physical server, a cloud server, etc., and is not limited herein. The electronic device 4 may include, but is not limited to, a processor 40, a memory 41. It will be appreciated by those skilled in the art that fig. 4 is merely an example of the electronic device 4 and is not meant to be limiting as to the electronic device 4, and may include more or fewer components than shown, or may combine certain components, or different components, e.g., a terminal may also include an input-output device, a network access device, a bus, etc.
The processor 40 may be a central processing unit (Central Processing Unit, CPU), other general purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), field-programmable gate arrays (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory 41 may be an internal storage unit of the electronic device 4, such as a hard disk or a memory of the electronic device 4. The memory 41 may also be an external storage device of the electronic device 4, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card) or the like, which are provided on the electronic device 4. Further, the memory 41 may also include both an internal storage unit and an external storage device of the electronic device 4. The memory 41 is used to store computer programs and other programs and data required by the terminal. The memory 41 may also be used to temporarily store data that has been output or is to be output.
The embodiment of the invention provides a computer readable storage medium, wherein the computer readable storage medium stores a computer program, and the computer program realizes the steps in the photovoltaic power generation system power generation amount prediction method embodiment when being executed by a processor.
The computer readable storage medium stores a computer program 42, the computer program 42 comprising program instructions which, when executed by the processor 40, implement all or part of the processes of the above described embodiments, or may be implemented by means of hardware associated with the instructions of the computer program 42, the computer program 42 being stored in a computer readable storage medium, the computer program 42, when executed by the processor 40, implementing the steps of the above described embodiments of the method. The computer program 42 comprises computer program code, which may be in the form of source code, object code, executable files, or in some intermediate form, among others. The computer readable medium may include: any entity or device capable of carrying computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer Memory, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), an electrical carrier signal, a telecommunications signal, a software distribution medium, and so forth.
The computer readable storage medium may be an internal storage unit of the terminal of any of the foregoing embodiments, such as a hard disk or a memory of the terminal. The computer readable storage medium may also be an external storage device of the terminal, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card) or the like, which are provided on the terminal. Further, the computer-readable storage medium may also include both an internal storage unit of the terminal and an external storage device. The computer-readable storage medium is used to store a computer program and other programs and data required for the terminal. The computer-readable storage medium may also be used to temporarily store data that has been output or is to be output.
It should be understood that the sequence number of each step in the foregoing embodiment does not mean that the execution sequence of each process should be determined by the function and the internal logic, and should not limit the implementation process of the embodiment of the present invention.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-described division of the functional units and modules is illustrated, and in practical application, the above-described functional distribution may be performed by different functional units and modules according to needs, i.e. the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-described functions. The functional units and modules in the embodiment may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit, where the integrated units may be implemented in a form of hardware or a form of a software functional unit. In addition, specific names of the functional units and modules are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present application. The specific working process of the units and modules in the above system may refer to the corresponding process in the foregoing method embodiment, which is not described herein again.
In the foregoing embodiments, the descriptions of the embodiments are emphasized, and in part, not described or illustrated in any particular embodiment, reference is made to the related descriptions of other embodiments.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
In the embodiments provided in the present invention, it should be understood that the disclosed apparatus/terminal and method may be implemented in other manners. For example, the apparatus/terminal embodiments described above are merely illustrative, e.g., the division of modules or units is merely a logical functional division, and there may be additional divisions when actually implemented, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection via interfaces, devices or units, which may be in electrical, mechanical or other forms.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed over a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.
The integrated modules/units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the present invention may implement all or part of the flow of the method of the above embodiment, or may be implemented by a computer program to instruct related hardware, and the computer program may be stored in a computer readable storage medium, where the computer program, when executed by a processor, may implement the steps of each of the method embodiments described above. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, executable files or in some intermediate form, etc. The computer readable medium may include: any entity or device capable of carrying computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer Memory, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), an electrical carrier signal, a telecommunications signal, a software distribution medium, and so forth.
The above embodiments are only for illustrating the technical solution of the present invention, and are not limiting; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention, and are intended to be included in the scope of the present invention.
Claims (10)
1. A method for predicting the amount of power generation of a photovoltaic power generation system, wherein the photovoltaic power generation system is divided into a plurality of power generation clusters in advance according to the consistency of photovoltaic output, each power generation cluster comprising a plurality of power stations, the method comprising:
acquiring an initial predicted value of a reference power station in a target period; wherein the reference power plant is any one of a plurality of power plants of a target power generation cluster; the target power generation cluster is any power generation cluster; the initial predicted value is power generation data obtained by prediction according to meteorological data of the reference power station in a target period;
acquiring a satellite cloud image of a target power generation cluster, and determining the radiation variation of each power station of the target power generation cluster in a target period according to the satellite cloud image;
and correcting the initial power generation data according to the radiation variation to obtain final power generation data of each power station of the target power generation cluster.
2. The photovoltaic power generation system power generation amount prediction method according to claim 1, wherein correcting the initial power generation data according to the radiation variation amount to obtain final power generation data of each power station of the target power generation cluster includes:
calculating the difference value of the radiation variation of each power station of the target power generation cluster and the radiation variation of the reference power station;
determining the power generation correction amount of each power station of the target power generation cluster according to the difference value and the radiation variation amount of the reference power station;
and determining final power generation data of each power station of the target power generation cluster according to the initial power generation data and the power generation correction amount of each power station of the target power generation cluster.
3. The method for predicting the power generation capacity of a photovoltaic power generation system according to claim 1, wherein determining the radiation variation of each power station of the target power generation cluster in the target period according to the satellite cloud image comprises:
determining cloud cover coefficients of all power stations in the target power generation cluster according to the satellite cloud image of the target power generation cluster at the current moment;
and determining the radiation variation of each power station of the target power generation cluster in the target period according to the cloud cover coefficient and the pre-established probability distribution model.
4. The method for predicting the power generation capacity of a photovoltaic power generation system according to claim 1, wherein determining the radiation variation of each power station of the target power generation cluster in the target period according to the satellite cloud image comprises:
according to the satellite cloud image of the target power generation cluster at the current moment, determining the cloud cover rate and the movement direction at the current moment;
the cloud cover rate and the movement direction at the current moment are input into a pre-established long-period and short-period memory network model to obtain the cloud state above each power station in a target power generation cluster at the target moment;
and determining the radiation variation quantity above each power station in the target power generation cluster in the target moment according to the cloud layer state above each power station.
5. The method for predicting power generation capacity of a photovoltaic power generation system according to claim 1, further comprising:
acquiring meteorological data and historical power generation data of all power stations in a historical period;
determining weather similarity among all power stations according to the weather data and the historical power generation data;
determining the spatial similarity among the power stations according to the geographic parameters of the power stations;
determining the output consistency of each power station in each weather according to the meteorological similarity and the spatial similarity;
traversing all power stations, and dividing the power stations with the output consistency larger than a preset threshold value into the same power generation cluster to obtain a plurality of power generation clusters.
6. The method for predicting power generation capacity of a photovoltaic power generation system of claim 5, further comprising:
acquiring the output consistency of each power station in the target power generation cluster in each weather;
calculating the sum of the output consistency in the clusters in each weather of each power station;
and taking the power station with the largest sum of the output consistency as the reference power station of the target power generation cluster.
7. The method for predicting power generation capacity of a photovoltaic power generation system according to any one of claims 1 to 6, further comprising:
acquiring meteorological data of a reference power station in a target period; wherein the meteorological data comprises at least one of: irradiance, humidity, temperature, visibility;
and inputting the meteorological data of the reference power station in the target period into a pre-trained convolutional neural network model to obtain an initial predicted value of the reference power station in the target period.
8. An electronic device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor, when executing the computer program, implements the steps of the photovoltaic power generation system power generation capacity prediction method of any of the preceding claims 1 to 7.
9. A distributed photovoltaic power generation system comprising a plurality of power stations and the electronic device of claim 8.
10. A computer readable storage medium, characterized in that the computer readable storage medium stores a computer program which, when executed by a processor, implements the steps of the photovoltaic power generation system power generation amount prediction method of any one of the preceding claims 1 to 7.
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