CN116706884A - Photovoltaic power generation amount prediction method, device, terminal and storage medium - Google Patents
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- H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
- H02J2300/20—The dispersed energy generation being of renewable origin
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- H02J2300/24—The renewable source being solar energy of photovoltaic origin
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Abstract
The application provides a photovoltaic power generation amount prediction method, a photovoltaic power generation amount prediction device, a terminal and a storage medium. The method comprises the following steps: acquiring a plurality of power generation history data of a target area; the power generation history data comprises power generation amount influence factor data and power generation amount; carrying out correlation analysis on the generated energy influence factor data and the generated energy to obtain at least two strong correlated generated energy influence factors; wherein the power generation amount influencing factors comprise photovoltaic equipment parameters; and predicting the power generation amount of the target area based on each strong correlated power generation amount influence factor. According to the application, a plurality of generated energy influence factors are analyzed and screened through the correlation, so that influence factors more related to the generated energy of the photovoltaic can be selected, the generated energy is predicted, and the prediction process is simplified; meanwhile, the influence of the photovoltaic equipment parameters on the generated energy is considered in the selection process, the photovoltaic equipment parameters are also used as influence factors, and the accuracy of the generated energy prediction can be improved.
Description
Technical Field
The application relates to the technical field of power dispatching, in particular to a photovoltaic power generation amount prediction method, a device, a terminal and a storage medium.
Background
Photovoltaic power generation is an important renewable energy source at present, and has the advantages of no pollution and low acquisition cost. However, photovoltaic power generation is greatly affected by weather, which has strong intermittence and volatility, resulting in unstable and discontinuous photovoltaic power generation. If a large amount of photovoltaic is connected into the power system without treatment, the stable operation of the power system is seriously affected.
In order to solve the above problems, the present research focuses on how to accurately predict the photovoltaic power generation amount according to various influencing factors, however, too many influencing factors can cause too large calculation amount of prediction, and it is difficult to improve the accuracy of prediction.
Disclosure of Invention
The embodiment of the application provides a method, a device, a terminal and a storage medium for predicting photovoltaic power generation amount, which are used for solving the problem of improving the accuracy of predicting the photovoltaic power generation amount.
In a first aspect, an embodiment of the present application provides a method for predicting photovoltaic power generation, including:
acquiring a plurality of power generation history data of a target area; the power generation history data comprises power generation amount influence factor data and power generation amount;
carrying out correlation analysis on the generated energy influence factor data and the generated energy to obtain at least two strong correlated generated energy influence factors; wherein the power generation amount influencing factors comprise photovoltaic equipment parameters;
and predicting the power generation amount of the target area based on each strong correlated power generation amount influence factor.
In one possible implementation manner, performing correlation analysis on the generated energy influence factor data and the generated energy to obtain at least two strongly correlated generated energy influence factors, including:
determining the weight of each generating capacity influence factor by an analytic hierarchy process, and sequencing each generating capacity influence factor according to the weight from large to small;
respectively selecting the first n generated energy influence factors, and combining the n generated energy influence factors to obtain m candidate schemes; wherein, n= {2,3, … …, m }, m is a preset value;
and respectively calculating gray correlation degrees of each candidate scheme and the generated energy, and taking each generated energy influence factor in the candidate scheme with the maximum gray correlation degree as a strong correlation generated energy influence factor.
In one possible implementation, the power generation amount influencing factors further include weather data;
when the strongly correlated power generation amount influencing factors include weather data and photovoltaic device parameters, predicting the power generation amount of the target area based on each strongly correlated power generation amount influencing factor includes:
acquiring weather data of a target area on a prediction day;
acquiring photovoltaic equipment parameters of a target area on a prediction day;
and inputting weather data of the target area on the prediction day and photovoltaic equipment parameters into a trained photovoltaic power generation amount calculation model to obtain the power generation amount of the target area on the prediction day.
In one possible implementation, obtaining weather data for a target area on a predicted day includes:
acquiring weather data corresponding to a plurality of historical dates; the weather data comprise wind directions, wind levels, cloud quantities and cloud positions corresponding to time periods in a day;
and inputting weather data corresponding to each historical date into the trained long-short-period memory neural network to obtain the weather data of the target area on the predicted date.
In one possible implementation, the photovoltaic device parameters include the type of photovoltaic device and the number of each type of photovoltaic device;
obtaining photovoltaic device parameters of the target area on a prediction day, including:
acquiring historical photovoltaic equipment parameters, historical photovoltaic equipment fault data and historical photovoltaic equipment newly-added data of a target area;
and determining the equipment parameters of the target area on the prediction day based on the historical photovoltaic equipment parameters, the historical fault data and the historical newly-added data equipment.
In a second aspect, an embodiment of the present application provides a device for predicting photovoltaic power generation, including:
the acquisition module is used for acquiring a plurality of power generation historical data of the target area; the power generation history data comprises power generation amount influence factor data and power generation amount;
the analysis module is used for carrying out correlation analysis on the generated energy influence factor data and the generated energy to obtain at least two strong correlated generated energy influence factors; wherein the power generation amount influencing factors comprise photovoltaic equipment parameters;
and the prediction module is used for predicting the power generation amount of the target area based on each strong correlated power generation amount influence factor.
In a third aspect, embodiments of the present application provide a terminal 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 method according to the first aspect or any one of the possible implementations of the first aspect, when the computer program is executed.
In a fourth aspect, embodiments of the present application provide a computer readable storage medium storing a computer program which, when executed by a processor, implements the steps of the method as described above in the first aspect or any one of the possible implementations of the first aspect.
The photovoltaic power generation amount prediction method, the photovoltaic power generation amount prediction device, the photovoltaic power generation amount prediction terminal and the photovoltaic power generation amount storage medium have the beneficial effects that:
according to the application, a plurality of generated energy influence factors are analyzed and screened through the correlation, so that influence factors more related to the generated energy of the photovoltaic can be selected, the generated energy is predicted, and the prediction process is simplified; meanwhile, the influence of the photovoltaic equipment parameters on the generated energy is considered in the selection process, the photovoltaic equipment parameters are also used as influence factors, and the accuracy of the generated energy prediction can be improved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, 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 application, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flowchart of a method for predicting photovoltaic power generation according to an embodiment of the present application;
fig. 2 is a schematic structural diagram of a photovoltaic power generation amount prediction apparatus according to an embodiment of the present application;
fig. 3 is a schematic diagram of a terminal according to an embodiment of the present application.
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 application. It will be apparent, however, to one skilled in the art that the present application 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 application with unnecessary detail.
For the purpose of making the objects, technical solutions and advantages of the present application more apparent, the following description will be made by way of specific embodiments with reference to the accompanying drawings.
Referring to fig. 1, a flowchart for implementing a photovoltaic power generation amount prediction method provided by an embodiment of the present application is shown, and details are as follows:
step 101, acquiring a plurality of power generation history data of a target area; wherein the power generation history data includes power generation amount influence factor data and power generation amount.
In the present embodiment, the target area refers to an area in which prediction of photovoltaic power generation is to be performed, and the target area may be one or more photovoltaic power generation stations, or may be a residential area or a commercial area in which photovoltaic devices are installed. The power generation amount influence factors refer to factors which influence the photovoltaic power generation amount, such as sunlight duration, sunlight angle and the like, and the power generation amount influence factor data in the power generation history data refer to the history values of various power generation amount influence factors.
Step 102, carrying out correlation analysis on generating capacity influence factor data and generating capacity to obtain at least two strong correlated generating capacity influence factors; wherein the power generation amount influencing factors comprise photovoltaic equipment parameters.
In this embodiment, the influence degrees of various power generation amount influence factors on the power generation amount are different, and the influence factor most relevant to the power generation amount is selected, so that the prediction process can be simplified on the premise of ensuring the prediction accuracy of the power generation amount. In order to select, correlation analysis can be performed on the generated energy influence factor data and generated energy, and the stronger the correlation is, the larger the influence of a certain generated energy influence factor on generated energy is.
And step 103, predicting the power generation amount of the target area based on each strong correlated power generation amount influence factor.
In the embodiment, the power generation amount is predicted based on each strong related power generation amount influence factor, so that the prediction process can be simplified on the premise of ensuring the power generation amount prediction precision.
In one possible implementation manner, performing correlation analysis on the generated energy influence factor data and the generated energy to obtain at least two strongly correlated generated energy influence factors, including:
determining the weight of each generating capacity influence factor by an analytic hierarchy process, and sequencing each generating capacity influence factor according to the weight from large to small;
respectively selecting the first n generated energy influence factors, and combining the n generated energy influence factors to obtain m candidate schemes; wherein, n= {2,3, … …, m }, m is a preset value;
and respectively calculating gray correlation degrees of each candidate scheme and the generated energy, and taking each generated energy influence factor in the candidate scheme with the maximum gray correlation degree as a strong correlation generated energy influence factor.
In the present embodiment, the weight of each power generation amount influence factor determined by the analytic hierarchy process is an objective weight. Each candidate scheme contains at least two and at most m generated energy influence factors, and generated energy influence factors contained in each candidate scheme are generated energy influence factors with larger weight, namely each candidate scheme is a better scheme.
The magnitude of the gray correlation degree can reflect the comprehensive correlation between the generated energy influence factor combination contained in the candidate schemes and the generated energy, and an optimal scheme can be selected from the candidate schemes through the gray correlation degree to predict the generated energy.
In one possible implementation, the power generation amount influencing factors further include weather data;
when the strongly correlated power generation amount influencing factors include weather data and photovoltaic device parameters, predicting the power generation amount of the target area based on each strongly correlated power generation amount influencing factor includes:
acquiring weather data of a target area on a prediction day;
acquiring photovoltaic equipment parameters of a target area on a prediction day;
and inputting weather data of the target area on the prediction day and photovoltaic equipment parameters into a trained photovoltaic power generation amount calculation model to obtain the power generation amount of the target area on the prediction day.
In this embodiment, the photovoltaic power generation amount calculation model is trained based on a sample data set, where the sample data set includes a plurality of samples, each sample is weather data and photovoltaic equipment parameters of a target area on a history date, and a sample tag is photovoltaic power generation amount of the target area on the history date. The trained photovoltaic power generation amount calculation model can determine the power generation amount of the target area on the prediction day according to the weather data of the target area on the prediction day and the photovoltaic equipment parameters.
In one possible implementation, obtaining weather data for a target area on a predicted day includes:
acquiring weather data corresponding to a plurality of historical dates; the weather data comprise wind directions, wind levels, cloud quantities and cloud positions corresponding to time periods in a day;
and inputting weather data corresponding to each historical date into the trained long-short-period memory neural network to obtain the weather data of the target area on the predicted date.
In this embodiment, the weather data affecting the photovoltaic power generation mainly lies in the sunlight condition, the sunlight condition of the target area is affected by the cloud cover size and the cloud position, the cloud position is affected by wind, and the cloud cover and the cloud position change are sustained slow change processes, so in this embodiment, the wind direction, the wind level, the cloud cover and the cloud position are used as inputs of the network, and the long-short-term memory neural network is used for weather data prediction.
In one possible implementation, the photovoltaic device parameters include the type of photovoltaic device and the number of each type of photovoltaic device;
obtaining photovoltaic device parameters of the target area on a prediction day, including:
acquiring historical photovoltaic equipment parameters, historical photovoltaic equipment fault data and historical photovoltaic equipment newly-added data of a target area;
and determining the equipment parameters of the target area on the prediction day based on the historical photovoltaic equipment parameters, the historical fault data and the historical newly-added data equipment.
In this embodiment, under the same weather conditions, the number, model and layout positions of the photovoltaic devices are different, which also results in different photovoltaic power generation amounts, and for the prediction of the medium-and-long-term photovoltaic power generation amounts, the current photovoltaic device parameters and the photovoltaic device parameters on the prediction day may have a large difference, so that the prediction of the photovoltaic device parameters on the prediction day is required. According to analysis, the main reasons for the change of the photovoltaic equipment parameters include the reduction of the available quantity caused by equipment faults and the increase of the available quantity caused by newly-added equipment, so that the photovoltaic equipment parameters on the prediction day can be predicted according to historical fault data and historical newly-added data. In addition, the new situation of the photovoltaic equipment from the current to the predicted day can be determined according to the construction intention of the resident photovoltaic equipment or the construction plan of the photovoltaic equipment.
According to the embodiment of the application, the influence factors of the generated energy are analyzed and screened through the correlation, the influence factors more relevant to the generated energy of the photovoltaic can be selected, the generated energy is predicted, and the prediction process is simplified; meanwhile, the influence of the photovoltaic equipment parameters on the generated energy is considered in the selection process, the photovoltaic equipment parameters are also used as influence factors, and the accuracy of the generated energy prediction can be improved.
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 application.
The following are device embodiments of the application, for details not described in detail therein, reference may be made to the corresponding method embodiments described above.
Fig. 2 shows a schematic structural diagram of a photovoltaic power generation amount prediction apparatus according to an embodiment of the present application, and for convenience of explanation, only the portions related to the embodiment of the present application are shown, which are described in detail below:
as shown in fig. 2, the photovoltaic power generation amount prediction apparatus 2 includes:
an acquisition module 21 for acquiring a plurality of power generation history data of a target area; the power generation history data comprises power generation amount influence factor data and power generation amount;
the analysis module 22 is configured to perform correlation analysis on the power generation amount influence factor data and the power generation amount, so as to obtain at least two power generation amount influence factors with strong correlation; wherein the power generation amount influencing factors comprise photovoltaic equipment parameters;
a prediction module 23 for predicting the power generation amount of the target region based on each of the strongly correlated power generation amount influencing factors.
In one possible implementation, the analysis module 22 is specifically configured to:
determining the weight of each generating capacity influence factor by an analytic hierarchy process, and sequencing each generating capacity influence factor according to the weight from large to small;
respectively selecting the first n generated energy influence factors, and combining the n generated energy influence factors to obtain m candidate schemes; wherein, n= {2,3, … …, m }, m is a preset value;
and respectively calculating gray correlation degrees of each candidate scheme and the generated energy, and taking each generated energy influence factor in the candidate scheme with the maximum gray correlation degree as a strong correlation generated energy influence factor.
In one possible implementation, the power generation amount influencing factors further include weather data;
when the strongly correlated power generation amount influencing factors include weather data and photovoltaic device parameters, the prediction module 23 is specifically configured to:
acquiring weather data of a target area on a prediction day;
acquiring photovoltaic equipment parameters of a target area on a prediction day;
and inputting weather data of the target area on the prediction day and photovoltaic equipment parameters into a trained photovoltaic power generation amount calculation model to obtain the power generation amount of the target area on the prediction day.
In one possible implementation, the obtaining module 21 is specifically configured to:
acquiring weather data corresponding to a plurality of historical dates; the weather data comprise wind directions, wind levels, cloud quantities and cloud positions corresponding to time periods in a day;
and inputting weather data corresponding to each historical date into the trained long-short-period memory neural network to obtain the weather data of the target area on the predicted date.
In one possible implementation, the photovoltaic device parameters include the type of photovoltaic device and the number of each type of photovoltaic device;
the acquisition module 21 is specifically configured to:
acquiring historical photovoltaic equipment parameters, historical photovoltaic equipment fault data and historical photovoltaic equipment newly-added data of a target area;
and determining the equipment parameters of the target area on the prediction day based on the historical photovoltaic equipment parameters, the historical fault data and the historical newly-added data equipment.
According to the embodiment of the application, the influence factors of the generated energy are analyzed and screened through the correlation, the influence factors more relevant to the generated energy of the photovoltaic can be selected, the generated energy is predicted, and the prediction process is simplified; meanwhile, the influence of the photovoltaic equipment parameters on the generated energy is considered in the selection process, the photovoltaic equipment parameters are also used as influence factors, and the accuracy of the generated energy prediction can be improved.
Fig. 3 is a schematic diagram of a terminal according to an embodiment of the present application. As shown in fig. 3, the terminal 3 of this embodiment includes: a processor 30, a memory 31 and a computer program 32 stored in said memory 31 and executable on said processor 30. The processor 30, when executing the computer program 32, implements the steps of the above-described embodiments of the method for predicting photovoltaic power generation, such as steps 101 to 103 shown in fig. 1. Alternatively, the processor 30 may perform the functions of the modules/units of the apparatus embodiments described above, such as the functions of the modules/units 21 to 23 shown in fig. 2, when executing the computer program 32.
Illustratively, the computer program 32 may be partitioned into one or more modules/units that are stored in the memory 31 and executed by the processor 30 to complete the present application. The one or more modules/units may be a series of computer program instruction segments capable of performing specific functions for describing the execution of the computer program 32 in the terminal 3. For example, the computer program 32 may be split into modules/units 21 to 23 shown in fig. 2.
The terminal 3 may be a computing device such as a desktop computer, a notebook computer, a palm computer, a cloud server, etc. The terminal 3 may include, but is not limited to, a processor 30, a memory 31. It will be appreciated by those skilled in the art that fig. 3 is merely an example of the terminal 3 and does not constitute a limitation of the terminal 3, and may include more or less components than illustrated, or may combine certain components, or different components, e.g., the terminal may further include an input-output device, a network access device, a bus, etc.
The processor 30 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 31 may be an internal storage unit of the terminal 3, such as a hard disk or a memory of the terminal 3. The memory 31 may be an external storage device of the terminal 3, 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 3. Further, the memory 31 may also include both an internal storage unit and an external storage device of the terminal 3. The memory 31 is used for storing the computer program as well as other programs and data required by the terminal. The memory 31 may also be used for temporarily storing data that has been output or is to be output.
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, the specific names of the functional units and modules are only for 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 application.
In the embodiments provided in the present application, 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 the modules or units is merely a logical function 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 on 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 application 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 application may implement all or part of the flow of the method of the above embodiment, or may be implemented by instructing related hardware by a computer program, where the computer program may be stored in a computer readable storage medium, and the computer program may implement the steps of the method embodiment for predicting photovoltaic power generation, when executed by a processor. Wherein the computer program comprises computer program code which may be in source code form, object code form, executable file or some intermediate form etc. The computer readable medium may include: any entity or device capable of carrying the 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 (Random Access Memory, RAM), an electrical carrier signal, a telecommunications signal, a software distribution medium, and so forth. It should be noted that the computer readable medium may include content that is subject to appropriate increases and decreases as required by jurisdictions in which such content is subject to legislation and patent practice, such as in certain jurisdictions in which such content is not included as electrical carrier signals and telecommunication signals.
The above embodiments are only for illustrating the technical solution of the present application, and not for limiting the same; although the application 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 application, and are intended to be included in the scope of the present application.
Claims (10)
1. A method for predicting photovoltaic power generation, comprising:
acquiring a plurality of power generation history data of a target area; wherein the power generation history data includes power generation amount influence factor data and power generation amount;
performing correlation analysis on the generated energy influence factor data and the generated energy to obtain at least two strong correlated generated energy influence factors; wherein the power generation amount influencing factors comprise photovoltaic equipment parameters;
and predicting the power generation amount of the target area based on each strong correlated power generation amount influence factor.
2. The method for predicting a photovoltaic power generation amount according to claim 1, wherein the performing correlation analysis on the power generation amount influence factor data and the power generation amount to obtain at least two strongly correlated power generation amount influence factors includes:
determining the weight of each generating capacity influence factor by an analytic hierarchy process, and sequencing each generating capacity influence factor according to the weight from large to small;
respectively selecting the first n generated energy influence factors, and combining the n generated energy influence factors to obtain m candidate schemes; wherein, n= {2,3, … …, m }, m is a preset value;
and respectively calculating gray correlation degrees of each candidate scheme and the generated energy, and taking each generated energy influence factor in the candidate scheme with the maximum gray correlation degree as a strong correlation generated energy influence factor.
3. The method for predicting a photovoltaic power generation amount according to claim 1, wherein the power generation amount influencing factor further includes weather data;
when the strongly correlated power generation amount influencing factors include weather data and photovoltaic device parameters, the predicting the power generation amount of the target area based on each strongly correlated power generation amount influencing factor includes:
acquiring weather data of the target area on a prediction day;
acquiring photovoltaic equipment parameters of the target area on a prediction day;
and inputting weather data and photovoltaic equipment parameters of the target area on a prediction day into a trained photovoltaic power generation amount calculation model to obtain the power generation amount of the target area on the prediction day.
4. A method of predicting photovoltaic power generation as claimed in claim 3, wherein the obtaining weather data for the target area on the predicted day comprises:
acquiring weather data corresponding to a plurality of historical dates; the weather data comprise wind directions, wind levels, cloud quantities and cloud positions corresponding to time periods in a day;
and inputting weather data corresponding to each historical date into a trained long-short-period memory neural network to obtain the weather data of the target area on the predicted date.
5. A method of predicting photovoltaic power generation in accordance with claim 3 wherein the photovoltaic device parameters include photovoltaic device type and number of each type of photovoltaic device;
the obtaining the photovoltaic equipment parameters of the target area on the prediction day comprises the following steps:
acquiring historical photovoltaic equipment parameters, historical photovoltaic equipment fault data and historical photovoltaic equipment newly-added data of the target area;
and determining the equipment parameters of the target area on a prediction day based on the historical photovoltaic equipment parameters, the historical fault data and the historical newly-added data equipment.
6. A photovoltaic power generation amount prediction apparatus, comprising:
the acquisition module is used for acquiring a plurality of power generation historical data of the target area; wherein the power generation history data includes power generation amount influence factor data and power generation amount;
the analysis module is used for carrying out correlation analysis on the generated energy influence factor data and the generated energy to obtain at least two strong correlated generated energy influence factors; wherein the power generation amount influencing factors comprise photovoltaic equipment parameters;
and the prediction module is used for predicting the power generation amount of the target area based on each strong correlated power generation amount influence factor.
7. The photovoltaic power generation amount prediction device according to claim 6, wherein the analysis module is specifically configured to:
determining the weight of each generating capacity influence factor by an analytic hierarchy process, and sequencing each generating capacity influence factor according to the weight from large to small;
respectively selecting the first n generated energy influence factors, and combining the n generated energy influence factors to obtain m candidate schemes; wherein, n= {2,3, … …, m }, m is a preset value;
and respectively calculating gray correlation degrees of each candidate scheme and the generated energy, and taking each generated energy influence factor in the candidate scheme with the maximum gray correlation degree as a strong correlation generated energy influence factor.
8. The photovoltaic power generation amount prediction apparatus according to claim 6, wherein the power generation amount influence factor further includes weather data;
the prediction module is specifically configured to:
when the strong correlated power generation amount influencing factors comprise weather data and photovoltaic equipment parameters, weather data of the target area on a predicted day are obtained;
acquiring photovoltaic equipment parameters of the target area on a prediction day;
and inputting weather data and photovoltaic equipment parameters of the target area on a prediction day into a trained photovoltaic power generation amount calculation model to obtain the power generation amount of the target area on the prediction day.
9. A terminal comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor implements the steps of the method according to any of the preceding claims 1 to 5 when the computer program is executed.
10. A computer readable storage medium storing a computer program, characterized in that the computer program when executed by a processor implements the steps of the method according to any of the preceding claims 1 to 5.
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| CN117239745B (en) * | 2023-11-16 | 2024-01-23 | 北京弘象科技有限公司 | Photovoltaic power generation amount prediction method and device, electronic equipment and storage medium |
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