CN117933531A - Distributed photovoltaic power generation power prediction system and method - Google Patents
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Abstract
The invention discloses a distributed photovoltaic power generation power prediction system and a method, which belong to the technical field of electric power sources.
Description
Technical Field
The invention belongs to the technical field of electric power energy, and particularly relates to a distributed photovoltaic power generation power prediction system and method.
Background
At present, the prediction of the distributed photovoltaic power generation power refers to the prediction of the power generation power of a distributed photovoltaic power generation system in a future period of time through a certain method and technology, the prediction is helpful for power grid dispatching, power system management and the optimal operation of photovoltaic power generation equipment, and the prediction of the distributed photovoltaic power generation power mainly depends on the characteristics of the photovoltaic power generation system and meteorological factors such as solar irradiance, temperature, humidity and the like;
for example, a method for predicting photovoltaic power generation power is disclosed in China patent publication No. CN116667318A, and belongs to the technical field of electric power energy. Which comprises the following steps: acquiring original meteorological data and photovoltaic output data of a photovoltaic power station; calculating the average value, standard deviation, skewness coefficient and kurtosis of historical power data in original meteorological data and photovoltaic output data of a photovoltaic power station as input factors of an improved K-means algorithm, carrying out cluster analysis on the input factors by adopting the improved K-means algorithm, and calculating the correlation degree of the meteorological data and the generated power in different cluster categories; and (3) taking the meteorological data with the correlation degree of the generated power being more than 0.6 as an input factor of a prediction model, inputting the meteorological data into an Attention-LSTM model, training the model to obtain optimal parameters, and outputting a final prediction result. The precision of photovoltaic power prediction can be well improved based on improved K-means clustering and an attribute-LSTM network model, and the method has engineering application value;
meanwhile, for example, in chinese patent publication No. CN114825328a, a short-term photovoltaic power prediction method and system are provided, including: acquiring an original meteorological data sequence and a photovoltaic power generation power data sequence, and carrying out normalization processing on the original meteorological data sequence; decomposing the photovoltaic power generation power data sequence to obtain N groups of eigenmode function components and residual error components; and carrying out normalization processing on the N groups of eigenmode function components and residual components, and inputting normalization processing results of the N groups of eigenmode function components and residual components and normalization processing results of an original meteorological data sequence into a BiGRU model after training to obtain a prediction result of short-term photovoltaic power generation.
The problems proposed in the background art exist in the above patents: in the prior art, the previous operation data of the photovoltaic power generation is usually collected and analyzed when the photovoltaic power generation power is predicted, the prior art cannot quickly clean and screen a large amount of data, the prediction accuracy of the photovoltaic power generation power prediction under the influence of severe environments (such as strong wind) is not high, the problems exist in the prior art, and the distributed photovoltaic power generation power prediction system and method are designed in order to solve the problems.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides a distributed photovoltaic power generation power prediction system and a method, wherein the invention acquires historical operation power data and historical meteorological data in the operation process of a historical photovoltaic power station, acquires photovoltaic power station operation damage data at corresponding positions of the historical meteorological data, guides the acquired historical operation power data and the historical meteorological data into a data screening strategy to screen similar scenes, screens out a plurality of similar scenes, guides the acquired photovoltaic power station operation damage data at corresponding positions of the historical meteorological data into an operation damage judgment strategy to construct a weather damage judgment model, guides the screened plurality of similar scene data and the historical operation power data corresponding to the similar scenes into a power generation power prediction strategy to conduct preliminary prediction of power generation, guides the power generation power obtained by the preliminary prediction into the weather damage judgment model to conduct final derivation of a power generation prediction value, and prevents interference of extraneous data based on the acquisition of the similar scenes and the construction of the weather damage judgment model, thereby improving the precision of the photovoltaic power generation power prediction under complex conditions.
In order to achieve the above purpose, the present invention provides the following technical solutions:
A method for predicting distributed photovoltaic power generation power comprises the following specific steps:
s1, acquiring historical operation power data and historical meteorological data in the operation process of a historical photovoltaic power station, and simultaneously acquiring photovoltaic power station operation damage data of a position corresponding to the historical meteorological data;
S2, importing the acquired historical operating power data and the historical meteorological data into a data screening strategy to screen similar scenes, and screening a plurality of similar scenes;
S3, importing the operation damage data of the photovoltaic power station at the corresponding position of the acquired historical meteorological data into an operation damage judgment strategy to construct a meteorological damage judgment model;
S4, importing the screened plurality of similar scene data and the historical operation power data corresponding to the similar scenes into a power generation power prediction strategy to perform preliminary prediction of the power generation power;
and S5, guiding the power generated by the preliminary prediction into a weather damage judgment model to finally derive a power generation predicted value.
Specifically, the step S1 includes the following specific steps:
s11, collecting historical moment meteorological data of a photovoltaic power station area through a meteorological collection module, wherein the meteorological data comprise radiation illuminance, wind power, precipitation, temperature and humidity, and acquiring historical operation power data of photovoltaic power station operation corresponding to the historical moment;
S12, extracting operation damage data of the photovoltaic power station at a corresponding position under the influence of historical meteorological data through an operation record of equipment overhaul, wherein the operation damage data comprise the number of damaged photovoltaic power generation modules;
s13, taking the historical meteorological data as a first-dimensional vector form, taking the historical operation power data as a second-dimensional vector form, taking the historical operation damage data as a third-dimensional vector form, and storing and transmitting the historical meteorological data, the historical operation power data and the historical operation damage data in a three-dimensional vector mode.
Specifically, the specific content of the data screening policy in S2 is as follows:
S21, setting a monitoring time interval, acquiring stored historical meteorological data and historical operation power data, extracting radiation illuminance, temperature and humidity data of each monitoring time interval in the historical meteorological data, simultaneously extracting radiation illuminance, temperature and humidity data in weather data of weather forecast in the time interval, and simultaneously acquiring historical operation power data corresponding to each monitoring time in the historical meteorological data;
S22, the radiation illuminance, temperature and humidity data of each monitoring time interval in the historical meteorological data and the radiation illuminance, temperature and humidity data in the weather data of the weather forecast in the current time interval are imported into a difference value calculation formula to calculate the difference value, wherein the difference value calculation formula is as follows:
Wherein a i is the duty ratio of the ith item in the radiation illuminance, temperature and humidity data in the weather data, k i is the ith item in the radiation illuminance, temperature and humidity data of each monitoring time interval in the historical weather data, k' i is the ith item in the radiation illuminance, temperature and humidity data in the weather data of the weather forecast in the current time interval, wherein/>
S23, extracting and comparing the calculated difference value of each monitoring time interval of the history with a set difference threshold, setting a monitoring scene of a corresponding history monitoring time interval of which the difference value is smaller than or equal to the set difference threshold as a similar scene, and setting a monitoring scene of a corresponding history monitoring time interval of which the difference value is larger than the set difference threshold as a non-similar scene, wherein the value taking process of a i and the difference threshold is that 500 groups of monitoring scenes of corresponding history monitoring time intervals of which the difference value is within five percent from the rated output power are taken, substituting the data of the monitoring scenes into a difference value calculation formula, calculating the difference value, substituting the obtained difference value into fitting software, and fitting the data to obtain a i which accords with the accuracy and the value of the difference threshold.
Specifically, the specific content of the damage judgment policy in S3 is:
Obtaining wind power size data and duration time data in historical meteorological data and operation damage data of a photovoltaic power station at a corresponding position, substituting the wind power size data and duration time data in the historical meteorological data and the operation damage data of the photovoltaic power station at the corresponding position into a damage judgment formula to construct a meteorological damage judgment model, wherein the damage judgment formula is as follows:
Wherein Q is weather damage determination data, Q 1 is weather damage determination data corresponding to historical weather data, t 1 is duration of exceeding a set wind in the historical weather data, t is duration of exceeding the set wind in the current weather data, F 1 is average wind in the historical weather data, F is average wind in the current weather data, V 1 is maximum wind in the historical weather data, V is maximum wind in the current weather data, λ 1 is duration duty ratio coefficient, λ 2 is average wind duty ratio coefficient, λ 3 is maximum wind duty ratio coefficient, wherein λ 1+λ2+λ3 =1.
Specifically, the prediction strategy of the generated power in S4 includes the following specific steps:
S41, acquiring a plurality of screened similar scene data and historical operation power data corresponding to the similar scenes, wherein the similar scene data comprise radiation illuminance, temperature and humidity data, constructing deep learning neural network models which are input into the radiation illuminance, the temperature and the humidity data and output into the operation power data;
S42, dividing the extracted plurality of similar scene data and the historical operation power data corresponding to the similar scenes into a 70% parameter training set and a 30% parameter testing set; inputting 70% of parameter training sets into a deep learning neural network model for training to obtain an initial deep learning neural network model; testing the initial deep learning neural network model by using 30% of parameter test sets, and outputting an optimal initial deep learning neural network model meeting the accuracy of the running power data as the deep learning neural network model, wherein an output strategy formula of a specific neuron in the deep learning neural network model is as follows:
Wherein/> For the output of n-layer s term neurons,/>For the connection weight of the n-1 layer neuron i and the n layer s item neuron,Representing the output of layer n-1 neuron i,/>A bias representing the linear relationship of the n-th layer neurons i and the n-1 layer s neurons, sigma () represents a Sigmoid activation function, and m is the number of terms of the n-1 layer neurons;
S43, acquiring the meteorological data, and importing the meteorological data into a constructed deep learning neural network model to output a preliminary predicted value W of the power generation power.
Specifically, the S5 includes the following specific contents:
Acquiring wind power size data and duration time data in the current weather data, importing the wind power size data and the duration time data into a weather damage judgment model to calculate weather damage judgment data, importing the calculated weather damage judgment data and a preliminary predicted value of power generation into a power generation predicted value calculation formula to calculate a predicted value, wherein the power generation predicted value calculation formula is as follows:
Wherein Q Z is the number of the whole photovoltaic modules of the photovoltaic power station at the corresponding position.
Specifically, a distributed photovoltaic power generation power prediction system is realized based on the above-mentioned distributed photovoltaic power generation power prediction method, which specifically includes: the system comprises a data acquisition module, a similar scene judgment module, a weather damage judgment model construction module, a generation power preliminary prediction module, a generation power output module and a control module, wherein the data acquisition module is used for acquiring historical operation power data and historical weather data in the operation process of a historical photovoltaic power station, and simultaneously acquiring photovoltaic power station operation damage data at a position corresponding to the historical weather data, the similar scene judgment module is used for guiding the acquired historical operation power data and the historical weather data into a data screening strategy to screen similar scenes, a plurality of similar scenes are screened out, and the weather damage judgment model construction module is used for guiding the acquired photovoltaic power station operation damage data at the position corresponding to the historical weather data into the operation damage judgment strategy to construct a weather damage judgment model.
Specifically, the power generation power preliminary prediction module is used for guiding the screened plurality of similar scene data and historical operation power data corresponding to the similar scenes into a power generation power prediction strategy to perform preliminary prediction of power generation, the power generation power output module is used for guiding the power generation power obtained by preliminary prediction into the weather damage judgment model to perform final export of a power generation power prediction value, and the control module is used for controlling the operation of the data acquisition module, the similar scene judgment module, the weather damage judgment model construction module, the power generation power preliminary prediction module and the power generation power output module.
Specifically, an electronic device includes: a processor and a memory, wherein the memory stores a computer program for the processor to call;
the processor executes the above-mentioned distributed photovoltaic power generation power prediction method by calling the computer program stored in the memory.
Specifically, a computer readable storage medium stores instructions that, when executed on a computer, cause the computer to perform a distributed photovoltaic power generation power prediction method as described above.
Compared with the prior art, the invention has the beneficial effects that:
According to the method, historical operation power data and historical meteorological data in the operation process of the historical photovoltaic power station are obtained, photovoltaic power station operation damage data in the corresponding position of the historical meteorological data are obtained at the same time, the obtained historical operation power data and the obtained historical meteorological data are imported into a data screening strategy to screen similar scenes, a plurality of similar scenes are screened out, the photovoltaic power station operation damage data in the corresponding position of the obtained historical meteorological data are imported into an operation damage judgment strategy to construct a meteorological damage judgment model, the screened plurality of similar scene data and the historical operation power data corresponding to the similar scenes are imported into a power generation power prediction strategy to conduct preliminary prediction of power generation, the power generation power obtained through the preliminary prediction is imported into the meteorological damage judgment model to conduct final derivation of a power generation prediction value, and the interference of irrelevant data is avoided based on the acquisition of the similar scenes and the construction of the meteorological damage judgment model, so that the photovoltaic power generation power prediction precision under complex weather conditions is improved.
Drawings
FIG. 1 is a schematic flow chart of a method for predicting power of distributed photovoltaic power generation;
FIG. 2 is a schematic diagram showing a specific flow of step S1 of a method for predicting power of distributed photovoltaic power generation according to the present invention;
FIG. 3 is a schematic diagram of a specific flow of step S4 of the method for predicting the power of distributed photovoltaic power generation according to the present invention;
fig. 4 is a schematic diagram of a distributed photovoltaic power generation power prediction system architecture according to the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments.
Example 1
Referring to fig. 1-3, an embodiment of the present invention is provided: a method for predicting distributed photovoltaic power generation power comprises the following specific steps:
s1, acquiring historical operation power data and historical meteorological data in the operation process of a historical photovoltaic power station, and simultaneously acquiring photovoltaic power station operation damage data of a position corresponding to the historical meteorological data;
in this embodiment, S1 includes the following specific steps:
s11, collecting historical moment meteorological data of a photovoltaic power station area through a meteorological collection module, wherein the meteorological data comprise radiation illuminance, wind power, precipitation, temperature and humidity, and acquiring historical operation power data of photovoltaic power station operation corresponding to the historical moment;
The following is a simple C language code example, which is used to collect weather data of the photovoltaic power station area at the historical moment and historical operation power data of the photovoltaic power station through the weather collection module, note that the code is only used for demonstration purposes, and may need to be adjusted according to actual requirements and data formats in practical application;
This code defines two structures for storing meteorological data and photovoltaic plant operational data, respectively. The 'GETWEATHERDATA' function is used to obtain meteorological data and the 'getPowerData' function is used to obtain photovoltaic power plant operational data. In the 'main' function, the two functions are called separately to acquire data and print it to the console. In practical applications, these two functions need to be adjusted according to the data source and format to read data from the corresponding data source (such as file, database or network request);
S12, extracting operation damage data of the photovoltaic power station at a corresponding position under the influence of historical meteorological data through an operation record of equipment overhaul, wherein the operation damage data comprise the number of damaged photovoltaic power generation modules;
S13, taking historical meteorological data as a first-dimensional vector form, historical operation power data as a second-dimensional vector form, historical operation damage data as a third-dimensional vector form, and storing and transmitting the historical meteorological data, the historical operation power data and the historical operation damage data in a three-dimensional vector mode;
S2, importing the acquired historical operating power data and the historical meteorological data into a data screening strategy to screen similar scenes, and screening a plurality of similar scenes;
in this embodiment, the specific content of the data filtering policy in S2 is as follows:
S21, setting a monitoring time interval, acquiring stored historical meteorological data and historical operation power data, extracting radiation illuminance, temperature and humidity data of each monitoring time interval in the historical meteorological data, simultaneously extracting radiation illuminance, temperature and humidity data in weather data of weather forecast in the time interval, and simultaneously acquiring historical operation power data corresponding to each monitoring time in the historical meteorological data;
S22, the radiation illuminance, temperature and humidity data of each monitoring time interval in the historical meteorological data and the radiation illuminance, temperature and humidity data in the weather data of the weather forecast in the current time interval are imported into a difference value calculation formula to calculate the difference value, wherein the difference value calculation formula is as follows:
Wherein a i is the duty ratio of the ith item in the radiation illuminance, temperature and humidity data in the weather data, k i is the ith item in the radiation illuminance, temperature and humidity data of each monitoring time interval in the historical weather data, k' i is the ith item in the radiation illuminance, temperature and humidity data in the weather data of the weather forecast in the current time interval, wherein/>
S23, extracting a difference value of each monitoring time interval of the history obtained by calculation, comparing the difference value with a set difference threshold, setting a monitoring scene of a corresponding history monitoring time interval of which the difference value is smaller than or equal to the set difference threshold as a similar scene, and setting a monitoring scene of a corresponding history monitoring time interval of which the difference value is larger than the set difference threshold as a non-similar scene, wherein the value taking process of a i and the difference threshold is that 500 groups of monitoring scenes of corresponding history monitoring time intervals of which the difference value is within five percent of rated output power are taken, substituting the data of the monitoring scenes into a difference value calculation formula, calculating the difference value, substituting the obtained difference value into fitting software, and fitting the data to obtain a i which accords with accuracy and the value of the difference threshold;
S3, importing the operation damage data of the photovoltaic power station at the corresponding position of the acquired historical meteorological data into an operation damage judgment strategy to construct a meteorological damage judgment model;
in this embodiment, the specific content of the damage determination policy in S3 is:
Obtaining wind power size data and duration time data in historical meteorological data and operation damage data of a photovoltaic power station at a corresponding position, substituting the wind power size data and duration time data in the historical meteorological data and the operation damage data of the photovoltaic power station at the corresponding position into a damage judgment formula to construct a meteorological damage judgment model, wherein the damage judgment formula is as follows:
Wherein Q is weather damage judgment data, Q 1 is weather damage judgment data corresponding to historical weather data, t 1 is duration exceeding a set wind in the historical weather data, t is duration exceeding the set wind in the current weather data, F 1 is average wind in the historical weather data, F is average wind in the current weather data, V 1 is maximum wind in the historical weather data, V is maximum wind in the current weather data, λ 1 is duration duty ratio coefficient, λ 2 is average wind duty ratio coefficient, λ 3 is maximum wind duty ratio coefficient, wherein λ 1+λ2+λ3 =1;
S4, importing the screened plurality of similar scene data and the historical operation power data corresponding to the similar scenes into a power generation power prediction strategy to perform preliminary prediction of the power generation power;
In the present embodiment, the generated power prediction strategy in S4 includes the following specific steps:
S41, acquiring a plurality of screened similar scene data and historical operation power data corresponding to the similar scenes, wherein the similar scene data comprise radiation illuminance, temperature and humidity data, constructing deep learning neural network models which are input into the radiation illuminance, the temperature and the humidity data and output into the operation power data;
S42, dividing the extracted plurality of similar scene data and the historical operation power data corresponding to the similar scenes into a 70% parameter training set and a 30% parameter testing set; inputting 70% of parameter training sets into a deep learning neural network model for training to obtain an initial deep learning neural network model; testing the initial deep learning neural network model by using 30% of parameter test sets, and outputting an optimal initial deep learning neural network model meeting the accuracy of the running power data as the deep learning neural network model, wherein an output strategy formula of a specific neuron in the deep learning neural network model is as follows:
Wherein/> For the output of n-layer s term neurons,/>For the connection weight of the n-1 layer neuron i and the n layer s item neuron,Representing the output of layer n-1 neuron i,/>A bias representing the linear relationship of the n-th layer neurons i and the n-1 layer s neurons, sigma () represents a Sigmoid activation function, and m is the number of terms of the n-1 layer neurons;
S43, acquiring the meteorological data, and importing the meteorological data into a constructed deep learning neural network model to output a preliminary predicted value W of the power generation power;
S5, the power generated by preliminary prediction is led into a weather damage judgment model to conduct final derivation of a power generation predicted value;
in this embodiment, S5 includes the following specific contents:
Acquiring wind power size data and duration time data in the current weather data, importing the wind power size data and the duration time data into a weather damage judgment model to calculate weather damage judgment data, importing the calculated weather damage judgment data and a preliminary predicted value of power generation into a power generation predicted value calculation formula to calculate a predicted value, wherein the power generation predicted value calculation formula is as follows:
Wherein Q Z is the number of the whole photovoltaic modules of the photovoltaic power station at the corresponding position.
The implementation of the embodiment can be realized: according to the method, historical operation power data and historical meteorological data in the operation process of the historical photovoltaic power station are obtained, photovoltaic power station operation damage data in the corresponding position of the historical meteorological data are obtained at the same time, the obtained historical operation power data and the obtained historical meteorological data are imported into a data screening strategy to screen similar scenes, a plurality of similar scenes are screened out, the photovoltaic power station operation damage data in the corresponding position of the obtained historical meteorological data are imported into an operation damage judgment strategy to construct a meteorological damage judgment model, the screened plurality of similar scene data and the historical operation power data corresponding to the similar scenes are imported into a power generation power prediction strategy to conduct preliminary prediction of power generation, the power generation power obtained through the preliminary prediction is imported into the meteorological damage judgment model to conduct final derivation of a power generation prediction value, and the interference of irrelevant data is avoided based on the acquisition of the similar scenes and the construction of the meteorological damage judgment model, so that the photovoltaic power generation power prediction precision under complex weather conditions is improved.
Example 2
As shown in fig. 4, a distributed photovoltaic power generation power prediction system is implemented based on the above-mentioned distributed photovoltaic power generation power prediction method, which specifically includes: the system comprises a data acquisition module, a similar scene judgment module, a weather damage judgment model construction module, a power generation preliminary prediction module, a power generation output module and a control module, wherein the data acquisition module is used for acquiring historical operation power data and historical weather data in the operation process of a historical photovoltaic power station, and simultaneously acquiring photovoltaic power station operation damage data at a position corresponding to the historical weather data; the power generation power preliminary prediction module is used for guiding the screened plurality of similar scene data and the historical operation power data corresponding to the similar scenes into a power generation power prediction strategy to conduct preliminary prediction of power generation, the power generation power output module is used for guiding the power generation power obtained through preliminary prediction into the weather damage judgment model to conduct final export of a power generation power prediction value, and the control module is used for controlling the operation of the data acquisition module, the similar scene judgment module, the weather damage judgment model construction module, the power generation power preliminary prediction module and the power generation power output module.
Example 3
The present embodiment provides an electronic device including: a processor and a memory, wherein the memory stores a computer program for the processor to call;
The processor executes a distributed photovoltaic power generation power prediction method as described above by invoking a computer program stored in the memory.
The electronic device may have a relatively large difference due to different configurations or performances, and may include one or more processors (Central Processing Units, CPU) and one or more memories, where at least one computer program is stored in the memories, and the computer program is loaded and executed by the processors to implement a distributed photovoltaic power generation power prediction method provided by the above method embodiments. The electronic device can also include other components for implementing the functions of the device, for example, the electronic device can also have wired or wireless network interfaces, input-output interfaces, and the like, for inputting and outputting data. The present embodiment is not described herein.
Example 4
The present embodiment proposes a computer-readable storage medium having stored thereon an erasable computer program;
The computer program, when run on a computer device, causes the computer device to perform one of the distributed photovoltaic power generation power prediction methods described above.
For example, the computer readable storage medium can be Read-Only Memory (ROM), random access Memory (Random Access Memory, RAM), compact disk Read-Only Memory (Compact Disc Read-Only Memory, CD-ROM), magnetic tape, floppy disk, optical data storage device, and the like.
It should be understood that, in various embodiments of the present application, the sequence numbers of the foregoing processes do not mean the order of execution, and the order of execution of the processes should be determined by the functions and internal logic thereof, and should not constitute any limitation on the implementation process of the embodiments of the present application.
It should be understood that determining B from a does not mean determining B from a alone, but can also determine B from a and/or other information.
The above embodiments may be implemented in whole or in part by software, hardware, firmware, or any other combination. When implemented in software, the above-described embodiments may be implemented in whole or in part in the form of a computer program product. The computer program product comprises one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, the processes or functions in accordance with embodiments of the present invention are produced in whole or in part. The computer may be a general purpose computer, a special purpose computer, a computer network, or other programmable apparatus. The computer instructions may be stored in or transmitted from one computer-readable storage medium to another, for example, by way of wired or/and wireless networks from one website site, computer, server, or data center to another. Computer readable storage media can be any available media that can be accessed by a computer or data storage devices, such as servers, data centers, etc. that contain one or more collections of available media. The usable medium may be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. The semiconductor medium may be a solid state disk.
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.
It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described systems, apparatuses and units may refer to corresponding procedures in the foregoing method embodiments, and are not repeated herein.
In the several embodiments provided by the present invention, it should be understood that the disclosed systems, devices, and methods may be implemented in other manners. For example, the apparatus embodiments described above are merely illustrative, e.g., the partitioning of units is merely one, and there may be additional partitioning in actual implementation, 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 with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other form.
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 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.
In the description of the present specification, the descriptions of the terms "one embodiment," "example," "specific example," and the like, mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiments or examples. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The preferred embodiments of the invention disclosed above are intended only to assist in the explanation of the invention. The preferred embodiments are not intended to be exhaustive or to limit the invention to the precise form disclosed. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the invention and the practical application, to thereby enable others skilled in the art to best understand and utilize the invention. The invention is limited only by the claims and the full scope and equivalents thereof.
Claims (10)
1. The distributed photovoltaic power generation power prediction method is characterized by comprising the following specific steps of:
s1, acquiring historical operation power data and historical meteorological data in the operation process of a historical photovoltaic power station, and simultaneously acquiring photovoltaic power station operation damage data of a position corresponding to the historical meteorological data;
S2, importing the acquired historical operating power data and the historical meteorological data into a data screening strategy to screen similar scenes, and screening a plurality of similar scenes;
S3, importing the operation damage data of the photovoltaic power station at the corresponding position of the acquired historical meteorological data into an operation damage judgment strategy to construct a meteorological damage judgment model;
S4, importing the screened plurality of similar scene data and the historical operation power data corresponding to the similar scenes into a power generation power prediction strategy to perform preliminary prediction of the power generation power;
and S5, guiding the power generated by the preliminary prediction into a weather damage judgment model to finally derive a power generation predicted value.
2. The method for predicting distributed photovoltaic power generation power according to claim 1, wherein S1 comprises the following specific steps:
s11, collecting historical moment meteorological data of a photovoltaic power station area through a meteorological collection module, wherein the meteorological data comprise radiation illuminance, wind power, precipitation, temperature and humidity, and acquiring historical operation power data of photovoltaic power station operation corresponding to the historical moment;
S12, extracting operation damage data of the photovoltaic power station at a corresponding position under the influence of historical meteorological data through an operation record of equipment overhaul, wherein the operation damage data comprise the number of damaged photovoltaic power generation modules;
s13, taking the historical meteorological data as a first-dimensional vector form, taking the historical operation power data as a second-dimensional vector form, taking the historical operation damage data as a third-dimensional vector form, and storing and transmitting the historical meteorological data, the historical operation power data and the historical operation damage data in a three-dimensional vector mode.
3. A method for predicting distributed photovoltaic power generation as claimed in claim 2, wherein: the specific content of the data screening strategy in S2 is as follows:
S21, setting a monitoring time interval, acquiring stored historical meteorological data and historical operation power data, extracting radiation illuminance, temperature and humidity data of each monitoring time interval in the historical meteorological data, simultaneously extracting radiation illuminance, temperature and humidity data in weather data of weather forecast in the time interval, and simultaneously acquiring historical operation power data corresponding to each monitoring time in the historical meteorological data;
S22, the radiation illuminance, temperature and humidity data of each monitoring time interval in the historical meteorological data and the radiation illuminance, temperature and humidity data in the weather data of the weather forecast in the current time interval are imported into a difference value calculation formula to calculate the difference value, wherein the difference value calculation formula is as follows:
Wherein a i is the duty ratio of the ith item in the radiation illuminance, temperature and humidity data in the weather data, k i is the ith item in the radiation illuminance, temperature and humidity data of each monitoring time interval in the historical weather data, k i' is the ith item in the radiation illuminance, temperature and humidity data in the weather data of the weather forecast in the current time interval, wherein/>
S23, extracting and comparing the calculated difference value of each monitoring time interval of the history with a set difference threshold, setting the monitoring scene of the corresponding history monitoring time interval with the difference value smaller than or equal to the set difference threshold as a similar scene, and setting the monitoring scene of the corresponding history monitoring time interval with the difference value larger than the set difference threshold as a non-similar scene.
4. The method for predicting distributed photovoltaic power generation power according to claim 3, wherein the specific content of the damage judgment strategy in S3 is as follows:
Obtaining wind power size data and duration time data in historical meteorological data and operation damage data of a photovoltaic power station at a corresponding position, substituting the wind power size data and duration time data in the historical meteorological data and the operation damage data of the photovoltaic power station at the corresponding position into a damage judgment formula to construct a meteorological damage judgment model, wherein the damage judgment formula is as follows:
Wherein Q is weather damage determination data, Q 1 is weather damage determination data corresponding to historical weather data, t 1 is duration of exceeding a set wind in the historical weather data, t is duration of exceeding the set wind in the current weather data, F 1 is average wind in the historical weather data, F is average wind in the current weather data, V 1 is maximum wind in the historical weather data, V is maximum wind in the current weather data, λ 1 is duration duty ratio coefficient, λ 2 is average wind duty ratio coefficient, λ 3 is maximum wind duty ratio coefficient, wherein λ 1+λ2+λ3 =1.
5. The method for predicting distributed photovoltaic power generation power according to claim 4, wherein the power generation prediction strategy in S4 comprises the following specific steps:
S41, acquiring a plurality of screened similar scene data and historical operation power data corresponding to the similar scenes, wherein the similar scene data comprise radiation illuminance, temperature and humidity data, constructing deep learning neural network models which are input into the radiation illuminance, the temperature and the humidity data and output into the operation power data;
S42, dividing the extracted plurality of similar scene data and the historical operation power data corresponding to the similar scenes into a 70% parameter training set and a 30% parameter testing set; inputting 70% of parameter training sets into a deep learning neural network model for training to obtain an initial deep learning neural network model; testing the initial deep learning neural network model by using 30% of parameter test sets, and outputting an optimal initial deep learning neural network model meeting the accuracy of the running power data as the deep learning neural network model, wherein an output strategy formula of a specific neuron in the deep learning neural network model is as follows:
Wherein/> For the output of n-layer s term neurons,/>For the connection weight of the n-1 layer neuron i and the n layer s item neuron,Representing the output of layer n-1 neuron i,/>A bias representing the linear relationship of the n-th layer neurons i and the n-1 layer s neurons, sigma () represents a Sigmoid activation function, and m is the number of terms of the n-1 layer neurons;
S43, acquiring the meteorological data, and importing the meteorological data into a constructed deep learning neural network model to output a preliminary predicted value W of the power generation power.
6. The method for predicting distributed photovoltaic power generation power according to claim 5, wherein S5 comprises the following specific contents:
Acquiring wind power size data and duration time data in the current weather data, importing the wind power size data and the duration time data into a weather damage judgment model to calculate weather damage judgment data, importing the calculated weather damage judgment data and a preliminary predicted value of power generation into a power generation predicted value calculation formula to calculate a predicted value, wherein the power generation predicted value calculation formula is as follows:
Wherein Q Z is the number of the whole photovoltaic modules of the photovoltaic power station at the corresponding position.
7. A distributed photovoltaic power generation power prediction system, which is implemented based on the distributed photovoltaic power generation power prediction method according to any one of claims 1 to 6, characterized in that the system specifically comprises: the system comprises a data acquisition module, a similar scene judgment module, a weather damage judgment model construction module, a generation power preliminary prediction module, a generation power output module and a control module, wherein the data acquisition module is used for acquiring historical operation power data and historical weather data in the operation process of a historical photovoltaic power station, and simultaneously acquiring photovoltaic power station operation damage data at a position corresponding to the historical weather data, the similar scene judgment module is used for guiding the acquired historical operation power data and the historical weather data into a data screening strategy to screen similar scenes, a plurality of similar scenes are screened out, and the weather damage judgment model construction module is used for guiding the acquired photovoltaic power station operation damage data at the position corresponding to the historical weather data into the operation damage judgment strategy to construct a weather damage judgment model.
8. The system for predicting power of distributed photovoltaic power generation according to claim 7, wherein the power generation preliminary prediction module is configured to import the screened several similar scene data and the historical operation power data corresponding to the similar scene into the power generation prediction strategy to perform preliminary prediction of the power generation, the power generation output module is configured to import the power generation obtained by the preliminary prediction into the weather damage judgment model to perform final export of the power generation prediction value, and the control module is configured to control operations of the data acquisition module, the similar scene judgment module, the weather damage judgment model construction module, the power generation preliminary prediction module, and the power generation output module.
9. An electronic device, comprising: a processor and a memory, wherein the memory stores a computer program for the processor to call;
The processor performs a distributed photovoltaic power generation power prediction method according to any one of claims 1 to 6 by invoking a computer program stored in the memory.
10. A computer-readable storage medium, characterized by: instructions stored thereon which, when executed on a computer, cause the computer to perform a distributed photovoltaic power generation power prediction method as claimed in any one of claims 1 to 6.
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