CN114282737A - Method and device for predicting short-term solar irradiation intensity and electronic equipment - Google Patents
Method and device for predicting short-term solar irradiation intensity and electronic equipment Download PDFInfo
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Abstract
The application discloses a method and a device for predicting short-term solar irradiation intensity and electronic equipment, wherein the method and the device are used for acquiring meteorological data of a target area; and processing the meteorological data by using a hybrid prediction model which is formed by mixing and constructing a GCN model and a cavity convolution model to obtain the short-term solar radiation intensity of the target area in a future period. According to the method, the meteorological data of the modular photovoltaic power station and the surrounding area of the modular photovoltaic power station are processed in two dimensions of time and space, the short-term solar irradiation intensity is predicted by fusing the two parts, and a control basis is provided for stable operation of a power system.
Description
Technical Field
The application relates to the technical field of new energy, in particular to a method and a device for predicting short-term solar irradiation intensity and electronic equipment.
Background
In recent years, with the shortage of conventional resources and the need for environmental protection, the demand for Renewable Energy Sources (RESs) has sharply increased. Among all renewable energy sources, solar energy, the most typical one, is attracting widespread attention due to its abundant resources and nearly ubiquitous accessibility. Meanwhile, it has many advantages compared to other forms of power generation such as hydroelectric power generation, and thus the scale of photovoltaic power generation has rapidly increased. In recent years, the global photovoltaic market is continuously increased, the installed capacity is increased to 99.8 gigawatts globally in 2018, the installed capacity is increased to 45 gigawatts in the current year in China, and the photovoltaic market is continuously increased at a high speed in the next years.
However, due to the dependence on the atmospheric temperature, the total cloud amount, the humidity and other immediate meteorological factors, the photovoltaic power generation has the problems of randomness, fluctuation, intermittency and the like. These uncertainties can degrade real-time control performance and compromise stable operation of the power system. In order to solve the above problems, it is necessary to predict the short-term irradiation intensity of the sun so as to predict the generated power of the photovoltaic power generation system in a short period of time, and provide a control basis for stable operation of the power system, but there is no tool capable of predicting the short-term irradiation intensity of the sun at present.
Disclosure of Invention
In view of the above, the present application provides a method, an apparatus and an electronic device for predicting short-term solar radiation intensity, which are used for predicting short-term solar radiation intensity.
In order to achieve the above object, the following solutions are proposed:
a method for predicting short-term solar radiation intensity is applied to electronic equipment and comprises the following steps:
acquiring meteorological data of a target area;
and processing the meteorological data based on a pre-constructed hybrid prediction model to obtain the short-term solar radiation intensity of the target area in a future period, wherein the hybrid prediction model is constructed by mixing a GCN model and a cavity convolution model.
Optionally, the method further comprises the steps of:
constructing the GCN model based on meteorological factor data of surrounding areas of the spatially combined photovoltaic power station;
the hole convolution model is constructed by modeling temporally the solar irradiance time series data for each of the surrounding regions.
Optionally, the building the GCN model based on the meteorological factor data of the surrounding area of the spatially combined photovoltaic power plant includes:
firstly, the distribution and the structured meteorological time series of the photovoltaic power station are defined as an undirected graph Gt＝(Vtε, W) in which VtIs a finite vertex, each vertex represents a meteorological factor at time t, ε is a set of edges, and W is the adjacency matrix of the graph;
and then, calculating the graph convolution network by using graph Fourier transform in a spectrum domain, and popularizing the CNN into the graph domain to obtain the GCN model.
Optionally, the constructing the hole convolution model by modeling the solar irradiance time-series data of each of the surrounding regions in time includes:
firstly, two expanding convolution operations are respectively carried out to obtain two products with the same size
The A and the B respectively obtain results through a sigmoid function and fusion operation, and finally Hadamard product is carried out;
then, the time convolution can be defined as
Obtaining the cavity convolution model, wherein sigma is sigmoid function,
is an element-level Hadamard product operation.
A device for predicting short-term solar radiation intensity is applied to electronic equipment and comprises:
a data acquisition module configured to acquire meteorological data of a target area;
and the prediction execution module is configured to process the meteorological data based on a pre-constructed hybrid prediction model to obtain the short-term solar radiation intensity of the target area in a future period, and the hybrid prediction model is constructed by mixing a GCN model and a void convolution model.
Optionally, the method further includes:
a first construction module configured to construct the GCN model based on meteorological factor data spatially combined with surrounding areas of a photovoltaic power plant;
a second construction module configured to construct the hole convolution model by modeling temporally the solar irradiance time series data for each of the surrounding regions.
Optionally, the first building module includes:
a first construction unit for defining the distribution and the structured meteorological time series of the photovoltaic power station as an undirected graph Gt＝(Vtε, W) in which VtIs a finite vertex, each vertex represents a meteorological factor at time t, ε is a set of edges, and W is the adjacency matrix of the graph;
and the second construction unit is used for calculating the graph convolution network by using graph Fourier transform in a spectrum domain, and popularizing the CNN into the graph domain to obtain the GCN model.
Optionally, the second building module includes:
a third construction unit for performing two times of dilation convolution operations to obtain two signals with the same size
The A and the B respectively obtain results through a sigmoid function and fusion operation, and finally Hadamard product is carried out;
a fourth construction unit for defining the time convolution as
Obtaining the cavity convolution model, wherein sigma is sigmoid function,
is an element-level Hadamard product operation.
An electronic device, characterized in that it is provided with a prediction apparatus as described above.
An electronic device, characterized in that at least one processor and a memory connected to the processor are provided, wherein:
the memory is for storing a computer program or instructions;
the processor is for the computer program or instructions to cause the electronic device to implement the prediction method as described above.
According to the technical scheme, the application discloses a method, a device and electronic equipment for predicting short-term solar irradiation intensity, and the method and the device are used for acquiring meteorological data of a target area; and processing the meteorological data by using a hybrid prediction model which is formed by mixing and constructing a GCN model and a cavity convolution model to obtain the short-term solar radiation intensity of the target area in a future period. According to the method, the meteorological data of the modular photovoltaic power station and the surrounding area of the modular photovoltaic power station are processed in two dimensions of time and space, the short-term solar irradiation intensity is predicted by fusing the two parts, and a control basis is provided for stable operation of a power system.
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In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a flowchart of a method for predicting short-term solar irradiance according to an embodiment of the present disclosure;
FIG. 2 is a flow chart of another method for predicting short-term solar irradiance according to an embodiment of the present disclosure;
FIG. 3 is a graph of a single-sided amplitude spectrum of a power conditioning series according to an embodiment of the present application;
FIG. 4 is a block diagram of a device for predicting short-term solar irradiance according to an embodiment of the present disclosure;
FIG. 5 is a block diagram of another apparatus for predicting short-term solar irradiance according to an exemplary embodiment of the present disclosure;
fig. 6 is a block diagram of an electronic device according to an embodiment of the present application.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
According to the method, the GCN is combined with meteorological factor data of the peripheral area of the photovoltaic power station, and the cavity convolution is used for connecting the solar irradiance change time sequence data of each area in series, so that a hybrid model with excellent performance is obtained, and the method can be used for short-term solar irradiance prediction. The short-term solar irradiance prediction method based on the GCN and the hollow convolution hybrid model mainly comprises the steps of modeling solar irradiance data of a photovoltaic power station and a surrounding area of the photovoltaic power station in two dimensions of time and space, and predicting the solar irradiance by fusing the two parts, wherein the specific content is described in the following embodiment.
Example one
Fig. 1 is a flowchart of a method for predicting short-term solar irradiance according to an embodiment of the present disclosure.
As shown in fig. 1, the prediction method provided by the present embodiment is applied to an electronic device for predicting the short-term solar radiation intensity of a target area where a photovoltaic power station is located, where the electronic device can be understood as a computer or a server with data calculation and information processing capabilities, and the prediction method includes the following steps:
and S1, acquiring meteorological data of the target area.
The target area refers to an area needing the short-term irradiation intensity of the sun for prediction, a corresponding photovoltaic power station exists in the area, and the meteorological data refers to the temperature, humidity, wind power, cloud layer, date, longitude and latitude and other data of the target area where the photovoltaic electrons are located when prediction is carried out.
And S2, predicting the short-term solar irradiation intensity of the target area by using meteorological data.
The method comprises the step of processing the meteorological data by using a hybrid prediction model formed by mixing a GCN model and a cavity convolution model to obtain the short-term solar irradiation intensity of the target area.
It can be seen from the above technical solutions that, the present embodiment provides a method for predicting short-term solar irradiation intensity, which is applied to electronic devices, and specifically, obtains meteorological data of a target area; and processing the meteorological data by using a hybrid prediction model which is formed by mixing and constructing a GCN model and a cavity convolution model to obtain the short-term solar radiation intensity of the target area in a future period. According to the method, the meteorological data of the modular photovoltaic power station and the surrounding area of the modular photovoltaic power station are processed in two dimensions of time and space, the short-term solar irradiation intensity is predicted by fusing the two parts, and a control basis is provided for stable operation of a power system.
In one embodiment of the present application, the following steps are also included, as shown in fig. 2.
And S01, constructing a GCN model based on the spatial combination of meteorological factor data of the area around the photovoltaic power station.
Firstly, the distribution and the structured meteorological time series of the photovoltaic power station are defined as an undirected graph Gt＝(Vtε, W) in which VtIs a finite vertex, each vertex representing a meteorological factor at time t, ε is a set of edges, and W is the adjacency matrix of the graph. The meteorological factor data prediction on the graph can be written as Eq (11):
the Graph Convolutional Network (GCN) is then generalized to the graph domain by computing with a graph fourier transform in the spectral domain. It can be written as Eq (12),
gθ(·)*x＝Ugθ(Λ)UTx (12)
where U is the eigenvector matrix L ═ I of the normalized graph LaplacianN-D-1/2AD-1/2U Λ U, its diagonal matrix of eigenvalues Λ, UTx is the graph fourier transform of x.
The data are organized into a map according to the distribution condition of the photovoltaic power station, the spatial information can be effectively utilized, meanwhile, map convolution operation is directly applied to the structured data, and deep patterns and features of a spatial domain are extracted. However, matrix multiplication of eigenvectors in Eq (2) can be computationally expensive for large graphs, and Chebyshev polynomial approximation and hierarchical linear equations can be used to overcome this problem.
To reduce the time complexity, the filter is composed of Chebyshev polynomials Tk(x) Is approximately to KthNext, the process is carried out. We can then rewrite the graph convolution to Eq (13),
wherein,
can pass through (UΛ U)T)k＝UΛkUTTo calculate. The time complexity of Eq (13) can be reduced by computing the K-partial convolution by polynomial approximation.
By limiting K to 1, the graph convolution function can become linear on graph laplace. In addition, since the neural network can adapt to scale changes, we can adapt to λmaxAn approximation is made 2. It can be written as Eq (14),
wherein theta is0And theta1Are two shared filter parameters. To reduce the occurrence of overfitting and numerical manipulation, θ can be0And theta1By exchanging for a parameter theta, let theta be theta0＝-；
And
it can be written as Eq (15),
s02, constructing a hole convolution model by modeling the solar irradiance time-series data of each target area in time.
It is known that RNN-like models are always time-consuming and cannot cope with variable data due to complex gating mechanisms, while CNNs have the advantage of fast training and can implement parallel training processes by stacking convolutional layers. Therefore, we apply the data with K in the time dimension of the input photovoltaic power plant and the solar irradiance data of the surrounding areatGated linear units of width kernel and one-dimensional dilation convolution.
The hole convolution can enlarge the field of view, and in the deep network, down-sampling is always performed to increase the field of view and reduce the amount of calculation, so that although the field of view can be increased, the spatial resolution is reduced. In order not to lose resolution and still enlarge the field of view, hole convolution can be used. This is useful in the prediction task. On one hand, the solar irradiance data in a wider space-time field can be learned due to the large receptive field, and on the other hand, the time point can be accurately positioned and predicted due to the high resolution.
Meanwhile, it can capture multi-scale context information: the cavity convolution has a parameter which can be set, and the specific meaning is that a plurality of 0 s are filled in a convolution kernel, so when different proportions are set, the receptive fields are different, and multi-scale solar irradiance data information is obtained. The input to the model may be considered solar irradiance sequence data of length M
Has CiA channel, a core size of
Firstly, after entering a model, carrying out expansion convolution operation twice respectively to obtain two products with the same size
To output of (c). It can be written as Eq (21),
where d is a dilution parameter that controls the skip distance,
is a nucleus, xtIs the t-th value of the sequence x.
Then, respectively carrying out sigmoid function and fusion operation on A and B, and finally carrying out Hadamard product to obtain a result. The sigmoid function helps to filter the inputs that help to discover the dynamically changing pattern of data, while the nonlinear gate can capture general information of the data. Finally, the time convolution can be defined as
Wherein σ is a sigmoid function;
is an element-level Hadamard product operation.
After the solar irradiance data are respectively modeled in the space-time field, the meteorological data can be processed through the mixed model.
Compared with the prior art, the application has the advantages and effects that:
the application relates to a short-term solar irradiance prediction method based on a GCN and a hole convolution mixed model, which uses a space-time joint characteristic to predict solar irradiance of a missing area for five days in the future. Since solar irradiance can only be collected during the day, the predicted time period is 8:00-18:00 per day, and the predicted results for the next 5 days are shown in table 1. Wherein, No-Spa is a method for removing the GCN model. The results for No-Spa are much more accurate than GRU and CNN, indicating that the GCN model can extract more global spatial information. The prediction result of the method (STM) is superior to that of No-Spa, and the fact that the capture of spatial and temporal characteristics is very important and effective for predicting solar irradiance is proved.
TABLE 1 solar irradiance prediction results
Visualization of the prediction results as shown in fig. 3, it can be seen that the prediction curve of the present method (STM) is closest to the true value curve of solar irradiance, while the curves of the other methods differ greatly from the true value curve in the regions where the fluctuation and oscillation occurs.
Example two
Fig. 4 is a block diagram of a device for predicting short-term solar irradiance according to an embodiment of the present disclosure.
As shown in fig. 4, the prediction apparatus provided in the present embodiment is applied to an electronic device for predicting the short-term solar radiation intensity of a target area in which a photovoltaic power station is located, the electronic device may be understood as a computer or a server having data calculation and information processing capabilities, the prediction apparatus may be regarded as a hardware module of the computer or the server itself, or a lissajous computer or the server, and the prediction apparatus includes a data acquisition module 10 and a prediction execution module 20.
The data acquisition module is used for acquiring meteorological data of a target area.
The target area refers to an area needing the short-term irradiation intensity of the sun for prediction, a corresponding photovoltaic power station exists in the area, and the meteorological data refers to the temperature, humidity, wind power, cloud layer, date, longitude and latitude and other data of the target area where the photovoltaic electrons are located when prediction is carried out.
The prediction execution module is used for predicting the solar short-term irradiation intensity of the target area by using meteorological data.
The method comprises the step of processing the meteorological data by using a hybrid prediction model formed by mixing a GCN model and a cavity convolution model to obtain the short-term solar irradiation intensity of the target area.
It can be seen from the above technical solutions that, the present embodiment provides a short-term solar irradiation intensity prediction apparatus, which is applied to electronic devices, specifically, to obtain meteorological data of a target area; and processing the meteorological data by using a hybrid prediction model which is formed by mixing and constructing a GCN model and a cavity convolution model to obtain the short-term solar radiation intensity of the target area in a future period. According to the method, the meteorological data of the modular photovoltaic power station and the surrounding area of the modular photovoltaic power station are processed in two dimensions of time and space, the short-term solar irradiation intensity is predicted by fusing the two parts, and a control basis is provided for stable operation of a power system.
In one embodiment of the present application, a first building block 30 and a second building block 40 are also included, as shown in FIG. 5.
The first construction module is used for constructing a GCN model based on spatial combination of meteorological factor data of the surrounding area of the photovoltaic power station. The module includes a first building element and a second building element.
The first construction unit is used for defining the distribution and the structured meteorological time series of the photovoltaic power station as an undirected graph Gt＝(Vtε, W) in which VtIs a finite vertex, each vertex representing a meteorological factor at time t, ε is a set of edges, and W is the adjacency matrix of the graph. The meteorological factor data prediction on the graph can be written as Eq (11):
the second construction unit is used for popularizing the Graph Convolutional Network (GCN) into a graph domain by calculating the CNN in the spectrum domain by graph Fourier transform. It can be written as Eq (12),
gθ(·)*x＝Ugθ(Λ)UTx (12)
where U is the eigenvector matrix L ═ I of the normalized graph LaplacianN-D-1/ 2AD-1/ 2U Λ U, its diagonal matrix of eigenvalues Λ, UTx is the graph fourier transform of x.
The data are organized into a map according to the distribution condition of the photovoltaic power station, the spatial information can be effectively utilized, meanwhile, map convolution operation is directly applied to the structured data, and deep patterns and features of a spatial domain are extracted. However, matrix multiplication of eigenvectors in Eq (2) can be computationally expensive for large graphs, and Chebyshev polynomial approximation and hierarchical linear equations can be used to overcome this problem.
To reduce the time complexity, the filter is composed of Chebyshev polynomials Tk(x) Is approximately to KthNext, the process is carried out. We can then rewrite the graph convolution to Eq (13),
wherein,
can pass through (UΛ U)T)k＝UΛkUTTo calculate. The time complexity of Eq (13) can be reduced by computing the K-partial convolution by polynomial approximation.
By limiting K to 1, the graph convolution function can become linear on graph laplace. In addition, since the neural network can adapt to scale changes, we can adapt to λmaxAn approximation is made 2. It can be written as Eq (14),
wherein theta is0And theta1Are two shared filter parameters. To reduce the occurrence of overfitting and numerical manipulation, θ can be0And theta1By exchanging for a parameter theta, let theta be theta0＝-；
And
it can be written as Eq (15),
the second construction module is used for constructing a hole convolution model by modeling the solar irradiance time-series data of each target area in time.
It is known that RNN-like models are always time-consuming and cannot cope with variable data due to complex gating mechanisms, while CNNs have the advantage of fast training and can implement parallel training processes by stacking convolutional layers. Therefore, we apply the data with K in the time dimension of the input photovoltaic power plant and the solar irradiance data of the surrounding areatGated linear units of width kernel and one-dimensional dilation convolution.
The hole convolution can enlarge the field of view, and in the deep network, down-sampling is always performed to increase the field of view and reduce the amount of calculation, so that although the field of view can be increased, the spatial resolution is reduced. In order not to lose resolution and still enlarge the field of view, hole convolution can be used. This is useful in the prediction task. On one hand, the solar irradiance data in a wider space-time field can be learned due to the large receptive field, and on the other hand, the time point can be accurately positioned and predicted due to the high resolution.
Meanwhile, it can capture multi-scale context information: the cavity convolution has a parameter which can be set, and the specific meaning is that a plurality of 0 s are filled in a convolution kernel, so when different proportions are set, the receptive fields are different, and multi-scale solar irradiance data information is obtained. The input to the model may be considered solar irradiance sequence data of length M
Has CiA channel, a core size of
The module includes a third building element and a fourth building element. The third construction unit is used for performing two times of expansion convolution operations after entering the model to obtain two products with the same size
To output of (c). It can be written as Eq (21),
where d is a dilution parameter that controls the skip distance,
is a nucleus, xtIs the t-th value of the sequence x.
And the fourth construction unit is used for respectively carrying out sigmoid function and fusion operation on the A and the B, and finally carrying out Hadamard product to obtain a result. The sigmoid function helps to filter the inputs that help to discover the dynamically changing pattern of data, while the nonlinear gate can capture general information of the data. Finally, the time convolution can be defined as
Wherein σ is a sigmoid function;
is an element-level Hadamard product operation.
After the solar irradiance data are respectively modeled in the space-time field, the meteorological data can be processed through the mixed model.
Compared with the prior art, the application has the advantages and effects that:
the application relates to a short-term solar irradiance prediction method based on a GCN and a hole convolution mixed model, which uses a space-time joint characteristic to predict solar irradiance of a missing area for five days in the future. Since solar irradiance can only be collected during the day, the predicted time period is 8:00-18:00 per day, and the predicted results for the next 5 days are shown in table 1. Wherein, No-Spa is a method for removing the GCN model. The results for No-Spa are much more accurate than GRU and CNN, indicating that the GCN model can extract more global spatial information. The prediction result of the method (STM) is superior to that of No-Spa, and the fact that the capture of spatial and temporal characteristics is very important and effective for predicting solar irradiance is proved.
TABLE 2 solar irradiance prediction results
Visualization of the prediction results as shown in fig. 3, it can be seen that the prediction curve of the present method (STM) is closest to the true value curve of solar irradiance, while the curves of the other methods differ greatly from the true value curve in the regions where the fluctuation and oscillation occur.
EXAMPLE III
The present embodiment provides an electronic device that can be understood as a computer or server having data calculation and information processing. The electronic equipment is provided with the device for predicting the short-term solar radiation intensity of the embodiment. The device is used for acquiring meteorological data of a target area; and processing the meteorological data by using a hybrid prediction model which is formed by mixing and constructing a GCN model and a cavity convolution model to obtain the short-term solar radiation intensity of the target area in a future period. According to the method, the meteorological data of the modular photovoltaic power station and the surrounding area of the modular photovoltaic power station are processed in two dimensions of time and space, the short-term solar irradiation intensity is predicted by fusing the two parts, and a control basis is provided for stable operation of a power system.
Example four
Fig. 6 is a block diagram of an electronic device according to an embodiment of the present application.
As shown in fig. 6, the electronic device provided in the present embodiment may be understood as a computer or a server having data calculation and information processing. The electronic device comprises at least a processor 101 and a memory 102, which are connected by a data bus 103.
The memory is used for storing corresponding computer programs or instructions, and the processor is used for executing the computer programs or instructions so as to enable the electronic equipment to realize the method for predicting the solar short-term irradiation intensity, which is described in the embodiment. The method is specifically used for acquiring meteorological data of a target area; and processing the meteorological data by using a hybrid prediction model which is formed by mixing and constructing a GCN model and a cavity convolution model to obtain the short-term solar radiation intensity of the target area in a future period. According to the method, the meteorological data of the modular photovoltaic power station and the surrounding area of the modular photovoltaic power station are processed in two dimensions of time and space, the short-term solar irradiation intensity is predicted by fusing the two parts, and a control basis is provided for stable operation of a power system.
The embodiments in the present specification are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, apparatus, or computer program product. Accordingly, embodiments of the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, embodiments of the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
Embodiments of the present invention are described with reference to flowchart illustrations and/or block diagrams of methods, terminal devices (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing terminal to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing terminal, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing terminal to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing terminal to cause a series of operational steps to be performed on the computer or other programmable terminal to produce a computer implemented process such that the instructions which execute on the computer or other programmable terminal provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
While preferred embodiments of the present invention have been described, additional variations and modifications of these embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including preferred embodiments and all such alterations and modifications as fall within the scope of the embodiments of the invention.
Finally, it should also be noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or terminal that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or terminal. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or terminal that comprises the element.
The technical solutions provided by the present invention are described in detail above, and the principle and the implementation of the present invention are explained in this document by applying specific examples, and the descriptions of the above examples are only used to help understanding the method and the core idea of the present invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present invention.
Claims (10)
1. A method for predicting short-term solar radiation intensity is applied to electronic equipment and is characterized by comprising the following steps:
acquiring meteorological data of a target area;
and processing the meteorological data based on a pre-constructed hybrid prediction model to obtain the short-term solar radiation intensity of the target area in a future period, wherein the hybrid prediction model is constructed by mixing a GCN model and a cavity convolution model.
2. The prediction method of claim 1, further comprising the steps of:
constructing the GCN model based on meteorological factor data of surrounding areas of the spatially combined photovoltaic power station;
the hole convolution model is constructed by modeling temporally the solar irradiance time series data for each of the surrounding regions.
3. The prediction method of claim 2, wherein said constructing the GCN model based on spatially integrating meteorological factor data for a surrounding area of a photovoltaic power plant comprises the steps of:
firstly, the distribution and the structured meteorological time series of the photovoltaic power station are defined as an undirected graph Gt＝(Vtε, W) in which VtIs a finite vertex, each vertex represents a meteorological factor at time t, ε is a set of edges, and W is the adjacency matrix of the graph;
and then, calculating the graph convolution network by using graph Fourier transform in a spectrum domain, and popularizing the CNN into the graph domain to obtain the GCN model.
4. The prediction method of claim 2, wherein said constructing said hole convolution model by modeling temporally solar irradiance time series data for each of said surrounding regions comprises the steps of:
firstly, two expanding convolution operations are respectively carried out to obtain two products with the same size
The A and the B respectively obtain results through a sigmoid function and fusion operation, and finally Hadamard product is carried out;
then, the time convolution can be defined as
Obtaining the cavity convolution model, wherein sigma is sigmoid function,
is an element-level Hadamard product operation.
5. A device for predicting short-term solar radiation intensity is applied to electronic equipment, and is characterized by comprising:
a data acquisition module configured to acquire meteorological data of a target area;
and the prediction execution module is configured to process the meteorological data based on a pre-constructed hybrid prediction model to obtain the short-term solar radiation intensity of the target area in a future period, and the hybrid prediction model is constructed by mixing a GCN model and a void convolution model.
6. The prediction apparatus of claim 5, further comprising:
a first construction module configured to construct the GCN model based on meteorological factor data spatially combined with surrounding areas of a photovoltaic power plant;
a second construction module configured to construct the hole convolution model by modeling temporally the solar irradiance time series data for each of the surrounding regions.
7. The prediction apparatus of claim 6, wherein the first construction module comprises:
a first construction unit for defining the distribution and the structured meteorological time series of the photovoltaic power station as an undirected graph Gt＝(Vtε, W) in which VtIs a finite vertex, each vertex represents a meteorological factor at time t, ε is a set of edges, and W is the adjacency matrix of the graph;
and the second construction unit is used for calculating the graph convolution network by using graph Fourier transform in a spectrum domain, and popularizing the CNN into the graph domain to obtain the GCN model.
8. The prediction apparatus of claim 6, wherein the second construction module comprises:
a third construction unit for performing two times of dilation convolution operations to obtain two signals with the same size
The A and the B respectively obtain results through a sigmoid function and fusion operation, and finally Hadamard product is carried out;
a fourth construction unit for defining the time convolution as
Obtaining the cavity convolution model, wherein sigma is sigmoid function,
is an element-level Hadamard product operation.
9. An electronic device, characterized in that a prediction apparatus according to any one of claims 5 to 8 is provided.
10. An electronic device, characterized in that at least one processor and a memory connected to the processor are provided, wherein:
the memory is for storing a computer program or instructions;
the processor is for the computer program or instructions to cause the electronic device to implement the prediction method of any one of claims 1 to 4.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115618714A (en) * | 2022-09-09 | 2023-01-17 | 北京思源知行科技发展有限公司 | Solar radiation probability forecasting method and related equipment |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20170351970A1 (en) * | 2016-06-07 | 2017-12-07 | International Business Machines Corporation | Solar irradiation modeling and forecasting using community based terrestrial sky imaging |
| CN111260154A (en) * | 2020-02-17 | 2020-06-09 | 河海大学 | Short-term solar radiation prediction method and device based on CNN-LSTM |
| CN111540199A (en) * | 2020-04-21 | 2020-08-14 | 浙江省交通规划设计研究院有限公司 | High-speed traffic flow prediction method based on multi-mode fusion and graph attention machine mechanism |
| CN111563611A (en) * | 2020-04-13 | 2020-08-21 | 北京工业大学 | Cloud data center renewable energy space-time prediction method for orientation graph convolutional network |
| CN111814398A (en) * | 2020-07-08 | 2020-10-23 | 国网河北省电力有限公司 | Map-based earth surface solar radiance prediction method integrating space-time attention |
-
2022
- 2022-01-26 CN CN202210094146.1A patent/CN114282737A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20170351970A1 (en) * | 2016-06-07 | 2017-12-07 | International Business Machines Corporation | Solar irradiation modeling and forecasting using community based terrestrial sky imaging |
| CN111260154A (en) * | 2020-02-17 | 2020-06-09 | 河海大学 | Short-term solar radiation prediction method and device based on CNN-LSTM |
| CN111563611A (en) * | 2020-04-13 | 2020-08-21 | 北京工业大学 | Cloud data center renewable energy space-time prediction method for orientation graph convolutional network |
| CN111540199A (en) * | 2020-04-21 | 2020-08-14 | 浙江省交通规划设计研究院有限公司 | High-speed traffic flow prediction method based on multi-mode fusion and graph attention machine mechanism |
| CN111814398A (en) * | 2020-07-08 | 2020-10-23 | 国网河北省电力有限公司 | Map-based earth surface solar radiance prediction method integrating space-time attention |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115618714A (en) * | 2022-09-09 | 2023-01-17 | 北京思源知行科技发展有限公司 | Solar radiation probability forecasting method and related equipment |
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