CN114819257A - Irradiation calculation method, device and equipment - Google Patents
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Abstract
The application discloses an irradiation calculation method, device and equipment. Wherein, the method comprises the following steps: acquiring satellite cloud picture characteristic data of a first area; acquiring first ground meteorological monitoring data of a first area; acquiring second ground meteorological monitoring data of a second area surrounding the first area; and inputting the satellite cloud picture characteristic data, the first ground meteorological monitoring data and the second ground meteorological monitoring data into a pre-trained irradiation calculation model, and outputting through the irradiation calculation model to obtain a target irradiation value of the first area. The method and the device solve the technical problem that the result error is large when the irradiation calculation is carried out in the related technology.
Description
Technical Field
The application relates to the technical field of photovoltaic power generation, in particular to an irradiation calculation method, device and equipment.
Background
In a photovoltaic power generation system, the output power of the system depends on the solar irradiation amount received by a solar panel to a great extent, so that accurate calculation of irradiation values in a photovoltaic power station area is required to accurately predict the output power of the photovoltaic power station.
At present, calculation is mainly performed in the industry based on satellite data, however, earth surface irradiation changes continuously and instantaneously in real time under many conditions, and because the satellite data is limited by space-time resolution and is influenced by superposition of high altitude and earth surface irradiation deviation, a single irradiation calculation model based on the satellite data has large errors under complex weather conditions.
In view of the above problems, no effective solution has been proposed.
Disclosure of Invention
The embodiment of the application provides an irradiation calculation method, an irradiation calculation device and irradiation calculation equipment, which are used for at least solving the technical problem of larger result error in irradiation calculation in the related technology.
According to an aspect of an embodiment of the present application, there is provided an irradiation calculation method including: acquiring satellite cloud picture characteristic data of a first area; acquiring first ground meteorological monitoring data of the first area; acquiring second ground meteorological monitoring data of a second area surrounding the first area; inputting the satellite cloud picture characteristic data, the first ground meteorological monitoring data and the second ground meteorological monitoring data into a pre-trained irradiation calculation model, and outputting through the irradiation calculation model to obtain a target irradiation value of the first area.
Optionally, the acquiring satellite cloud map feature data of the first region includes: acquiring a satellite cloud picture of the first area; inputting the satellite cloud picture into a pre-trained target neural network, and outputting to obtain the satellite cloud picture characteristic data, wherein the satellite cloud picture characteristic data at least comprises: the type, thickness, moving speed and moving direction of the cloud layer, and the longitude and latitude of the first area.
Optionally, the obtaining first ground meteorological monitoring data for the first area comprises: acquiring the first ground meteorological monitoring data monitored by the environmental monitoring equipment in the first area, wherein the first ground meteorological monitoring data at least comprises: the wind speed, wind direction, temperature, humidity, air pressure of the first area; acquiring second terrestrial meteorological monitoring data of a second area surrounding the first area comprises: acquiring the second meteorology monitoring data monitored by the environment monitoring equipment in the second area, wherein the second meteorology monitoring data at least comprises: and the wind speed, wind direction, temperature, humidity, air pressure and irradiation value of the second area.
Optionally, the training process of the irradiation calculation model includes: acquiring multiple groups of sample data acquired at multiple preset moments, wherein each group of sample data comprises: the satellite cloud picture characteristic data, the first ground meteorological monitoring data, the second ground meteorological monitoring data and the irradiation value of the first area central point are acquired at the same preset time; training a target neural network model based on the multiple groups of sample data to obtain the irradiation calculation model, wherein the target neural network model at least comprises: an attention layer, a recurrent neural network layer, and a fully-connected layer, wherein the recurrent neural network layer comprises one of: long and short term memory network, gate control circulation unit.
Optionally, for any group of sample data, performing normalization processing on the sample data; inputting the satellite cloud picture characteristic data, the first ground meteorological monitoring data, the second ground meteorological monitoring data and the irradiation value of the central point of the first area corresponding to the sample data into the attention layer to obtain a first output matrix, wherein the irradiation value of the central point of the first area is used as a sample label; inputting the first output matrix into the recurrent neural network layer to obtain a second output matrix; inputting the second output matrix into the full-connection layer to obtain a prediction result; adjusting model parameters of the target neural network model based on the sample labels and the prediction results; and performing iterative training on the target neural network model based on the multiple groups of sample data, determining target model parameters, and obtaining the irradiation calculation model.
Optionally, a first input feature is determined based on the satellite cloud image feature data, a second input feature is determined based on the first ground meteorological monitoring data, a third input feature is determined based on the second ground meteorological monitoring data, and an irradiation value of a central point of the first area is used as a sample tag to construct an input matrix; copying the input matrix through the attention layer to obtain a first matrix, a second matrix and a third matrix; performing dot product calculation on the transposed matrixes of the first matrix and the second matrix to obtain a first result; zooming the first result based on a preset zooming coefficient to obtain a second result; performing softmax processing on the second result to obtain a third result; and carrying out tensor multiplication calculation on the third result and the third matrix to obtain the first output matrix.
Optionally, determining a first preset weight corresponding to the satellite cloud picture characteristic data, a second preset weight corresponding to the first terrestrial meteorological monitoring data, and a third preset weight corresponding to the second terrestrial meteorological monitoring data; determining the first input feature based on the first preset weight and the satellite cloud picture feature data; determining the second input feature based on the second preset weight and the first ground meteorological monitoring data; and determining the third input characteristic based on the third preset weight and the second terrestrial weather monitoring data.
Optionally, the multiple groups of sample data are sequentially input into the target neural network model for iterative training, a target loss function is constructed, and model parameters of the target neural network model are dynamically optimized based on a gradient descent optimization algorithm to obtain the target model parameters, wherein the target loss function includes one of the following: mean square error loss function, cross entropy loss function.
Optionally, the first area is an area where a solar panel of the photovoltaic power station is located, and after a target irradiation value of the first area is obtained through the irradiation calculation model output, target output power of the photovoltaic power station in a future target time period is predicted based on the target irradiation value, wherein the target output power is used for evaluating reliability of the photovoltaic power station accessing to a power grid.
According to another aspect of the embodiments of the present application, there is also provided an irradiation calculation apparatus, including: the first acquisition module is used for acquiring satellite cloud picture characteristic data of a first area; the second acquisition module is used for acquiring first ground meteorological monitoring data of the first area; the third acquisition module is used for acquiring second ground meteorological monitoring data of a second area around the first area; and the calculation module is used for inputting the satellite cloud picture characteristic data, the first ground meteorological monitoring data and the second ground meteorological monitoring data into a pre-trained irradiation calculation model, and outputting the irradiation value of the first area through the irradiation calculation model.
According to another aspect of the embodiments of the present application, there is also provided a non-volatile storage medium, which includes a stored program, wherein when the program runs, a device in which the non-volatile storage medium is located is controlled to execute the irradiation calculation method.
According to another aspect of the embodiments of the present application, there is also provided an irradiation computing apparatus, including: a memory in which a computer program is stored, and a processor configured to execute the irradiation calculation method described above by the computer program.
In the embodiment of the application, the satellite cloud picture characteristic data of a first area, the first ground meteorological monitoring data of the first area and the second ground meteorological monitoring data of a second area surrounding the first area are firstly obtained, then the satellite cloud picture characteristic data, the first ground meteorological monitoring data and the second ground meteorological monitoring data are input into a pre-trained irradiation calculation model, and a target irradiation value of the first area is obtained through the output of the irradiation calculation model. The irradiation calculation model comprehensively calculates the irradiation value based on the satellite observation data and the ground monitoring data, simultaneously takes the advantages of large satellite observation coverage rate and high ground monitoring accuracy into consideration, can realize mutual correction of the satellite observation data and the ground monitoring data, effectively improves the irradiation calculation precision, and solves the technical problem of large result error in irradiation calculation in the related technology.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the application and together with the description serve to explain the application and not to limit the application. In the drawings:
FIG. 1 is a schematic flow chart diagram of an irradiance calculation method according to an embodiment of the present application;
FIG. 2 is a schematic diagram of sample data collection according to an embodiment of the present application;
FIG. 3 is a schematic structural diagram of an irradiation computing device according to an embodiment of the present application.
Detailed Description
In order to make the technical solutions better understood by those skilled in the art, 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 partial embodiments of the present application, but not all 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.
It should be noted that the terms "first," "second," and the like in the description and claims of this application and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the application described herein are capable of operation in sequences other than those illustrated or described herein. Furthermore, the terms "comprises," "comprising," and any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
Example 1
Generally, earth surface irradiation is continuously and instantaneously changed in real time, and due to the time-space resolution limit of satellite data and the influence of superposition of high altitude and earth surface irradiation deviation, the single irradiation calculation based on the satellite data has larger error under the condition of complex weather; although the irradiation data based on ground equipment monitoring is simple and accurate, the coverage area of single-point equipment is small, the cost is high if large-area multipoint equipment is required to be installed, and later multipoint equipment is difficult to maintain.
In order to solve the above problem, embodiments of the present application provide a scheme for comprehensively calculating an irradiation value based on satellite observation data and ground monitoring data, which can achieve mutual correction of the satellite observation data and the ground monitoring data. The advantage that it utilizes near source of ground monitoring and the degree of accuracy is high can effectively promote the degree of accuracy when the irradiation calculates, takes into account the big advantage of satellite observation coverage simultaneously, only needs a small amount of irradiation collection equipment of ground installation, can obtain the accurate irradiation data of large tracts of land target area, reduces hardware purchase and later stage manual maintenance cost.
In particular, embodiments of the present application provide an irradiance calculation method, it should be noted that the steps illustrated in the flowchart of the drawings may be performed in a computer system such as a set of computer-executable instructions, and that while a logical order is illustrated in the flowchart, in some cases the steps illustrated or described may be performed in an order different than here.
Fig. 1 is a schematic flowchart of an alternative irradiation calculation method according to an embodiment of the present application, and as shown in fig. 1, the method at least includes steps S102-S108, where:
and S102, acquiring satellite cloud picture characteristic data of the first area.
Step S104, first ground meteorological monitoring data of the first area are obtained.
And S106, acquiring second ground surface weather monitoring data of a second area around the first area.
And S108, inputting the satellite cloud picture characteristic data, the first ground meteorological monitoring data and the second ground meteorological monitoring data into a pre-trained irradiation calculation model, and outputting through the irradiation calculation model to obtain a target irradiation value of the first area.
Generally, the purpose of irradiation calculation is to provide reference data for the generated energy revenue of a newly-built photovoltaic power station in a target area by evaluating the irradiation level of the area, or to quantify the generation efficiency and the personnel operation and maintenance level of the built power station, and therefore, the first area mainly refers to the area where the solar panel of the photovoltaic power station is located. In some optional embodiments of the present application, when calculating the irradiation value of the first region, sample data of the first region at different times may be collected first for training the irradiation calculation model.
Specifically, multiple sets of sample data acquired at multiple preset times may be acquired first, where each set of sample data includes: the method comprises the steps of acquiring satellite cloud picture characteristic data, first ground meteorological monitoring data, second ground meteorological monitoring data and an irradiation value of a first region central point at the same preset time; training a target neural network model based on multiple groups of sample data to obtain an irradiation calculation model, wherein the target neural network model at least comprises: an attention layer, a recurrent neural network layer and a fully-connected layer, wherein the recurrent neural network layer comprises one of: long and Short term Memory network LSTM (Long Short Memory network), gate control loop unit GRU (gate recovery Unit).
When acquiring the satellite cloud picture characteristic data of the first area, the satellite cloud picture of the first area, such as the satellite cloud picture transmitted by the wind cloud meteorological satellite, may be acquired first, and then the satellite cloud picture is input to a pre-trained target neural network, and the satellite cloud picture characteristic data is output, where the target neural network is mainly used to extract cloud layer characteristics, and the satellite cloud picture characteristic data at least includes: the cloud layer type, the cloud layer thickness, the cloud layer moving speed, the cloud layer moving direction, the longitude and latitude of the first area, particularly the longitude and latitude of the central point position of the first area and the like.
When the ground meteorological monitoring data is acquired, the influence of the surrounding environment on the irradiation value of the first area is considered, and besides the first ground meteorological monitoring data of the first area, second ground meteorological monitoring data of a second area surrounding the first area also needs to be acquired.
Specifically, when acquiring the first ground meteorological monitoring data of the first area, the first ground meteorological monitoring data monitored by the environment monitoring device (such as an irradiation monitor) in the first area may be acquired, where the first ground meteorological monitoring data at least includes: wind speed, wind direction, temperature, humidity, air pressure and the like of the first area.
When acquiring the second meteorology monitoring data of the second area surrounding the first area, it may be determined that the surrounding area within a preset range (e.g. within 80 km) from the first area is the second area, and the second meteorology monitoring data monitored by the environment monitoring device in the second area is acquired, wherein the second meteorology monitoring data at least includes: and the wind speed, wind direction, temperature, humidity, air pressure, irradiation value and the like of the second area.
Meanwhile, the irradiation value of the central point of the first area is obtained through the irradiation monitor of the central point of the first area, and the value can be used as a true value of the irradiation value of the first area and used as a sample label.
FIG. 2 is a schematic diagram illustrating sample data acquisition, wherein satellite cloud characteristic data of a first region is obtained by acquiring a satellite cloud transmitted by a satellite and performing characteristic extraction; acquiring first ground meteorological monitoring data of a first area through an irradiation monitor 1 of the first area; and acquiring second ground meteorological monitoring data of the second area through the irradiation monitors 2-9 of the second area around the ground meteorological monitoring system. It can be seen that the quantity of the irradiation monitors required when the ground meteorological monitoring data is acquired is not large, and the irradiation monitors only need to be uniformly distributed.
When the sample data is collected, the collection time and the collection frequency can be set, for example, a target time period is determined, and the sample data is collected every 20s in the target time period, so that a plurality of groups of sample data corresponding to a plurality of preset moments are obtained.
Then, the target neural network model can be iteratively trained based on multiple groups of sample data to obtain a final irradiation calculation model.
In some alternative embodiments of the present application, a target neural network model of the GRU + Attention combination may be constructed based on the pyrch. The pytorech is an open-source Python deep learning framework, can realize strong GPU acceleration and supports a dynamic neural network; the GRU is a variant of the LSTM, mainly comprises an update gate and a reset gate, can better capture the dependence relationship with larger interval in time sequence data compared with the LSTM, and has simpler structure, so the GRU is preferably selected by the recurrent neural network layer; attention is used to improve the interpretability of the target neural network model.
By utilizing the accuracy and the training timeliness advantages of the GRU on sequence data calculation and utilizing the Attention to mine the potential relation before different meteorological data, the input characteristic weight of the target neural network model can be more reasonable, and therefore mutual correction of satellite observation data and ground monitoring data is achieved.
In the model training process, for any group of sample data, firstly, carrying out normalization processing on the group of sample data, and then inputting satellite cloud picture characteristic data, first ground meteorological monitoring data, second ground meteorological monitoring data and the irradiation value of the central point of a first region corresponding to the group of sample data into Attention layer Attention to obtain a first output matrix, wherein the irradiation value of the central point of the first region is used as a sample label.
Optionally, in the Attention, a first input feature may be determined based on the satellite cloud image feature data, a second input feature may be determined based on the first terrestrial meteorological monitoring data, a third input feature may be determined based on the second terrestrial meteorological monitoring data, and the irradiation value of the first area center point may be used as a sample tag to construct the input matrix.
Considering that the influence degrees of the satellite data and the ground monitoring data on the final irradiation value are different, different weights can be set for the satellite data and the ground monitoring data. Specifically, a first preset weight corresponding to the satellite cloud picture characteristic data, a second preset weight corresponding to the first terrestrial meteorological monitoring data, and a third preset weight corresponding to the second terrestrial meteorological monitoring data may be determined; then, determining a first input characteristic based on the first preset weight and the satellite cloud picture characteristic data; determining a second input characteristic based on a second preset weight and the first ground meteorological monitoring data; and determining a third input characteristic based on the third preset weight and the second ground meteorological monitoring data. Generally, the first predetermined weight is greater than the second predetermined weight and greater than the third predetermined weight.
Then, the first input feature may be recorded as module data 1, the second input feature may be recorded as module data 2, the third input feature may be recorded as module data 3, the irradiation value of the center point of the first region may be used as a sample label and recorded as Y-L, and an input matrix tensor may be constructed in a format of [ [ module data 1, module data 2, and module data 3]]，Y-L](ii) a Then copying an input matrix tensor through Attention layer Attention to obtain a first matrix Q, a second matrix K and a third matrix V; performing dot product calculation on the transposed matrix K of the first matrix Q and the second matrix K to obtain a first result; based on preset scaling factor
(where d is the length of the first matrix Q) scaling the first result to obtain a second result; performing softmax processing on the second result to obtain a third result; carrying out tensor multiplication calculation on the third result and the third matrix to obtain a first output matrix Attention out The specific calculation formula is as follows:
then, inputting the first output matrix into a recurrent neural network layer, namely inputting the first output matrix into a GRU (generalized regression Unit), and obtaining a second output matrix, wherein a dropout function is usually nested in the GRU layer, and the specific proportion can be selected to be 0.5; then inputting the second output matrix into the full-connection layer to obtain a prediction result; adjusting model parameters of the target neural network model based on the sample label and the prediction result; and performing iterative training on the target neural network model based on multiple groups of sample data, determining final target model parameters, and obtaining an irradiation calculation model.
During iterative training, a target loss function, such as a commonly used mean square error loss function MSE or a Cross Entropy loss function Cross Engine, can be constructed, and model parameters of a target neural network model are dynamically optimized based on a gradient descent optimization algorithm to obtain final target model parameters. For example, 500 rounds of iterative training can be performed on the target neural network model, model parameters are dynamically optimized through a commonly used Adam optimization algorithm, and a model parameter file is generated after model training is completed to obtain a final irradiation calculation model.
After the irradiation calculation model is obtained, when the irradiation value of the first area at the target moment needs to be calculated, the target irradiation value of the first area at the target moment can be obtained only by acquiring the satellite cloud picture characteristic data, the first ground meteorological monitoring data and the second ground meteorological monitoring data of the first area at the target moment and inputting the data into the trained irradiation calculation model. Because the data are obtained by mutual correction based on satellite observation data and ground monitoring data, the accuracy is higher, and the quantity of the requirements on ground environment monitoring hardware equipment is not high.
After the target irradiation value of the first area is obtained, the target output power of the photovoltaic power station in a future target time period can be predicted based on the target irradiation value, the reliability of the photovoltaic power station accessing to the power grid can be evaluated through the target output power, and a basis is provided for the photovoltaic power station accessing to the power grid.
In the embodiment of the application, satellite cloud picture characteristic data of a first area, first ground meteorological monitoring data of the first area and second ground meteorological monitoring data of a second area surrounding the first area are obtained firstly, then the satellite cloud picture characteristic data, the first ground meteorological monitoring data and the second ground meteorological monitoring data are input into a pre-trained irradiation calculation model, and a target irradiation value of the first area is obtained through output of the irradiation calculation model. The irradiation calculation model comprehensively calculates the irradiation value based on the satellite observation data and the ground monitoring data, simultaneously takes the advantages of high satellite observation coverage rate and high ground monitoring accuracy into consideration, can realize mutual correction of the satellite observation data and the ground monitoring data, effectively improves the irradiation calculation precision, and solves the technical problem of large result error in irradiation calculation in the related technology.
Example 2
According to an embodiment of the present application, there is also provided an irradiation calculation apparatus for implementing the irradiation calculation method, as shown in fig. 3, the apparatus at least includes a first obtaining module 30, a second obtaining module 32, a third obtaining module 34 and a calculating module 36, where:
the first obtaining module 30 is configured to obtain satellite cloud map feature data of a first area.
The second obtaining module 32 is configured to obtain first ground weather-monitoring data of the first area.
The third obtaining module 34 is configured to obtain second ground surface weather monitoring data of a second area around the first area.
And the calculation module 36 is configured to input the satellite cloud image characteristic data, the first ground meteorological monitoring data, and the second ground meteorological monitoring data into a pre-trained irradiation calculation model, and output the irradiation value of the first region through the irradiation calculation model.
In some optional embodiments of the present application, the first region mainly refers to a region where a solar panel of the photovoltaic power station is located, and when an irradiation value of the first region is calculated, sample data of the first region at different times may be collected first for training an irradiation calculation model.
Specifically, when the satellite cloud picture feature data of the first area is obtained, the first obtaining module may first obtain the satellite cloud picture of the first area, for example, obtain the satellite cloud picture transmitted by the weather satellite of wind and cloud, then input the satellite cloud picture into a pre-trained target neural network, and output the satellite cloud picture feature data, where the target neural network is mainly used to extract cloud layer features, and the satellite cloud picture feature data at least includes: the cloud layer type, the cloud layer thickness, the cloud layer moving speed, the cloud layer moving direction, the longitude and latitude of the first area, particularly the longitude and latitude of the central point position of the first area and the like.
In acquiring the first ground meteorological monitoring data of the first area, the second acquisition module may acquire the first ground meteorological monitoring data monitored by an environmental monitoring device (e.g., an irradiation monitor) in the first area, wherein the first ground meteorological monitoring data at least includes: wind speed, wind direction, temperature, humidity, air pressure and the like of the first area.
When acquiring the second meteorology monitoring data of the second area surrounding the first area, the third acquiring module may determine the surrounding area within a preset range (e.g. within 80 km) from the first area as the second area, and acquire the second meteorology monitoring data monitored by the environment monitoring device in the second area, wherein the second meteorology monitoring data at least includes: and the wind speed, wind direction, temperature, humidity, air pressure, irradiation value and the like of the second area.
Optionally, the irradiation computing apparatus further includes a model training module, configured to acquire multiple sets of sample data acquired at multiple preset times, where each set of sample data includes: the method comprises the steps of acquiring satellite cloud picture characteristic data, first ground meteorological monitoring data, second ground meteorological monitoring data and an irradiation value of a first region central point at the same preset time; training a target neural network model based on multiple groups of sample data to obtain an irradiation calculation model, wherein the target neural network model at least comprises: the system comprises an attention layer, a recurrent neural network layer and a full connection layer, wherein the recurrent neural network layer comprises one of the following: long and short term memory networks LSTM, gated loop units GRU.
The irradiation calculation device further comprises a fourth acquisition module, wherein the fourth acquisition module is used for acquiring the irradiation value of the first area central point through the irradiation monitor of the first area central point, and the value can be used as a real value of the irradiation value of the first area and used as a sample label.
When the model training module collects sample data, the acquisition time and the acquisition frequency can be set, for example, a target time period is determined, and the sample data is collected every 20s in the target time period, so that multiple groups of sample data corresponding to multiple preset moments are obtained.
After sample data is obtained, the model training module can perform iterative training on the target neural network model based on multiple groups of sample data to obtain a final irradiation calculation model.
In some alternative embodiments of the present application, a target neural network model of the GRU + Attention combination may be constructed based on the pyrch. The pytorech is an open-source Python deep learning framework, can realize strong GPU acceleration and supports a dynamic neural network; the GRU is a variant of the LSTM, mainly comprises an update gate and a reset gate, can better capture the dependence relationship with larger interval in time sequence data compared with the LSTM, and has simpler structure, so the GRU is preferably selected by the recurrent neural network layer; the Attention is used to improve the interpretability of the target neural network model.
By utilizing the accuracy and the training timeliness advantages of the GRU on sequence data calculation and utilizing the Attention to mine the potential relation before different meteorological data, the input characteristic weight of the target neural network model can be more reasonable, and therefore mutual correction of satellite observation data and ground monitoring data is achieved.
In the model training process, for any group of sample data, firstly carrying out normalization processing on the group of sample data, and then inputting satellite cloud picture characteristic data, first ground meteorological monitoring data, second ground meteorological monitoring data and the irradiation value of the central point of a first region corresponding to the group of sample data into the Attention layer Attention to obtain a first output matrix, wherein the irradiation value of the central point of the first region is used as a sample label.
Optionally, in the Attention, a first input feature may be determined based on the satellite cloud image feature data, a second input feature may be determined based on the first terrestrial meteorological monitoring data, a third input feature may be determined based on the second terrestrial meteorological monitoring data, and the irradiation value of the first area center point may be used as a sample tag to construct the input matrix.
Considering that the influence degrees of the satellite data and the ground monitoring data on the final irradiation value are different, different weights can be set for the satellite data and the ground monitoring data. Specifically, a first preset weight corresponding to the satellite cloud picture characteristic data, a second preset weight corresponding to the first terrestrial meteorological monitoring data, and a third preset weight corresponding to the second terrestrial meteorological monitoring data may be determined; then, determining a first input characteristic based on the first preset weight and the satellite cloud picture characteristic data; determining a second input characteristic based on a second preset weight and the first ground meteorological monitoring data; and determining a third input characteristic based on the third preset weight and the second ground meteorological monitoring data. Generally, the first predetermined weight is greater than the second predetermined weight and greater than the third predetermined weight.
Then, the first input feature may be recorded as module data 1, the second input feature may be recorded as module data 2, the third input feature may be recorded as module data 3, the irradiation value of the first area center point is used as a sample label and recorded as Y-L, and an input matrix tensor is constructed in a format of [ [ module data 1, module data 2, module data 3], Y-L ]; then copying an input matrix tensor through Attention layer Attention to obtain a first matrix Q, a second matrix K and a third matrix V; performing dot product calculation on the transposed matrix K of the first matrix Q and the second matrix K to obtain a first result; scaling the first result based on a preset scaling factor √ d (where d is the length of the first matrix Q), obtaining a second result; performing softmax processing on the second result to obtain a third result; carrying out tensor multiplication calculation on the third result and the third matrix to obtain a first output matrix Attention _ out, wherein the specific calculation formula is as follows:
then, inputting the first output matrix into a recurrent neural network layer, namely inputting the first output matrix into a GRU (generalized regression Unit), and obtaining a second output matrix, wherein a dropout function is usually nested in the GRU layer, and the specific proportion can be selected to be 0.5; then inputting the second output matrix into the full-connection layer to obtain a prediction result; adjusting model parameters of the target neural network model based on the sample label and the prediction result; and performing iterative training on the target neural network model based on multiple groups of sample data, determining final target model parameters, and obtaining an irradiation calculation model.
During iterative training, a target loss function, such as a commonly used mean square error loss function MSE or a Cross Entropy loss function Cross Engine, can be constructed, and model parameters of a target neural network model are dynamically optimized based on a gradient descent optimization algorithm to obtain final target model parameters. For example, 500 rounds of iterative training can be performed on the target neural network model, model parameters are dynamically optimized through a commonly used Adam optimization algorithm, and a model parameter file is generated after model training is completed to obtain a final irradiation calculation model.
After the irradiation calculation model is obtained, when the irradiation value of the first area at the target moment needs to be calculated, the target irradiation value of the first area at the target moment can be obtained only by acquiring the satellite cloud picture characteristic data, the first ground meteorological monitoring data and the second ground meteorological monitoring data of the first area at the target moment and inputting the data into the trained irradiation calculation model. Because the data are obtained by mutual correction based on satellite observation data and ground monitoring data, the accuracy is higher, and the quantity of the requirements on ground environment monitoring hardware equipment is not high.
Optionally, the irradiation calculation device further includes a prediction module, and after the target irradiation value of the first region is obtained, the target output power of the photovoltaic power station in a future target time period is predicted based on the target irradiation value, and the reliability of the photovoltaic power station accessing to the power grid can be evaluated through the target output power, so that a basis is provided for the photovoltaic power station accessing to the power grid.
Example 3
According to an embodiment of the present application, there is also provided a nonvolatile storage medium including a stored program, wherein, when the program is executed, a device in which the nonvolatile storage medium is located is controlled to execute the irradiation calculation method in embodiment 1.
There is also provided, in accordance with an embodiment of the present application, an irradiation computing apparatus, including: a memory in which a computer program is stored, and a processor configured to execute the irradiation calculation method in embodiment 1 by the computer program.
Specifically, the following steps are implemented when the program runs: acquiring satellite cloud picture characteristic data of a first area; acquiring first ground meteorological monitoring data of a first area; acquiring second ground meteorological monitoring data of a second area surrounding the first area; and inputting the satellite cloud picture characteristic data, the first ground meteorological monitoring data and the second ground meteorological monitoring data into a pre-trained irradiation calculation model, and outputting through the irradiation calculation model to obtain a target irradiation value of the first area.
The above-mentioned serial numbers of the embodiments of the present application are merely for description and do not represent the merits of the embodiments.
In the above embodiments of the present application, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.
In the embodiments provided in the present application, it should be understood that the disclosed technology can be implemented in other ways. The above-described embodiments of the apparatus are merely illustrative, and for example, a division of a unit may be a division of a logic function, and an actual implementation may have another division, for example, a plurality of units or components may be combined or may be integrated into another system, or some features may be omitted, or may not be executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, units or modules, and may be in an electrical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application may be substantially implemented or contributed to by the prior art, or all or part of the technical solution may be embodied in a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method of the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic or optical disk, and other various media capable of storing program codes.
The foregoing is only a preferred embodiment of the present application and it should be noted that those skilled in the art can make several improvements and modifications without departing from the principle of the present application, and these improvements and modifications should also be considered as the protection scope of the present application.
Claims (12)
1. An irradiance calculation method, comprising:
acquiring satellite cloud picture characteristic data of a first area;
acquiring first ground meteorological monitoring data of the first area;
acquiring second ground meteorological monitoring data of a second area around the first area;
inputting the satellite cloud picture characteristic data, the first ground meteorological monitoring data and the second ground meteorological monitoring data into a pre-trained irradiation calculation model, and outputting through the irradiation calculation model to obtain a target irradiation value of the first area.
2. The method of claim 1, wherein obtaining satellite cloud characterization data for the first region comprises:
acquiring a satellite cloud picture of the first area;
inputting the satellite cloud picture into a pre-trained target neural network, and outputting to obtain the satellite cloud picture characteristic data, wherein the satellite cloud picture characteristic data at least comprises: the type, thickness, moving speed and moving direction of the cloud layer, and the longitude and latitude of the first area.
3. The method of claim 1,
obtaining first ground meteorological monitoring data for the first region, comprising: acquiring the first ground meteorological monitoring data monitored by the environmental monitoring equipment in the first area, wherein the first ground meteorological monitoring data at least comprises: the wind speed, wind direction, temperature, humidity, air pressure of the first area;
acquiring second terrestrial meteorological monitoring data of a second area surrounding the first area comprises: acquiring the second meteorology monitoring data monitored by the environment monitoring equipment in the second area, wherein the second meteorology monitoring data at least comprises: and the wind speed, wind direction, temperature, humidity, air pressure and irradiation value of the second area.
4. The method of claim 1, wherein the training process of the irradiance calculation model comprises:
acquiring multiple groups of sample data acquired at multiple preset moments, wherein each group of sample data comprises: the satellite cloud picture characteristic data, the first ground meteorological monitoring data, the second ground meteorological monitoring data and the irradiation value of the first area central point are acquired at the same preset time;
training a target neural network model based on the multiple groups of sample data to obtain the irradiation calculation model, wherein the target neural network model at least comprises: an attention layer, a recurrent neural network layer, and a fully-connected layer, wherein the recurrent neural network layer comprises one of: long and short term memory network, gate control circulation unit.
5. The method of claim 4, wherein training a target neural network model based on the plurality of sets of sample data to obtain the irradiance calculation model comprises:
for any group of sample data, carrying out normalization processing on the sample data;
inputting the satellite cloud picture characteristic data, the first ground meteorological monitoring data, the second ground meteorological monitoring data and the irradiation value of the central point of the first area corresponding to the sample data into the attention layer to obtain a first output matrix, wherein the irradiation value of the central point of the first area is used as a sample label;
inputting the first output matrix into the recurrent neural network layer to obtain a second output matrix;
inputting the second output matrix into the full-connection layer to obtain a prediction result;
adjusting model parameters of the target neural network model based on the sample labels and the prediction results;
and performing iterative training on the target neural network model based on the multiple groups of sample data, determining target model parameters, and obtaining the irradiation calculation model.
6. The method of claim 5, wherein inputting the satellite cloud characteristic data, the first ground meteorological monitoring data, the second ground meteorological monitoring data, and the irradiance value of the first region center point corresponding to the sample data into the attention layer to obtain a first output matrix comprises:
determining a first input feature based on the satellite cloud picture feature data, determining a second input feature based on the first ground meteorological monitoring data, determining a third input feature based on the second ground meteorological monitoring data, and constructing an input matrix by taking the irradiation value of the central point of the first area as a sample label;
copying the input matrix through the attention layer to obtain a first matrix, a second matrix and a third matrix;
performing dot product calculation on the transposed matrixes of the first matrix and the second matrix to obtain a first result;
zooming the first result based on a preset zooming coefficient to obtain a second result;
performing softmax processing on the second result to obtain a third result;
and carrying out tensor multiplication calculation on the third result and the third matrix to obtain the first output matrix.
7. The method of claim 6, wherein determining a first input feature based on the satellite cloud characteristics data, determining a second input feature based on the first terrestrial weather monitoring data, determining a third input feature based on the second terrestrial weather monitoring data, comprises:
determining a first preset weight corresponding to the satellite cloud picture characteristic data, a second preset weight corresponding to the first ground meteorological monitoring data and a third preset weight corresponding to the second ground meteorological monitoring data;
determining the first input feature based on the first preset weight and the satellite cloud picture feature data;
determining the second input feature based on the second preset weight and the first ground meteorological monitoring data;
and determining the third input characteristic based on the third preset weight and the second terrestrial weather monitoring data.
8. The method of claim 5, wherein iteratively training the target neural network model based on the plurality of sets of sample data to determine target model parameters comprises:
sequentially inputting the multiple groups of sample data into the target neural network model for iterative training, constructing a target loss function, and dynamically optimizing the model parameters of the target neural network model based on an optimization algorithm of gradient descent to obtain the target model parameters, wherein the target loss function comprises one of the following: mean square error loss function, cross entropy loss function.
9. The method of claim 1, wherein the first area is an area where a solar panel of a photovoltaic power station is located, and after the target irradiance value of the first area is obtained through the output of the irradiance calculation model, the method further comprises:
and predicting the target output power of the photovoltaic power station in a future target time period based on the target irradiation value, wherein the target output power is used for evaluating the reliability of the photovoltaic power station accessing to the power grid.
10. An irradiance computing device, comprising:
the first acquisition module is used for acquiring satellite cloud picture characteristic data of a first area;
the second acquisition module is used for acquiring first ground meteorological monitoring data of the first area;
the third acquisition module is used for acquiring second ground meteorological monitoring data of a second area surrounding the first area;
and the calculation module is used for inputting the satellite cloud picture characteristic data, the first ground meteorological monitoring data and the second ground meteorological monitoring data into a pre-trained irradiation calculation model, and obtaining the irradiation value of the first area through the output of the irradiation calculation model.
11. A non-volatile storage medium, comprising a stored program, wherein the program, when executed, controls a device in which the non-volatile storage medium is located to perform the irradiance calculation method according to any one of claims 1 to 9.
12. An irradiated computing device, comprising: a memory in which a computer program is stored, and a processor configured to execute the irradiation calculation method according to any one of claims 1 to 9 by the computer program.
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