CN116739152A - New energy power prediction model construction method and new energy power prediction method - Google Patents
New energy power prediction model construction method and new energy power prediction method Download PDFInfo
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Abstract
The application discloses a new energy power prediction model construction method and a new energy power prediction method, and belongs to the field of new energy. The construction method of the new energy power prediction model comprises the following steps: dividing a target area into a plurality of grids based on longitude and latitude grids of historical weather grid point data; dividing the grids by using a grid clustering method based on the installed capacity and the grid distance of the power station in each grid to obtain a plurality of subareas; and constructing a new energy power prediction model based on the installed capacity of the power station in the subarea, the historical weather grid point data corresponding to at least part of grids in each subarea and the historical total output data corresponding to the target area. The method for constructing the new energy power prediction model can construct the new energy power prediction model, so that the power of the power station in the target area in the future period is predicted based on the new energy power prediction model, and the accuracy and the precision of a power prediction result are improved.
Description
Technical Field
The application belongs to the field of new energy, and particularly relates to a new energy power prediction model construction method and a new energy power prediction method.
Background
For safe and stable operation of the electric power system, the power of the new energy needs to be predicted. In the related art, the sum of power prediction results of a plurality of power stations is determined as the total power of all power stations in a target area, and a common new energy power prediction method needs a large amount of historical power generation power data of the power stations, so that the difficulty is high, and the prediction results are inaccurate.
Disclosure of Invention
The present application aims to solve at least one of the technical problems existing in the prior art. Therefore, the application provides a new energy power prediction model construction method and a new energy power prediction method, which improve the accuracy and precision of the new energy power prediction model; in addition, the historical weather grid point data, the historical total output data and the installed capacity are easy to obtain, a large amount of labor cost is saved, the operation is convenient, and the efficiency of power prediction is improved.
In a first aspect, the present application provides a method for constructing a new energy power prediction model, where the method includes:
Dividing a target area into a plurality of grids based on longitude and latitude grids of historical weather grid point data;
dividing the grids by using a grid clustering method based on the installed capacity and the grid distance of the power station in each grid to obtain a plurality of subareas; the sub-area comprises at least one grid;
a new energy power prediction model is constructed based on the installed capacity of the power station in the subareas, historical weather grid point data corresponding to at least part of grids in each subarea and historical total output data corresponding to the target area; and the historical time period corresponding to the historical weather grid point data is the same as the historical time period corresponding to the historical total output data.
According to the method for constructing the new energy power prediction model, the target area is divided based on the longitude and latitude grids to obtain a plurality of grids, then the grids with the installed capacity being adjacent dense and the grid distance being smaller than the target threshold value are combined into a sub-area, so that a plurality of sub-areas are obtained, the new energy power prediction model is constructed based on the installed capacity of the power station in the sub-areas, the historical weather grid point data corresponding to at least part of the grids in each sub-area and the historical total output data corresponding to the target area, and the accuracy and the precision of the new energy power prediction model are improved; in addition, the historical weather grid point data, the historical total output data and the installed capacity are easy to obtain, a large amount of labor cost is saved, the operation is convenient, and the efficiency of power prediction is improved.
The method for constructing the new energy power prediction model according to an embodiment of the present application divides the plurality of grids by using a grid clustering method based on the installed capacity and the grid distance of the power station in each grid, and obtains a plurality of sub-areas, including:
determining the installed density corresponding to each grid based on the installed capacity of the power station in each grid;
and carrying out density clustering on the grids by using a grid clustering method based on the installed density and the grid distance to obtain the plurality of subareas.
The method for constructing the new energy power prediction model according to the embodiment of the present application constructs the new energy power prediction model based on the installed capacity of the power station in the sub-area, the historical weather grid point data corresponding to at least part of the grids in each sub-area, and the historical total output data corresponding to the target area, and includes:
determining a target number based on the installed capacity;
based on the historical weather grid point data corresponding to each subarea and the historical total output data corresponding to the target area, acquiring a correlation coefficient between the historical weather grid point data corresponding to each subarea and the historical total output data corresponding to the target area;
Acquiring the historical weather lattice point data of the target number with the highest correlation coefficient from the historical weather lattice point data corresponding to the plurality of subareas;
and constructing the new energy power prediction model based on the historical meteorological grid point data of the target quantity and the historical total output data.
The method for constructing the new energy power prediction model according to the embodiment of the application, which is based on the installed capacity, determines the target number, comprises the following steps:
determining the number of weather grid points corresponding to a target subarea in the plurality of subareas based on the installed capacity of the power station in the target subarea and the installed capacity of the power station in the subarea with the minimum installed capacity;
and calculating the least common multiple of the number of the weather lattice points corresponding to each subarea, and determining the target number.
The method for constructing the new energy power prediction model according to the embodiment of the application, based on the historical weather lattice data of the target number and the historical total output data, includes:
determining any one of the historical weather lattice point data of the target number and the historical total output data corresponding to the historical weather lattice point data as a training sample, and obtaining a plurality of training samples;
Randomly dividing the plurality of training samples into a training set and a verification set;
training the new energy power prediction model based on the training set to obtain a trained new energy power prediction model;
optimizing the trained new energy power prediction model based on the verification set.
In a second aspect, the present application provides a new energy power prediction method based on the method for constructing a new energy power prediction model according to the first aspect, where the method includes:
and inputting target weather lattice point data of a target moment corresponding to a target area into the new energy power prediction model, and obtaining the predicted total power of the power station in the target area corresponding to the target weather lattice point data, which is output by the new energy power prediction model.
According to the new energy power prediction method provided by the embodiment of the application, the grid clustering method is used for clustering grids based on the installed capacity and the grid distance of the power station by combining the grid characteristic of the weather forecast numerical mode data and the installed capacity density aggregation characteristic of the regional photovoltaic power station to obtain a plurality of subareas, and then the new energy power prediction model is built based on the representative weather grid point data and the total output data of the power station in the target region, so that the built model has better accuracy, the more accurate prediction total power output by the new energy power prediction model can be obtained in the application process, and the accurate prediction of the power generation power of the power station in the target region is realized.
In a third aspect, the present application provides a device for constructing a new energy power prediction model, where the device includes:
the first processing module is used for dividing the target area into a plurality of grids based on longitude and latitude grids of the historical weather grid point data;
the second processing module is used for dividing the grids by using a grid clustering method based on the installed capacity and the grid distance of the power station in each grid to obtain a plurality of subareas; the sub-area comprises at least one grid;
the third processing module is used for constructing a new energy power prediction model based on the installed capacity of the power station in the subarea, the historical weather grid point data corresponding to at least part of grids in each subarea and the historical total output data corresponding to the target area; and the historical time period corresponding to the historical weather grid point data is the same as the historical time period corresponding to the historical total output data.
According to the construction device of the new energy power prediction model provided by the embodiment of the application, the target area is divided based on the longitude and latitude grids to obtain a plurality of grids, then the grids with the installed capacity being adjacent dense and the grid distance being smaller than the target threshold value are combined into one sub-area, so that a plurality of sub-areas are obtained, the new energy power prediction model is constructed based on the installed capacity of the power station in the sub-areas, the historical weather grid point data corresponding to at least part of the grids in each sub-area and the historical total output data corresponding to the target area, and the accuracy and the precision of the new energy power prediction model are improved; in addition, the historical weather grid point data, the historical total output data and the installed capacity are easy to obtain, a large amount of labor cost is saved, the operation is convenient, and the efficiency of power prediction is improved.
In a fourth aspect, the present application provides a new energy power prediction apparatus, the apparatus comprising:
and the fourth processing module is used for inputting target weather lattice point data of a target moment corresponding to a target area into the new energy power prediction model and obtaining the predicted total power of the power station in the target area corresponding to the target weather lattice point data, which is output by the new energy power prediction model.
According to the new energy power prediction device provided by the embodiment of the application, the grid clustering method is used for clustering grids based on the installed capacity and the grid distance of the power station by combining the grid characteristic of the weather forecast numerical mode data and the installed capacity density aggregation characteristic of the regional photovoltaic power station to obtain a plurality of subareas, and then the new energy power prediction model is built based on the representative weather grid point data and the total output data of the power station in the target region, so that the built model has better accuracy, the more accurate prediction total power output by the new energy power prediction model can be obtained in the application process, and the accurate prediction of the power generation power of the power station in the target region is realized.
In a fifth aspect, the present application provides a plant controller comprising:
A new energy power generation device;
the construction device of the new energy power prediction model according to the third aspect; the construction device of the new energy power prediction model is electrically connected with the new energy power generation device;
the new energy power prediction device according to the fourth aspect; the new energy power prediction device is electrically connected with the new energy power generation device.
According to the power station controller provided by the embodiment of the application, the prediction total power of the power station in the target area can be predicted based on the new energy power prediction model by constructing the new energy power prediction model, so that the accurate prediction of the power generation power of the power station in the target area is realized.
In a fifth aspect, the present application provides a non-transitory computer readable storage medium, on which a computer program is stored, which when executed by a processor implements the method for constructing a new energy power prediction model and the method for predicting new energy power according to the second aspect described above.
In a sixth aspect, the present application provides a computer program product comprising a computer program which, when executed by a processor, implements the method for constructing a new energy power prediction model as described in the first aspect and the method for predicting new energy power as described in the second aspect.
The above technical solutions in the embodiments of the present application have at least one of the following technical effects:
dividing a target area based on longitude and latitude grids to obtain a plurality of grids, merging the grids with adjacent dense installed capacity and grid distance smaller than a target threshold value into a sub-area to obtain a plurality of sub-areas, and constructing a new energy power prediction model based on the installed capacity of a power station in the sub-areas, historical meteorological grid point data corresponding to at least part of the grids in each sub-area and historical total output data corresponding to the target area, so that the accuracy and precision of the new energy power prediction model are improved; in addition, the historical weather grid point data, the historical total output data and the installed capacity are easy to obtain, a large amount of labor cost is saved, the operation is convenient, and the efficiency of power prediction is improved.
Further, the installed density corresponding to each grid is determined based on the installed capacity of the power station in each grid, then a grid clustering method is used based on the installed density and the grid distance, a plurality of grids with the installed capacity being adjacent dense and the grid distance being smaller than a target threshold value are combined into a sub-area, so that a plurality of sub-areas are obtained, the sub-areas are reasonably divided, and a new energy power prediction model can be built based on the sub-areas obtained by dividing in application.
Furthermore, by combining the grid characteristics of the weather forecast numerical mode data and the installed capacity density aggregation characteristics of the regional photovoltaic power stations, the grids are clustered based on the installed capacity of the power stations to obtain a plurality of subareas, then a new energy power prediction model is built based on representative weather lattice point data and total output data of the power stations in the target region, the built model has good accuracy, and therefore more accurate prediction total power output by the new energy power prediction model can be obtained in the application process, and accurate prediction of the generated power of the power stations in the target region is realized.
Still further, by constructing the new energy power prediction model, the predicted total power of the power station in the target area can be predicted based on the new energy power prediction model, and accurate prediction of the generated power of the power station in the target area can be realized.
Additional aspects and advantages of the application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the application.
Drawings
The foregoing and/or additional aspects and advantages of the application will become apparent and may be better understood from the following description of embodiments taken in conjunction with the accompanying drawings in which:
FIG. 1 is a schematic flow chart of a method for constructing a new energy power prediction model according to an embodiment of the present application;
FIG. 2 is a schematic structural diagram of a device for constructing a new energy power prediction model according to an embodiment of the present application;
FIG. 3 is a schematic flow chart of a new energy power prediction method according to an embodiment of the present application;
fig. 4 is a schematic structural diagram of a new energy power prediction device according to an embodiment of the present application;
fig. 5 is a schematic structural diagram of a power station controller according to an embodiment of the present application.
Detailed Description
The technical solutions of the embodiments of the present application will be clearly described below with reference to the drawings in the embodiments of the present application, and it is apparent that the described embodiments are some embodiments of the present application, but not all embodiments. All other embodiments, which are obtained by a person skilled in the art based on the embodiments of the present application, fall within the scope of protection of the present application.
The terms first, second and the like in the description and in the claims, 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 may be interchanged, as appropriate, such that embodiments of the present application may be implemented in sequences other than those illustrated or described herein, and that the objects identified by "first," "second," etc. are generally of a type, and are not limited to the number of objects, such as the first object may be one or more. Furthermore, in the description and claims, "and/or" means at least one of the connected objects, and the character "/", generally means that the associated object is an "or" relationship.
The method for constructing the new energy power prediction model according to the embodiment of the present application is described below with reference to fig. 1.
It should be noted that, the execution body of the method for constructing the new energy power prediction model may be a power station controller, or may be a device for constructing the new energy power prediction model that is disposed on the power station controller, or may also be a server that is electrically connected to the power station controller, or may also be a user terminal that is communicatively connected to the power station controller, including but not limited to a mobile terminal and a non-mobile terminal.
For example, mobile terminals include, but are not limited to, cell phones, PDA smart terminals, tablet computers, vehicle-mounted smart terminals, and the like; non-mobile terminals include, but are not limited to, PC-side and the like.
As shown in fig. 1, the method for constructing the new energy power prediction model includes: step 110, step 120 and step 130.
Step 110, dividing the target area into a plurality of grids based on longitude and latitude grids of the historical weather grid point data.
In this step, historical weather lattice data may be derived based on weather site data, where weather site data is based on measured observation stations.
In practical application, site data which are unevenly distributed in space can be merged according to a certain geometric form, the average value of the site data in each lattice is obtained, and then the average value is placed in the center of the lattice to obtain lattice point data.
Historical weather lattice data may include irradiance, temperature, humidity, pressure, wind speed, and the like.
Points on the latitude and longitude grid may represent values of weather over the latitude and longitude.
The target region is a region for predicting the generated power of the power station in the region.
At least one power station is disposed within the target area.
The target area may be divided into multiple grids based on latitude and longitude grids of historical weather grid point data.
The size of the grid may be user-defined based, for example, the grid size may be defined as 0.25°n by 0.25°s.
According to the application, the target area is divided based on the meshing data of the weather forecast numerical model, so that the technical problem of data error amplification caused by secondary processing of the weather forecast numerical model result is avoided.
Step 120, dividing a plurality of grids by using a grid clustering method based on the installed capacity and the grid distance of the power station in each grid to obtain a plurality of subareas; the sub-area comprises at least one grid.
In this step the power station may be a photovoltaic power station, a tidal power station, a wind power station, a hydroelectric power station, etc.
The installed capacity of a power plant is used to characterize the construction scale and the power production capacity of the power plant.
It will be appreciated that the greater the installed capacity of the plant, the greater the impact on the total output of the plant in the grid.
The grid distance is the distance between the grids.
The subareas are obtained by dividing a plurality of grids.
The sub-area comprises at least one grid.
Clustering the grids, namely merging the grids with the installed capacity being adjacent dense and with the grid distance smaller than the target threshold value into a sub-area, so as to obtain a plurality of sub-areas;
the target threshold may be customized based on a user, which is not limited by the present application.
The clustering method may include a Partition-based method (Partition-based Methods), a Density-based method (Density-based Methods), a hierarchical clustering method (Hierarchical Methods), and the like.
The inventor finds that the dividing of the target area based on the sum of the number of the photovoltaic power stations is unreasonable in the related technology in the research and development process.
In the application, under the condition that the installed capacity of the power station is larger, the influence on the total output of the power station in the grids is larger, a plurality of grids are divided based on the installed capacity of the power station in each grid, so that a plurality of subareas are obtained, and the subareas are reasonably divided.
In some embodiments, step 120 may include:
determining the installed density corresponding to each grid based on the installed capacity of the power station in each grid;
And performing density clustering on the grids by using a grid clustering method based on the installed density and the grid distance to obtain a plurality of subareas.
In this embodiment, the installed density is the sum of the installed capacities of the power stations in the grid.
The density clustering algorithm may include: DBSCAN, MDCA, OPTICS and dencolue, etc., may be selected based on user needs, and the application is not limited.
In actual execution, the formula may be based on:
di= Σinstalled capacity of a plant falling within a grid
Determining the installed density corresponding to the grid, wherein Di is the installed density corresponding to the grid;
the multiple grids can then be clustered based on an OPTICS density clustering algorithm to obtain N sub-regions.
According to the method for constructing the new energy power prediction model, which is provided by the embodiment of the application, the installed density corresponding to each grid is determined based on the installed capacity of the power station in each grid, then the grid clustering method is used based on the installed density and the grid distance, and a plurality of grids with the installed capacity being adjacent dense and the grid distance being smaller than the target threshold value are combined into one subarea, so that a plurality of subareas are acquired, the subareas are reasonably divided, and the new energy power prediction model can be constructed based on the subareas obtained by dividing in application.
130, constructing a new energy power prediction model based on the installed capacity of the power station in each subarea, the historical weather grid point data corresponding to at least part of grids in each subarea and the historical total output data corresponding to the target area; the historical time period corresponding to the historical weather lattice data is the same as the historical time period corresponding to the historical total output data.
In this step, the historical total output data is the sum of the output data of all the power stations in the target area in the historical time period.
The new energy power prediction model is used for predicting the power of the power station in the target area in a future time period.
The new energy power prediction model may be a neural network model.
The historical time period corresponding to the historical weather lattice data is the same as the historical time period corresponding to the historical total output data, for example, the historical time period can be one year or half a year, and the application is not limited based on user definition.
According to the method, the target area is divided into a plurality of grids based on longitude and latitude grids of historical weather grid point data, and the target area is divided based on the grid data of a weather forecast numerical model, so that the technical problem that data errors are amplified due to secondary processing of weather forecast numerical model results is avoided;
Based on the installed capacity and the grid distance of the power stations in each grid, a grid clustering method is used for dividing the grids to obtain a plurality of subareas, and the grids with the installed capacity being adjacent dense and the grid distance being smaller than the target threshold value can be combined into one subarea, so that the subareas are obtained, and the subareas are reasonably divided.
According to the method for constructing the new energy power prediction model, the target area is divided based on the longitude and latitude grids to obtain a plurality of grids, then the grids with the installed capacity being adjacent dense and the grid distance being smaller than the target threshold value are combined into a sub-area, so that a plurality of sub-areas are obtained, the new energy power prediction model is constructed based on the installed capacity of the power station in the sub-areas, the historical weather grid point data corresponding to at least part of the grids in each sub-area and the historical total output data corresponding to the target area, and the accuracy and the precision of the new energy power prediction model are improved; in addition, the historical weather grid point data, the historical total output data and the installed capacity are easy to obtain, a large amount of labor cost is saved, the operation is convenient, and the efficiency of power prediction is improved.
In some embodiments, step 130 may include:
determining a target number based on the installed capacity;
based on the historical weather lattice point data corresponding to each subarea and the historical total output data corresponding to the target area, acquiring a correlation coefficient between the historical weather lattice point data corresponding to each subarea and the historical total output data corresponding to the target area;
acquiring historical weather lattice point data of a target number with the highest correlation coefficient from the corresponding sub-areas;
and constructing a new energy power prediction model based on the historical meteorological lattice data and the historical total output data of the target quantity.
In this embodiment, the target quantity is used to select a portion of the data from a plurality of historical weather lattice data.
The correlation coefficient between the historical weather lattice data and the historical total output data is used for representing the correlation degree between the historical weather lattice data and the historical total output data.
In the actual execution process, the target number may be determined based on the installed capacity;
the formula can then be based on:
determining a correlation coefficient between the historical weather lattice data and the historical total output data, wherein X is the historical weather lattice data, Y is the historical total output data, r (X, Y) is the correlation coefficient, cov (X, Y) is the covariance between the historical weather lattice data and the historical total output data, var [ X ] is the variance of the historical weather lattice data, var [ Y ] is the variance of the historical total output data;
Sequencing the plurality of correlation coefficients, and acquiring the historical weather lattice point data with the highest target number of the correlation coefficients from the plurality of historical weather lattice point data;
and then, a new energy power prediction model is constructed based on the historical meteorological lattice data and the historical total output data of the target quantity.
According to the method for constructing the new energy power prediction model, the new energy power prediction model is constructed by acquiring the historical weather lattice point data with the highest target number of the correlation coefficient from the historical weather lattice point data, and then based on the historical weather lattice point data with the representative target number and the historical total output data, the constructed model prediction effect is good, and the more accurate predicted power of the power station in the target area can be acquired based on the new energy power prediction model.
In some embodiments, determining the target number based on the installed capacity may include:
determining the number of weather grid points corresponding to the target subarea based on the installed capacity of the power station in the target subarea and the installed capacity of the power station in the subarea with the minimum installed capacity;
and calculating the least common multiple of the number of the weather lattice points corresponding to each subarea, and determining the target number.
In this embodiment, the target sub-region is any one of a plurality of sub-regions.
The number of the weather lattice points is the number of the weather lattice points selected from the target subarea.
The least common multiple corresponding to the number of weather lattice points corresponding to the plurality of sub-regions can be calculated, and then the least common multiple is determined as the target number.
In the actual implementation process, the installed capacity of the power station in the target area can be set to be 1w, and the target area can comprise 3 sub-areas, wherein the installed capacity of the power station in the first sub-area is 3k, the installed capacity of the power station in the second sub-area is 3k, and the installed capacity of the power station in the third sub-area is 4k;
based on the formula:
dn=int (installed capacity of power station in target subregion/Min (installed capacity of power station in each subregion))
Determining the number of weather lattice points corresponding to a target subarea in a plurality of subareas;
the number of first weather grid points corresponding to the first subarea can be determined to be 1, the number of second weather grid points corresponding to the second subarea is determined to be 1, and the number of third weather grid points corresponding to the third subarea is determined to be 2;
and then the least common multiple 2 among the first weather lattice point number 1, the second weather lattice point number 1 and the third weather lattice point number 2 can be obtained, and the least common multiple 2 is determined as the target number.
According to the method for constructing the new energy power prediction model, provided by the embodiment of the application, the number of the weather lattice points corresponding to the plurality of subareas is determined according to the installed capacity proportion of the power station in each subarea, then the target number is determined based on the number of the weather lattice points, and in the subsequent application, the historical weather lattice point data of the target number can be selected based on the correlation coefficient between the historical weather lattice point data and the historical total output data, so that the new energy power prediction model is constructed.
In some embodiments, constructing the new energy power prediction model based on the target number of historical meteorological grid data and the historical total output data may include:
determining any one of the historical weather lattice point data of the target number and the historical total output data corresponding to the historical weather lattice point data as a training sample, and obtaining a plurality of training samples;
randomly dividing a plurality of training samples into a training set and a verification set;
training the new energy power prediction model based on the training set to obtain a trained new energy power prediction model;
and optimizing the trained new energy power prediction model based on the verification set.
In this embodiment, the training samples are used to construct a new energy power prediction model.
One training sample includes any one of the historical weather lattice data of the target number and the historical total output data corresponding to the historical weather lattice data.
The plurality of training samples may be randomly divided into a training set and a validation set.
The training set is used for training the new energy power prediction model.
The verification set is used for optimizing the trained new energy power prediction model.
In the actual execution process, 75% of training samples in the plurality of training samples can be obtained as a training set, and the remaining 25% of training samples in the plurality of training samples can be obtained as a verification set;
then the number of layers of the hidden layer and the number of neurons of the hidden layer of the network in the new energy power prediction model can be set;
training the new energy power prediction model based on 75% of training set, specifically, training the new energy power prediction model by taking sample historical weather lattice point data in the training set as a sample and taking sample historical total output data corresponding to the sample historical weather lattice point data as a sample label;
the new energy power prediction model after training is optimized based on 25% of verification set, specifically, the historical weather lattice point data in the verification set can be input into the new energy power prediction model after training, the predicted total output data output by the new energy power prediction model after training is obtained, and then the new energy power prediction model after training is optimized based on the difference between the predicted total output data and the historical total output data corresponding to the historical weather lattice point data.
According to the method for constructing the new energy power prediction model, any one of the historical weather lattice point data of the target number and the historical total output data corresponding to the historical weather lattice point data are determined to be one training sample, a plurality of training samples are obtained, then the training samples are divided into a training set and a verification set, the new energy power prediction model is trained based on the training set, and the new energy power prediction model after the training is optimized based on the verification set, so that the prediction accuracy and precision of the new energy power prediction model are improved.
The device for constructing the new energy power prediction model provided by the application is described below, and the device for constructing the new energy power prediction model described below and the method for constructing the new energy power prediction model described above can be correspondingly referred to each other.
According to the method for constructing the new energy power prediction model, provided by the embodiment of the application, the execution subject can be a device for constructing the new energy power prediction model. In the embodiment of the application, the construction device of the new energy power prediction model provided by the embodiment of the application is described by taking the construction method of the new energy power prediction model executed by the construction device of the new energy power prediction model as an example.
The embodiment of the application also provides a device for constructing the new energy power prediction model.
As shown in fig. 2, the device for constructing the new energy power prediction model includes: a first processing module 210, a second processing module 220, and a third processing module 230.
A first processing module 210, configured to divide the target area into a plurality of grids based on longitude and latitude grids of the historical weather grid point data;
a second processing module 220, configured to divide a plurality of grids by using a grid clustering method based on the installed capacity and the grid distance of the power station in each grid, and obtain a plurality of sub-areas; the sub-area comprises at least one grid;
the third processing module 230 is configured to construct a new energy power prediction model based on the installed capacity of the power station in the sub-area, the historical weather grid point data corresponding to at least part of the grids in each sub-area, and the historical total output data corresponding to the target area; the historical time period corresponding to the historical weather lattice data is the same as the historical time period corresponding to the historical total output data.
According to the construction device of the new energy power prediction model provided by the embodiment of the application, the target area is divided based on the longitude and latitude grids to obtain a plurality of grids, then the grids with the installed capacity being adjacent dense and the grid distance being smaller than the target threshold value are combined into one sub-area, so that a plurality of sub-areas are obtained, the new energy power prediction model is constructed based on the installed capacity of the power station in the sub-areas, the historical weather grid point data corresponding to at least part of the grids in each sub-area and the historical total output data corresponding to the target area, and the accuracy and the precision of the new energy power prediction model are improved; in addition, the historical weather grid point data, the historical total output data and the installed capacity are easy to obtain, a large amount of labor cost is saved, the operation is convenient, and the efficiency of power prediction is improved.
In some embodiments, the second processing module 220 may also be configured to:
determining the installed density corresponding to each grid based on the installed capacity of the power station in each grid;
and performing density clustering on the grids by using a grid clustering method based on the installed density and the grid distance to obtain a plurality of subareas.
In some embodiments, the apparatus for constructing a new energy power prediction model may further include:
a fifth processing module for determining a target number based on the installed capacity;
the sixth processing module is used for acquiring a correlation coefficient between the historical weather lattice data corresponding to each subarea and the historical total output data corresponding to the target area based on the historical weather lattice data corresponding to each subarea and the historical total output data corresponding to the target area;
the seventh processing module is used for acquiring the historical weather lattice point data of the target number with the highest correlation coefficient from the historical weather lattice point data corresponding to the plurality of subareas;
and the eighth processing module is used for constructing a new energy power prediction model based on the historical weather lattice data and the historical total output data of the target quantity.
In some embodiments, the fifth processing module may also be configured to:
determining the number of weather grid points corresponding to the target subarea based on the installed capacity of the power station in the target subarea and the installed capacity of the power station in the subarea with the minimum installed capacity;
And calculating the least common multiple of the number of the weather lattice points corresponding to each subarea, and determining the target number.
In some embodiments, the eighth processing module may be further configured to:
determining any one of the historical weather lattice point data of the target number and the historical total output data corresponding to the historical weather lattice point data as a training sample, and obtaining a plurality of training samples;
randomly dividing a plurality of training samples into a training set and a verification set;
training the new energy power prediction model based on the training set to obtain a trained new energy power prediction model;
and optimizing the trained new energy power prediction model based on the verification set.
The device for constructing the new energy power prediction model in the embodiment of the application can be a device with an operating system. The operating system may be an Android operating system, an IOS operating system, or other possible operating systems, and the embodiment of the present application is not limited specifically.
The device for constructing the new energy power prediction model provided by the embodiment of the application can realize each process realized by the method embodiment of fig. 1, and in order to avoid repetition, the description is omitted here.
The following describes a new energy power prediction method based on the new energy power prediction model construction method according to any embodiment of the present application with reference to fig. 3, where the new energy power prediction method includes:
Step 310, inputting the target weather lattice point data of the target time corresponding to the target area into the new energy power prediction model, and obtaining the predicted total power of the power station in the target area corresponding to the target weather lattice point data output by the new energy power prediction model.
In this step, the target time may be the current time.
The target weather lattice point data is weather lattice point data corresponding to a target area at the target moment.
The predicted total power is the total power of the power stations in the target area at the future time.
The predicted total power may be predicted based on a new energy power prediction model.
The inventor finds that the sum of power prediction results of a plurality of power stations is determined to be the predicted total power of all the power stations in a target area in the related technology in the research and development process, and the conventional new energy power prediction method needs historical power generation power data of a large number of power stations, so that the difficulty is high, and the prediction result is inaccurate.
According to the method, the new energy power prediction model is built based on the historical weather lattice point data, the historical total output data of the target area and the coordinates and the installed capacity of the power station in the target area, so that the data for building the model can be quickly and easily obtained, the labor cost is reduced, and the prediction efficiency is improved; and the prediction result is accurate based on the prediction total power of the power station in the target area predicted by the constructed new energy power prediction model.
According to the new energy power prediction method provided by the embodiment of the application, the grid clustering method is used for clustering grids based on the installed capacity and the grid distance of the power station by combining the grid characteristic of the weather forecast numerical mode data and the installed capacity density aggregation characteristic of the regional photovoltaic power station to obtain a plurality of subareas, and then the new energy power prediction model is built based on the representative weather grid point data and the total output data of the power station in the target region, so that the built model has better accuracy, the more accurate prediction total power output by the new energy power prediction model can be obtained in the application process, and the accurate prediction of the power generation power of the power station in the target region is realized.
The new energy power prediction device provided by the application is described below, and the new energy power prediction device described below and the new energy power prediction method described above can be referred to correspondingly.
According to the new energy power prediction method provided by the embodiment of the application, the execution subject can be a new energy power prediction device. In the embodiment of the application, a new energy power prediction method executed by a new energy power prediction device is taken as an example, and the new energy power prediction device provided by the embodiment of the application is described.
The embodiment of the application also provides a new energy power prediction device.
As shown in fig. 4, the new energy power prediction apparatus includes: a fourth processing module 410.
The fourth processing module 410 is configured to input target weather grid point data at a target time corresponding to the target area to the new energy power prediction model, and obtain the predicted total power of the power station in the target area corresponding to the target weather grid point data output by the new energy power prediction model.
According to the new energy power prediction device provided by the embodiment of the application, the grid clustering method is used for clustering grids based on the installed capacity and the grid distance of the power station by combining the grid characteristic of the weather forecast numerical mode data and the installed capacity density aggregation characteristic of the regional photovoltaic power station to obtain a plurality of subareas, and then the new energy power prediction model is built based on the representative weather grid point data and the total output data of the power station in the target region, so that the built model has better accuracy, the more accurate prediction total power output by the new energy power prediction model can be obtained in the application process, and the accurate prediction of the power generation power of the power station in the target region is realized.
The new energy power prediction device in the embodiment of the application can be a device with an operating system. The operating system may be an Android operating system, an IOS operating system, or other possible operating systems, and the embodiment of the present application is not limited specifically.
The new energy power prediction device provided by the embodiment of the present application can implement each process implemented by the method embodiment of fig. 3, and in order to avoid repetition, a description thereof will not be repeated here.
The embodiment of the application also provides a power station controller.
The power station controller includes: new energy power generation device, new energy power prediction model's construction device and new energy power prediction device.
In this embodiment, the new energy power generation device may be a photovoltaic power generation device, or may be a water power generation device, or may also be a wind power generation device, and the present application is not limited thereto.
The new energy power prediction model constructing device is the new energy power prediction model constructing device described in any embodiment above.
The new energy power prediction model constructing device is electrically connected with the new energy power generation device.
The new energy power prediction device is the new energy power prediction device described in any of the above embodiments.
The new energy power prediction device is electrically connected with the new energy power generation device.
As shown in fig. 5, the power station controller provided in the embodiment of the present application includes a processor 501, a memory 502, and a computer program stored in the memory 502 and capable of running on the processor 501, where the program, when executed by the processor 501, implements the above-mentioned method for constructing the new energy power prediction model and the respective processes of the new energy power prediction method embodiment, and can achieve the same technical effects, so that repetition is avoided and no further description is given here.
It should be noted that, the power station controller in the embodiment of the present application includes the mobile power station controller and the non-mobile power station controller described above.
According to the power station controller provided by the embodiment of the application, the prediction total power of the power station in the target area can be predicted based on the new energy power prediction model by constructing the new energy power prediction model, so that the accurate prediction of the power generation power of the power station in the target area is realized.
In another aspect, the present application further provides a computer program product, where the computer program product includes a computer program stored on a non-transitory computer readable storage medium, where the computer program includes program instructions, when the program instructions are executed by a computer, the computer is capable of executing the above-mentioned methods for constructing the new energy power prediction model and the processes of the embodiments of the new energy power prediction method, and the same technical effects can be achieved, so that repetition is avoided, and no further description is given here.
In still another aspect, the present application further provides a non-transitory computer readable storage medium, on which a computer program is stored, where the computer program is implemented when executed by a processor to perform the above-mentioned methods for constructing the new energy power prediction model and the respective processes of the embodiments of the new energy power prediction method, and achieve the same technical effects, and for avoiding repetition, a detailed description is omitted herein.
In still another aspect, an embodiment of the present application further provides a chip, where the chip includes a processor and a communication interface, where the communication interface is coupled to the processor, and the processor is configured to execute a program or an instruction, to implement the above method for constructing the new energy power prediction model and each process of the new energy power prediction method embodiment, and achieve the same technical effect, so that repetition is avoided, and no further description is given here.
It should be understood that the chips referred to in the embodiments of the present application may also be referred to as system-on-chip chips, chip systems, or system-on-chip chips, etc.
The apparatus embodiments described above are merely illustrative, wherein the elements illustrated as separate elements may or may not be physically separate, and the elements shown as elements may or may not be physical elements, may be located in one place, or may be distributed over a plurality of network elements. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art will understand and implement the present application without undue burden.
From the above description of the embodiments, it will be apparent to those skilled in the art that the embodiments may be implemented by means of software plus necessary general hardware platforms, or of course may be implemented by means of hardware. Based on this understanding, the foregoing technical solution may be embodied essentially or in a part contributing to the prior art in the form of a software product, which may be stored in a computer readable storage medium, such as ROM/RAM, a magnetic disk, an optical disk, etc., including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the method described in the respective embodiments or some parts of the embodiments.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present application, and are not limiting; although the application has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present application.
Claims (11)
1. The method for constructing the new energy power prediction model is characterized by comprising the following steps of:
dividing a target area into a plurality of grids based on longitude and latitude grids of historical weather grid point data;
dividing the grids by using a grid clustering method based on the installed capacity and the grid distance of the power station in each grid to obtain a plurality of subareas; the sub-area comprises at least one grid;
a new energy power prediction model is constructed based on the installed capacity of the power station in the subareas, historical weather grid point data corresponding to at least part of grids in each subarea and historical total output data corresponding to the target area; and the historical time period corresponding to the historical weather grid point data is the same as the historical time period corresponding to the historical total output data.
2. The method for constructing a new energy power prediction model according to claim 1, wherein the dividing the plurality of grids by using a grid clustering method based on the installed capacity and the grid distance of the power station in each grid, and obtaining a plurality of sub-regions, comprises:
determining the installed density corresponding to each grid based on the installed capacity of the power station in each grid;
And carrying out density clustering on the grids by using a grid clustering method based on the installed density and the grid distance to obtain the plurality of subareas.
3. The method for constructing a new energy power prediction model according to claim 1 or 2, wherein the constructing a new energy power prediction model based on the installed capacity of the power station in the sub-area, the historical weather grid point data corresponding to at least part of the grids in each sub-area, and the historical total output data corresponding to the target area includes:
determining a target number based on the installed capacity;
based on the historical weather grid point data corresponding to each subarea and the historical total output data corresponding to the target area, acquiring a correlation coefficient between the historical weather grid point data corresponding to each subarea and the historical total output data corresponding to the target area;
acquiring the historical weather lattice point data of the target number with the highest correlation coefficient from the historical weather lattice point data corresponding to the plurality of subareas;
and constructing the new energy power prediction model based on the historical meteorological grid point data of the target quantity and the historical total output data.
4. The method for constructing a new energy power prediction model according to claim 3, wherein the determining the target number based on the installed capacity includes:
determining the number of weather grid points corresponding to a target subarea in the plurality of subareas based on the installed capacity of the power station in the target subarea and the installed capacity of the power station in the subarea with the minimum installed capacity;
and calculating the least common multiple of the number of the weather lattice points corresponding to each subarea, and determining the target number.
5. The method for constructing a new energy power prediction model according to claim 3, wherein the constructing the new energy power prediction model based on the historical weather lattice data of the target number and the historical total output data includes:
determining any one of the historical weather lattice point data of the target number and the historical total output data corresponding to the historical weather lattice point data as a training sample, and obtaining a plurality of training samples;
randomly dividing the plurality of training samples into a training set and a verification set;
training the new energy power prediction model based on the training set to obtain a trained new energy power prediction model;
Optimizing the trained new energy power prediction model based on the verification set.
6. A new energy power prediction method based on the construction method of the new energy power prediction model according to any one of claims 1 to 5, characterized by comprising:
and inputting target weather lattice point data of a target moment corresponding to a target area into the new energy power prediction model, and obtaining the predicted total power of the power station in the target area corresponding to the target weather lattice point data, which is output by the new energy power prediction model.
7. The device for constructing the new energy power prediction model is characterized by comprising the following components:
the first processing module is used for dividing the target area into a plurality of grids based on longitude and latitude grids of the historical weather grid point data;
the second processing module is used for dividing the grids by using a grid clustering method based on the installed capacity and the grid distance of the power station in each grid to obtain a plurality of subareas; the sub-area comprises at least one grid;
the third processing module is used for constructing a new energy power prediction model based on the installed capacity of the power station in the subarea, the historical weather grid point data corresponding to at least part of grids in each subarea and the historical total output data corresponding to the target area; and the historical time period corresponding to the historical weather grid point data is the same as the historical time period corresponding to the historical total output data.
8. A new energy power prediction apparatus based on the construction method of the new energy power prediction model according to any one of claims 1 to 5, characterized by comprising:
and the fourth processing module is used for inputting target weather lattice point data of a target moment corresponding to a target area into the new energy power prediction model and obtaining the predicted total power of the power station in the target area corresponding to the target weather lattice point data, which is output by the new energy power prediction model.
9. A plant controller comprising:
a new energy power generation device;
the construction device of the new energy power prediction model according to claim 7; the construction device of the new energy power prediction model is electrically connected with the new energy power generation device;
the new energy power prediction apparatus according to claim 8; the new energy power prediction device is electrically connected with the new energy power generation device.
10. A non-transitory computer readable storage medium having stored thereon a computer program, characterized in that the computer program, when executed by a processor, implements the new energy power prediction model construction method according to any one of claims 1 to 5 or the new energy power prediction method according to claim 6.
11. A computer program product comprising a computer program, characterized in that the computer program, when executed by a processor, implements the new energy power prediction model construction method according to any one of claims 1-5 or the new energy power prediction method according to claim 6.
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| CN117613909A (en) * | 2024-01-24 | 2024-02-27 | 国能日新科技股份有限公司 | Distributed photovoltaic regional weather modeling method and device |
| CN117713039A (en) * | 2023-11-01 | 2024-03-15 | 宁夏青铜峡市华能雷避窑光伏发电有限公司 | Power plant power generation control method based on regional new energy power generation prediction |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117713039A (en) * | 2023-11-01 | 2024-03-15 | 宁夏青铜峡市华能雷避窑光伏发电有限公司 | Power plant power generation control method based on regional new energy power generation prediction |
| CN117613909A (en) * | 2024-01-24 | 2024-02-27 | 国能日新科技股份有限公司 | Distributed photovoltaic regional weather modeling method and device |
| CN117613909B (en) * | 2024-01-24 | 2024-04-12 | 国能日新科技股份有限公司 | Distributed photovoltaic regional weather modeling method and device |
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