CN115983121A - Coastal NWP data grid refinement method and device based on double mapping - Google Patents
Coastal NWP data grid refinement method and device based on double mapping Download PDFInfo
- Publication number
- CN115983121A CN115983121A CN202211713006.4A CN202211713006A CN115983121A CN 115983121 A CN115983121 A CN 115983121A CN 202211713006 A CN202211713006 A CN 202211713006A CN 115983121 A CN115983121 A CN 115983121A
- Authority
- CN
- China
- Prior art keywords
- data
- nwp
- fine grid
- grid
- fine
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Images
Landscapes
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention discloses a coastal NWP data grid refining method and device based on double mapping, and relates to the technical field of wind power. One embodiment of the method comprises: determining the corresponding relation of the measured coarse grid NWP data and the measured fine grid meteorological monitoring data in a historical period as a first mapping; interpolating the actually measured coarse grid NWP data according to a preset kernel function to obtain fine grid NWP data; inputting the fine-grid NWP data into a second mapping for prediction under the condition that the fine-grid NWP data meets a high-precision condition constrained by actually-measured fine-grid meteorological monitoring data, and obtaining fine-grid NWP predicted data in a future period; and converting the fine grid NWP predicted data into first fine grid meteorological predicted data of a future period by utilizing a first mapping, and inputting the first fine grid meteorological predicted data into a wind power prediction model to predict wind power. According to the embodiment, accurate prediction of the offshore wind power can be realized based on the measured coarse grid NWP data.
Description
Technical Field
The invention relates to the technical field of wind power, in particular to a coastal NWP data grid refining method and device based on double mapping.
Background
In recent years, the installed capacity of offshore wind power in China is rapidly developed, but the matching technology is relatively immature, wherein high-precision wind power prediction is the basis of wind power grid connection and safe and reliable control, and NWP (numerical weather prediction) of a fine grid is the basis of high-precision high-resolution wind power prediction and is an internationally recognized difficulty and a challenging problem at present. At present, NWP data which can be obtained from weather stations and weather stations are both in a coarse grid mode, and if high-precision wind power prediction is performed on the basis of the NWP data in the coarse grid mode, the problem to be solved in the technical field of wind power is urgent.
Disclosure of Invention
In view of this, embodiments of the present invention provide a coastal NWP data grid refinement method and apparatus based on double mapping, which can implement accurate prediction of offshore wind power based on coarse grid NWP data.
To achieve the above object, according to one aspect of the present invention, a coastal NWP data grid refinement method based on dual mapping is provided.
The coastal NWP data grid refining method based on double mapping comprises the following steps: determining the corresponding relation of actual measurement coarse grid numerical weather forecast NWP data and actual measurement fine grid weather monitoring data in a historical period as a first mapping; interpolating the actually measured coarse grid NWP data according to a preset kernel function to obtain fine grid NWP data; under the condition that the fine grid NWP data meet a preset high-precision condition constrained by the actually-measured fine grid meteorological monitoring data, inputting the fine grid NWP data into a preset second mapping for prediction to obtain fine grid NWP predicted data in a future period; and converting the fine grid NWP predicted data into first fine grid meteorological predicted data in a future period by using a first mapping, and inputting the first fine grid meteorological predicted data into a pre-trained wind power prediction model to predict wind power.
Optionally, the interpolating the measured coarse grid NWP data according to the preset kernel function to obtain the fine grid NWP data includes: for any position to be inserted, acquiring a preset number of reference data of a neighborhood of the position to be inserted; wherein the reference data belongs to the measured coarse grid NWP data; and determining a weight value of each reference data for the position to be inserted according to the kernel function, and calculating a weighted sum of the reference data based on the weight values to serve as the fine mesh NWP data of the position to be inserted.
Optionally, the determining, according to the kernel function, a weight value of each reference data for the location to be inserted includes: for any reference data, dividing the reference data and the kernel function calculation result of the position to be inserted by the sum of each reference data and the kernel function calculation result of the position to be inserted to obtain the weight value of the reference data for the position to be inserted.
Optionally, satisfying the high accuracy condition includes: and converting the fine grid NWP data into fine grid meteorological data by utilizing a first mapping, calculating the deviation between the fine grid meteorological data and the actually measured fine grid meteorological monitoring data, and determining that the deviation is smaller than a preset precision threshold value.
Optionally, the method further comprises: inputting the actually measured fine grid meteorological monitoring data into a second mapping to obtain second fine grid meteorological prediction data in a future period, and inputting the second fine grid meteorological prediction data into the wind power prediction model to obtain a wind power prediction result of the second fine grid meteorological prediction data in the current test period; and comparing the wind power prediction results of the first fine grid meteorological prediction data and the second fine grid meteorological prediction data in the current test period, and using the fine grid meteorological prediction data corresponding to a better wind power prediction result as the input data of the wind power prediction model in the current application period.
To achieve the above object, according to another aspect of the present invention, a coastal NWP data grid refinement apparatus based on dual mapping is provided.
The coastal NWP data grid refining device based on double mapping comprises the following steps: a first mapping establishing unit configured to: determining the corresponding relation of actual measurement coarse grid numerical weather forecast NWP data and actual measurement fine grid weather monitoring data in a historical period as a first mapping; a refinement unit to: interpolating the measured coarse grid NWP data according to a preset kernel function to obtain fine grid NWP data; a second mapping prediction unit to: under the condition that the fine grid NWP data meet a preset high-precision condition constrained by the actually-measured fine grid meteorological monitoring data, inputting the fine grid NWP data into a preset second mapping for prediction to obtain fine grid NWP predicted data in a future period; a wind power prediction unit to: and converting the fine grid NWP predicted data into first fine grid meteorological predicted data of a future period by utilizing a first mapping, and inputting the first fine grid meteorological predicted data into a wind power prediction model trained in advance to predict wind power.
Optionally, the refining unit is further configured to: for any position to be inserted, acquiring a preset number of reference data of a neighborhood of the position to be inserted; wherein the reference data belongs to the measured coarse grid NWP data; for any reference data, dividing the reference data and the kernel function calculation result of the position to be inserted by the sum of each reference data and the kernel function calculation result of the position to be inserted to obtain the weight value of the reference data for the position to be inserted; and calculating the reference data based on the weighted sum of the weight values as the fine mesh NWP data of the position to be inserted.
Optionally, satisfying the high precision condition includes: converting the fine grid NWP data into fine grid meteorological data by utilizing a first mapping, calculating the deviation between the fine grid meteorological data and the actually measured fine grid meteorological monitoring data, and determining that the deviation is smaller than a preset precision threshold; the wind power prediction unit is further to: inputting the actually measured fine grid meteorological monitoring data into a second mapping to obtain second fine grid meteorological prediction data in a future period, and inputting the second fine grid meteorological prediction data into the wind power prediction model to obtain a wind power prediction result of the second fine grid meteorological prediction data in the current test period; and comparing the wind power prediction results of the first fine grid meteorological prediction data and the second fine grid meteorological prediction data in the current test period, and using the fine grid meteorological prediction data corresponding to a better wind power prediction result as the input data of the wind power prediction model in the current application period.
To achieve the above object, according to still another aspect of the present invention, there is provided an electronic apparatus.
An electronic device of the present invention includes: one or more processors; the storage device is used for storing one or more programs, and when the one or more programs are executed by the one or more processors, the one or more processors realize the coastal NWP data grid refinement method based on double mapping provided by the invention.
To achieve the above object, according to still another aspect of the present invention, there is provided a computer-readable storage medium.
The invention relates to a computer readable storage medium, on which a computer program is stored, which when executed by a processor implements the coastal NWP data grid refinement method based on dual mapping provided by the invention.
According to the technical scheme of the invention, the embodiment of the invention has the following advantages or beneficial effects:
firstly, determining the corresponding relation of the actually measured coarse grid NWP data and the actually measured fine grid meteorological monitoring data in a historical period as a first mapping, and interpolating the actually measured coarse grid NWP data according to a preset kernel function to obtain fine grid NWP data. And then judging whether the fine grid NWP data meets a high-precision condition constrained by the actually-measured fine grid meteorological monitoring data, if not, adjusting the kernel function or the bandwidth in the kernel function to re-interpolate, and if so, inputting the fine grid NWP data into a second mapping for prediction to obtain fine grid NWP predicted data in a future period. Thereafter, the fine-grid NWP prediction data is converted into first fine-grid meteorological prediction data for a future period using a first mapping, and the first fine-grid meteorological prediction data is input into a pre-trained wind power prediction model to predict wind power. Through the processing, high-precision wind power prediction based on NWP data and constrained by actual measurement fine grid meteorological monitoring data is achieved, and the problem that only coarse grid NWP data can be obtained at present and therefore high-precision prediction cannot be achieved is solved through NWP grid refinement processing under the constraint of the actual measurement fine grid meteorological monitoring data and NWP prediction under a fine grid. Furthermore, as a more optimal scheme, the predicted path is compared with a predicted path which does not depend on NWP data and only depends on actual measurement fine grid meteorological monitoring data, and a path with a better selection effect is preferentially selected for actual wind power prediction, so that the reliability of the wind power prediction scheme is further guaranteed.
Further effects of the above-mentioned non-conventional alternatives will be described below in connection with the embodiments.
Drawings
The drawings are included to provide a better understanding of the invention and are not to be construed as unduly limiting the invention. Wherein:
fig. 1 is a schematic diagram of main steps of a coastal NWP data grid refinement method based on double mapping in an embodiment of the present invention;
FIG. 2 is a schematic diagram illustrating a schematic diagram of a coastal NWP data grid refinement method based on double mapping in an embodiment of the present invention;
FIG. 3 is a schematic diagram of a part of a coastal NWP data grid refinement apparatus based on dual mapping according to an embodiment of the present invention;
FIG. 4 is an exemplary system architecture diagram in which embodiments of the present invention may be employed;
fig. 5 is a schematic structural diagram of an electronic device for implementing the coastal NWP data grid refinement method based on dual mapping in the embodiment of the present invention.
Detailed Description
Exemplary embodiments of the invention are described below with reference to the accompanying drawings, in which various details of embodiments of the invention are included to assist understanding, and which are to be considered as merely exemplary. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the invention. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.
It should be noted that the embodiments of the present invention and the technical features of the embodiments may be combined with each other without conflict.
Fig. 1 is a schematic diagram illustrating main steps of a coastal NWP data grid refinement method based on dual mapping according to an embodiment of the present invention.
As shown in fig. 1, the coastal NWP data grid refinement method based on dual mapping according to the embodiment of the present invention can be specifically performed according to the following steps:
step S101: and determining the corresponding relation of the measured coarse grid NWP data and the measured fine grid meteorological monitoring data in the historical period as a first mapping.
In the field of wind power technology, there are two kinds of raw data for predicting wind power, one is measured coarse grid NWP data, that is, meteorological data issued by wayware organizations such as weather stations and meteorological stations, and these data may include time, three-dimensional coordinates (such as longitude, latitude, altitude), temperature, humidity, wind direction, wind speed, and air pressure, that is, meteorological data capable of representing temperature, humidity, wind direction, wind speed, and air pressure at a certain time and a certain three-dimensional spatial position. The measured coarse grid NWP data may include data of a historical time period and may also include predicted data of a future time period. The other is measured fine grid meteorological monitoring data which is usually collected and recorded by a generator fan recorder or a surrounding monitoring network, wherein the data can also comprise time, three-dimensional coordinates, temperature, humidity, wind direction, wind speed, air pressure and the like, but only comprises historical period data and no future period data. It can be understood that the measured coarse grid NWP data and the measured fine grid meteorological monitoring data respectively belong to two independent systems (the former belongs to a general weather forecasting system, and the latter is internal data of a professional wind power system), and the two systems cannot be directly fused for use and need to be converted when being combined for use.
It should be noted that, in the embodiment of the present invention, a coarse grid refers to a large interval of data in a time and space scale, and a fine grid refers to a small interval of data in a time and space scale, for example, a coarse grid may be determined when a time interval is greater than or equal to 1 hour and a space interval is greater than or equal to 1 kilometer, and a coarse grid may be determined when a time interval is less than half an hour and a space interval is less than 500 meters. It can be understood that specific definitions of the coarse mesh and the fine mesh may be flexibly formulated according to an actual scene, and in the following description, the coarse mesh is taken as: the time interval is equal to 1 hour and the spatial interval is equal to 1 km, the fine grid is: the time interval equals 15 minutes and the space interval equals 300 meters as an example.
In step S101, the correspondence between the measured coarse grid NWP data and the measured fine grid meteorological monitoring data in the historical period is determined as a first mapping. In practical applications, the measured coarse grid NWP data D1 corresponding to a certain spatial point can be expressed as
I is a positive integer, and>
for discretized time, ->
，/>
Corresponding meteorological data such as temperature, humidity, wind speed, wind direction, air pressure and the like. LikeAdditionally, the measured fine grid weather monitoring data D2 corresponding to a spatial point may be expressed as ^ er>
，/>
，/>
For corresponding meteorological data such as temperature, humidity, wind speed, wind direction, air pressure, etc., the first mapping f1 can be expressed as a polynomial regression model as follows:
wherein z represents a portion of the measured coarse grid NWP data,
representing partially measured fine-grid meteorological monitoring data, k being a power, n being the maximum value of k, and->
Is the coefficient of each term. When determining each parameter in the first mapping, a plurality of sets of measured coarse grid NWP data and measured fine grid meteorological monitoring data corresponding to the same historical time point may be selected, and the polynomial regression model may be solved using these data to determine each parameter of the first mapping, thereby determining the first mapping.
Step S102: and interpolating the actually measured coarse grid NWP data according to a preset kernel function to obtain fine grid NWP data.
In this step, kernel function interpolation is performed on the measured coarse mesh NWP data, so as to obtain fine mesh NWP data. Specifically, for any position to be inserted, reference data of a preset number of neighborhood of the position to be inserted are obtained first, and the reference data belong to measured coarse grid NWP data. And then determining a weight value of each reference data aiming at the position to be inserted according to the kernel function, and calculating a weighted sum of the reference data based on the weight values to be used as fine mesh NWP data of the position to be inserted. As a preferable scheme, the above weight value may be calculated by: for any reference data, dividing the reference data and the kernel function calculation result of the position to be inserted by the sum of each reference data and the kernel function calculation result of the position to be inserted to obtain the weight value of the reference data for the position to be inserted.
Taking the interpolation distance of a time axis, the time interval of the actually measured coarse grid NWP data is 1 hour, and the interpolation needs to be performed at an interval of 15 minutes, then for a 1 point 15,1 point 30,1 point 45 which needs to perform interpolation of three points between a 1 point and a 2 point, taking the NWP data z of the 1 point 15 as an example, firstly, reference data such as the actually measured coarse grid NWP data z1 of the 1 point and the actually measured coarse grid NWP data z2 of the 2 point are selected according to a preset rule, and then, weight values w1 and w2 of z1 and z2 for z are respectively calculated, that is:
where K represents a pre-set kernel function, which may be in the form of a gaussian function or the like.
Thereafter, the reference data based on the above weight values may be weighted and taken as the fine mesh NWP data of the position to be inserted, thereby implementing refinement of the coarse mesh NWP data, that is:
step S103: and under the condition that the fine grid NWP data meet a preset high-precision condition which is restricted by the actually measured fine grid meteorological monitoring data, inputting the fine grid NWP data into a preset second mapping for prediction to obtain fine grid NWP predicted data in a future period.
In this step, the fine-grid NWP data formed in step S102 is checked by using the actually measured fine-grid meteorological monitoring data to prevent inaccuracy of the interpolation process. Specifically, the fine grid NWP data is first converted into fine grid meteorological data by using a first mapping (that is, the fine grid NWP data of the weather forecast system is converted into the fine grid meteorological data of the wind power system), and the deviation between the fine grid meteorological data and the actually measured fine grid meteorological monitoring data at corresponding time points is calculated. Because the fine grid meteorological data and the actually measured fine grid meteorological monitoring data are fine grid data and have the same time and space scale intervals, the meteorological data deviation at the corresponding time point can be comprehensively calculated, and the deviation can be the sum, the weighted sum, the average value and the weighted average value of the deviation at each time point. Finally, judging whether the deviation is smaller than a preset precision threshold value, if so, executing the next step through verification; if not, then the kernel needs to be adjusted or replaced (the bandwidth parameters in the kernel can be adjusted first, and then the kernel replacement is tried if the effect is not good).
When the above check passes, the fine mesh NWP data may be input to a second mapping for prediction, obtaining fine mesh NWP prediction data for future time periods. Illustratively, [ wang 1] may be a Prophet model composed of a time series of trend terms, seasonal terms, emergency terms, and random fluctuation terms, and parameters of the model may be determined by training future time period data in the coarse mesh NWP data as tags.
Step S104: and converting the fine grid NWP predicted data into first fine grid meteorological predicted data in a future period by utilizing a first mapping, and inputting the first fine grid meteorological predicted data into a wind power prediction model trained in advance to predict wind power.
The fine-grid NWP prediction data is prediction data of future time, belongs to a weather forecast system, and needs to be converted into a wind power system to perform wind power prediction, so that the fine-grid NWP prediction data is converted into first fine-grid meteorological prediction data of the wind power system by using a first mapping in the step. Finally, the first fine-mesh meteorological prediction data may be input into a pre-trained wind power prediction model to predict wind power.
As a better technical scheme, on the basis of the predicted path, a completely different predicted path can be designed and compared, and finally the wind power prediction in practical application is executed by using the more optimal path. This predicted path is: inputting the actually measured fine grid meteorological monitoring data into a second mapping to obtain second fine grid meteorological prediction data in a future period, and inputting the second fine grid meteorological prediction data into a wind power prediction model to obtain a wind power prediction result of the second fine grid meteorological prediction data in the current test period. And finally, comparing the wind power prediction results of the first fine grid meteorological prediction data and the second fine grid meteorological prediction data in the current test period, and taking the fine grid meteorological prediction data corresponding to a better wind power prediction result as the input data of the wind power prediction model in the current application period. In this way, higher system reliability can be achieved.
Fig. 2 is a schematic diagram of a principle of a coastal NWP data grid refinement method based on dual mapping in an embodiment of the present invention, and refer to fig. 2. After the first mapping f1 and the second mapping f2 are established, kernel function interpolation is firstly carried out on the measured coarse grid NWP data to obtain fine grid NWP data. And then converting the data into fine grid meteorological data by utilizing the first mapping, and verifying the data by using the actually measured fine grid meteorological monitoring data. And inputting the NWP data of the fine grids into a second mapping after the verification is passed to obtain the NWP predicted data of the fine grids, wherein the second mapping needs to be restrained by actually measuring the NWP data of the coarse grids. And then, the fine-grid NWP predicted data form first fine-grid meteorological predicted data entering a wind power prediction model through first mapping, and the first fine-grid meteorological predicted data are a first predicted path. The second prediction path is that the actually measured fine grid meteorological monitoring data directly enters a second mapping to form second fine grid meteorological prediction data and enters a wind power prediction model. And finally, comparing the prediction results of the two prediction paths, and preferentially selecting one of the prediction results to be used for actual wind power prediction.
In the technical scheme of the embodiment of the invention, based on the data precision level of the current weather related industry, the offshore NWP data optimization research and application of the fine grid are realized by adopting the data processing method based on machine learning, and the method has positive exploration significance for further improving the offshore wind power prediction precision, developing new energy industries, particularly the offshore wind power industry and realizing the aim of 'two carbons'.
It should be noted that for the above-mentioned embodiments of the method, for convenience of description, the embodiments are described as a series of combinations of actions, but those skilled in the art should understand that the present invention is not limited by the described order of actions, and that some steps may in fact be performed in other orders or simultaneously. Moreover, those skilled in the art will appreciate that the embodiments described in the specification are presently preferred and that no acts or modules are necessarily required to implement the invention.
To facilitate a better implementation of the above-described aspects of embodiments of the present invention, the following also provides relevant means for implementing the above-described aspects.
Referring to fig. 3, a coastal NWP data grid refinement apparatus 300 based on dual mapping according to an embodiment of the present invention may include: a first mapping establishing unit 301, a refining unit 302, a second mapping predicting unit 303 and a wind power predicting unit 304.
Wherein, the first mapping establishing unit 301 is configured to: determining the corresponding relation of actual measurement coarse grid numerical weather forecast NWP data and actual measurement fine grid weather monitoring data in a historical period as a first mapping; the refinement unit 302 is configured to: interpolating the actually measured coarse grid NWP data according to a preset kernel function to obtain fine grid NWP data; the second mapping prediction unit 303 is configured to: under the condition that the fine grid NWP data meet a preset high-precision condition constrained by the actually-measured fine grid meteorological monitoring data, inputting the fine grid NWP data into a preset second mapping for prediction to obtain fine grid NWP predicted data in a future period; the wind power prediction unit 304 is configured to: and converting the fine grid NWP predicted data into first fine grid meteorological predicted data of a future period by utilizing a first mapping, and inputting the first fine grid meteorological predicted data into a wind power prediction model trained in advance to predict wind power.
In an embodiment of the present invention, the refining unit 302 is further configured to: for any position to be inserted, acquiring a preset number of reference data of a neighborhood of the position to be inserted; wherein the reference data belongs to the measured coarse grid NWP data; for any reference data, dividing the reference data and the kernel function calculation result of the position to be inserted by the sum of each reference data and the kernel function calculation result of the position to be inserted to obtain the weight value of the reference data for the position to be inserted; and calculating the reference data based on the weighted sum of the weight values as the fine mesh NWP data of the position to be inserted.
Preferably, satisfying the high accuracy condition includes: converting the fine grid NWP data into fine grid meteorological data by utilizing a first mapping, calculating the deviation between the fine grid meteorological data and the actually measured fine grid meteorological monitoring data, and determining that the deviation is smaller than a preset precision threshold; the wind power prediction unit 304 is further configured to: inputting the actually measured fine grid meteorological monitoring data into a second mapping to obtain second fine grid meteorological prediction data in a future period, and inputting the second fine grid meteorological prediction data into the wind power prediction model to obtain a wind power prediction result of the second fine grid meteorological prediction data in the current test period; and comparing the wind power prediction results of the first fine grid meteorological prediction data and the second fine grid meteorological prediction data in the current test period, and using the fine grid meteorological prediction data corresponding to a better wind power prediction result as the input data of the wind power prediction model in the current application period.
According to the technical scheme of the embodiment of the invention, firstly, the corresponding relation of the actually measured coarse grid NWP data and the actually measured fine grid meteorological monitoring data in a historical period is determined as a first mapping, and the actually measured coarse grid NWP data is interpolated according to a preset kernel function to obtain the fine grid NWP data. And then judging whether the fine grid NWP data meets a high-precision condition constrained by the actually-measured fine grid meteorological monitoring data, if not, adjusting the kernel function or the bandwidth in the kernel function to interpolate again, and if so, inputting the fine grid NWP data into a second mapping for prediction to obtain fine grid NWP predicted data in a future period. Thereafter, the fine-grid NWP prediction data is converted into first fine-grid meteorological prediction data for a future period using a first mapping, and the first fine-grid meteorological prediction data is input into a pre-trained wind power prediction model to predict wind power. Through the processing, high-precision wind power prediction based on NWP data and constrained by actual measurement fine grid meteorological monitoring data is achieved, and the problem that only coarse grid NWP data can be obtained at present and therefore high-precision prediction cannot be achieved is solved through NWP grid refinement processing under the constraint of the actual measurement fine grid meteorological monitoring data and NWP prediction under a fine grid. Furthermore, as a more optimal scheme, the predicted path is compared with a predicted path which does not depend on NWP data and only depends on actual measurement fine grid meteorological monitoring data, and a path with a better selection effect is preferentially selected for actual wind power prediction, so that the reliability of the wind power prediction scheme is further guaranteed.
Fig. 4 illustrates an exemplary system architecture 400 to which a dual mapping-based coastal NWP data grid refinement method or a dual mapping-based coastal NWP data grid refinement apparatus of an embodiment of the present invention may be applied.
As shown in fig. 4, the system architecture 400 may include terminal devices 401, 402, 403, a network 404, and a server 405 (this architecture is merely an example, and the components included in a particular architecture may be adapted according to application specific circumstances). The network 404 serves as a medium for providing communication links between the terminal devices 401, 402, 403 and the server 405. Network 404 may include various types of connections, such as wire, wireless communication links, or fiber optic cables.
A user may use terminal devices 401, 402, 403 to interact with a server 405 over a network 404 to receive or send messages or the like. The terminal devices 401, 402, 403 may have installed thereon various communication client applications, such as a wind power prediction application (for example only).
The terminal devices 401, 402, 403 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smart phones, tablet computers, laptop portable computers, desktop computers, and the like.
The server 405 may be a server providing various services, such as a background server (for example only) providing support for a wind power prediction application operated by a user with the terminal device 401, 402, 403. The backend server may process the received wind power prediction request or the like and feed back the processing result (e.g. the predicted wind power-just an example) to the terminal devices 401, 402, 403.
It should be noted that the method for refining the coastal NWP data grid based on the double mapping provided by the embodiment of the present invention is generally executed by the server 405, and accordingly, the device for refining the coastal NWP data grid based on the double mapping is generally disposed in the server 405.
It should be understood that the number of terminal devices, networks, and servers in fig. 4 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation.
The invention also provides electronic equipment. The electronic device of the embodiment of the invention comprises: one or more processors; and the storage device is used for storing one or more programs, and when the one or more programs are executed by the one or more processors, the one or more processors realize the coastal NWP data grid refinement method based on the double mapping provided by the invention.
Referring now to FIG. 5, shown is a block diagram of a computer system 500 suitable for use in implementing an electronic device of an embodiment of the present invention. The electronic device shown in fig. 5 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present invention.
As shown in fig. 5, the computer system 500 includes a Central Processing Unit (CPU) 501 that can perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM) 502 or a program loaded from a storage section 508 into a Random Access Memory (RAM) 503. In the RAM503, various programs and data necessary for the operation of the computer system 500 are also stored. The CPU501, ROM 502, and RAM503 are connected to each other through a bus 504. An input/output (I/O) interface 505 is also connected to bus 504.
The following components are connected to the I/O interface 505: an input portion 506 including a keyboard, a mouse, and the like; an output portion 507 including a display such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, and a speaker; a storage portion 508 including a hard disk and the like; and a communication section 509 including a network interface card such as a LAN card, a modem, or the like. The communication section 509 performs communication processing via a network such as the internet. The driver 510 is also connected to the I/O interface 505 as necessary. A removable medium 511 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 510 as necessary, so that a computer program read out therefrom is mounted into the storage section 508 as necessary.
In particular, the processes described in the main step diagrams above may be implemented as computer software programs, according to embodiments of the present disclosure. For example, embodiments of the invention include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method illustrated in the main step diagram. In the above-described embodiment, the computer program can be downloaded and installed from a network through the communication section 509, and/or installed from the removable medium 511. The computer program performs the above-described functions defined in the system of the present invention when executed by the central processing unit 501.
It should be noted that the computer readable medium shown in the present invention can be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present invention, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In the present invention, a computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, fiber optic cable, RF, etc., or any suitable combination of the foregoing.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams or flowchart illustration, and combinations of blocks in the block diagrams or flowchart illustration, can be implemented by special purpose hardware-based systems that perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The units described in the embodiments of the present invention may be implemented by software or hardware. The described units may also be provided in a processor, and may be described as: a processor includes a first map creation unit, a refinement unit, a second map prediction unit, and a wind power prediction unit. Where the names of these units do not in some cases constitute a limitation of the unit itself, for example, the refinement unit may also be described as a "unit providing fine mesh NWP data to the second mapped prediction unit".
As another aspect, the present invention also provides a computer-readable medium that may be contained in the apparatus described in the above embodiments; or may be separate and not incorporated into the device. The computer readable medium carries one or more programs which, when executed by the apparatus, cause the apparatus to perform steps comprising: determining the corresponding relation of actual measured coarse grid numerical weather forecast NWP data and actual measured fine grid meteorological monitoring data in a historical period as first mapping; interpolating the measured coarse grid NWP data according to a preset kernel function to obtain fine grid NWP data; under the condition that the fine grid NWP data meet a preset high-precision condition constrained by the actually-measured fine grid meteorological monitoring data, inputting the fine grid NWP data into a preset second mapping for prediction to obtain fine grid NWP predicted data in a future period; and converting the fine grid NWP predicted data into first fine grid meteorological predicted data of a future period by utilizing a first mapping, and inputting the first fine grid meteorological predicted data into a wind power prediction model trained in advance to predict wind power.
In the technical scheme of the embodiment of the invention, high-precision wind power prediction based on NWP data and constrained by actual measurement fine grid meteorological monitoring data is realized, and the problem that only coarse grid NWP data can be obtained at present and therefore high-precision prediction cannot be realized is solved through NWP grid refinement processing under the constraint of the actual measurement fine grid meteorological monitoring data and NWP prediction under a fine grid. Furthermore, as a more optimal scheme, the predicted path is compared with a predicted path which does not depend on NWP data and only depends on actual measurement fine grid meteorological monitoring data, and a path with a better selection effect is preferentially selected for actual wind power prediction, so that the reliability of the wind power prediction scheme is further guaranteed.
The above-described embodiments should not be construed as limiting the scope of the invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may occur depending on design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
The details of prophets in the filing book are well known and need not be written in the patent.
Claims (10)
1. A coastal NWP data grid refinement method based on double mapping is characterized by comprising the following steps:
determining the corresponding relation of actual measured coarse grid numerical weather forecast NWP data and actual measured fine grid meteorological monitoring data in a historical period as first mapping;
interpolating the measured coarse grid NWP data according to a preset kernel function to obtain fine grid NWP data;
under the condition that the fine grid NWP data meet a preset high-precision condition constrained by the actually-measured fine grid meteorological monitoring data, inputting the fine grid NWP data into a preset second mapping for prediction to obtain fine grid NWP predicted data in a future period;
and converting the fine grid NWP predicted data into first fine grid meteorological predicted data of a future period by utilizing a first mapping, and inputting the first fine grid meteorological predicted data into a wind power prediction model trained in advance to predict wind power.
2. The method according to claim 1, wherein the interpolating the measured coarse grid NWP data according to a preset kernel function to obtain fine grid NWP data comprises:
for any position to be inserted, acquiring a preset number of reference data of a neighborhood of the position to be inserted; wherein the reference data belongs to the measured coarse grid NWP data;
determining a weight value of each reference data aiming at the position to be inserted according to the kernel function, and calculating the weighted sum of the reference data based on the weight values to be used as the fine mesh NWP data of the position to be inserted.
3. The method of claim 2, wherein determining the weight value of each reference data for the location to be inserted according to the kernel function comprises:
for any reference data, dividing the reference data and the kernel function calculation result of the position to be inserted by the sum of each reference data and the kernel function calculation result of the position to be inserted to obtain the weight value of the reference data for the position to be inserted.
4. The method of claim 1, wherein satisfying the high-precision condition comprises:
and converting the fine grid NWP data into fine grid meteorological data by utilizing a first mapping, calculating the deviation between the fine grid meteorological data and the actually measured fine grid meteorological monitoring data, and determining that the deviation is smaller than a preset precision threshold value.
5. The method of claim 1, further comprising:
inputting the actually measured fine grid meteorological monitoring data into a second mapping to obtain second fine grid meteorological prediction data in a future period, and inputting the second fine grid meteorological prediction data into the wind power prediction model to obtain a wind power prediction result of the second fine grid meteorological prediction data in the current test period;
and comparing the wind power prediction results of the first fine grid meteorological prediction data and the second fine grid meteorological prediction data in the current test period, and using the fine grid meteorological prediction data corresponding to a better wind power prediction result as the input data of the wind power prediction model in the current application period.
6. A coastal NWP data grid refining device based on double mapping is characterized by comprising the following components:
a first mapping establishing unit configured to: determining the corresponding relation of actual measurement coarse grid numerical weather forecast NWP data and actual measurement fine grid weather monitoring data in a historical period as a first mapping;
a refinement unit to: interpolating the actually measured coarse grid NWP data according to a preset kernel function to obtain fine grid NWP data;
a second mapping prediction unit to: under the condition that the fine grid NWP data meet a preset high-precision condition constrained by the actually-measured fine grid meteorological monitoring data, inputting the fine grid NWP data into a preset second mapping for prediction to obtain fine grid NWP predicted data in a future period;
a wind power prediction unit to: and converting the fine grid NWP predicted data into first fine grid meteorological predicted data in a future period by using a first mapping, and inputting the first fine grid meteorological predicted data into a pre-trained wind power prediction model to predict wind power.
7. The apparatus of claim 6, wherein the refining unit is further configured to:
for any position to be inserted, acquiring a preset number of reference data of a neighborhood of the position to be inserted; wherein the reference data belongs to the measured coarse grid NWP data; for any reference data, dividing the reference data and the kernel function calculation result of the position to be inserted by the sum of each reference data and the kernel function calculation result of the position to be inserted to obtain the weight value of the reference data for the position to be inserted; and calculating the reference data based on the weighted sum of the weight values as the fine mesh NWP data of the position to be inserted.
8. The apparatus of claim 6, wherein satisfying the high accuracy condition comprises: converting the fine grid NWP data into fine grid meteorological data by utilizing a first mapping, calculating the deviation between the fine grid meteorological data and the actually measured fine grid meteorological monitoring data, and determining that the deviation is smaller than a preset precision threshold;
the wind power prediction unit is further to: inputting the actually measured fine grid meteorological monitoring data into a second mapping to obtain second fine grid meteorological prediction data in a future period, and inputting the second fine grid meteorological prediction data into the wind power prediction model to obtain a wind power prediction result of the second fine grid meteorological prediction data in the current test period; and comparing the wind power prediction results of the first fine grid meteorological prediction data and the second fine grid meteorological prediction data in the current test period, and using the fine grid meteorological prediction data corresponding to a better wind power prediction result as the input data of the wind power prediction model in the current application period.
9. An electronic device, comprising:
one or more processors;
a storage device for storing one or more programs,
when executed by the one or more processors, cause the one or more processors to implement the method of any one of claims 1-5.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the method according to any one of claims 1-5.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211713006.4A CN115983121A (en) | 2022-12-30 | 2022-12-30 | Coastal NWP data grid refinement method and device based on double mapping |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211713006.4A CN115983121A (en) | 2022-12-30 | 2022-12-30 | Coastal NWP data grid refinement method and device based on double mapping |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN115983121A true CN115983121A (en) | 2023-04-18 |
Family
ID=85962269
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202211713006.4A Pending CN115983121A (en) | 2022-12-30 | 2022-12-30 | Coastal NWP data grid refinement method and device based on double mapping |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115983121A (en) |
-
2022
- 2022-12-30 CN CN202211713006.4A patent/CN115983121A/en active Pending
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US11169911B2 (en) | Method and apparatus for performing a fitting calculation on test data and generating data fluctuation values | |
| CN109697522B (en) | Data prediction method and device | |
| CN113777552B (en) | Method and system for predicting metering error of capacitive voltage transformer | |
| CN110972261A (en) | Base station fingerprint database establishing method, device, server and storage medium | |
| CN111062525B (en) | Base station address selection method, device, equipment and storage medium for expanding cell | |
| CN112308281A (en) | Temperature information prediction method and device | |
| CN112285807A (en) | Meteorological information prediction method and device | |
| WO2023134188A1 (en) | Index determination method and apparatus, and electronic device and computer-readable medium | |
| CN109799550A (en) | Method and apparatus for predicting rainfall intensity | |
| US20200389026A1 (en) | Electric power generation prediction method based on expected value calculation, electric power generation prediction system based on expected value calculation, and electric power generation prediction program based on expected value calculation | |
| CN114500339A (en) | Node bandwidth monitoring method and device, electronic equipment and storage medium | |
| CN113344737B (en) | Device control method, device, electronic device and computer readable medium | |
| US9019122B2 (en) | Information retrieval for boundary reading processing | |
| CN113722369A (en) | Method, device, equipment and storage medium for predicting field monitoring data | |
| CN110850296A (en) | Method and device for evaluating health degree of battery | |
| CN112995893A (en) | Fingerprint positioning method, system, server and storage medium | |
| CN115983121A (en) | Coastal NWP data grid refinement method and device based on double mapping | |
| CN115617633A (en) | Icing monitoring terminal maintenance method and related equipment | |
| KR20170060361A (en) | Device for analyzing power demands and system comprising the same | |
| CN115453661A (en) | Weather forecasting method, weather forecasting device, weather forecasting equipment and storage medium | |
| CN115391746A (en) | Interpolation method, device, electronic device and medium for meteorological element data | |
| CN113902230A (en) | Electric quantity deviation control method, system, storage medium and electronic equipment | |
| CN113723712B (en) | Wind power prediction method, system, equipment and medium | |
| US9635441B2 (en) | Information retrieval for service point channels | |
| CN109684430A (en) | Method and apparatus for output information |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |