Rapid generation method and system for wind resource atlas of designated area
By simulating partitions and medium -scale meteorological models for specified areas, combined with power reduction scale and random forest regression models, the problem of difficulty to quickly generate high -precision wind resource maps in existing technologies has been solved. Essence
Patent Information
- Application Number
- CN202411576828.1
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-11-06
- Publication Date
- 2025-01-24
AI Technical Summary
The prior art is difficult to quickly generate high-precision wind resource maps for designated areas, especially in the automatic coupling of wind tower data and mesoscale data.
By zoning designated areas, conducting mesoscale meteorological model simulations, dynamic downscaling, splicing wind resource maps, and generating high-precision wind resource maps through spatial matching of wind tower data and training of random forest regression models.
The rapid generation of high -precision wind resource maps has been achieved, the efficiency of the production of wind resource map generation is improved, the medium -scale wind resource data platform in all regions is supported, and it has the ability to flexibly import or call the existing wind testing of wind and measure data.
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Figure CN119357438A_ABST
Abstract
Description
Technical Field
[0001] The present invention belongs to the technical field of wind power, and in particular relates to a method and system for quickly generating a wind resource map of a specified area. Background Art
[0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art.
[0003] In order to achieve efficient wind resource assessment and project planning, how to quickly generate a resource map of a specified area is a challenging technical goal.
[0004] At present, the wind farm modeling methods mainly used in the wind power industry include linear models, computational fluid dynamics (CFD) and mesoscale numerical models.
[0005] Mesoscale meteorological models (such as WRF) provide a more comprehensive tool for simulating the spatiotemporal distribution characteristics of wind fields by solving equations and parameterizing different processes in the weather system. Compared with traditional methods, mesoscale models can more accurately consider the turbulence closure scheme of the atmospheric boundary layer, while comprehensively considering multiple atmospheric physical properties such as thermal stability. This makes mesoscale models more accurate and applicable in the assessment and prediction of wind resources in large areas. At present, mesoscale meteorological models provide a more comprehensive and efficient method by solving equations and parameterizing atmospheric physical processes, which can more accurately simulate the spatiotemporal distribution characteristics of wind fields in a specified area.
[0006] The current technical challenge is how to automatically couple wind tower data with mesoscale data to generate and correct wind resource maps, and be able to flexibly output wind resource conditions at any point; at the same time, it is necessary to provide the function of downloading mesoscale reanalysis data at any point, and have the ability to integrate other third-party mesoscale reanalysis products; in addition, it is necessary to support mesoscale wind resource data platforms in all regions, and realize the import or call of existing wind tower wind measurement data to achieve the generation of high-precision wind resource maps in all regions. Summary of the invention
[0007] In order to solve the technical problems existing in the above-mentioned background technology, the present invention provides a method and system for quickly generating a wind resource map of a specified area, studies the coupling calculation method of wind measurement data of a wind tower in a specified area and mesoscale data, generates a high-precision wind resource map, and on this basis, establishes a relationship with reanalysis data, realizes the conversion of reanalysis data into high-resolution wind resource data, and generates a more comprehensive high-precision wind resource map.
[0008] In order to achieve the above object, the present invention adopts the following technical solution: A first aspect of the present invention provides a method for quickly generating a wind resource map of a specified area, comprising: The designated area is divided into several sub-areas. For each sub-area, a mesoscale meteorological model simulation is performed in a designated year to obtain background field data. For the background field data of each sub-area, dynamic downscaling is performed to obtain a 100-meter resolution wind resource map of a specified year; the 100-meter resolution wind resource maps of all areas of a specified year are spliced to obtain a 100-meter resolution wind resource map of a specified area of a specified year; the wind tower data of a specified area of a specified year are obtained, and after spatial matching of the wind tower data with the 100-meter resolution wind resource map of a specified area of a specified year, the 100-meter resolution wind resource map of a specified area of a specified year is corrected; Obtain the reanalysis data of the specified year, use the reanalysis data of the specified year as input, and the corrected 100-meter resolution wind resource map of the specified area in the specified year as output to train the random forest regression model; obtain the reanalysis data of non-specified years, perform statistical downscaling through the trained random forest regression model, and obtain the 100-meter resolution wind resource map of the specified area in the non-specified year.
[0009] Furthermore, when simulating the mesoscale meteorological model for a specified year, the cumulus convection physical parameterization scheme selects the boundary layer parameterization scheme, the cloud microphysics parameterization scheme selects the Thompson dual-parameter scheme, the planetary boundary layer physical parameterization scheme selects the MYNN scheme, the shortwave radiation physical parameterization scheme selects the RRTMG scheme, the longwave radiation physical parameterization scheme adopts the RRTMG scheme, and the pavement process physical parameterization scheme adopts the NOAH-MP scheme.
[0010] Furthermore, the method for correcting the wind resource atlas with a resolution of 100 meters in the designated area and designated year is: weighted averaging the wind tower data and the wind field data in the wind resource atlas with a resolution of 100 meters in the designated area and designated year.
[0011] Furthermore, the step of dynamic downscaling includes: converting the format of background field data to obtain format-converted meteorological initial field data; acquiring terrain elevation and land use type data, resampling, and obtaining binary geographic information data; and running the California meteorological model based on the binary geographic information data and the format-converted meteorological initial field data to achieve dynamic downscaling.
[0012] A second aspect of the present invention provides a system for quickly generating a wind resource map of a specified area, comprising: A mesoscale simulation module is configured to: partition a designated area to obtain a plurality of sub-areas, and for each sub-area, perform a mesoscale meteorological model simulation for a designated year to obtain background field data; The dynamic downscaling module is configured to: perform dynamic downscaling on the background field data of each sub-area to obtain a 100-meter resolution wind resource map of a specified year; splice the 100-meter resolution wind resource maps of all areas in a specified year to obtain a 100-meter resolution wind resource map of a specified area in a specified year; obtain the wind tower data of a specified area in a specified year, perform spatial matching between the wind tower data and the 100-meter resolution wind resource map of a specified area in a specified year, and then calibrate the 100-meter resolution wind resource map of a specified area in a specified year; The statistical downscaling module is configured as follows: obtaining reanalysis data of a specified year, taking the reanalysis data of a specified year as input, and outputting a corrected wind resource map of a specified area in a specified year with a resolution of 100 meters, and training a random forest regression model; obtaining reanalysis data of non-specified years, and performing statistical downscaling through the trained random forest regression model to obtain a wind resource map of a specified area in a non-specified year with a resolution of 100 meters.
[0013] Furthermore, when simulating the mesoscale meteorological model for a specified year, the cumulus convection physical parameterization scheme selects the boundary layer parameterization scheme, the cloud microphysics parameterization scheme selects the Thompson dual-parameter scheme, the planetary boundary layer physical parameterization scheme selects the MYNN scheme, the shortwave radiation physical parameterization scheme selects the RRTMG scheme, the longwave radiation physical parameterization scheme adopts the RRTMG scheme, and the pavement process physical parameterization scheme adopts the NOAH-MP scheme.
[0014] Furthermore, the method for correcting the wind resource atlas with a resolution of 100 meters in the designated area and designated year is: weighted averaging the wind tower data and the wind field data in the wind resource atlas with a resolution of 100 meters in the designated area and designated year.
[0015] Furthermore, the step of dynamic downscaling includes: converting the format of background field data to obtain format-converted meteorological initial field data; acquiring terrain elevation and land use type data, resampling, and obtaining binary geographic information data; and running the California meteorological model based on the binary geographic information data and the format-converted meteorological initial field data to achieve dynamic downscaling.
[0016] A third aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps in the method for rapidly generating a wind resource map of a specified area as described above.
[0017] The fourth aspect of the present invention provides a computer device, comprising a computer-readable storage medium, a processor, and a computer program stored on the computer-readable storage medium and executable on the processor, wherein when the processor executes the program, the steps in the method for rapidly generating a wind resource map of a specified area as described above are implemented.
[0018] Compared with the prior art, the present invention has the following beneficial effects: The present invention studies the coupling calculation method of wind tower wind data and mesoscale data in a specified area to generate a high-precision wind resource map, and on this basis, establishes a relationship with reanalysis data to realize the conversion of reanalysis data into high-resolution wind resource data.
[0019] The present invention partitions the designated area to realize the generation of high-precision wind resource maps of all regions and can flexibly output the wind resource conditions at any point.
[0020] The present invention realizes the automatic coupling of wind tower data and mesoscale data without complicated manual operation, thus improving the efficiency of wind resource map generation.
[0021] The present invention can flexibly import or call the wind measurement data of existing wind towers through spatial matching. This comprehensive support feature ensures wide applicability, enabling it to play a role in a variety of application scenarios. BRIEF DESCRIPTION OF THE DRAWINGS
[0022] The accompanying drawings in the specification, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.
[0023] Figure 1 It is a flow chart of a method for quickly generating a wind resource map of a specified area according to the first embodiment of the present invention; Figure 2 It is a structural diagram of a computer device according to a fourth embodiment of the present invention. DETAILED DESCRIPTION
[0024] To make the purpose, technical solution and advantages of the embodiments of the present invention more clear, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention.
[0025] It should be noted that the following detailed descriptions are all illustrative and intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meanings as those commonly understood by those skilled in the art to which the present invention belongs.
[0026] Embodiment 1 This embodiment provides a method for quickly generating a wind resource map of a specified area.
[0027] This embodiment provides a method for quickly generating a wind resource map of a specified area, studies the coupling calculation method of wind tower wind data and mesoscale data in the specified area, generates a high-precision wind resource map, and provides a function of querying wind resource data at any point, including but not limited to outputting a wind resource map of any area (KMZ / KML, tif).
[0028] The present embodiment provides a method for quickly generating a wind resource map of a designated area. The method comprises the following steps: partitioning a designated area to obtain a plurality of sub-areas, and for each sub-area, performing a mesoscale meteorological model simulation of a designated year to obtain background field data; performing dynamic downscaling on the background field data of each sub-area to obtain a 100-meter resolution wind resource map of a designated year; splicing the 100-meter resolution wind resource maps of all areas of a designated year to obtain a 100-meter resolution wind resource map of a designated area of a designated year; obtaining wind tower data of a designated area of a designated year, spatially matching the wind tower data with the 100-meter resolution wind resource map of a designated area of a designated year, and then correcting the 100-meter resolution wind resource map of a designated area of a designated year; obtaining reanalysis data of a designated year, taking the reanalysis data of a designated year as input and the corrected 100-meter resolution wind resource map of a designated area of a designated year as output, and training a random forest regression model; obtaining reanalysis data of a non-designated year (multi-year ERA5 reanalysis data or updated ERA5 reanalysis data), and performing statistical downscaling on the trained random forest regression model to obtain a 100-meter resolution wind resource map of a non-designated year of a designated area.
[0029] This embodiment provides a method for quickly generating a wind resource map for a specified area, such as Figure 1 As shown, the following steps are included: Step 1: Background field simulation: The whole country is divided into different regions, and the WRF simulation background field is obtained by running the regions.
[0030] Step 101: Divide the whole country (the whole scope, i.e., the designated area) into several sub-areas.
[0031] Step 102: In each sub-region, a specific year is selected, such as 2022 with more observation data, and a mesoscale meteorological model (WRF model, Weather Research & Forecasting Model) is used to perform high-resolution simulation (abbreviated as mesoscale meteorological model simulation or WRF mesoscale simulation) to obtain background field data for the sub-region; after all sub-regions are run, background field data with a resolution of 2km×2km for the entire range is obtained.
[0032] In step 102, the WRF model version used is V4.5, the land cover is the 30″ (about 1km) resolution product of MODIS (Moderate Resolution Imaging Spectroradiometer), the soil texture uses the 30″ product released by Beijing Normal University, and the terrain uses the 30-meter COP-DEM (Copernicus DEM, Copernicus Digital Elevation Model). The driving data uses the FNL global analysis data (Final Operational Global Analysis, FNL data) of NCEP (National Centers for Environmental Prediction, the National Center for Environmental Prediction of the United States).
[0033] In the mesoscale meteorological model simulation, sensitivity experiments are designed to design different experimental groups for parameter schemes that may have a greater impact on the near-surface wind simulation, including boundary layer parameterization schemes and microphysical parameterization schemes, and select different parameterization schemes for comparison. For example, the boundary layer scheme selects MYNN, YSU, Pleim-Xiu, etc. The WRF model is run separately according to the set experimental groups to ensure that each experiment is carried out under the same initial and boundary conditions.
[0034] Based on the observation data of the actual wind tower, the simulation results of different experimental groups were verified and the performance of different schemes was evaluated. The best performing scheme was selected and the results are as follows: In the simulation of mesoscale meteorological model, four-dimensional relaxation approximation (Nugding) technology is used to make the model output close to the reanalysis data by adding forcing terms in the prediction equation. The parameterization schemes are as follows: the GF scheme (Grenier-Bretherton-McCaa boundary layer parameterization scheme) is selected for the cumulus convection physical parameterization scheme, the Thompson dual parameter scheme is selected for the cloud microphysics parameterization scheme, the MYNN scheme (Mellor-Yamada-Nakanishi-Niino scheme) is selected for the planetary boundary layer physical parameterization scheme, the RRTMG scheme is selected for the shortwave radiation physical parameterization scheme, the RRTMG scheme (Rapid Radiative Transfer Model for GCMs) is selected for the longwave radiation physical parameterization scheme, and the NOAH-MP (Noah Land Surface Model with Multi-Physics) scheme is selected for the road surface process physical parameterization scheme.
[0035] Step 2: Regional downscaling: Downscale the background field data of each region to a resolution of 100 meters to obtain a 100-meter resolution wind resource map for a specified year.
[0036] Downscaling technology is a method of converting large-scale, low-resolution model output information into small-scale, high-resolution regional climate change information. Dynamical downscaling refers to regional climate models (GCMs) or global reanalysis data providing initial conditions and boundary conditions to higher-resolution regional climate models (RCMs). RCMs, through internal physical processes, can not only retain the large-scale characteristics of GCMs, but also simulate more regional-scale climate characteristics than GCMs. Statistical downscaling is based on statistical relationships to achieve the downscaling process. Statistical downscaling has a small amount of calculation and saves machine time. Its research area and specific implementation plan have greater flexibility.
[0037] Specifically, the 2km×2km background field data of each sub-area obtained in step 1 were dynamically downscaled using the CALMET (California Meteorological Model) technology to obtain 100-meter resolution wind resource maps at altitudes of 100 meters, 120 meters, 140 meters, 160 meters, and 180 meters for each sub-area within the overall range.
[0038] The steps of dynamic downscaling using CALMET technology are as follows: Step 201, static data preparation: Based on the downloaded terrain elevation (Shuttle Radar Topography Mission version 4.1, SRTM V4.1) and land use type data (The first 10-m resolution global land cover map, FROM-GLC10), the terrain elevation and land use type data are resampled to the target data resolution of 200 meters using the bilinear interpolation method based on Python, and output as binary geographic information data GEO.DAT that can be read by CALMET; Step 202, data preprocessing: the background field data of each region obtained from the WRF output in step 1 is converted into a format using the data interface program of CALWRF, the format of the meteorological initial field data in CALMET is converted, and the original wrfout data in NetCDF (network Common Data Form) format is formatted into a binary 3D.DAT format readable by CALMET; Step 203, dynamic downscaling: based on the static data and initial meteorological field data obtained above, the CALMET mode is run to refine the wind field and output 200-meter resolution data to achieve dynamic downscaling; Step 204, data post-processing: extract wind field variables from the CALMET output data, and finally export it to the commonly used NetCDF format.
[0039] Step 205, wind field stitching: The 100-meter resolution data obtained by downscaling each region is stitched using the ESMF (Earth System Modeling Framework) technology. Specifically: according to the geographical scope (longitude and latitude boundaries) and grid resolution of each sub-region, the position of each sub-region in the target grid is determined; the wind field data such as wind speed and wind direction of each sub-region is superimposed on the corresponding area of the target grid; for each altitude layer, the input and output grids are defined respectively, and the same re-gridding and stitching steps as above are performed, and finally a complete data map of the whole country (i.e., a map of the whole country with a resolution of 100 meters for a specified year) is obtained.
[0040] Step 206, wind measurement data coupling: collect observation data of wind towers in the required area, including meteorological elements such as wind speed and wind direction, and record the geographical coordinate information of the wind towers.
[0041] The wind tower data is spatially matched with the downscaled wind field data to ensure that the wind tower data can correspond to the relevant model output area. The measured wind speed and wind direction data of the wind tower are compared with the wind field data output by the model, and the wind speed and wind direction are corrected at the grid points output by the model using the interpolation method.
[0042] For areas near wind towers, the downscaled wind data is partially corrected by weighted averaging and other methods (i.e., weighted averaging of wind tower data and wind field data in the 100-meter resolution wind resource map for a specified area and year) to improve its consistency with the measured data. The corrected wind field data is combined with the downscaled data to ensure that the obtained high-resolution wind resource map is more accurate, especially in areas monitored by wind towers, and can better reflect the actual wind field conditions.
[0043] Step 3: Establish statistical relationships.
[0044] Step 301, data acquisition and preprocessing: Use Python CDS-API to obtain the ERA5 (European Centre for Medium-Range Weather Forecasts Reanalysis v5) reanalysis data for the same period as the simulation results (2022), and select meteorological elements suitable for forecasting as forecast factors.
[0045] Step 302: Perform pre-processing steps such as format conversion and unit unification on the data acquired in step 301 to ensure data consistency.
[0046] Step 303, data division and model training: The high-precision wind resource data, wind tower observation data and prediction factors in the ERA5 reanalysis data obtained in step 2 are divided into a training set and a validation set, using about 70% of the data as the training set and 30% as the validation set.
[0047] Step 304: Use the training set to train the statistical model, select the random forest regression model, input the data of the training set into the selected statistical model, and adjust the model parameters to achieve the best fit between the independent variable (ERA5 reanalysis data) and the target variable (i.e., the 100-meter resolution wind resource map of the specified year). By minimizing the error function (mean square error MSE, etc.) or maximizing the correlation (R², etc.), the model parameters are continuously adjusted to ensure that the ERA5 data can accurately fit the high-precision wind resource data. Through methods such as grid search or random search, the hyperparameters in the model (such as the tree depth of the random forest, the number of layers and learning rate of the neural network, etc.) are optimized to improve the prediction accuracy of the model.
[0048] Step 305, statistical model validation: input the validation set data into the trained model to evaluate the prediction effect of the model. The evaluation indicators include: root mean square error (RMSE): measures the difference between the model prediction value and the true value. The smaller the value, the higher the model accuracy; determination coefficient (R²): indicates the model's ability to explain the target variable. The closer the value is to 1, the better the model fit effect; mean absolute error (MAE): measures the average deviation between the predicted value and the actual value.
[0049] Step 306, generalization ability check: Apply the model to different climate conditions (such as monsoon season, typhoon conditions, etc.) to evaluate its performance under various meteorological conditions. Analyze the performance differences of the model based on the wind field characteristics in different seasons to ensure the accuracy of the wind resource map throughout the year. Apply the model to new geographical areas (such as deep sea areas, coastal areas, etc.) to evaluate the spatial generalization ability of the model. Ensure that the model is not only applicable to the training area, but also can accurately predict wind resources in new areas.
[0050] Step 4: Statistical downscaling.
[0051] Step 401, data acquisition and preprocessing: Use Python CDS-API to obtain ERA5 reanalysis data for many years, ensuring that the time span covers a sufficiently long meteorological record (e.g., 20 years or longer) to provide a basis for long-term wind resource assessment. Perform the preprocessing of step 302 to ensure data consistency.
[0052] Step 402, application of statistical downscaling model: Use the random forest model trained in step 3 and apply it to the ERA5 reanalysis data for many years, perform independent downscaling predictions on the reanalysis data for each year, and generate a high-precision wind resource map for each year. Processing each year separately can better capture extreme weather events. For example, if there is a significant wind field anomaly in a certain year, the model can identify and process this special situation separately and generate a more accurate map, which is more reliable, especially when evaluating the safety of wind farms or long-term wind energy utilization.
[0053] In step 4, the statistical relationship between the nationwide high-precision wind resource data obtained in step 3 and the prediction factors in the ERA5 reanalysis data is used.
[0054] Step 403, data splicing: The downscaled data of each region for many years are spliced using ESMF technology to obtain a wind resource map of the whole country for many years.
[0055] Step 404, quality inspection: Compare the generated wind resource map with the measured wind resource data or other high-resolution data sets, and use indicators such as RMSE (root mean square error) and R² (coefficient of determination) to evaluate the accuracy of the model to ensure that the results meet the quality requirements.
[0056] The wind resource map is corrected and adjusted through the measured data of the wind tower. According to the observation position of the wind tower, the measured data of the wind tower is matched with the wind field data output by the model by using spatial interpolation or weighted average method to correct the wind speed and wind direction, ensuring that the results near the wind tower are more accurate.
[0057] Step 5: Update the graph.
[0058] The previous steps are all for historical data, and this step is to continuously update the map as the ERA5 data is updated. For example, step 3 selects 2022, and step 4 selects 30 years from 1992 to 2021. This step can select the latest release, such as 2023.
[0059] Step 501, data update and preprocessing: regularly obtain the latest ERA5 reanalysis data through Python CDS-API or other automated data acquisition interfaces.
[0060] Step 502, matching historical statistical model: using the statistical model established in step 3, the real-time meteorological data is matched with the historical ERA5 data. The model will automatically convert the real-time low-resolution meteorological data into high-resolution wind resource data based on the past statistical relationship. And use ESMF technology to splice and obtain a nationwide data map.
[0061] Step 503: Update the high-precision wind resource map and update the resource data by time.
[0062] Through the above steps, statistical relationships can be established and verified, statistical downscaling can be achieved, and maps can be quickly updated to ensure that the system can continue to provide high-precision wind resource information.
[0063] The method for quickly generating a wind resource map for a specified area provided in this embodiment has the following key advantages: Efficient data coupling: Compared with traditional methods, the automatic acquisition and preprocessing technology of mesoscale data in this embodiment improves the accuracy of the mesoscale simulation field, realizes the automatic coupling of wind tower data and mesoscale data, eliminates the need for tedious manual operations, and improves the efficiency of wind resource map generation.
[0064] This embodiment supports the mesoscale wind resource data platform of all regions and can flexibly import or call the wind measurement data of existing wind towers. This comprehensive support feature ensures wide applicability and enables it to play a role in a variety of application scenarios.
[0065] High-precision output: By comprehensively using CALMET technology, statistical downscaling technology, Earth system model framework ESMF technology and other technical means, high-precision downscaling simulation of wind fields is achieved, providing a solid technical foundation for generating high-resolution and high-precision wind resource maps.
[0066] On the basis of the above beneficial effects, this embodiment fully demonstrates its excellent performance in the field of wind resources through quantitative data verification and theoretical analysis. The realization of these beneficial effects makes this embodiment more feasible and provides a reliable and efficient solution to the problems existing in the prior art.
[0067] Embodiment 2 This embodiment provides a system for quickly generating a wind resource map of a specified area, which specifically includes: A mesoscale simulation module is configured to: partition a designated area to obtain a plurality of sub-areas, and for each sub-area, perform a mesoscale meteorological model simulation for a designated year to obtain background field data; The dynamic downscaling module is configured to: perform dynamic downscaling on the background field data of each sub-area to obtain a 100-meter resolution wind resource map of a specified year; splice the 100-meter resolution wind resource maps of all areas in a specified year to obtain a 100-meter resolution wind resource map of a specified area in a specified year; obtain the wind tower data of a specified area in a specified year, perform spatial matching between the wind tower data and the 100-meter resolution wind resource map of a specified area in a specified year, and then calibrate the 100-meter resolution wind resource map of a specified area in a specified year; The statistical downscaling module is configured as follows: obtaining reanalysis data of a specified year, taking the reanalysis data of a specified year as input, and outputting a corrected wind resource map of a specified area in a specified year with a resolution of 100 meters, and training a random forest regression model; obtaining reanalysis data of non-specified years, and performing statistical downscaling through the trained random forest regression model to obtain a wind resource map of a specified area in a non-specified year with a resolution of 100 meters.
[0068] Among them, when simulating the mesoscale meteorological model for a specified year, the cumulus convection physical parameterization scheme selects the boundary layer parameterization scheme, the cloud microphysics parameterization scheme selects the Thompson dual-parameter scheme, the planetary boundary layer physical parameterization scheme selects the MYNN scheme, the shortwave radiation physical parameterization scheme selects the RRTMG scheme, the longwave radiation physical parameterization scheme adopts the RRTMG scheme, and the pavement process physical parameterization scheme adopts the NOAH-MP scheme.
[0069] The method for correcting the wind resource atlas with a resolution of 100 meters in the designated area and the designated year is as follows: weighted averaging the wind tower data and the wind field data in the wind resource atlas with a resolution of 100 meters in the designated area and the designated year.
[0070] The steps of dynamic downscaling include: converting the background field data into a format to obtain the meteorological initial field data after format conversion; acquiring terrain elevation and land use type data, resampling, and obtaining binary geographic information data; and running the California meteorological model based on the binary geographic information data and the meteorological initial field data after format conversion to achieve dynamic downscaling.
[0071] It should be noted here that each module in this embodiment corresponds to each step in Example 1 one by one, and the specific implementation process is the same, which will not be repeated here.
[0072] Embodiment 3 This embodiment provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the steps in the method for quickly generating a wind resource map of a specified area as described in the first embodiment above are implemented.
[0073] Embodiment 4 This embodiment provides a computer device, such as Figure 2As shown, it includes a computer-readable storage medium 1003, a processor 1001, a communication interface 1002, and a computer program stored on the computer-readable storage medium 1003 and executable on the processor 1001, wherein the processor 1001, the communication interface 1002, and the computer-readable storage medium 1003 can be connected via a bus or other means. The communication interface 1002 is used to receive and send data, and when the processor 1001 executes the program, the steps in the method for quickly generating a wind resource map of a specified area as described in the first embodiment above are implemented.
[0074] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.
[0075] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.
[0076] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.
[0077] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.
Claims
1. A method for quickly generating a wind resource map of a specified area, characterized in that: include: The designated area is divided into several sub-areas. For each sub-area, a mesoscale meteorological model simulation is performed in a designated year to obtain background field data. For the background field data of each sub-area, dynamic downscaling is performed to obtain a 100-meter resolution wind resource map of a specified year; the 100-meter resolution wind resource maps of all areas of a specified year are spliced to obtain a 100-meter resolution wind resource map of a specified area of a specified year; the wind tower data of a specified area of a specified year are obtained, and after spatial matching of the wind tower data with the 100-meter resolution wind resource map of a specified area of a specified year, the 100-meter resolution wind resource map of a specified area of a specified year is corrected; Obtain the reanalysis data of the specified year, use the reanalysis data of the specified year as input, and the corrected 100-meter resolution wind resource map of the specified area in the specified year as output to train the random forest regression model; obtain the reanalysis data of non-specified years, perform statistical downscaling through the trained random forest regression model, and obtain the 100-meter resolution wind resource map of the specified area in the non-specified year.
2. A method for quickly generating a wind resource map of a specified area according to claim 1, characterized in that: When simulating the mesoscale meteorological model for the specified year, the cumulus convection physical parameterization scheme selects the boundary layer parameterization scheme, the cloud microphysics parameterization scheme selects the Thompson dual-parameter scheme, the planetary boundary layer physical parameterization scheme selects the MYNN scheme, the shortwave radiation physical parameterization scheme selects the RRTMG scheme, the longwave radiation physical parameterization scheme adopts the RRTMG scheme, and the road surface process physical parameterization scheme adopts the NOAH-MP scheme.
3. A method for quickly generating a wind resource map of a designated area according to claim 1, characterized in that: The method for correcting the wind resource atlas with a resolution of 100 meters in the designated area and designated year is: weighted averaging the wind tower data and the wind field data in the wind resource atlas with a resolution of 100 meters in the designated area and designated year.
4. A method for quickly generating a wind resource map of a designated area according to claim 1, characterized in that: The steps of dynamic downscaling include: converting the format of background field data to obtain format-converted initial meteorological field data; acquiring terrain elevation and land use type data, resampling, and obtaining binary geographic information data; and running the California meteorological model based on the binary geographic information data and the format-converted initial meteorological field data to achieve dynamic downscaling.
5. A system for quickly generating a wind resource map of a specified area, characterized in that: include: A mesoscale simulation module is configured to: partition a designated area to obtain a plurality of sub-areas, and for each sub-area, perform a mesoscale meteorological model simulation for a designated year to obtain background field data; The dynamic downscaling module is configured to: perform dynamic downscaling on the background field data of each sub-area to obtain a 100-meter resolution wind resource map of a specified year; splice the 100-meter resolution wind resource maps of all areas in a specified year to obtain a 100-meter resolution wind resource map of a specified area in a specified year; obtain the wind tower data of a specified area in a specified year, perform spatial matching between the wind tower data and the 100-meter resolution wind resource map of a specified area in a specified year, and then calibrate the 100-meter resolution wind resource map of a specified area in a specified year; The statistical downscaling module is configured as follows: obtaining reanalysis data of a specified year, taking the reanalysis data of a specified year as input, and outputting a corrected wind resource map of a specified area in a specified year with a resolution of 100 meters, and training a random forest regression model; obtaining reanalysis data of non-specified years, and performing statistical downscaling through the trained random forest regression model to obtain a wind resource map of a specified area in a non-specified year with a resolution of 100 meters.
6. A system for rapidly generating a wind resource map of a designated area as claimed in claim 5, characterized in that: When simulating the mesoscale meteorological model for the specified year, the cumulus convection physical parameterization scheme selects the boundary layer parameterization scheme, the cloud microphysics parameterization scheme selects the Thompson dual-parameter scheme, the planetary boundary layer physical parameterization scheme selects the MYNN scheme, the shortwave radiation physical parameterization scheme selects the RRTMG scheme, the longwave radiation physical parameterization scheme adopts the RRTMG scheme, and the road surface process physical parameterization scheme adopts the NOAH-MP scheme.
7. A system for rapidly generating a wind resource map of a designated area as claimed in claim 5, characterized in that: The method for correcting the wind resource atlas with a resolution of 100 meters in the designated area and designated year is: weighted averaging the wind tower data and the wind field data in the wind resource atlas with a resolution of 100 meters in the designated area and designated year.
8. A system for rapidly generating a wind resource map of a designated area as claimed in claim 5, characterized in that: The steps of dynamic downscaling include: converting the format of background field data to obtain format-converted initial meteorological field data; acquiring terrain elevation and land use type data, resampling, and obtaining binary geographic information data; and running the California meteorological model based on the binary geographic information data and the format-converted initial meteorological field data to achieve dynamic downscaling.
9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of a method for quickly generating a wind resource map of a specified area as described in any one of claims 1 to 7 are implemented.
10. A computer device comprising a computer-readable storage medium, a processor, and a computer program stored in the computer-readable storage medium and executable on the processor, characterized in that: When the processor executes the program, the steps in the method for quickly generating a wind resource map of a specified area according to any one of claims 1 to 7 are implemented.