CN111325376A - Wind speed prediction method and device - Google Patents
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Abstract
The invention provides a wind speed prediction method and a wind speed prediction device, wherein the wind speed prediction method comprises the following steps: acquiring meteorological simulation data of a target area in a preset meteorological mode; dividing each grid point of a target area into different groups according to wind speed change characteristics of simulated wind speed at each grid point of the target area derived from the meteorological simulation data, wherein the grid points in the same group have the same or similar wind speed change characteristics; determining the distribution area of each divided group; the actual wind speed at another point in the area of each group distribution than the point is predicted based on the actual wind speed observed at any point in the area of each group distribution. The wind speed prediction method and the device can not only accurately evaluate wind resources under the condition of insufficient wind measurement even no wind measurement, but also effectively reduce the uncertainty of the calculation of the generated energy of the wind power plant.
Description
Technical Field
The application relates to the technical field of wind power generation, in particular to a wind speed prediction method and device.
Background
The problem of accuracy of calculation of the power generation amount of the wind power plant is a key problem which is constantly addressed by the wind power industry, and the accuracy of calculation of the power generation amount of the wind power plant depends on the accuracy of anemometry data. However, with the reduction of investment and construction period of the wind power plant and the insufficient recognition of the importance of the anemometric data, if the wind power project starts to evaluate wind power resources of the wind power plant under the condition of insufficient anemometry or no anemometry at all, great uncertainty is brought to the calculation of the generated energy of the wind power plant and the estimation of the corresponding income.
Therefore, a wind speed prediction method and a wind speed prediction device capable of solving the above problems are urgently needed so as to provide a more accurate and reliable data basis for the construction of the wind power plant and the evaluation of the power generation amount thereof.
Disclosure of Invention
The invention aims to provide a wind speed prediction method and a wind speed prediction device.
According to an aspect of the present invention, there is provided a wind speed prediction method, the method including: acquiring meteorological simulation data of a target area in a preset meteorological mode; dividing each grid point of a target area into different groups according to wind speed change characteristics of simulated wind speed at each grid point of the target area derived from the meteorological simulation data, wherein the grid points in the same group have the same or similar wind speed change characteristics; determining the distribution area of each divided group; the actual wind speed at another point in the area of each group distribution than the point is predicted based on the actual wind speed observed at any point in the area of each group distribution.
Preferably, the step of determining the distributed areas of the divided groups includes: the areas in which the divided respective groups are distributed are determined based on the areas surrounded by the grid points in the divided respective groups.
Preferably, the step of predicting the actual wind speed at another point in the area where each group is distributed than the point includes: determining a deviation of the actual wind speed observed at any point in the area of each cohort distribution from a simulated wind speed for that point derived from the meteorological simulation data as a deviation of the actual wind speed in the area of each cohort distribution; predicting an actual wind speed at another point than the point in the area of each group distribution based on a deviation of the actual wind speed in the area of each group distribution and a simulated wind speed at another point than the point in the area of each group distribution derived from the meteorological simulation data.
Preferably, the predetermined meteorological pattern is a mesoscale meteorological pattern.
Preferably, the wind speed variation characteristic is a characteristic of a variation in wind speed with time.
According to another aspect of the present invention, there is provided a wind speed prediction apparatus, the apparatus comprising: the weather acquisition unit is used for acquiring weather simulation data of a target area in a preset weather mode; the group dividing unit is used for dividing each grid point of a target area into different groups according to the wind speed change characteristics of the simulated wind speed at each grid point of the target area derived from the meteorological simulation data, wherein the grid points in the same group have the same or similar wind speed change characteristics; the area determining unit is used for determining the distribution area of each divided group; a wind speed prediction unit that predicts an actual wind speed at another point than the point in the area of each group distribution based on an actual wind speed observed at any point in the area of each group distribution.
Preferably, the area determination unit determines the areas distributed by the divided respective groups based on areas surrounded by the grid points in the divided respective groups.
Preferably, the wind speed prediction unit includes: a deviation determining subunit that determines a deviation of an actual wind speed observed at any point in the area of each cluster distribution from a simulated wind speed for the point derived from the meteorological simulation data as a deviation of the actual wind speed in the area of each cluster distribution; a wind speed prediction subunit predicting an actual wind speed at another point than the point in the area of each group distribution based on a deviation of the actual wind speed in the area of each group distribution and a simulated wind speed at another point than the point in the area of each group distribution derived from the meteorological simulation data.
Preferably, the predetermined meteorological pattern is a mesoscale meteorological pattern.
Preferably, the wind speed variation characteristic is a characteristic of a variation in wind speed with time.
According to another aspect of the invention, a computer-readable storage medium is provided, in which a computer program is stored, which, when being executed by a processor, carries out the wind speed prediction method as described above.
According to another aspect of the present invention, there is provided a computer apparatus comprising: a processor; a memory storing a computer program which, when executed by the processor, implements a wind speed prediction method as described above.
The wind speed prediction method and the device provided by the invention can not only accurately evaluate wind resources under the condition of insufficient wind measurement even no wind measurement, but also effectively reduce the uncertainty of calculation of the generated energy of the wind power plant, thereby providing more accurate and reliable data basis for site selection construction of the wind power plant and evaluation of the generated energy thereof.
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The above and other objects and features of the present invention will become more apparent from the following description taken in conjunction with the accompanying drawings, in which:
FIG. 1 is a flow chart illustrating a wind speed prediction method according to an exemplary embodiment of the invention;
fig. 2 is a block diagram illustrating a structure of a wind speed prediction apparatus according to an exemplary embodiment of the present invention.
Detailed Description
The general idea of the invention is that under the condition of insufficient wind measurement even no wind measurement, meteorological simulation data in a preset meteorological mode is used as wind measurement data, and the actual wind speed at each position point in the wind power plant is evaluated by carrying out cluster analysis on the wind speed change characteristics on the meteorological simulation data and converting the clusters into clusters distributed in spatial regions, so that the evaluation on the power generation amount of the wind power plant is realized. In addition, the uncertainty caused by the deviation of the meteorological simulation data relative to the actual wind speed to the calculation and the evaluation of the power generation amount of the wind power plant is also considered, and the actual wind speed is evaluated and the evaluated actual wind speed is corrected at the same time so as to further ensure the accuracy of the calculation of the power generation amount of the wind power plant.
Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings.
FIG. 1 is a flowchart illustrating a wind speed prediction method according to an exemplary embodiment of the present invention.
In step 110, meteorological simulation data for a target area in a predetermined meteorological pattern may be acquired.
In specific implementation, various meteorological modes can be selected for a target area to perform numerical simulation for wind speed in a required time period so as to obtain simulated wind speed at each grid point in a target area, thereby preparing for subsequent cluster analysis.
In view of the fact that the mesoscale meteorological model may provide meteorological simulation data as time series data of simulated wind speeds at various points in the target area over a desired time period, in one exemplary embodiment, meteorological simulation data for the target area in the mesoscale meteorological model may be acquired such that the acquired meteorological simulation data is more reflective of changes in flow fields and weather conditions within the target area, and is also more closely approximate to the actual changes in wind speeds within the target area. The mesoscale meteorological model may optionally include, but is not limited to, one of the following: a Weather forecast mode (WRF), a Mesoscale non-hydrostatic mode (MM 5), a Regional atmosphere simulation system (RAMS), and the like, and the Mesoscale Weather mode may also be selected from other various Weather modes with simulation capability, a marine mode, a marine coupling mode, and the like.
In step 120, the grid points of the target area may be divided into different groups according to wind speed variation characteristics of simulated wind speeds at the grid points of the target area derived from the meteorological simulation data, wherein the grid points in the same group have the same or similar wind speed variation characteristics.
The wind speed variation characteristic in step 120 depends on the meteorological simulation data derived in the meteorological mode, for example, in the mesoscale meteorological mode, the wind speed variation characteristic may be a characteristic of the variation of the wind speed with time, because the meteorological simulation data (i.e., the time series data of the simulated wind speed) acquired in this mode is more reflective of the variation of the flow field and the weather condition in the target area.
For example, if the target region is divided into 90 × 90 grids under a predetermined meteorological pattern, time series data of simulated wind speeds at the corresponding grid points are extracted from the meteorological simulation data for cluster analysis, and in particular, the sum of distances from a cluster center to each time series vector can be used as a cluster index, and the time series data can be divided into n different groups based on the cluster index.
In step 130, the area in which the divided groups are distributed may be determined.
In one exemplary embodiment, the areas distributed by the divided respective groups may be determined based on the areas surrounded by the grid points in the divided respective groups. In addition, in order to distinguish the areas distributed by different groups, the areas distributed by different groups (the same color area represents the same group/category) can be marked by different colors in Google earth (a virtual globe software developed by Google corporation), so as to draw a spatial area clustering effect graph of the wind speed distribution condition of the target area. In this way, clustering by wind speed variation characteristics may be converted into clustering to spatial regions of wind speed distribution conditions, and time-series data of simulated wind speeds at respective grid points included in the region of each group distribution have the same/similar wind speed variation characteristics. Based on this, the wind speed variation characteristics at various locations within the target area are spatially profiled.
It should be understood that the above-mentioned manner of determining the group distribution area is merely exemplary, and the present invention is not limited thereto, and any other suitable manner that facilitates determining the group distribution area may be applied to the present invention.
In step 140, the actual wind speed at another point in the area of each group distribution than the point may be predicted based on the actual wind speed observed at any point in the area of each group distribution.
In an alternative embodiment, the deviation of the actual wind speed observed at any point in the area of each cohort distribution from the simulated wind speed for that point derived from the meteorological simulation data may be determined as a deviation of the actual wind speed in the area of each cohort distribution; then, the actual wind speed at another point than the point in the area of each cluster distribution is predicted based on the deviation of the actual wind speed in the area of each cluster distribution and a simulated wind speed at another point than the point in the area of each cluster distribution derived from the meteorological simulation data. For example, in a case where the deviation is positive, a result of adding the deviation to the simulated wind speed at the other point is determined as the simulated wind speed at the other point; and in the case that the deviation is negative, determining the result of subtracting the deviation from the simulated wind speed at the other point as the simulated wind speed at the other point. In this way, not only can correction of the predicted wind speed be achieved, but also the influence of the deviation between the meteorological simulation data and the actual wind speed on the estimation of the wind farm power generation amount can be minimized.
In a further embodiment, all the simultaneously observed data points in the target region may be labeled on the region distribution map of the cluster, so as to determine the available observation points in the target region, and further determine a distribution region having the same/similar wind speed variation characteristics as those of the observed data points, so as to provide more data bases for predicting the actual wind speed at each position point in the target region.
It should be appreciated that the above-described manner of predicting the actual wind speed is also merely exemplary, and the present invention is not limited thereto, and any other suitable manner that contributes to improving the accuracy of wind speed prediction may be applied to the present invention.
Furthermore, before proceeding to step 140, the actual wind speeds observed at any point in the area where each cluster is distributed (e.g., the wind tower) may also be filtered according to a predetermined criterion (e.g., IEC standard) to filter out abnormal data that is outside of the normal range. By way of example, data including, but not limited to, wind speed greater than 50m/s, wind speed less than 0m/s, temperature less than-45 ℃, temperature greater than 50 ℃, air pressure less than 60kPa, and air pressure greater than 120kPa may be considered as data outside the normal range, data that is constant over a certain time range may be considered as frozen data, and data that has an inconsistent wind speed trend and a low channel value may be considered as abnormal data. In this way, these anomaly data may be screened from the observed actual wind speed for cleaning, further ensuring the validity and accuracy of the data.
In the above manner, the actual wind speeds of the regions where the respective groups are distributed in the target region may be predicted, and a wind speed prediction model of the target region may be constructed based on the predicted actual wind speeds. In order to enable the predicted result of the constructed wind speed prediction model to be more accurate, various modes can be used for carrying out effect test and model training on the wind speed prediction model.
In one exemplary embodiment, the actual wind speed Y1 observed at point A in a region S distributed over any one group of target regions and a contemporaneous simulated wind speed X1 for point A derived from the meteorological simulation data may be first obtained, and an initial wind speed prediction model for the region S is constructed based on the actual wind speed Y1 and contemporaneous simulated wind speed X1, trained to obtain a wind speed prediction model M for the region S; an actual wind speed Y2 observed at another point in the region S (point B) and a contemporaneous simulated wind speed X2 derived from the meteorological simulation data for point B may then be obtained and the wind speed prediction model M is validated using the actual wind speed Y2 and contemporaneous simulated wind speed X2. Specifically, the contemporaneous simulated wind speed X2 may be input into a wind speed prediction model M to output a predicted actual wind speed P2 for point B, the predicted actual wind speed P2 may then be compared with the acquired actual wind speed Y2, if the compared index reaches a predetermined threshold value T0 (which may be set empirically), the effect of the wind speed prediction model M is verified, and the saved wind speed prediction model M is recorded and determined as the wind speed prediction model of the region to provide a calculation reference for future prediction; if the compared index cannot reach the preset threshold value T0, the effect of the wind speed prediction model M is poor, and the wind speed prediction model can be reconstructed by re-acquiring the spatial clustering characteristics or modifying the parameters of the wind speed prediction model or replacing the wind speed prediction model.
As an example, indices of the above-described comparison may be used including, but not limited to, Pearson correlation coefficients, root mean square errors, average absolute errors, etc., which may be calculated using the following equations, respectively:
in the above formulae, r denotes a Pearson correlation coefficient, RMSE denotes a root mean square error, MAE denotes an average absolute error, n denotes the number of samples, X denotes a maximum number of samples, andiand YiRespectively representing predictions at B pointsThe actual wind speed and the observed actual wind speed,
and
respectively representing the mean of the predicted actual wind speeds and the mean of the observed actual wind speeds at point B, SXAnd SYThe variance of the predicted actual wind speed and the variance of the observed actual wind speed at point B are respectively represented.
It should be noted that, according to the implementation requirement, each step described above in the present invention can be further divided into more steps, and two or more steps or partial operations of the steps can be combined into a new step to implement the present invention.
By adopting the implementation process, the wind resource can be accurately evaluated under the condition of insufficient wind measurement even no wind measurement, and the uncertainty of calculation of the generated energy of the wind power plant can be effectively reduced, so that more accurate and reliable data basis is provided for site selection construction of the wind power plant and evaluation of the generated energy.
Fig. 2 is a block diagram illustrating a structure of a wind speed prediction apparatus according to an exemplary embodiment of the present invention.
Referring to fig. 2, the wind speed prediction apparatus may include a weather acquisition unit 210, a group division unit 220, an area determination unit 230, and a wind speed determination unit 240. The weather acquisition unit 210 may be used to acquire weather simulation data of the target area in a predetermined weather pattern. The group classification unit 220 may be used to classify respective grid points of a target area into different groups according to wind speed variation characteristics of simulated wind speeds at the respective grid points of the target area derived from the meteorological simulation data, wherein the grid points in the same group have the same or similar wind speed variation characteristics. The region determining unit 230 may be used to determine regions in which the divided groups are distributed. The wind speed prediction unit 240 may be used to predict the actual wind speed at another point than the point in the area of each group distribution based on the actual wind speed observed at any point in the area of each group distribution.
As mentioned above, in the wind speed prediction device, the meteorological model can adopt a mesoscale meteorological model, so that the obtained meteorological simulation data can reflect the changes of the flow field and the weather condition in the target area better, and the meteorological simulation data is closer to the actual condition of the wind speed change in the target area. Accordingly, in the mesoscale meteorological mode, the wind speed variation characteristic may be a characteristic of a change in wind speed over time.
As described above, in the wind speed prediction apparatus, the region determining unit 230 may determine the regions distributed by the divided respective groups based on the regions surrounded by the grid points in the divided respective groups.
As described above, in the wind speed prediction apparatus, the wind speed prediction unit 240 may further include a deviation determination subunit (not shown) and a wind speed prediction subunit (not shown). The deviation determination subunit may be used to determine a deviation of the actual wind speed observed at any point in the area of each cohort distribution from a simulated wind speed for that point derived from the meteorological simulation data as a deviation of the actual wind speed in the area of each cohort distribution. The wind speed prediction subunit may be used to predict the actual wind speed at another point than the point in the area of each group distribution based on a deviation of the actual wind speed in the area of each group distribution and a simulated wind speed at another point than the point in the area of each group distribution derived from the meteorological simulation data.
By adopting the implementation process, the wind resource can be accurately evaluated under the condition of insufficient wind measurement even no wind measurement, and the uncertainty of calculation of the generated energy of the wind power plant can be effectively reduced, so that more accurate and reliable data basis is provided for site selection construction of the wind power plant and evaluation of the generated energy.
There is also provided, in accordance with an exemplary embodiment of the present invention, a computer-readable storage medium storing a computer program. The computer readable storage medium stores a computer program which, when executed by a processor, causes the processor to perform a method of determining wind speed according to the present invention. The computer readable recording medium is any data storage device that can store data read by a computer system. Examples of the computer-readable recording medium include: read-only memory, random access memory, read-only optical disks, magnetic tapes, floppy disks, optical data storage devices, and carrier waves (such as data transmission through the internet via wired or wireless transmission paths).
There is also provided, in accordance with an exemplary embodiment of the present invention, a computer apparatus. The computer device includes a processor and a memory. The memory is for storing a computer program. The computer program is executed by a processor causing the processor to perform the method of determining wind speed according to the invention.
While the present application has been shown and described with reference to preferred embodiments, it will be understood by those skilled in the art that various changes and modifications may be made to these embodiments without departing from the spirit and scope of the present application as defined by the following claims.
Claims (12)
1. A method of wind speed prediction, the method comprising:
acquiring meteorological simulation data of a target area in a preset meteorological mode;
dividing each grid point of a target area into different groups according to wind speed change characteristics of simulated wind speed at each grid point of the target area derived from the meteorological simulation data, wherein the grid points in the same group have the same or similar wind speed change characteristics;
determining the distribution area of each divided group;
the actual wind speed at another point in the area of each group distribution than the point is predicted based on the actual wind speed observed at any point in the area of each group distribution.
2. The wind speed prediction method of claim 1, wherein the step of determining the area in which the divided groups are distributed comprises:
the areas in which the divided respective groups are distributed are determined based on the areas surrounded by the grid points in the divided respective groups.
3. The wind speed prediction method according to claim 1, wherein the step of predicting the actual wind speed at another point in the area where each group is distributed than the point comprises:
determining a deviation of the actual wind speed observed at any point in the area of each cohort distribution from a simulated wind speed for that point derived from the meteorological simulation data as a deviation of the actual wind speed in the area of each cohort distribution;
predicting an actual wind speed at another point than the point in the area of each group distribution based on a deviation of the actual wind speed in the area of each group distribution and a simulated wind speed at another point than the point in the area of each group distribution derived from the meteorological simulation data.
4. The method of wind speed prediction according to claim 1, wherein the predetermined meteorological pattern is a mesoscale meteorological pattern.
5. The wind speed prediction method of claim 4, wherein the wind speed variation characteristic is a characteristic of a variation in wind speed with time.
6. A wind speed prediction apparatus, characterized in that the apparatus comprises:
the weather acquisition unit is used for acquiring weather simulation data of a target area in a preset weather mode;
the group dividing unit is used for dividing each grid point of a target area into different groups according to the wind speed change characteristics of the simulated wind speed at each grid point of the target area derived from the meteorological simulation data, wherein the grid points in the same group have the same or similar wind speed change characteristics;
the area determining unit is used for determining the distribution area of each divided group;
a wind speed prediction unit that predicts an actual wind speed at another point than the point in the area of each group distribution based on an actual wind speed observed at any point in the area of each group distribution.
7. The wind speed prediction apparatus according to claim 6, wherein the area determination unit determines the area in which the divided groups are distributed based on an area surrounded by grid points in the divided groups.
8. The wind speed prediction device according to claim 6, wherein the wind speed prediction unit comprises:
a deviation determining subunit that determines a deviation of an actual wind speed observed at any point in the area of each cluster distribution from a simulated wind speed for the point derived from the meteorological simulation data as a deviation of the actual wind speed in the area of each cluster distribution;
a wind speed prediction subunit predicting an actual wind speed at another point than the point in the area of each group distribution based on a deviation of the actual wind speed in the area of each group distribution and a simulated wind speed at another point than the point in the area of each group distribution derived from the meteorological simulation data.
9. The wind speed prediction device of claim 6, wherein the predetermined meteorological pattern is a mesoscale meteorological pattern.
10. The wind speed prediction device of claim 9, wherein the wind speed variation characteristic is a characteristic of a variation in wind speed with time.
11. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the method of wind speed prediction according to any one of claims 1-5.
12. A computer device, characterized in that the computer device comprises:
a processor;
memory storing a computer program which, when executed by a processor, carries out the method of wind speed prediction according to any one of claims 1-5.
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| CN111932400A (en) * | 2020-08-06 | 2020-11-13 | 南通大学 | Wind/storage integrated power scheduling plan optimization implementation method |
| CN112613633A (en) * | 2020-09-30 | 2021-04-06 | 江苏金风软件技术有限公司 | Meteorological element prediction method and device for wind power plant |
| CN114137636A (en) * | 2021-11-11 | 2022-03-04 | 四川九通智路科技有限公司 | Regional meteorological monitoring management method and system for annular pressure sensor |
| CN116258023A (en) * | 2023-05-15 | 2023-06-13 | 中国船舶集团风电发展有限公司 | Wind speed prediction method of wind power plant and terminal equipment |
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