Wind condition prediction device and wind condition prediction method

Abstract

To allow a precise prediction of the wind state of a year from wind state measurement data for a short period of time in a wind state prediction target site.SOLUTION: A wind state prediction device 100 includes: a function acquisition unit 112 for acquiring a local wind state function, which shows a local wind state of a prediction target point, and a large-area wind state function, which shows a large-area wind state; and a wind state prediction unit 113 for correcting the large-area wind state function by using a parameter in the local wind state function, and predicting the wind state of the prediction target site by using the corrected large-area wind state function.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present invention relates to a wind condition prediction device and a wind condition prediction method for predicting wind conditions at a prediction target point with high accuracy. [Background technology]

[0002] When selecting a construction site for a wind power plant, it is necessary to install wind condition measuring instruments and measure wind conditions (how the wind blows, for example, the distribution of wind speed and direction) for a year in order to predict the amount of power generated. Because installing wind condition measuring instruments requires a lot of cost, it is desirable to identify locations with good wind conditions with high accuracy in advance. Currently, annual wind conditions are predicted based on global wind forecast data released by meteorological agencies, but global data does not take into account information such as the topography of the area near the construction site, which tends to increase prediction errors.

[0003] To address these issues, Patent Documents 1 and 2 have been disclosed. The weather forecasting device described in Patent Document 1 performs a local wind condition simulation based on global wind condition forecast data, and predicts wind conditions while taking into account the effects of topography, etc. The local wind condition simulation is not performed under all conditions, but is performed for representative wind conditions, and the annual average wind speed is predicted from the results.

[0004] The wind condition prediction system described in Patent Document 2 aims to predict annual wind speeds taking into account local conditions such as topography by correcting global wind condition prediction data based on short-term measurement data measured by a Doppler lidar or the like, which is easier to install than a wind condition measuring instrument. During the period when wind speeds are measured, the system calculates the time correlation between the global wind condition prediction data and the measured data, and uses the correlation coefficient to correct the global wind condition prediction data for periods when no measurement is performed to calculate the average wind speed for the year. [Prior art documents] [Patent documents]

[0005] [Patent Document 1] JP 2019-203728 A [Patent Document 2] JP 2020-159725 A Summary of the Invention [Problem to be solved by the invention]

[0006] The weather forecasting device of Patent Document 1 is capable of predicting detailed wind conditions near a target prediction point by local simulation. However, since the accuracy of the local simulation decreases due to an error between global wind condition data and actual wind conditions, it is difficult to improve the accuracy of the annual average wind speed prediction value.

[0007] The wind condition forecasting system described in Patent Document 2 is capable of grasping errors in global wind condition data based on short-term wind speed measurement data. However, there is often a difference between errors during observed periods and errors during unobserved periods, and it is often difficult to improve accuracy by only mathematical correction based on time correlation.

[0008] The present invention has been made in consideration of the above background, and has an objective of providing a wind condition prediction device and a wind condition prediction method that enable highly accurate prediction of annual wind conditions from short-term wind condition measurement data at a point where wind conditions are to be predicted. [Means for solving the problem]

[0009] In order to solve the above problems, the wind condition prediction device according to the present invention includes a function acquisition unit that acquires a local wind condition function, which is a function that indicates the local wind conditions at a prediction target point, and a global wind condition function, which is a function that indicates the global wind conditions, and a function acquisition unit that acquires the global wind condition function by using parameters included in the local wind condition function. The global wind function is calculated by replacing the parameters in and a wind condition prediction unit for predicting the wind conditions at the prediction target point using the corrected global wind condition function. The function acquisition unit derives and acquires the local wind condition function, which indicates the distribution of wind speed in the altitude direction, from local wind condition data of the prediction target point based on a wind condition physics model near the ground surface in which wind speed increases from the ground toward the sky, and the local wind condition data is actual measured values ​​of wind conditions measured at the prediction target point during a first period, and the global wind condition function is a function derived based on the wind condition physics model from global wind condition data including the prediction target point during a second period longer than the first period. Effect of the Invention

[0010] According to the present invention, it is possible to provide a wind condition prediction device and a wind condition prediction method that enable highly accurate prediction of annual wind conditions from short-term wind condition measurement data at a point where wind conditions are to be predicted. Problems, configurations and effects other than those described above will become clear from the description of the following embodiments. [Brief description of the drawings]

[0011] [Figure 1] FIG. 2 is a functional block diagram of the wind condition prediction device according to the present embodiment. [Diagram 2] 1 is a graph showing the relationship between altitude and wind speed according to the present embodiment. [Diagram 3] 4 is a flowchart of a wind condition prediction process according to the embodiment. [Figure 4] 13 is a flowchart of a wind condition prediction process according to a modified example of the embodiment. [Diagram 5] 13 is a rose map showing the occurrence frequency distribution of wind direction and wind speed according to a modified example of the present embodiment. [Figure 6] 13 is a flowchart of a wind condition prediction process according to a modified example of the embodiment. [Figure 7] 13 is a flowchart of a wind condition prediction process according to a modified example of the embodiment. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0012] <Outline of the wind forecast device> A wind condition prediction device in a form (embodiment) for carrying out the present invention will be described below. The wind condition prediction device obtains a wind condition profile, which is a distribution of wind speed in the height direction, from short-term wind condition measurement data at a prediction target point based on a wind condition physics model near the ground surface. Next, the wind condition prediction device corrects the wind condition profile in the global wind conditions based on this wind condition profile, and predicts the wind conditions for the year.

[0013] Wind resource profiles are determined by the terrain, and the influence of terrain does not change even during periods when no observations are made. Therefore, even corrections based on wind resource profiles estimated from short-term measurements are valid throughout the year. This allows for highly accurate prediction of wind conditions. Wind conditions include average wind speed, occurrence rate of wind speed and direction, average wind speed by wind direction, wind resource profile, etc.

[0014] <Overall configuration of the wind condition prediction device> 1 is a functional block diagram of a wind condition prediction device 100 according to this embodiment. The wind condition prediction device 100 is a computer, and includes a control unit 110, a storage unit 120, and an input / output unit 180. User interface devices such as a display, keyboard, and mouse are connected to the input / output unit 180. The input / output unit 180 may include a communication device, and may be capable of transmitting and receiving data to and from other devices. A media drive may also be connected to the input / output unit 180, and data may be exchanged using a recording medium.

[0015] The storage unit 120 includes storage devices such as a read only memory (ROM), a random access memory (RAM), and a solid state drive (SSD). Global wind condition data 130, local wind condition data 140, and a program 121 are stored in the storage unit 120. The program 121 includes a description of the procedure for the wind condition prediction process (see FIG. 3) described below.

[0016] <Global wind data> The global wind condition data 130 is meteorological data called Grid Point Value (GPV) issued by meteorological organizations such as the Japan Meteorological Agency, and analysis results obtained by global meteorological analysis. In the global meteorological analysis, the Weather Research and Forecasting model (WRF) or the like is used, and calculations are performed at a resolution of several hundred meters to several kilometers. In the global wind condition analysis, meteorological data issued by meteorological organizations is used for boundary conditions and initial conditions, but the resolution is several kilometers to several tens of kilometers.

[0017] <Local wind data> The local wind condition data 140 includes data measured by a wind condition measuring instrument or a Doppler lidar in the vicinity of the prediction target point, data calculated by a local wind condition analysis that takes into account the undulations of the terrain, etc. To measure the wind conditions, a wind condition measuring instrument is used, which is a rod-shaped structure several tens of meters in height equipped with a sensor that measures wind speed and direction. Other instruments that are used include a Doppler lidar that measures wind speed and direction by utilizing the reflection of a laser or the like from aerosols in the atmosphere. These measuring instruments are capable of measuring the wind conditions in the altitude direction of the prediction target point and in its vicinity.

[0018] The global wind condition data 130 is data for a long period (second period) in years, whereas the local wind condition data 140 is data based on observations for a short period (first period) of, for example, several days to several months. Also, although the local wind condition data 140 was observed for a long period in years, some data may be missing due to equipment malfunction, and the usable data may be for a short period.

[0019] Local wind analysis uses methods such as computational fluid analysis, with local topographical data recorded at an elevation resolution of several meters to several tens of meters issued by organizations such as the Geospatial Information Authority of Japan, and global meteorological data such as GPV as analysis conditions. When performing computational fluid analysis, a three-dimensional computational grid is generated with a resolution that allows for understanding the changes in wind conditions caused by local topographical changes at the target prediction point.

[0020] In the local wind condition analysis, the initial conditions and boundary conditions are determined from the global wind condition data. In this case, the values ​​of the global wind condition data may be used as they are, or values ​​that have been processed in some way may be used. For example, in the computational grid of the global wind condition data 130, the data of the grid point closest to the target point of prediction may be used, or the average value of the neighboring grid points may be used, or the boundary conditions may be changed depending on the position. Also, the period of the data may be limited. The local wind analysis may be a method for analyzing a steady state, or a non-steady state analysis that also captures time-varying conditions. The local wind data obtained in this way includes detailed changes in wind conditions near the ground surface at the target point, which are not included in the global wind data.

[0021] <Control Unit> Control unit 110 is configured to include a CPU (Central Processing Unit) and is equipped with a data acquisition unit 111, a function acquisition unit 112, and a wind condition prediction unit 113. Data acquisition unit 111 acquires global wind condition data and local wind condition data for a prediction target point, and stores them in global wind condition data 130 and local wind condition data 140, respectively.

[0022] <Control section: Function acquisition section> The function acquisition unit 112 acquires a function indicating wind conditions from the global wind condition data 130 and the local wind condition data 140. For example, the function acquisition unit 112 acquires a function indicating a wind condition profile (distribution of wind speed in the altitude direction) by wind direction. Below, the function indicating the wind condition profile is also referred to as a wind condition profile function. Moreover, the wind condition profile function acquired from the global wind condition data 130 is also referred to as a global wind condition profile function (global wind condition function), and the wind condition profile function acquired from the local wind condition data 140 is also referred to as a local wind condition profile function (local wind condition function).

[0023] FIG. 2 is a graph 210 showing the relationship between altitude and wind speed according to this embodiment. It can be seen that the wind speed slows down as it approaches the ground surface, and increases as the altitude increases. Theoretically, there is a relationship between altitude and wind speed, for example, known as the logarithmic law. In detail, wind speed is proportional to the logarithm of the altitude divided by the surrounding ground surface roughness, and to the friction speed, which is a parameter indicating the strength of wind turbulence (see equation (1) below). Furthermore, the zero plane displacement may be taken into account for the altitude (see equation (2) below). Alternatively, it may be a function including the temperature.

[0024] Wind speed = proportionality coefficient x friction speed x ln (altitude / surface roughness) (1) Wind speed = proportionality coefficient × friction velocity × ln((height - zero surface displacement) / surface roughness) (2)

[0025] The wind profile (wind profile function) in the case of following the logarithmic law as shown in Equation (1) and Equation (2) is expressed as a logarithmic function including parameters such as friction speed, ground surface roughness, zero surface displacement, and temperature, but may be expressed as other functions such as a power function or a polynomial function. Alternatively, the wind profile function may be a function (mapping) that shows the distribution of wind speed against height using a statistical method. Alternatively, the wind profile function may be a function (mapping) that is calculated by combining multiple formulas such as machine learning technology including a neural network.

[0026] The wind profile function acquired by the function acquisition unit 112 includes parameters such as the friction speed, surface roughness, zero plane displacement, and air temperature as described above, as well as parameters such as the Obukhov length and heat flux, or parameters determined from these parameters. Some of these parameters are determined by the local topography of the prediction target point, particularly the topography on the windward side, or are significantly influenced by the topography. Such parameters are specific to the prediction target point. For example, the surface roughness and zero plane displacement are determined by the topography on the windward side.

[0027] The function acquisition unit 112 acquires the type of wind condition profile function (logarithmic function, polynomial function, neural network, etc.) at the prediction target point from the local wind condition data 140 and parameters determined by the local (nearby) topography corresponding to this type for each wind direction. The function acquisition unit 112 also acquires the type of wind condition profile function in the vicinity of the prediction target point from the global wind condition data 130 and parameters corresponding to this type for each wind direction.

[0028] <Control unit: Wind condition prediction unit> The wind condition prediction unit 113 calculates the annual wind conditions at the prediction target point by correcting the function indicating the wind conditions of the global wind condition data 130 using the function indicating the wind conditions of the local wind condition data 140. For example, the wind condition prediction unit 113 corrects the global wind condition profile function using parameters included in the local wind condition profile function and determined by the topography in the vicinity of the prediction target point.

[0029] For example, when the wind condition profile function is expressed in the form of formula (2), the wind condition prediction unit 113 corrects the global wind condition profile function by replacing the ground surface roughness and zero plane displacement obtained from the global wind condition data 130 with the ground surface roughness and zero plane displacement obtained from the local wind condition data 140. Using this corrected global wind condition profile function, the wind condition prediction unit 113 calculates the annual wind conditions (wind speed) at the prediction target point. In other words, the global wind condition profile function (global wind condition function) is corrected using parameters included in the local wind condition profile function (local wind condition function), and the corrected global wind condition profile function is used to predict the wind conditions (annual wind conditions) at the prediction target point.

[0030] <Wind condition forecast processing> FIG. 3 is a flowchart of the wind condition prediction process according to this embodiment. In step S11, the wind condition prediction unit 113 divides the global wind condition data 130 and the local wind condition data 140 by wind direction. The width of the wind direction division is, for example, a predetermined angle (such as 30° or 22.5°).

[0031] In step S12, the wind condition prediction unit 113 starts the process of repeating steps S13 and S14 for each wind direction divided in step S11. In step S13, the function acquisition unit 112 acquires a wind condition profile function of the global wind condition data 130 and the local wind condition data 140. For example, the function acquisition unit 112 selects a logarithmic function as the function, and calculates a proportionality coefficient, a friction speed, a ground surface roughness, and a zero plane displacement as parameters.

[0032] In step S14, the wind condition prediction unit 113 corrects the global wind condition profile function. For example, the wind condition prediction unit 113 replaces the ground roughness and zero plane displacement included in the global wind condition profile function with the ground roughness and zero plane displacement included in the local wind condition profile function. Note that the ground roughness and zero plane displacement are parameters determined by the topography on the windward side of the prediction target point. In step S15, the wind condition prediction unit 113 predicts wind conditions using the global wind condition profile function corrected in step S14.

[0033] <Features of the wind forecast device> In global meteorological analysis, the area to be analyzed is divided into meshes (grids) of several hundred meters to several kilometers, and data such as wind speed and wind direction at the intersections and center points of the grid are obtained. The prediction target point is not necessarily the intersections or center points of the grid. In addition, there may be changes in topography around the prediction target point that cannot be resolved by a grid of several hundred meters to several kilometers, and there may be changes in the ground surface altitude. For this reason, there is a large error in predicting wind conditions using only the global wind condition data 130.

[0034] The wind condition prediction device 100 corrects the global wind condition profile function using the local wind condition profile function to predict the wind conditions at the prediction target point. For example, the wind condition prediction device 100 performs the correction by replacing some of the parameters included in the global wind condition profile function with parameters of the local wind condition profile function that are determined by the terrain. Even if the local wind condition data is for a short period of time, it is possible to predict the annual wind conditions with high accuracy because the parameters of the local wind condition profile function reflect the topography.

[0035] <Modification: Correction taking into account the frequency of occurrence of wind direction and speed, part 1> In the above embodiment, the parameters included in the global wind profile function are replaced with the parameters included in the local wind profile function. Instead of simply replacing the parameters, the parameters may be calculated based on the frequency of occurrence of wind direction and wind speed and then replaced.

[0036] FIG. 4 is a flowchart of a wind condition prediction process according to a modified example of this embodiment. Step S21 is similar to step S11 (see FIG. 3). In step S22, the wind condition prediction unit 113 calculates the friction speed of the global wind condition profile function based on the occurrence frequency for each wind direction. In detail, the wind condition prediction unit 113 weights the friction speed for each wind direction of the local wind condition profile function by the occurrence frequency for each wind direction obtained from the global wind condition data 130, and sets the result as the friction speed of the global wind condition profile function. In other words, the friction speed of the global wind condition profile is calculated by equation (3) with Σ as the sum for the wind direction.

[0037] Friction velocity of global wind profile = Σ(Occurrence frequency of each wind direction obtained from global wind condition data × Friction velocity for each wind direction of the local wind profile function) (3)

[0038] Steps S23 to S26 are similar to steps S12 to S15, respectively, except that the friction speed of the corrected global wind condition profile function in step S25 is the friction speed calculated in step S22.

[0039] In the embodiment described above, the friction speed is not corrected. In contrast, in the modified example, the friction speed according to the topography is used, which improves the accuracy of the prediction.

[0040] <Modification: Correction method 2 that takes into account the frequency of occurrence of wind direction and speed> 4, the correction is made based on the occurrence frequency of the wind direction obtained from the global wind condition data 130. The correction may also be made based on the occurrence frequency of the wind speed for each wind direction obtained from the global wind condition data 130.

[0041] 5 is a rose map 220 showing the frequency distribution of wind direction and wind speed according to a modified example of this embodiment. In the rose map 220, the wind direction is divided into 30° intervals, and the wind speed is divided into five intervals of 5 m / s from the darker hatching to the lighter hatching. The rose map 220 shows that the frequency of winds in the 90° to 180° directions is high, and within this, the frequency of wind speeds of 10 to 20 m / s is high.

[0042] 6 is a flowchart of a wind condition prediction process according to a modified example of this embodiment. Steps S31 to S33 and S35 are similar to steps S11 to S13 and S15 in FIG. In step S34, the wind condition prediction unit 113 corrects the global wind condition profile function. The wind condition prediction unit 113 replaces the ground roughness and zero plane displacement included in the global wind condition profile function with the ground roughness and zero plane displacement included in the local wind condition profile function. Furthermore, the wind condition prediction unit 113 calculates a friction speed weighted by the occurrence frequency of the wind speed obtained from the global wind condition data for the friction speed by wind speed included in the local wind condition profile function, and replaces and corrects the friction speed included in the global wind condition profile function with this friction speed. In other words, the friction speed of the global wind condition profile is calculated by equation (4) with Σ as the sum for the wind speed. Note that this correction is performed for each wind direction in response to the repeated processing for each wind direction in steps S33 to S34.

[0043] Friction velocity of global wind profile by wind direction = Σ(Occurrence frequency of wind speed by wind direction obtained from global wind condition data) × Friction velocity for each wind speed and wind direction of the local wind profile function (4)

[0044] In the embodiment described above, the friction speed is not corrected. In contrast, in the modified example, the friction speed according to the topography is used, which improves the accuracy of the prediction.

[0045] <<Variation: Correction taking into account temperature>> In the above-described embodiment and modified examples, the wind condition prediction unit 113 corrects the global wind condition profile function for each wind direction, but the correction may be performed for each temperature (temperature category) in addition to the wind direction.

[0046] FIG. 7 is a flowchart of a wind condition prediction process according to a modified example of this embodiment. In step S41, the wind condition prediction unit 113 divides the global wind condition data 130 and the local wind condition data 140 by wind direction and temperature. The temperature is divided by the temperature at the ground surface or by the distribution in the altitude direction near the ground surface. In step S42, the wind condition prediction unit 113 starts the process of repeating steps S43 and S44 for each wind direction and temperature obtained in step S41.

[0047] In step S43, the function acquisition unit 112 determines a wind condition profile function of the global wind condition data 130 and the local wind condition data 140. For example, the function acquisition unit 112 selects a logarithmic function as the function, and calculates a proportionality coefficient, a friction speed, a ground surface roughness, and a zero plane displacement using the logarithmic function as parameters. In step S44, the wind condition prediction unit 113 corrects the global wind condition profile function. For example, the wind condition prediction unit 113 replaces the ground roughness and zero plane displacement included in the global wind condition profile function with the ground roughness and zero plane displacement included in the local wind condition profile function.

[0048] In the embodiment described above, the parameters are corrected for each wind direction. In contrast, in the modified example, the parameters are corrected for each wind direction and temperature, improving the accuracy of prediction.

[0049] Other Modifications Although several embodiments of the present invention have been described above, these embodiments are merely illustrative and do not limit the technical scope of the present invention. The present invention can take various other embodiments, and various modifications such as omissions and substitutions can be made without departing from the gist of the present invention. These embodiments and their modifications are included in the scope and gist of the invention described in this specification, etc., and are included in the scope of the invention described in the claims and their equivalents. [Explanation of symbols]

[0050] 100 Wind forecast device 112 Function Acquisition Section 113 Wind Forecasting Department 130 Global Wind Data 140 Local wind data

Claims

1. a function acquisition unit that acquires a local wind function that is a function indicating the local wind conditions at a prediction target point and a global wind function that is a function indicating the global wind conditions; a wind condition prediction unit that uses parameters included in the local wind condition function to replace parameters included in the global wind condition function to correct the global wind condition function, and predicts wind conditions at the prediction target point using the corrected global wind condition function, The function acquisition unit The local wind function indicating the distribution of wind speed in the height direction is derived and obtained from the local wind condition data of the prediction target point based on a wind condition physics model near the ground surface in which the wind speed increases from the ground to the sky; The local wind condition data is an actual measurement value of wind conditions measured at the prediction target point during a first period, The global wind function is is a function derived based on the wind physics model from global wind condition data including the prediction target point during a second period longer than the first period. A wind condition prediction device characterized by:

2. a function acquisition unit that acquires a local wind function that is a function indicating the local wind conditions at a prediction target point and a global wind function that is a function indicating the global wind conditions; a wind condition prediction unit that uses parameters included in the local wind condition function to replace parameters included in the global wind condition function to correct the global wind condition function, and predicts wind conditions at the prediction target point using the corrected global wind condition function, The function acquisition unit The local wind function indicating the distribution of wind speed in the height direction is derived and obtained from the local wind condition data of the prediction target point based on a wind condition physics model near the ground surface in which the wind speed increases from the ground to the sky; The local wind condition data is The prediction is derived from a numerical analysis using topographical data in the vicinity of the prediction target point and the global wind conditions in a first period as inputs; The global wind function is is a function derived based on the wind physics model from global wind condition data including the prediction target point during a second period longer than the first period. A wind condition prediction device characterized by:

3. The wind condition prediction unit is The global wind condition function is corrected using a parameter included in the local wind condition function that has been changed according to the occurrence frequency of the wind direction or the wind speed at the prediction target point, and the wind condition at the prediction target point is predicted using the corrected global wind condition function.

3. The wind condition prediction device according to claim 1 or 2.

4. The parameters of the local wind condition function used to correct the global wind condition function are parameters determined by the topography in the vicinity of the prediction target point.

3. The wind condition prediction device according to claim 1 or 2.

5. The wind forecasting device, A step of obtaining a local wind function which is a function indicating the local wind conditions at a prediction target point, and a global wind function which is a function indicating the global wind conditions; a step of replacing parameters included in the global wind condition function with parameters included in the local wind condition function to correct the global wind condition function, and predicting wind conditions at the prediction target point using the corrected global wind condition function; When obtaining the local wind function, The local wind function indicating the distribution of wind speed in the height direction is derived and obtained from the local wind condition data of the prediction target point based on a wind condition physics model near the ground surface in which the wind speed increases from the ground to the sky, The local wind condition data is an actual measurement value of wind conditions measured at the prediction target point during a first period, The global wind function is is a function derived based on the wind physics model from global wind condition data including the prediction target point during a second period longer than the first period. A wind condition forecasting method comprising:

6. The wind forecasting device, A step of obtaining a local wind function which is a function indicating the local wind conditions at a prediction target point, and a global wind function which is a function indicating the global wind conditions; a step of replacing parameters included in the global wind condition function with parameters included in the local wind condition function to correct the global wind condition function, and predicting wind conditions at the prediction target point using the corrected global wind condition function; When obtaining the local wind function, The local wind function indicating the distribution of wind speed in the height direction is derived and obtained from the local wind condition data of the prediction target point based on a wind condition physics model near the ground surface in which the wind speed increases from the ground to the sky, The local wind condition data is The prediction is derived from a numerical analysis using topographical data in the vicinity of the prediction target point and the global wind conditions in a first period as inputs; The global wind function is is a function derived based on the wind physics model from global wind condition data including the prediction target point during a second period longer than the first period. A wind condition forecasting method comprising:

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