CN109934378A - A kind of power forecasting method and device - Google Patents
A kind of power forecasting method and device Download PDFInfo
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
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- Y02A90/00—Technologies having an indirect contribution to adaptation to climate change
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- Y04S10/00—Systems supporting electrical power generation, transmission or distribution
- Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications
Abstract
The invention belongs to technical field of data processing more particularly to a kind of power forecasting methods and device.First position data including acquiring wind power plant geographic location, and the second position data and weather forecasting data of N number of weather forecasting point geographic location of the acquisition apart from wind power plant recently；The plane perspective view for reflecting the relative position of wind power plant and N number of weather forecasting point is generated, and determines the second coordinate data of the first coordinate data of coordinate points where wind power plant and N number of weather forecasting point place coordinate points in plane perspective view；On the basis of coordinate points where wind power plant, according to the first coordinate data and the second coordinate data, interpolation calculation is carried out to the weather forecasting data of N number of weather forecasting point, obtains weather forecasting data at wind power plant；According to the weather forecasting data at wind power plant, the wind power prediction value of wind power plant is calculated.Using aforesaid way, the accuracy of the wind power prediction of wind power plant can be improved, provide technical support for the traffic control of wind-powered electricity generation.
Description
Technical field
The invention belongs to technical field of data processing more particularly to a kind of power forecasting methods and device.
Background technique
The a series of global problem such as increasingly depleted of global warming and conventional fossil energy, causes people
To the extensive concern of new energy.Wind energy is a kind of renewable sources of energy of no pollution to the environment, right as the principal mode of future source of energy
The life style of the mankind, survival and development are all of great significance from now on.In recent years, with advances in technology with environmental protection
It is required that wind-power electricity generation has obtained development continuously and healthily in China.
In order to effectively utilize wind energy resources, being incorporated into the power networks for wind power plant requires to be equipped with wind power prediction system.If
It is not previously predicted the support of system, or prediction is not accurate enough, wind power plant is possible to processing of being rationed the power supply, so as to cause wind power plant
Effective installed capacity cannot be fully utilized
At present for the technology of wind power prediction, using nearest apart from weather forecast mesh point according to some wind power plant
Weather data, pass through neural network algorithm carry out the prediction of wind power data.Because weather forecast provided by now is grid
Point form, the weather data predicted under the position has been corresponded at each mesh point, and wind power plant present position is not necessarily in net
On lattice point, the weather data of prediction and the practical weather data of wind power plant position in the middle position of grid, may be caused
There is difference, this will increase the error of wind power prediction, the generating efficiency of wind power plant be affected, so that wind-power electricity generation cannot be abundant
Utilization, and when predicting that error is very big, prediction result will be serious unfounded, and some security risks may be brought to wind power plant.
Summary of the invention
For the shortcomings of the prior art, the present invention provides a kind of power forecasting method and device, purpose
It is the accuracy in order to improve the wind power prediction of wind power plant, provides technical support for the traffic control of wind-powered electricity generation, ensure power train
The safe and stable operation of system and wind power plant.
In order to achieve the above-mentioned object of the invention, the present invention is achieved through the following technical solutions:
A kind of power forecasting method, comprising:
Step 1. acquires the first position data of wind power plant geographic location, and, it acquires apart from the wind power plant most
The second position data of close N number of weather forecasting point geographic location and the weather forecasting at N number of weather forecasting point
Data, N are the positive integer more than or equal to four；
Step 2. be based on the first position data and the second position data, generate for reflect the wind power plant and
The plane perspective view of the relative position of N number of weather forecasting point, and determine the place of wind power plant described in the plane perspective view
Second coordinate data of coordinate points where first coordinate data of coordinate points and N number of weather forecasting point；
Step 3. is sat on the basis of coordinate points where the wind power plant according to first coordinate data and described second
Data are marked, interpolation calculation is carried out to the weather forecasting data of N number of weather forecasting point, obtains the day at the wind power plant
Gas prediction data；
Step 4. calculates the wind power prediction value of the wind power plant according to the weather forecasting data at the wind power plant.
According to first coordinate data and second coordinate data in the step 3, to N number of weather
The weather forecasting data of future position carry out interpolation calculation, obtain the weather forecasting data at the wind power plant, specifically include:
On the basis of the abscissa of coordinate points where the wind power plant, according to the abscissa of first coordinate data, with
And the abscissa of second coordinate data, interpolation calculation is carried out to the weather forecasting data of N number of weather forecasting point, is obtained
M weather forecasting data, M are the positive integer more than or equal to 2；
On the basis of the ordinate of coordinate points where the wind power plant, according to the ordinate of first coordinate data, with
And the ordinate of second coordinate data, interpolation calculation is carried out to the M weather forecasting data and is obtained at the wind power plant
Weather forecasting data.
According to first coordinate data and second coordinate data in the step 3, to N number of weather
The weather forecasting data of future position carry out interpolation calculation, obtain the weather forecasting data at the wind power plant, specifically include:
On the basis of the ordinate of coordinate points where the wind power plant, according to the ordinate of first coordinate data, with
And the ordinate of second coordinate data, interpolation calculation is carried out to the weather forecasting data of N number of weather forecasting point, is obtained
P weather forecasting data, P are the positive integer more than or equal to 2；
On the basis of the abscissa of coordinate points where the wind power plant, according to the abscissa of first coordinate data, with
And the abscissa of second coordinate data, interpolation calculation is carried out to the P weather forecasting data and is obtained at the wind power plant
Weather forecasting data.
The plane for reflecting the relative position of the wind power plant and N number of weather forecasting point is generated in the step 2
Before perspective view, further includes:
Weather forecasting data at N number of weather forecasting point are normalized.
In the step 3 after the weather forecasting data being calculated at the wind power plant, the method also includes:
The reality for acquiring the wind power plant goes out force data；
The weather forecasting data according at the wind power plant calculate the wind power prediction value of the wind power plant, tool
Body includes:
By at the wind power plant normalized weather forecasting data and the reality go out force data and be input to reversed biography
It broadcasts neural network model to be learnt, calculates the wind power prediction value of the wind power plant.
A kind of powder prediction device, comprising: acquisition module, processing module, interpolation calculation module and prediction module；
The acquisition module, for acquiring the first position data of wind power plant geographic location, and, it acquires apart from institute
State the second position data of the nearest N number of weather forecasting point geographic location of wind power plant and at N number of weather forecasting point
Weather forecasting data, N is positive integer more than or equal to four；
The processing module is generated for being based on the first position data and the second position data for reflecting
The plane perspective view of the relative position of the wind power plant and N number of weather forecasting point, and determine institute in the plane perspective view
The second of coordinate points sits where first coordinate data of coordinate points where stating wind power plant and N number of weather forecasting point
Mark data；
The interpolation calculation module is used on the basis of coordinate points where the wind power plant, according to first number of coordinates
Accordingly and second coordinate data obtains the weather forecasting data progress interpolation calculation of N number of weather forecasting point
Weather forecasting data at the wind power plant；
The prediction module, for calculating the wind of the wind power plant according to the weather forecasting data at the wind power plant
Power prediction value.
The interpolation calculation module is specifically used for: on the basis of the abscissa of coordinate points where the wind power plant, according to institute
The abscissa of the first coordinate data and the abscissa of second coordinate data are stated, to the weather of N number of weather forecasting point
Prediction data carries out interpolation calculation, obtains M weather forecasting data, and M is the positive integer more than or equal to 2；
On the basis of the ordinate of coordinate points where the wind power plant, according to the ordinate of first coordinate data, with
And the ordinate of second coordinate data, interpolation calculation is carried out to the M weather forecasting data and is obtained at the wind power plant
Weather forecasting data.
The interpolation calculation module, additionally it is possible to be used for:
On the basis of the ordinate of coordinate points where the wind power plant, according to the ordinate of first coordinate data, with
And the ordinate of second coordinate data, interpolation calculation is carried out to the weather forecasting data of N number of weather forecasting point, is obtained
P weather forecasting data, P are the positive integer more than or equal to 2；
On the basis of the abscissa of coordinate points where the wind power plant, according to the abscissa of first coordinate data, with
And the abscissa of second coordinate data, interpolation calculation is carried out to the P weather forecasting data and is obtained at the wind power plant
Weather forecasting data.
The processing module: the weather forecasting data at N number of weather forecasting point are normalized.
The prediction module: the reality for recording the wind power plant goes out force data；By the weather forecasting number at the wind power plant
According to and the reality of the record go out force data and be input to back propagation artificial neural network model, by the way that the wind-powered electricity generation is calculated
The wind power prediction value of field.
A kind of powder prediction device, which is characterized in that further include: processor, memory and bus, the storage
Device is stored with the executable machine readable instructions of the processor, when electronic equipment operation, the processor and the storage
By bus communication between device, the step of aforementioned power prediction technique is executed when the machine readable instructions are executed by the processor
Suddenly.
A kind of powder prediction device, further includes: a kind of computer storage medium stores on the computer readable storage medium
The step of having computer program, aforementioned power prediction technique is executed when which is run by processor.
In the embodiment of the present application, pass through the weather forecasting number of the weather forecasting point of the nearest at least four of wind power plant of adjusting the distance
According to interpolation calculation is carried out, the weather forecasting data at wind power plant can be accurately obtained, and then can be pre- according to obtained weather
Measured data carries out wind power prediction.With in the prior art according to the weather forecasting of a weather forecasting point nearest apart from wind power plant
Weather condition and then calculating wind power prediction value at data-speculative wind power plant are compared, what mode provided by the present application deduced
Weather forecasting the data precision at wind power plant is higher, wind power prediction more can be close to actual conditions, so as to effectively improve
The accuracy of wind power prediction.
Further, power forecasting method provided by the present application, can also be using aerial prospective to wind power plant and weather forecasting
Point carries out perspective and generates plane perspective view, opposite between wind power plant and weather forecasting point so as to more intuitively reflect
Position.Furthermore it is also possible to the weather forecasting data of weather forecasting point are mapped between [0,1] by normalized, thus
It is convenient for two-way interpolation calculation.
Other feature and advantage of the application will illustrate in the following description, also, partly become from specification
It obtains it is clear that understand through the implementation of the invention.The purpose of the application and other advantages are in specification, claims
And specifically noted structure is achieved and obtained in attached drawing.
To enable the above objects, features, and advantages of the application to be clearer and more comprehensible, preferred embodiment is cited below particularly, and cooperate
Appended attached drawing, is described in detail below.
Detailed description of the invention
Technical solution in ord to more clearly illustrate embodiments of the present application, below will be to needed in the embodiment attached
Figure is briefly described, it should be understood that the following drawings illustrates only some embodiments of the application, therefore is not construed as pair
The restriction of range for those of ordinary skill in the art without creative efforts, can also be according to this
A little attached drawings obtain other relevant attached drawings.
Fig. 1 is the schematic diagram of a scenario of existing power prediction；
Fig. 2 is the flow diagram of power prediction provided in an embodiment of the present invention；
Fig. 3 is the schematic diagram of a scenario of power prediction provided in an embodiment of the present invention；
Fig. 4 is interpolation calculation schematic diagram provided in an embodiment of the present invention；
Fig. 5 is the structural schematic diagram of neural network model provided in an embodiment of the present invention；
Fig. 6 is the structural schematic diagram of powder prediction device provided in an embodiment of the present invention；
Fig. 7 is the structural schematic diagram of a kind of electronic equipment 70 provided in an embodiment of the present invention.
In figure: weather forecasting point A, weather forecasting point B, weather forecasting point C, weather forecasting point D, interpolation calculation obtain pre-
Measuring point E, the future position F that difference is calculated, wind power plant G, acquisition module 61, processing module 62, interpolation calculation module 63, prediction
Module 64, electronic equipment 70, processor 71, memory 72, bus 73.
Specific embodiment
To keep the purposes, technical schemes and advantages of the embodiment of the present application clearer, below in conjunction with the embodiment of the present application
Middle attached drawing, the technical scheme in the embodiment of the application is clearly and completely described, it is clear that described embodiment is only
It is some embodiments of the present application, instead of all the embodiments.The application being usually described and illustrated herein in the accompanying drawings is real
The component for applying example can be arranged and be designed with a variety of different configurations.Therefore, below to the application's provided in the accompanying drawings
The detailed description of embodiment is not intended to limit claimed scope of the present application, but is merely representative of the selected reality of the application
Apply example.Based on embodiments herein, those skilled in the art institute obtained without making creative work
There are other embodiments, shall fall in the protection scope of this application.
At present in " wind power prediction ", generally using the mesh point of the weather forecasting nearest apart from some wind power plant at
Weather forecasting data, by neural network algorithm to the wind power plant carry out the prediction of wind power data.Provided by present
Weather forecasting is using mesh point form, for example, the weather forecasting in Liaoning Province area is divided into 66*59 mesh point, often
A mesh point corresponds to the latitude and longitude coordinates in some geographical location, carries out corresponding to 66*59 mesh point respectively when weather forecasting
The weather condition of geographical location predicted, obtain weather forecasting data.But due in the deployment of practical wind power plant,
Everything is not necessarily correct in geographical location locating for wind power plant should be on the geographical location that mesh point represents, it is also possible to corresponding in the centre of grid
Position, so the practical weather data of corresponding weather forecasting data and wind power plant position may have difference at mesh point.
For example, referring to schematic diagram of a scenario shown in FIG. 1, if the position wind power plant G is in the point, it is by chance not at carry out day
At the mesh point of gas prediction.In this case, when carrying out wind power prediction to wind power plant, using apart from wind power plant in existing way
The weather forecasting data of nearest mesh point, i.e. weather forecasting point A, and this data is considered as to the weather forecasting data of wind power plant,
It is input to neural network algorithm and carries out wind power prediction.It needs first to predict weather data when due to power prediction, and this
The practical weather condition of the weather forecasting data and wind power plant position that obtain under kind of method has difference, cause to wind power into
Error when row prediction is larger.Based on this, the embodiment of the invention provides a kind of wind power prediction method and devices, according to two-wire
Property interpolation method, accurate can extrapolate the weather forecasting data of wind power plant present position, so as to more accurately meter
Calculate wind power prediction data.
Technical solution provided by the present application is described in detail combined with specific embodiments below.It is to be appreciated that
It may include multiple wind power plants in the deployment of wind power plant, illustrate by taking single wind power plant as an example in the following description to the wind-powered electricity generation
Field carries out the process of power prediction.
Embodiment 1:
A kind of power forecasting method, it is shown in Figure 2, it is that the process of power prediction provided in an embodiment of the present invention is illustrated
Figure, comprising the following steps:
S201, the first position data for acquiring wind power plant geographic location and the N number of weather nearest apart from wind power plant
The second position data of future position geographic location and the weather forecasting data at N number of weather forecasting point.
In this step, for convenience of the weather forecasting to wind power plant, the geographical location at wind power plant and distance can be acquired
The geographical location of the nearest N number of weather forecasting point of wind power plant, and record the first position data of wind power plant geographic location with
And the second position data of N number of weather forecasting point.Wherein, first position data and second position data include wind power plant with it is N number of
The latitude and longitude information etc. in the geographical location where weather forecasting point.
Wherein, the N number of weather forecasting point nearest apart from wind power plant can be understood as centered on wind power plant in each side
The weather forecasting nearest apart from wind power plant point upwards.In the present invention, at least four weather forecasting point can be acquired, i.e. N be greater than
Integer equal to four.For purposes of illustration only, being hereinafter described by taking N=4 as an example.
For example, referring to shown in Fig. 3, centered on wind power plant G, positive direction northwest, positive northeastward, positive southwestward,
And just the southeast is upward, can select tetra- weather forecasting points of A, B, C, D nearest apart from the wind power plant respectively.Due to day
The variation of gas will affect the power of wind power plant output, therefore when carrying out the prediction of power to wind power plant, weather is important naturally
Influence factor.Therefore, in the embodiment of the present application, the weather for needing to acquire the 4 weather forecasting points nearest apart from this wind power plant is pre-
Measured data, and these weather future positions are exactly the geographical location that weather forecast can be arrived with Accurate Prediction, so only need to be pre- to weather
The data of report are acquired.
Specifically, the weather forecasting data of each weather forecasting point of acquisition may include: surface pressure, apart from ground 2m
The temperature at place, the relative humidity at the 2m of ground, wind speed, wind direction, cloud amount, latent heat flux, Sensible Heating Flux, radiation flux, drop
Water etc..Wherein, precipitation may include gross precipitation, Large-Scale Precipitation amount, convective precipitation amount etc.；Radiation flux includes ground
Downward shortwave radiation flux, averaged long wave radiation flux etc. upwardly；Wind speed includes wind speed at the 10m of ground, apart from ground
Wind speed at 30m, the wind speed at the 70m of ground, the wind speed at the 100m of ground, the wind speed at the 120m of ground, away from
Wind speed at 170m, the wind speed at the 300m of ground, wind speed at the 500m of ground etc. from the ground；Wind direction includes distance ground
Wind direction at the 10m of face, the wind direction at the 30m of ground, the wind direction at the 70m of ground, the wind direction at the 100m of ground, away from
From the ground the wind direction at 120m, the wind direction at the 170m of ground, the wind direction at the 300m of ground, at the 500m of ground
Wind direction etc..
It can be first after collecting every weather forecasting data for the more convenient weather forecasting data for calculating wind power plant
It is normalized, weather forecasting data is mapped between [0,1].Specifically, during normalized, for not
The weather forecasting data of same type can use different normalized modes.
In one example, first kind weather forecasting data may include wind speed, wind direction, temperature, air pressure, temperature prediction data,
For the first kind weather forecasting data, normalized process can use ratio method, specifically:
For i-th weather forecasting data in first kind weather forecasting data, wherein i-th weather forecasting data can be with
Refer to wind speed, wind direction, temperature, air pressure, temperature any one weather forecasting data, can will currently at the weather forecasting point predict
To i-th weather forecasting data and the i-th weather forecasting data before predicted at the weather forecasting point maximum
Ratio is taken between the absolute value of value.By taking i-th weather forecasting data is temperature as an example, the normalized process of temperature can join
According to following formula:
In formula, TgFor the temperature value after normalized, TtIt is predicted at weather forecasting point when currently to carry out power prediction
Temperature value, | Tt|maxMaximum value for the temperature absolute value predicted at the weather forecasting point before.
In another example, the second class weather forecasting data may include precipitation, cloud amount, heat and radiation flux prediction number
According to being directed to the second class weather forecasting data, it is pre- that the every weather for including in the second class weather forecasting data can be preset first
The first preset value, the second preset value, the first preset threshold, the second preset threshold and the impact factor coefficient of measured data.Wherein,
The impact factor coefficient of each single item weather forecasting data can be according to this weather forecasting data to power prediction effect
And sets itself, the first preset value, the second preset value, the first preset threshold and the second preset threshold can also be according to these
The concrete condition of weather forecasting data carrys out sets itself, and the application does not limit this.
Wherein, for jth item weather forecasting data in the second class weather forecasting data, wherein jth item weather forecasting data
Any one weather forecasting data that can refer to total precipitation, cloud amount, heat and radiation flux return jth item weather forecasting data
One changes calculating principle are as follows:
(a) it when jth item weather forecasting data are less than or equal to the first preset value of jth item weather forecasting data, determines
Jth item weather forecasting data after normalized are zero；
(b) it when jth item weather forecasting data are greater than or equal to the second preset value of jth item weather forecasting data, determines
Jth item weather forecasting size of data after normalized is impact factor coefficient；
(c) when jth item weather forecasting data are greater than the first preset value of jth item weather forecasting data and are less than jth Xiang Tian
Jth item weather forecasting data when the second preset value of gas prediction data, after determining normalized are as follows: [(jth item weather is pre-
The first preset threshold of measured data-jth item weather forecasting data)/(the second preset threshold-jth of jth item weather forecasting data
First preset threshold of item weather forecasting data)] × impact factor coefficient.
By the way that the weather forecasting data at weather forecasting point are normalized, can be more convenient for carrying out in next step double
To interpolation calculation.
S203, first position data and second position data are based on, generated for reflecting wind power plant and N number of weather forecasting point
Relative position plane perspective view, and determine the first coordinate data and N of coordinate points where wind power plant in plane perspective view
Second coordinate data of coordinate points where a weather forecasting point.
In specific implementation, in the first position data for collecting wind power plant and the second position of 4 weather forecasting points
After data, the plane perspective view of the relative position of wind power plant and 4 weather forecasting points is generated using the method for aerial prospective.
It is set out with a certain viewpoint, wind power plant in realistic space and the latitude and longitude coordinates of weather forecasting point is utilized into Perspective Principles, are integrated into
In same plane rectangular coordinate system, then their coordinate data of point generation on the basis of the viewpoint of aerial prospective, determines plane
The first coordinate data of coordinate points and 4 weather forecasting point A, weather forecasting point B, weather are pre- where wind power plant G in perspective view
Second coordinate data of coordinate points where measuring point C and weather forecasting point D.
This step converts the first position data of collected wind power plant and the second position data of 4 weather forecasting points
For the first coordinate data and the second coordinate data in plane perspective view, the longitude and latitude on geographical location is changed into coordinate system
In transverse and longitudinal coordinate, with this more convenient calculating to weather forecasting and power prediction.
On the basis of S204, the coordinate points where the wind power plant, according to the first coordinate data and the second coordinate data, to N number of
The weather forecasting data of weather forecasting point carry out interpolation calculation, obtain the weather forecasting data at wind power plant.
It in specific implementation, can be there are two types of mode when carrying out interpolation calculation:
Mode one: first on the basis of the abscissa of coordinate points where the wind power plant, according to the abscissa of the first coordinate data, with
And second coordinate data abscissa, interpolation calculation is carried out to the weather forecasting data of N number of weather forecasting point, obtains M weather
Prediction data, M are the positive integer more than or equal to 2；Again on the basis of the ordinate of coordinate points where wind power plant, sat according to first
The ordinate of data and the ordinate of the second coordinate data are marked, interpolation calculation is carried out to M weather forecasting data and obtains wind-powered electricity generation
Weather forecasting data at.
Illustratively, referring to shown in Fig. 4, by taking N=4, M=2 as an example, in the plane perspective view of generation, obtained wind
Coordinate points where electric field are G (x, y), coordinate points where 4 weather forecasting points be respectively C (x1, y1), A (x1, y2), D (x2,
y1)、B(x2,y2)。
In conjunction with weather forecastings data such as collected wind speed, wind directions on four weather forecasting points, the number of linear interpolation is utilized
Theory can first carry out the interpolation calculation on abscissa, obtain obtaining with the consistent interpolation calculation of wind power plant G point abscissa
2 weather forecasting data at the future position F that future position E and difference are calculated, shown in following formula:
Wherein, f (A), f (B), f (C), f (D), f (E), f (F) refer to the weather forecasting data of each point.
Further, ordinate is carried out between the future position F obtained future position E of interpolation calculation and difference being calculated
On interpolation calculation, the 2 weather forecasting numbers for the future position F that the future position E and difference obtained in conjunction with interpolation calculation is calculated
According to the weather forecasting data of wind power plant G point can be obtained, be shown below:
Based on above-mentioned two formula, pass through the weather forecasting data f (G) of the available wind power plant of bilinear interpolation.
Mode two: first on the basis of the ordinate of coordinate points where the wind power plant, according to the ordinate of the first coordinate data, with
And second coordinate data ordinate, interpolation calculation is carried out to the weather forecasting data of N number of above-mentioned weather forecasting point, obtains P
Weather forecasting data；Again on the basis of the abscissa of coordinate points where wind power plant, the abscissa of the first coordinate data and second
The abscissa of coordinate data carries out interpolation calculation to P weather forecasting data and obtains the weather forecasting data at the wind power plant.
This mode two is identical as the theory of mode one, and difference is first to combine the weather forecasting number on 4 weather forecasting points
It is obtained and corresponding weather forecasting under consistent 2 coordinate points of wind power plant G point ordinate according to the interpolation calculation carried out on ordinate
Data, then the weather forecasting data that the interpolation calculation on abscissa obtains wind power plant G point are carried out to this 2 coordinate points.Specific mistake
Journey can be found in the process enumerated in mode one, and which is not described herein again.
The step in, M and P can be understood as the half of weather forecasting point number N, can first carry out in a kind of mode
Linear interpolation on abscissa direction carries out the linear interpolation in ordinate direction again, and another way is first to carry out ordinate side
Upward linear interpolation carries out the linear interpolation on abscissa direction again, the weather forecasting at wind power plant that both modes obtain
Data are identical.It is calculated by linear interpolation, the weather forecasting data at wind power plant can be made more accurate, with more practical meaning
Justice.
S205, according to the weather forecasting data at wind power plant, calculate the wind power prediction value of wind power plant.
In specific implementation, when calculating the wind power prediction value of wind power plant, the practical power output of wind power plant can be acquired first
Data, by the weather forecasting data at wind power plant and actually, force data is input to backpropagation (Back out later
Propagation, BP) neural network model, the wind power prediction value of the wind power plant can be calculated.
Wherein, the reality of wind power plant go out force data can be in wind power plant normal power generation from the automatic detection in wind power plant
Equipment directly reads and records, can be according to the actual power efficiency of wind-driven generator in wind power plant in the wind farm power ration period
The reality that the wind power plant is calculated in the period of rationing the power supply goes out force data.
It referring to Figure 5, is the structural schematic diagram of BP neural network module in the embodiment of the present application.BP network is defeated except inputting
Outside egress, there are also one or more layers to imply node, in node layer can without connection.Input signal is from input layer
It is successively transmitted through each implicit node, is then passed to output node layer, the output of a node layer under the influence of the output of every node layer.
Specifically, the weather forecasting data of obtained wind power plant can be input to BP nerve net in the embodiment of the present application
The input layer of network model, by the hidden layer of the practical power output data configuration of wind power plant to BP neural network, such hidden layer it is defeated
It is that the reality of wind power plant under different weather goes out force data out, can be obtained by model internal calculation again after hidden layer output
And export the power prediction value of wind power plant.Wherein the treatment process of concrete model internal calculation can refer to the prior art, the application
In be no longer described in detail.
Power forecasting method provided in an embodiment of the present invention, first can according to aerial prospective theory generate wind power plant and
The plane perspective view of weather forecasting point, the weather forecasting data that can use multiple future positions later pass through bilinear interpolation meter
Calculation obtains the weather forecasting data at wind power plant, then carries out power prediction by back propagation artificial neural network model, improves function
The accuracy of rate prediction provides technical support for the traffic control of wind-powered electricity generation, ensures the safe and stable fortune of electric system and wind power plant
Row.
Embodiment 2
As shown in fig. 6, the structural schematic diagram of the powder prediction device provided for the embodiment of the present application 2, comprising: acquisition module
61, processing module 62, interpolation calculation module 63 and prediction module 64；Wherein,
Acquisition module 61, for acquiring the first position data of wind power plant geographic location, and, described in acquisition distance
Second position data of the nearest N number of weather forecasting point geographic location of wind power plant and at N number of weather forecasting point
Weather forecasting data, N are the positive integer more than or equal to four；
Processing module 62 is generated for being based on the first position data and the second position data for reflecting
The plane perspective view of the relative position of wind power plant and N number of weather forecasting point is stated, and is determined described in the plane perspective view
Second coordinate of coordinate points where first coordinate data of coordinate points where wind power plant and N number of weather forecasting point
Data；
Interpolation calculation module 63 is used on the basis of coordinate points where the wind power plant, according to first coordinate data
And second coordinate data, interpolation calculation is carried out to the weather forecasting data of N number of weather forecasting point, obtains institute
State the weather forecasting data at wind power plant；
Prediction module 64, for calculating the wind function of the wind power plant according to the weather forecasting data at the wind power plant
Rate predicted value.
Further, interpolation calculation module 63 is specifically used for:
On the basis of the abscissa of coordinate points where the wind power plant, according to the abscissa of first coordinate data, with
And the abscissa of second coordinate data, interpolation calculation is carried out to the weather forecasting data of N number of weather forecasting point, is obtained
M weather forecasting data, M are the positive integer more than or equal to 2；
On the basis of the ordinate of coordinate points where the wind power plant, according to the ordinate of first coordinate data, with
And the ordinate of second coordinate data, interpolation calculation is carried out to the M weather forecasting data and is obtained at the wind power plant
Weather forecasting data.
Further, interpolation calculation module 63 is specifically used for:
On the basis of the ordinate of coordinate points where the wind power plant, according to the ordinate of first coordinate data, with
And the ordinate of second coordinate data, interpolation calculation is carried out to the weather forecasting data of N number of weather forecasting point, is obtained
P weather forecasting data, P are the positive integer more than or equal to 2；
On the basis of the abscissa of coordinate points where the wind power plant, according to the abscissa of first coordinate data, with
And the abscissa of second coordinate data, interpolation calculation is carried out to the P weather forecasting data and is obtained at the wind power plant
Weather forecasting data.
Further, processing module 62 are also used to:
Weather forecasting data at N number of weather forecasting point are normalized.
Further, prediction module 64 are also used to:
The reality for recording the wind power plant goes out force data；
The reality of weather forecasting data and the record at the wind power plant is gone out into force data and is input to backpropagation
Neural network model, by the wind power prediction value that the wind power plant is calculated.
Embodiment 3
As shown in fig. 7, the structural schematic diagram of a kind of electronic equipment 70 provided for the embodiment of the present invention 3, comprising: processor
71, memory 72 and bus 73.
The memory 72 is stored with the executable machine readable instructions of the processor 71, for example, the acquisition mould in Fig. 6
Block 61, processing module 62, interpolation calculation module 63 and prediction module 64 is corresponding executes instruction, when electronic equipment 70 is run
When, it is communicated between the processor 71 and the memory 72 by bus 73, the machine readable instructions are by the processor
71 execute following processing when executing:
The first position data of wind power plant geographic location are acquired, and, it acquires apart from nearest N number of of the wind power plant
The second position data of weather forecasting point geographic location and the weather forecasting data at N number of weather forecasting point, N
For the positive integer more than or equal to four；
Based on the first position data and the second position data, generate for reflecting the wind power plant and the N
The plane perspective view of the relative position of a weather forecasting point, and determine coordinate points where wind power plant described in the plane perspective view
The first coordinate data and N number of weather forecasting point where coordinate points the second coordinate data；
On the basis of coordinate points where the wind power plant, according to first coordinate data and second number of coordinates
According to the weather forecasting data progress interpolation calculation of N number of weather forecasting point, the weather obtained at the wind power plant is pre-
Measured data；
According to the weather forecasting data at the wind power plant, the wind power prediction value of the wind power plant is calculated.
In a kind of embodiment, in the processing that above-mentioned processor 71 executes, it is described according to first coordinate data and
Second coordinate data carries out interpolation calculation to the weather forecasting data of N number of weather forecasting point, obtains the wind
Weather forecasting data at electric field, specifically include:
On the basis of the abscissa of coordinate points where the wind power plant, according to the abscissa of first coordinate data, with
And the abscissa of second coordinate data, interpolation calculation is carried out to the weather forecasting data of N number of weather forecasting point, is obtained
M weather forecasting data, M are the positive integer more than or equal to 2；
On the basis of the ordinate of coordinate points where the wind power plant, according to the ordinate of first coordinate data, with
And the ordinate of second coordinate data, interpolation calculation is carried out to the M weather forecasting data and is obtained at the wind power plant
Weather forecasting data.
In another embodiment, in the processing that above-mentioned processor 71 executes, it is described according to first coordinate data with
And second coordinate data, interpolation calculation is carried out to the weather forecasting data of N number of weather forecasting point, is obtained described
Weather forecasting data at wind power plant, specifically include:
On the basis of the ordinate of coordinate points where the wind power plant, according to the ordinate of first coordinate data, with
And the ordinate of second coordinate data, interpolation calculation is carried out to the weather forecasting data of N number of weather forecasting point, is obtained
P weather forecasting data, P are the positive integer more than or equal to 2；
On the basis of the abscissa of coordinate points where the wind power plant, according to the abscissa of first coordinate data, with
And the abscissa of second coordinate data, interpolation calculation is carried out to the P weather forecasting data and is obtained at the wind power plant
Weather forecasting data.
In specific implementation, it in the processing that above-mentioned processor 71 executes, is generating for reflecting the wind power plant and the N
Before the plane perspective view of the relative position of a weather forecasting point, further includes:
Weather forecasting data at N number of weather forecasting point are normalized.
In specific implementation, pre- in the weather being calculated at the wind power plant in the processing that above-mentioned processor 71 executes
After measured data, the method also includes:
The reality for acquiring the wind power plant goes out force data；
The weather forecasting data according at the wind power plant calculate the wind power prediction value of the wind power plant, tool
Body includes:
By at the wind power plant weather forecasting data and the reality go out force data and be input to backpropagation neural network
Network model, by the wind power prediction value that the wind power plant is calculated.
Embodiment 4:
The embodiment of the present application 4 additionally provides a kind of computer readable storage medium, deposits on the computer readable storage medium
The step of containing computer program, the method for power prediction executed when which is run by processor.
Specifically, which can be general storage medium, such as mobile disk, hard disk, on the storage medium
Computer program when being run, the method for being able to carry out above-mentioned power prediction, to solve in existing way using according to certain
A wind power plant weather data nearest apart from weather forecast mesh point, carrying out the prediction of wind power data by neural network algorithm makes
Obtain the not high problem of forecasting accuracy.The computer program product of the method for power prediction provided by the embodiment of the present application, packet
The computer readable storage medium for storing program code is included, the instruction that program code includes can be used for executing previous methods implementation
Method in example, specific implementation can be found in embodiment of the method, and details are not described herein.
It is apparent to those skilled in the art that for convenience and simplicity of description, the method for foregoing description
It with the specific work process of device, can refer to corresponding processes in the foregoing method embodiment, details are not described herein.
If function is realized in the form of SFU software functional unit and when sold or used as an independent product, can store
In a computer readable storage medium.Based on this understanding, the technical solution of the disclosure is substantially in other words to existing
Having the part for the part or the technical solution that technology contributes can be embodied in the form of software products, the computer
Software product is stored in a storage medium, including some instructions are used so that a computer equipment (can be personal meter
Calculation machine, server or network equipment etc.) execute each embodiment method of the disclosure all or part of the steps.And it is above-mentioned
Storage medium includes: USB flash disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory
The various media that can store program code such as (RAM, Random Access Memory), magnetic or disk.
More than, the only specific embodiment of the disclosure, but the protection scope of the disclosure is not limited thereto, and it is any to be familiar with
Those skilled in the art can easily think of the change or the replacement in the technical scope that the disclosure discloses, and should all cover
Within the protection scope of the disclosure.Therefore, the protection scope of the disclosure should be subject to the protection scope in claims.
Claims (10)
1. a kind of power forecasting method characterized by comprising
Step 1. acquires the first position data of wind power plant geographic location, and, acquire the N nearest apart from the wind power plant
The second position data of a weather forecasting point geographic location and the weather forecasting data at N number of weather forecasting point,
N is the positive integer more than or equal to four；
Step 2. is based on the first position data and the second position data, generates for reflecting the wind power plant and described
The plane perspective view of the relative position of N number of weather forecasting point, and determine coordinate where wind power plant described in the plane perspective view
Second coordinate data of coordinate points where first coordinate data of point and N number of weather forecasting point；
Step 3. is on the basis of coordinate points where the wind power plant, according to first coordinate data and second number of coordinates
According to the weather forecasting data progress interpolation calculation of N number of weather forecasting point, the weather obtained at the wind power plant is pre-
Measured data；
Step 4. calculates the wind power prediction value of the wind power plant according to the weather forecasting data at the wind power plant.
2. a kind of power forecasting method according to claim 1, which is characterized in that according to described first in the step 3
Coordinate data and second coordinate data carry out interpolation meter to the weather forecasting data of N number of weather forecasting point
It calculates, obtains the weather forecasting data at the wind power plant, specifically include:
On the basis of the abscissa of coordinate points where the wind power plant, according to abscissa, the Yi Jisuo of first coordinate data
The abscissa for stating the second coordinate data carries out interpolation calculation to the weather forecasting data of N number of weather forecasting point, obtains M
Weather forecasting data, M are the positive integer more than or equal to 2；
On the basis of the ordinate of coordinate points where the wind power plant, according to ordinate, the Yi Jisuo of first coordinate data
The ordinate for stating the second coordinate data carries out interpolation calculation to the M weather forecasting data and obtains the day at the wind power plant
Gas prediction data.
3. a kind of power forecasting method according to claim 1, which is characterized in that with the wind power plant in the step 3
It is pre- to N number of weather according to first coordinate data and second coordinate data on the basis of the coordinate points of place
The weather forecasting data of measuring point carry out interpolation calculation, obtain the weather forecasting data at the wind power plant, specifically further include:
On the basis of the ordinate of coordinate points where the wind power plant, according to ordinate, the Yi Jisuo of first coordinate data
The ordinate for stating the second coordinate data carries out interpolation calculation to the weather forecasting data of N number of weather forecasting point, obtains P
Weather forecasting data, P are the positive integer more than or equal to 2；
On the basis of the abscissa of coordinate points where the wind power plant, according to abscissa, the Yi Jisuo of first coordinate data
The abscissa for stating the second coordinate data carries out interpolation calculation to the P weather forecasting data and obtains the day at the wind power plant
Gas prediction data.
4. a kind of power forecasting method according to claim 1, which is characterized in that generate in the step 2 for reflecting
The plane perspective view of the relative position of the wind power plant and N number of weather forecasting point, before also further include:
Weather forecasting data at N number of weather forecasting point are normalized.
5. according to claim 1 to a kind of 4 any power forecasting methods, which is characterized in that calculated in the step 3
Weather forecasting data at the wind power plant, later further include:
The reality for acquiring the wind power plant goes out force data；
The weather forecasting data according at the wind power plant, calculate the wind power prediction value of the wind power plant, specific to wrap
It includes:
By at the wind power plant weather forecasting data and the reality go out force data and be input to reverse transmittance nerve network mould
Type, by the wind power prediction value that the wind power plant is calculated.
6. a kind of powder prediction device characterized by comprising acquisition module, processing module, interpolation calculation module and prediction mould
Block；
The acquisition module, for acquiring the first position data of wind power plant geographic location, and, it acquires apart from the wind
The second position data of the nearest N number of weather forecasting point geographic location of electric field and the day at N number of weather forecasting point
Gas prediction data, N are the positive integer more than or equal to four；
The processing module generates described for reflecting for being based on the first position data and the second position data
The plane perspective view of the relative position of wind power plant and N number of weather forecasting point, and determine wind described in the plane perspective view
Second number of coordinates of coordinate points where first coordinate data of coordinate points where electric field and N number of weather forecasting point
According to；
The interpolation calculation module, on the basis of coordinate points where the wind power plant, according to first coordinate data with
And second coordinate data, interpolation calculation is carried out to the weather forecasting data of N number of weather forecasting point, is obtained described
Weather forecasting data at wind power plant；
The prediction module, for calculating the wind power of the wind power plant according to the weather forecasting data at the wind power plant
Predicted value.
7. a kind of powder prediction device according to claim 6, which is characterized in that the interpolation calculation module is specific to use
In:
On the basis of the abscissa of coordinate points where the wind power plant, according to abscissa, the Yi Jisuo of first coordinate data
The abscissa for stating the second coordinate data carries out interpolation calculation to the weather forecasting data of N number of weather forecasting point, obtains M
Weather forecasting data, M are the positive integer more than or equal to 2；
On the basis of the ordinate of coordinate points where the wind power plant, according to ordinate, the Yi Jisuo of first coordinate data
The ordinate for stating the second coordinate data carries out interpolation calculation to the M weather forecasting data and obtains the day at the wind power plant
Gas prediction data；
The interpolation calculation module, additionally it is possible to be used for:
On the basis of the ordinate of coordinate points where the wind power plant, according to ordinate, the Yi Jisuo of first coordinate data
The ordinate for stating the second coordinate data carries out interpolation calculation to the weather forecasting data of N number of weather forecasting point, obtains P
Weather forecasting data, P are the positive integer more than or equal to 2；
On the basis of the abscissa of coordinate points where the wind power plant, according to abscissa, the Yi Jisuo of first coordinate data
The abscissa for stating the second coordinate data carries out interpolation calculation to the P weather forecasting data and obtains the day at the wind power plant
Gas prediction data.
8. a kind of powder prediction device according to claim 6, which is characterized in that
The processing module: the weather forecasting data at N number of weather forecasting point are normalized；
The prediction module: the reality for recording the wind power plant goes out force data, by the wind power plant weather forecasting data, with
And the reality of the record goes out force data and is input to back propagation artificial neural network model, by the wind that the wind power plant is calculated
Power prediction value.
9. a kind of powder prediction device according to claim 6, which is characterized in that further include: processor, memory and total
Line, the memory are stored with the executable machine readable instructions of the processor, when electronic equipment operation, the processor
By bus communication between the memory, such as claim is executed when the machine readable instructions are executed by the processor
The step of 1-5 any described power forecasting method.
10. a kind of powder prediction device according to claim 6, which is characterized in that further include: one kind is computer-readable to deposit
Storage media is stored with computer program on the computer readable storage medium, execution when which is run by processor
The step of power forecasting method a method as claimed in any one of claims 1 to 5.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2021179698A1 (en) * | 2020-03-12 | 2021-09-16 | 深圳前海微众银行股份有限公司 | Power generation capacity prediction method, apparatus and device, and computer-readable storage medium |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104376509A (en) * | 2014-11-26 | 2015-02-25 | 国家电网公司 | Wind power plant theoretical power determining method with wind direction effects considered |
| CN106875038A (en) * | 2017-01-03 | 2017-06-20 | 北京国能日新系统控制技术有限公司 | Based on the wind power prediction method and device for gathering local multiple spot Different climate feature |
| CN107341134A (en) * | 2017-06-27 | 2017-11-10 | 洛阳市气象局 | A kind of method of logarithm value forecast lattice point temperature forecast data process of refinement |
-
2018
- 2018-12-30 CN CN201811647777.1A patent/CN109934378A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104376509A (en) * | 2014-11-26 | 2015-02-25 | 国家电网公司 | Wind power plant theoretical power determining method with wind direction effects considered |
| CN106875038A (en) * | 2017-01-03 | 2017-06-20 | 北京国能日新系统控制技术有限公司 | Based on the wind power prediction method and device for gathering local multiple spot Different climate feature |
| CN107341134A (en) * | 2017-06-27 | 2017-11-10 | 洛阳市气象局 | A kind of method of logarithm value forecast lattice point temperature forecast data process of refinement |
Non-Patent Citations (3)
| Title |
|---|
| 叶金印等: "气象遥感图像及格点场重采样插值方法", 《计算机工程与应用》 * |
| 李得勤等: "基于数值模式的风速预报方法研究", 《太阳能学报》 * |
| 杨志凌等: "应用粒子群优化算法的短期风电功率预测", 《电网技术》 * |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2021179698A1 (en) * | 2020-03-12 | 2021-09-16 | 深圳前海微众银行股份有限公司 | Power generation capacity prediction method, apparatus and device, and computer-readable storage medium |
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