CN110263998A - Multi-source numerical weather forecast set bilayer modification method - Google Patents
Multi-source numerical weather forecast set bilayer modification method Download PDFInfo
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Abstract
A kind of multi-source numerical weather forecast set bilayer modification method is proposed, technical field of new energy power generation is belonged to, which comprises acquisition multi-source numerical weather forecast data survey wind data with corresponding reality；The set and corresponding practical survey wind data, logarithm data of weather forecast formed according to multi-source numerical weather forecast data carries out first layer amendment；Different weather scenes is divided according to the revised multi-source numerical weather forecast data of first layer；Analyze the correlation under different weather scene at each spatial position between multi-source numerical weather forecast data and corresponding practical survey wind data；The mutual correction model of numerical weather forecast is established for each weather scene according to relevance ranking, logarithm data of weather forecast carries out second layer amendment.This method improves the accuracy of weather forecast, improves the precision of prediction of wind power.
Description
Technical field
The invention belongs to technical field of new energy power generation more particularly to a kind of multi-source numerical weather forecast set bilayer to correct
Method.
Background technique
Extensive renewable energy access power grid is one of the prominent features of various countries' electric system future development.With wind-powered electricity generation
Power supply accounting in countries in the world rises year by year, and the intrinsic intermittence of wind-powered electricity generation and fluctuation lead to large-scale wind power integration meeting
Great impact is caused to the safe and stable and economical operation of power grid, accurate wind power prediction is to solve the problems, such as this necessity
One of means.Numerical weather forecast (NWP) is the key input and wind-powered electricity generation function of wind power prediction and its analysis of uncertainty
The main source of rate prediction error.In most cases, numerical weather forecast bring error is twice of prediction algorithm or more,
Therefore improving numerical weather forecast precision is to improve the key link of wind farm power prediction precision.
The amendment of existing numerical weather forecast mainly combines actual measurement wind speed to single position by statistical method both at home and abroad
The systematic error of point value weather forecast is modified.Such method mostly just uses the numerical weather forecast of single source,
It is closed by establishing the mapping in a certain location point historical series between synchronization numerical weather forecast data and measured data respectively
System, and then the numerical weather forecast data of future time instance are modified.But China has a vast territory, climate type and orographic condition
Complicated multiplicity, considerably increases the difficulty of numerical weather forecast (NWP) and subsequent wind power prediction.Existing Numerical Weather is pre-
Report correction method for repairment to have the following problems: single source numerical weather forecast is limited to the adaptability of complicated weather condition, becomes shadow
One of an important factor for ringing wind power prediction precision；From the point of view of physical modeling process, the calculating of Mesoscale Meteorology is differentiated
Rate is too low, and the Regional factors such as landform, roughness, wake flow can not be embodied in its described flow field, and single position is caused to be counted
Value data of weather forecast has often lacked the time and space usage relationship of practical wind wave process within the scope of Large Scale Wind Farm Integration, or needs to consume
Take a large amount of computing resources to be solved, needs to consider the correlative character of practical wind regime at multi-point for above-mentioned minute yardstick factor
It is corrected again.
Summary of the invention
In order to solve the problems, such as to be proposed above, the present invention proposes a kind of multi-source numerical weather forecast set bilayer amendment side
Method.
According to an aspect of the present invention, it proposes a kind of multi-source numerical weather forecast set bilayer modification method, the party
Method includes: step 1: it acquires the multi-source numerical weather forecast data at multiple spatial positions and surveys wind data with corresponding reality, it is right
Above-mentioned data are cleaned and are pre-processed；Step 2: being directed to the spatial position, pre- according to the multi-source Numerical Weather at the position
Set (NWP data) and corresponding practical survey wind data of the count off according to formation, establish numerical weather forecast from local correction model, logarithm
It is worth data of weather forecast and carries out first layer amendment；Step 3: according to the revised multi-source numerical weather forecast data set of first layer
It closes result and divides different weather scenes；Step 4: multi-source Numerical Weather at each spatial position under analysis different weather scene
Forecast data aggregated result and the corresponding practical correlation surveyed between wind data；Step 5: it according to relevance ranking, chooses not on the same day
Numerical value is established for each weather scene with the actual measurement the most similar numerical weather forecast data of air speed data under gas field scape
The mutual correction model of weather forecast, logarithm data of weather forecast carry out second layer amendment.
According to an aspect of the present invention, the multi-source numerical weather forecast data refer to different initial fields, different parameters
Numerical weather forecast result when scheme, difference under secondary or different computation models.
According to an aspect of the present invention, in step 1, the cleaning and pretreatment include identification to abnormal data
Interpolation with deleting and to missing data.
According to an aspect of the present invention, in step 2, with the multi-source numerical weather forecast number at the spatial position
It is combined into input according to the collection of formation, the practical survey wind data in corresponding position is learning objective, establishes the custom holotype of numerical weather forecast
Type eliminates the Systematic Errors in numerical simulation.
According to an aspect of the present invention, the practical wind data of surveying includes that anemometer measurement is simultaneously in wind power plant operation data
The actual measurement wind data that anemometer measures and records at the practical survey wind data of record and anemometer tower position.
According to an aspect of the present invention, in step 3, using Fuzzy C-Means Cluster Algorithm by numerical weather forecast number
According to different weather scenes is divided into, numerical weather forecast data set X={ x is given1,x2,…,xn, wherein n is the number of sample
Amount, k are the class number of weather scene, mj(j=1,2 ..., k) is the center of each cluster, μj(xi) it is that i-th of sample is corresponding
The subordinating degree function of jth class, then the cluster loss function J based on subordinating degree functionfAre as follows:
Wherein b > 1, for the constant of the fog-level of control cluster result.
Enable JfTo mjAnd μj(xi) partial derivative be 0, acquire the necessary condition of above formula minimum:
Then the step of Fuzzy C-Means Cluster Algorithm are as follows:
(1) cluster classification number C is given, iteration convergence condition is set, initializes each cluster centre；
(2) following operation is repeated, until the angle value that is subordinate to of each sample is stablized:
A. subordinating degree function is calculated according to formula (4) with current cluster centre；
B. the center of each cluster is recalculated according to formula (3) with current subordinating degree function.
When algorithmic statement, just obtains all kinds of cluster centres and each sample and angle value is subordinate to for all kinds of, thus
Fuzzy clustering is completed to divide.
According to an aspect of the present invention, in step 4, by calculating multi-source numerical weather forecast data acquisition system result
With the practical Pearson correlation coefficient for surveying wind data, Two-Dimensional Correlativity evaluations matrix is constructed.
According to an aspect of the present invention, in step 5, for certain weather scene, with numerical value day under the weather scene
At gas forecast data and actual measurement air speed data the numerical weather forecast data and other positions to be verified of similar location the most
Numerical weather forecast data be input, the practical survey wind data at other positions to be verified is learning objective, establishes the day
The mutual Knowledge Verification Model of numerical weather forecast under gas field scape.
It can be seen that the present invention is aiming at the problems existing in the prior art, using the numerical weather forecast data in a variety of sources
Its adaptability to complicated weather condition is improved, using numerical weather forecast data at multiple spatial positions, in independent position
The multi-point numerical weather forecast that different weather scene is carried out from the basis of correcting mutually verifies, to correct minute yardstick factor, this
Outside, numerical weather forecast amendment of the invention includes the double-deck amendment, i.e., surveys wind data using history and carry out from correcting and multi-point
The mutual verification of numerical weather forecast improves the precision of prediction of wind power to improve the accuracy of weather forecast.
Detailed description of the invention
Fig. 1 is the flow chart of multi-source numerical weather forecast set bilayer modification method.
Specific embodiment
Technical solution of the present invention is described in further detail with specific embodiment with reference to the accompanying drawing.
Fig. 1 is the flow chart of multi-source numerical weather forecast set bilayer modification method, as shown in Figure 1, the method includes
Following steps:
Step 1: acquiring the multi-source numerical weather forecast data at multiple spatial positions and survey wind data with corresponding reality,
Above-mentioned data are cleaned and pre-processed.
Specifically, acquisition spatial position multi-source numerical weather forecast data (NWP-1, NWP-2 ..., NWP-n) with it is right
It answers the actual measurement at position to survey wind data, abnormal data is identified according to range check, correlation test and trend test criterion and incites somebody to action
It is deleted, and using nearest neighbor interpolation algorithm and linear interpolation algorithm interpolation missing data, restores reducible data, after guaranteeing
Continuous step learning model input data is accurate corresponding with label data.At the beginning of the multi-source numerical weather forecast data refer to difference
Beginning field, different parameters scheme, it is different when time or different computation models under numerical weather forecast as a result, specifically include wind speed,
Wind direction, temperature, air pressure, humidity and atmospheric density data.
Step 2: being directed to the spatial position, the set formed according to the multi-source numerical weather forecast data at the position
(NWP data) and it is corresponding it is practical survey wind data, establish numerical weather forecast from local correction model, logarithm data of weather forecast into
The amendment of row first layer.
Specifically, it is directed to some spatial position, the set formed with the multi-source numerical weather forecast data at the position
For input, the practical survey wind data in corresponding position (wind speed, wind direction, temperature, air pressure, humidity and atmospheric density data) is study mesh
Mark establishes numerical weather forecast from local correction model, the systematicness eliminated in numerical simulation is missed using random forests algorithm
Difference.As shown in Figure 1, the actual measurement wind data (wind speed, wind direction etc.) for having instrument for wind measurement to measure and record in wind power plant operation data, is surveyed
Wind tower data are the actual measurement wind datas (wind speed, wind direction etc.) that instrument for wind measurement measures and records at anemometer tower position, and herein is more
A spatial position refers to each Wind turbines and each anemometer tower.
N is randomly selected from numerical weather forecast and practical survey in wind data training set S using the Bootstrap methods of sampling
A training sample subset S1,S2,…,SNFor constructing N decision tree, the size of each training subset is about original training set
2/3rds, sampling is random and sampling with replacement every time, so that there are certain repetition, mesh for the sample in training subset
Be in order to make the decision tree in forest be unlikely to generate locally optimal solution；Then these decision trees are combined, with each
The result that is returned as random forest of average value of tree output result；To guarantee randomness when decision tree building, avoided
Fitting problems randomly select m1 kind as random character variable participative decision making when each decision tree constructs from m attribute
The sampling of the fission process of tree node, referred to as random attribute subspace, wherein m1 takes less than or equal to log2(m+1) maximum is just whole
Number；Furthermore the quantity N needs of stochastic decision tree adjust；Finally based on the regression result of random forests algorithm are as follows:
In formula: X is input variable, h (X；θk) it is single decision-tree model, wherein k=1,2 ..., N, N are decision tree
Number, θkFor the parameter of single decision tree.
Step 3: different day gas fields is divided according to the revised multi-source numerical weather forecast data acquisition system result of first layer
Scape.
In one embodiment, numerical weather forecast data are divided into not by step 3 using Fuzzy C-Means Cluster Algorithm
Same weather scene gives numerical weather forecast data set X={ x1,x2,…,xn, wherein n is the quantity of sample, and k is weather
The class number of scene, mj(j=1,2 ..., k) is the center of each cluster, μj(xi) it is that i-th of sample corresponds to being subordinate to for jth class
Function is spent, then the cluster loss function J based on subordinating degree functionfIt is writeable are as follows:
Wherein b > 1, for the constant of the fog-level of control cluster result.
Enable JfTo mjAnd μj(xi) partial derivative be 0, acquire the necessary condition of above formula minimum:
Then the step of Fuzzy C-Means Cluster Algorithm are as follows:
(1) cluster classification number C is given, iteration convergence condition is set, initializes each cluster centre；
(2) following operation is repeated, until the angle value that is subordinate to of each sample is stablized:
A. subordinating degree function is calculated according to formula (4) with current cluster centre；
B. the center of each cluster is recalculated according to formula (3) with current subordinating degree function；
When algorithmic statement, just obtains all kinds of cluster centres and each sample and angle value is subordinate to for all kinds of, thus
Fuzzy clustering is completed to divide.
Step 4: under analysis different weather scene at each spatial position multi-source numerical weather forecast data acquisition system result with
The corresponding practical correlation surveyed between wind data.
Two-Dimensional Correlativity evaluations matrix is constructed by calculating the Pearson correlation coefficient of the two according to one embodiment.
Step 5: according to relevance ranking, choose under different weather scene with the actual measurement the most similar numerical value of air speed data
Data of weather forecast, i.e., with the practical numerical weather forecast for surveying the most similar position of wind data as mutual at different spatial
The input of Knowledge Verification Model establishes the mutual correction model of numerical weather forecast, logarithm weather forecast number for each weather scene
According to progress second layer amendment.
According to one embodiment, for certain weather scene, with numerical weather forecast data under the weather scene and actual measurement
The numerical weather forecast number at numerical weather forecast data and other points to be verified at air speed data point the most similar
According to input, the practical survey wind data at other points to be verified establishes the day using random forests algorithm for learning objective
The mutual Knowledge Verification Model of numerical weather forecast under gas field scape, further, it is also possible to be modified using other algorithms, such as deep learning
Stacking automatic coding machine in algorithm etc..
Above-mentioned specific example is only to illustrate, not as limiting the scope of the invention.Those skilled in the art can root
According to needing the specific example proposed to the application to modify or adjust, these modifications or adjustment equally fall into the application and are wanted
Within the scope of asking protection.
Claims (8)
1. a kind of multi-source numerical weather forecast set bilayer modification method, which is characterized in that this method comprises:
Step 1: the multi-source numerical weather forecast data at multiple spatial positions are acquired and survey wind data with corresponding reality, to upper
Data are stated to be cleaned and pre-processed；
Step 2: being directed to the spatial position, the set (NWP formed according to the multi-source numerical weather forecast data at the position
Data) and it is corresponding it is practical survey wind data, establish numerical weather forecast from local correction model, logarithm data of weather forecast carries out the
One layer of amendment；
Step 3: different weather scenes is divided according to the revised multi-source numerical weather forecast data acquisition system result of first layer；
Step 4: multi-source numerical weather forecast data acquisition system result and corresponding at each spatial position under analysis different weather scene
The practical correlation surveyed between wind data；
Step 5: according to relevance ranking, choose under different weather scene with the actual measurement the most similar Numerical Weather of air speed data
Forecast data establishes the mutual correction model of numerical weather forecast for each weather scene, and logarithm data of weather forecast carries out
Second layer amendment.
2. according to the method described in claim 1, its feature exists:
The multi-source numerical weather forecast data refer to secondary or different calculating when different initial fields, different parameters scheme, difference
Numerical weather forecast result under model.
3. according to the method described in claim 1, it is characterized by:
In step 1, the cleaning and pretreatment include identification to abnormal data and deletion and insert to missing data
It mends.
4. according to the method described in claim 1, it is characterized by:
In step 2, input, corresponding position are combined into the collection that the multi-source numerical weather forecast data at the spatial position are formed
Setting the practical wind data of surveying in place is learning objective, and establishing numerical weather forecast from local correction model, elimination numerical simulation is
System property error.
5. according to the method described in claim 4, it is characterized by:
The practical wind data of surveying includes the practical survey wind data and survey wind that anemometer measures and records in wind power plant operation data
The actual measurement wind data that anemometer measures and records at tower position.
6. according to the method described in claim 1, it is characterized by:
In step 3, numerical weather forecast data are divided into using Fuzzy C-Means Cluster Algorithm by different weather scenes, are given
Fixed number value data of weather forecast collection X={ x1,x2,…,xn, wherein n is the quantity of sample, and k is the class number of weather scene,
mj(j=1,2 ..., k) is the center of each cluster, μj(xi) it is the subordinating degree function that i-th of sample corresponds to jth class, then based on person in servitude
The cluster loss function J of category degree functionfAre as follows:
Wherein b > 1, for the constant of the fog-level of control cluster result；
Enable JfTo mjAnd μj(xi) partial derivative be 0, acquire the necessary condition of above formula minimum:
Then the step of Fuzzy C-Means Cluster Algorithm are as follows:
(1) cluster classification number C is given, iteration convergence condition is set, initializes each cluster centre；
(2) following operation is repeated, until the angle value that is subordinate to of each sample is stablized:
A. subordinating degree function is calculated according to formula (4) with current cluster centre；
B. the center of each cluster is recalculated according to formula (3) with current subordinating degree function；
When algorithmic statement, all kinds of cluster centres and each sample is just obtained and angle value are subordinate to for all kinds of, to complete
Fuzzy clustering divides.
7. according to the method described in claim 1, it is characterized by:
It is related to the practical survey Pearson came of wind data by calculating multi-source numerical weather forecast data acquisition system result in step 4
Coefficient constructs Two-Dimensional Correlativity evaluations matrix.
8. according to the method described in claim 1, it is characterized by:
In step 5, for certain weather scene, with numerical weather forecast data under the weather scene and actual measurement air speed data
The numerical weather forecast data of similar location and the numerical weather forecast data at other positions to be verified the most are input,
Practical survey wind data at other positions to be verified is learning objective, establishes the mutual school of numerical weather forecast under the weather scene
Test model.
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| CN111309852A (en) * | 2020-03-16 | 2020-06-19 | 青岛百洋智能科技股份有限公司 | Method, system, apparatus and storage medium for generating a visual decision tree (set) model |
| CN113537648A (en) * | 2021-09-16 | 2021-10-22 | 国能日新科技股份有限公司 | Wind speed prediction method and device based on set data |
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| CN110934061A (en) * | 2019-12-26 | 2020-03-31 | 裕华生态环境股份有限公司 | Garden irrigation water-saving system |
| CN111309852A (en) * | 2020-03-16 | 2020-06-19 | 青岛百洋智能科技股份有限公司 | Method, system, apparatus and storage medium for generating a visual decision tree (set) model |
| CN111309852B (en) * | 2020-03-16 | 2021-09-03 | 青岛百洋智能科技股份有限公司 | Method, system, device and storage medium for generating visual decision tree set model |
| CN113610287A (en) * | 2021-07-27 | 2021-11-05 | 远景智能国际私人投资有限公司 | Optical power forecasting method and device, computer equipment and storage medium |
| CN113537648A (en) * | 2021-09-16 | 2021-10-22 | 国能日新科技股份有限公司 | Wind speed prediction method and device based on set data |
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