CN105654207A  Wind power prediction method based on wind speed information and wind direction information  Google Patents
Wind power prediction method based on wind speed information and wind direction information Download PDFInfo
 Publication number
 CN105654207A CN105654207A CN201610007840.XA CN201610007840A CN105654207A CN 105654207 A CN105654207 A CN 105654207A CN 201610007840 A CN201610007840 A CN 201610007840A CN 105654207 A CN105654207 A CN 105654207A
 Authority
 CN
 China
 Prior art keywords
 wind
 day
 data
 prediction
 power
 Prior art date
 Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
 Pending
Links
 238000010606 normalization Methods 0.000 claims abstract description 25
 238000004458 analytical methods Methods 0.000 claims abstract description 7
 238000003064 k means clustering Methods 0.000 claims abstract description 6
 206010008190 Cerebrovascular accident Diseases 0.000 claims description 9
 208000006011 Stroke Diseases 0.000 claims description 9
 230000000875 corresponding Effects 0.000 claims description 9
 238000007621 cluster analysis Methods 0.000 claims description 6
 230000001537 neural Effects 0.000 claims description 5
 238000000605 extraction Methods 0.000 claims description 4
 238000005311 autocorrelation function Methods 0.000 claims description 3
 230000005540 biological transmission Effects 0.000 claims description 3
 230000002596 correlated Effects 0.000 claims description 3
 238000000034 methods Methods 0.000 claims description 3
 239000010410 layers Substances 0.000 description 18
 210000002569 neurons Anatomy 0.000 description 6
 230000004913 activation Effects 0.000 description 2
 230000000694 effects Effects 0.000 description 2
 238000010586 diagrams Methods 0.000 description 1
 210000002364 input neuron Anatomy 0.000 description 1
 230000001105 regulatory Effects 0.000 description 1
 230000001932 seasonal Effects 0.000 description 1
 238000004088 simulation Methods 0.000 description 1
 238000009987 spinning Methods 0.000 description 1
 230000001429 stepping Effects 0.000 description 1
 230000001629 suppression Effects 0.000 description 1
 230000001131 transforming Effects 0.000 description 1
Classifications

 G—PHYSICS
 G06—COMPUTING; CALCULATING; COUNTING
 G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
 G06Q10/00—Administration; Management
 G06Q10/04—Forecasting or optimisation, e.g. linear programming, "travelling salesman problem" or "cutting stock problem"

 G—PHYSICS
 G06—COMPUTING; CALCULATING; COUNTING
 G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
 G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
 G06Q50/06—Electricity, gas or water supply
Abstract
The invention relates to a wind power prediction method based on the wind speed information and the wind direction information. The method comprises the steps of collecting and arranging the historical wind data of a wind power plant and the actual wind power output data, conducting the normalization pretreatment on the data as a training sample, then conducting the similarity analysis and the clustering analysis, and dividing data objects similar in characteristic attribute as one type, respectively calculating the cluster center for each type of data based on the kmeans clustering algorithm, predicting an active power average value on a prediction day based on an autoregressive model structure, constructing a model to establish different types of wind power prediction models with the combination of the active power average value with the data of wind speed and wind direction of each type jointly as the input and the actually measured active power data on the prediction day as the output, judging the type of normalized daily vectors highest in similarity on the prediction day, and obtaining the predicted wind power on the prediction day based on a wind power prediction model of the same type. The above method is high in prediction accuracy and high in reliability.
Description
Technical field
The present invention relates to wind energy turbine set generated power forecasting technology, particularly a kind of wind power forecasting method based on wind speed, wind direction information.
Background technology
Wind energy is a kind of typical nonpollution renewable energy, due to its aboundresources, possess the condition of largescale development, therefore suffer from paying close attention to widely, becoming the principal mode of future source of energy, the development and utilization of wind energy has become the of paramount importance Renewable Energy Development direction of China. Along with gradually stepping up of the accumulative installed capacity of windpowered electricity generation, the randomness of its generated output and undulatory property are more and more obvious on the impact of gridconnected rear grid balance. The reliability of stable operation and electric power system in order to ensure electrical network, electric power system must be carried out effectively plan and scheduling, it is thus desirable to windpower electricity generation power is carried out Accurate Prediction, this is directly connected to the equilibrium of supply and demand of electrical network, also directly affects the operation cost of gridconnected system. 2011, National Energy Board has issued " wind park power prediction forecast management Tentative Measures ", force wind energy turbine set that Wind power forecasting system is installed, and since 1 day January in 2012, do not report and submit the wind energy turbine set of windpower electricity generation power forecast result also not allow to be incorporated into the power networks on request.
When formulating generation schedule and arranging scheduling, need to ensure that electricity generation system has stronger complex tracking ability, can adapt to minute level or the load fluctuation of hour level, thus it requires system has enough spinning reserve capacities, it is thus desirable to the generated output of winddriven generator and wind energy turbine set to be carried out the prediction of ultrashort term (< 4h) and shortterm (< 72h), and wind power prediction can reduce wind power cost under ensureing the steady of electric power system and the premise of safety accurately, it is possible to reach to improve the purpose of wind power value. The importance of Wind power forecasting as can be seen here.
Summary of the invention
The invention aims to the wind power forecasting method based on wind speed, wind direction information providing a kind of reliability high, utilize wind speed, wind direction information realization ultrashort term or shortterm windelectricity power prediction.
The technical scheme is that
A kind of wind power forecasting method based on wind speed, wind direction information, comprises the following steps:
Step one, carry out collecting to the history wind data of wind energy turbine set and corresponding actual wind power output data and arrange, wherein history wind data includes the wind speed of differing heights, the wind direction information that anemometer tower obtains, and actual wind power output data are the actual active power of wind energy turbine set realtime Transmission;
Step 2, the history wind data of described wind energy turbine set and corresponding actual wind power output data are normalized after pretreatment as training sample, then according to the normalization data sequence of the wind speed of different time points, wind direction and corresponding actual active power is carried out similarity analysis and cluster analysis by time sequencing, data object similar for characteristic attribute is divided into same class, adopt K means clustering algorithm after determining final number of categories K, calculate the cluster centre of each class data respectively;
The actual active power of history before step 3, extraction training Day, autoregression model structure prediction is adopted to go out the active power mean value on the training Day same day, it is combined collectively as input with the wind speed of each apoplexy due to endogenous wind, wind direction data, the actual measurement active power data on training Day same day are modeled as output, by the training to each class data sample, set up inhomogeneous wind power prediction model, namely set up K wind power prediction model;
The normalization data of the active power mean value of step 4, the extraction prediction wind speed on same day day, wind direction and prediction, the normalization day vector on same day predicted composition day, calculate this normalization day vectorial Similarity Parameter with each class cluster centre of K apoplexy due to endogenous wind respectively, judge a class the highest with the normalization day vector similarity of prediction day, utilize such wind power prediction model to obtain the prepower scale of windpowered electricity generation on prediction same day day.
The abovementioned wind power forecasting method based on wind speed, wind direction information, the method carrying out similarity analysis in step 2 is the size adopting Pearson productmoment correlation coefficient to carry out quantitative description similarity, and the computing formula of described Pearson productmoment correlation coefficient is as follows:
Wherein, E represents mathematic expectaion; X and Y represents the normalization day vector that wind speed and direction data on the same day are not constituted respectively; R represents correlation coefficient, and span is1��1;  R  < 0.4 is low linear correlation; 0.4�� R  < 0.7 is notable linear correlation; 0.7�� R  < 1 is correlated with for highly linear.
The abovementioned wind power forecasting method based on wind speed, wind direction information, concretely comprising the following steps of described K means clustering algorithm:
A, all data being divided into K initial classes, choosing K sample point is initial cluster center, is designated as z_{1}(l),z_{2}(l),��,z_{k}(l), wherein, initial value l=1;
B, according to Nearest Neighbor Method, all samples are assigned to the K class �� representated by each cluster centre_{j}(K), in, all kinds of comprised sample number is N_{j}(l), wherein, criterion function is Euclidean distance, and definition is:
In formula, d represents not Euclidean distance between normalization day vector X, Y on the same day; The dimension of m vector X and Y; The subvector of x and y vector X, Y;
C, calculate all kinds of mean vector, and using this vector as new cluster centre:In formula, j=1,2 ..., k; I=1,2 ..., N_{j}(l);
If d is z_{j}(l+1)��z_{j}L (), represents that cluster result is not best, then returns b, continue iterative computation;
If z_{j}(l+1)=z_{j}L (), iterative process terminates, and cluster result now is exactly optimum cluster result.
The abovementioned wind power forecasting method based on wind speed, wind direction information, K=5.
The abovementioned wind power forecasting method based on wind speed, wind direction information, the expression of the autoregression model structure in step 3 is:
Wherein, p_{t}Represent the active power mean value of training Day; p_{t1}��p_{t2}Represent the active power mean value of two days before training Day respectively;For Parameters of Autoregressive Models, { ��_{t}For White Noise Model, by adopting sample autocorrelation function to determine rank and method of least square carries out parameter estimation, obtain
The abovementioned wind power forecasting method based on wind speed, wind direction information, the wind power prediction model in step 3 selects general regression neural network.
The abovementioned wind power forecasting method based on wind speed, wind direction information, predicts in step 4 that the normalization day vectorial similarity measurement with each class cluster centre of K apoplexy due to endogenous wind on same day day adopts Euclidean distance to measure.
The invention has the beneficial effects as follows:
Owing to the selection of training sample directly influences the precision of forecast model, so the method adopting cluster analysis before modeling, wind speed with training Day differing heights, wind direction information is as clustering target, adopt Euclidean distance as the measurement criterion of similarity, first sample data is carried out cluster analysis, further according to the sample class the most close with training Day same day, in conjunction with history active power as the training sample modeled, shown by simulation result, after the method adopting cluster analysis carries out data prediction, improve the precision of prediction of forecast model greatly, reliability is high, can under the premise ensureing electric power system stable operation, save operating cost, be conducive to improving the economic benefit that wind energy turbine set is run.
Accompanying drawing explanation
Fig. 1 is the theory diagram of the wind power forecasting method of the present invention.
Detailed description of the invention
As it is shown in figure 1, this wind power forecasting method, comprise the following steps:
1, carry out the history wind data of wind energy turbine set and corresponding actual wind power output data collecting arranging, wherein wind data includes the wind speed of differing heights, the wind direction information that anemometer tower obtains, and actual wind power output data are the actual active power of wind energy turbine set realtime Transmission. In the present embodiment, collect and arrange 10 meters of height wind speed that in April, 2014 to May, anemometer tower obtained, 10 meters of height wind directions, 30 meters of height wind speed, 30 meters of height wind directions, 50 meters of height wind speed and 50 meters of height wind directions, and the actual active power of wind energy turbine set in this period, the resolution of data is 15min, and each day is 96 time points.
2, to 10 meters of height wind speed that in April, 2014 to May, anemometer tower obtained, 10 meters of height wind directions, 30 meters of height wind speed, 30 meters of height wind directions, 50 meters of height wind speed and 50 meters of height wind directions, and the pretreatment that in this period, the actual active power of wind energy turbine set is normalized is as training sample, then according to the normalization data sequence of the wind speed of different time points, wind direction and corresponding actual active power is carried out similarity analysis by time sequencing, wherein, selecting Pearson productmoment correlation coefficient to carry out the size of quantitative description similarity, computing formula is as follows:
In formula, E mathematic expectaion; X, Y represents the normalization day vector that wind speed and direction data on the same day are not constituted, in the present embodiment, described day vector be made up of 10 meters of 96 time points height wind speed, 10 meters of height wind directions, 30 meters of height wind speed, 30 meters of height wind directions, 50 meters of height wind speed and 50 meters of height wind direction data; The value of correlation coefficient is1��1, and its character is as follows: as R > 0, represents two variable positive correlations, as R < 0, represents that two variablees are negative correlation; When  R =1, represent that two variablees are fairly linear relevant, be functional relationship; As R=0, represent between two variablees without linear relationship; As 0 <  R  < 1, representing that two variablees exist a degree of linear correlation, and  R  is closer to 1, two variable linearly relations are more close;  R , closer to 0, represents that the linear correlation of two variablees is more weak; Generally can by three grades of divisions,  R  < 0.4 is low linear correlation; 0.4�� R  < 0.7 is notable linear correlation; 0.7�� R  < 1 is correlated with for highly linear.
After obtaining different wind speed, wind direction seasonal effect in time series correlation coefficient, historical time data is carried out cluster analysis, data object similar for characteristic attribute is divided into same class.In the present embodiment, select K means clustering algorithm the most classical in Dynamic Clustering Algorithm, it is determined that final number of categories K, and calculate the cluster centre of each class data respectively. In the present embodiment, K=5, calculate the cluster centre of 5 class data respectively. Concretely comprise the following steps:
E, all data being divided into K initial classes, choosing K sample point is initial cluster center, is designated as z_{1}(l),z_{2}(l),��,z_{k}(l), wherein, initial value l=1;
F, according to Nearest Neighbor Method, all samples are assigned to the K class �� representated by each cluster centre_{j}(K), in, all kinds of comprised sample number is N_{j}(l), wherein, criterion function is Euclidean distance, and definition is:
In formula, d represents not Euclidean distance between normalization day vector X, Y on the same day; The dimension of m vector X and Y; The subvector of x and y vector X, Y;
G, calculate all kinds of mean vector, and using this vector as new cluster centre:In formula, j=1,2 ..., k; I=1,2 ..., N_{j}(l);
If h is z_{j}(l+1)��z_{j}L (), represents that cluster result is not best, then returns b, continue iterative computation;
If i is z_{j}(l+1)=z_{j}L (), iterative process terminates, and cluster result now is exactly optimum cluster result.
3, extracting the actual active power of history before training Day, adopt autoregression model structure prediction to go out the active power mean value on the training Day same day, in the present embodiment, the expression of autoregression model structure is:
Wherein, p_{t}Represent the active power mean value of training Day; p_{t1}��p_{t2}Represent the active power mean value of two days before training Day respectively;For Parameters of Autoregressive Models, { ��_{t}For White Noise Model, by adopting sample autocorrelation function to determine rank and method of least square carries out parameter estimation, obtain
Then the active power mean value on training Day same day is combined collectively as input with the wind speed of each apoplexy due to endogenous wind, wind direction data, the actual measurement active power data on training Day same day are modeled as output, by the training to each class data sample, set up inhomogeneous wind power prediction model, namely set up K wind power prediction model. in the present embodiment, wind power prediction model selects general regression neural network (GRNN), this model is by input layer, hidden layer and output layer are constituted, hidden layer is radial direction basic unit, Gaussian transformation function is adopted to control hidden layer output, thus playing the activation suppressing output unit, in the input space, Gaussian function is symmetrical in acceptance region, the impact exported by network is exponentially decayed by input neuron with the distance between input vector, in GRNN, each trained vector has a corresponding radially base neuron in hidden layer, hidden layer neuron stores each trained vector, when network inputs a new vector, distance between new vector and each unit weight vector of hidden layer can be calculated by below equation:
Dist= XW^{1}
In formula, X is input vector; W^{1}For hidden layer unit weight vector; Dist is the spacing of input vector and weight vector. The Gaussian function output of hidden layer calculates as follows:
In formula, s is window width, if dist=s, then distance n after adjusting^{1}=  dist   �� b^{1}=0.8326, Gaussian function output valve is 0.5, and being equivalent to correlation coefficient is 0.5; If dist is much larger than s, Gaussian function exports close to 0. along with a^{1}Increase, hidden layer output be gradually reduced, variable s plays a part window, say, that s size plays output layer neuronal activation effect, and s is more big, b^{1}More little, hidden layer neuron and input vector are apart from reduced, and therefore, the neuron number being activated in window increases, otherwise, s is more little, and b is more big, hidden layer neuron is exaggerated with input vector distance, and hidden layer output reduces, and the neuron number being activated in window reduces.
GRNN output layer is linear layer, and output calculates as follows:
a^{2}=n^{2}=W^{2}a^{1}+b^{2}
In formula, W^{2}It is the 2nd layer of weights.
Therefore, the feature of general regression neural network (GRNN) is that the artificial parameter regulated is few, only one of which threshold value s. The study of network all relies on data sample. This feature determines GRNN to avoid artificial subjective hypothesis on the impact predicted the outcome to greatest extent. To sum up, for inhomogeneous training sample, in the present embodiment, the input number of nodes of GRNN is 7, and output node number is 1, and the window width parameter s value of network is 0.21.
4, prediction day is extracted (in the present embodiment, prediction is set as10 days on the 1st June in 2014 day) wind speed on the same day, wind direction and prediction the normalization data of active power mean value, the normalization day vector on same day predicted composition day, calculate this normalization day vectorial Similarity Parameter with the 5 each class cluster centres of apoplexy due to endogenous wind respectively, judge a class the highest with the normalization day vector similarity of prediction day, and utilize such wind power prediction model to obtain the prepower scale of windpowered electricity generation on prediction same day day. In the present embodiment, the normalization day vector on prediction same day day adopts Euclidean distance algorithm with the calculating of the Similarity Parameter of each class cluster centre of K apoplexy due to endogenous wind, prediction same day day day vector to the Euclidean distance respectively 0.32,0.43,0.83,0.12,0.48 of each class, nearest with the cluster centre of prediction day vector distance the 3rd class, namely the highest with the 3rd class similarity, therefore selecting the forecast model being training sample with the 3rd class, can obtain its forecast error NMAE (standard absolute value mean error) is 10.47%. Obtain owing to the parameter of forecast model is both for similar sample training, therefore there is higher precision of prediction.
Claims (7)
1., based on a wind power forecasting method for wind speed, wind direction information, comprise the following steps:
Step one, carry out collecting to the history wind data of wind energy turbine set and corresponding actual wind power output data and arrange, wherein history wind data includes the wind speed of differing heights, the wind direction information that anemometer tower obtains, and actual wind power output data are the actual active power of wind energy turbine set realtime Transmission;
Step 2, the history wind data of described wind energy turbine set and corresponding actual wind power output data are normalized after pretreatment as training sample, then according to the normalization data sequence of the wind speed of different time points, wind direction and corresponding actual active power is carried out similarity analysis and cluster analysis by time sequencing, data object similar for characteristic attribute is divided into same class, adopt K means clustering algorithm after determining final number of categories K, calculate the cluster centre of each class data respectively;
The actual active power of history before step 3, extraction training Day, autoregression model structure prediction is adopted to go out the active power mean value on the training Day same day, it is combined collectively as input with the wind speed of each apoplexy due to endogenous wind, wind direction data, the actual measurement active power data on training Day same day are modeled as output, by the training to each class data sample, set up inhomogeneous wind power prediction model, namely set up K wind power prediction model;
The normalization data of the active power mean value of step 4, the extraction prediction wind speed on same day day, wind direction and prediction, the normalization day vector on same day predicted composition day, calculate this normalization day vectorial Similarity Parameter with each class cluster centre of K apoplexy due to endogenous wind respectively, judge a class the highest with the normalization day vector similarity of prediction day, and utilize such wind power prediction model to obtain the prepower scale of windpowered electricity generation on prediction same day day.
2. the wind power forecasting method based on wind speed, wind direction information according to claim 1, it is characterized in that: the method carrying out similarity analysis in step 2 is the size adopting Pearson productmoment correlation coefficient to carry out quantitative description similarity, and the computing formula of described Pearson productmoment correlation coefficient is as follows:
Wherein, E represents mathematic expectaion;X and Y represents the normalization day vector that wind speed and direction data on the same day are not constituted respectively; R represents correlation coefficient, and span is1��1;  R  < 0.4 is low linear correlation; 0.4�� R  < 0.7 is notable linear correlation; 0.7�� R  < 1 is correlated with for highly linear.
3. the wind power forecasting method based on wind speed, wind direction information according to claim 1, it is characterised in that: concretely comprising the following steps of described K means clustering algorithm:
A, all data being divided into K initial classes, choosing K sample point is initial cluster center, is designated as z_{1}(l),z_{2}(l),��,z_{k}(l), wherein, initial value l=1;
B, according to Nearest Neighbor Method, all samples are assigned to the K class �� representated by each cluster centre_{j}(K), in, all kinds of comprised sample number is N_{j}(l), wherein, criterion function is Euclidean distance, and definition is:
In formula, d represents not Euclidean distance between normalization day vector X, Y on the same day; The dimension of m vector X and Y; The subvector of x and y vector X, Y;
C, calculate all kinds of mean vector, and using this vector as new cluster centre:In formula, j=1,2 ..., k; I=1,2 ..., N_{j}(l);
If d is z_{j}(l+1)��z_{j}L (), represents that cluster result is not best, then returns b, continue iterative computation;
If z_{j}(l+1)=z_{j}L (), iterative process terminates, and cluster result now is exactly optimum cluster result.
4. the wind power forecasting method based on wind speed, wind direction information according to claim 1 or 3, it is characterised in that: K=5.
5. the wind power forecasting method based on wind speed, wind direction information according to claim 1, it is characterised in that: the expression of the autoregression model structure in step 3 is:
Wherein, p_{t}Represent the active power mean value of training Day; p_{t1}��p_{t2}Represent the active power mean value of two days before training Day respectively;For Parameters of Autoregressive Models, { ��_{t}For White Noise Model, by adopting sample autocorrelation function to determine rank and method of least square carries out parameter estimation, obtain
6. the wind power forecasting method based on wind speed, wind direction information according to claim 1, it is characterised in that: the wind power prediction model in step 3 selects general regression neural network.
7. the wind power forecasting method based on wind speed, wind direction information according to claim 1, it is characterised in that: step 4 being predicted, the normalization day vector on same day day adopts Euclidean distance algorithm with the calculating of the Similarity Parameter of each class cluster centre of K apoplexy due to endogenous wind.
Priority Applications (1)
Application Number  Priority Date  Filing Date  Title 

CN201610007840.XA CN105654207A (en)  20160107  20160107  Wind power prediction method based on wind speed information and wind direction information 
Applications Claiming Priority (1)
Application Number  Priority Date  Filing Date  Title 

CN201610007840.XA CN105654207A (en)  20160107  20160107  Wind power prediction method based on wind speed information and wind direction information 
Publications (1)
Publication Number  Publication Date 

CN105654207A true CN105654207A (en)  20160608 
Family
ID=56491543
Family Applications (1)
Application Number  Title  Priority Date  Filing Date 

CN201610007840.XA Pending CN105654207A (en)  20160107  20160107  Wind power prediction method based on wind speed information and wind direction information 
Country Status (1)
Country  Link 

CN (1)  CN105654207A (en) 
Cited By (17)
Publication number  Priority date  Publication date  Assignee  Title 

CN106779203A (en) *  20161208  20170531  贵州电网有限责任公司电力科学研究院  A kind of plateau mountain area wind power forecasting method based on different wind speed section 
CN106849066A (en) *  20170307  20170613  云南电网有限责任公司电力科学研究院  A kind of regional wind power prediction method 
CN106933778A (en) *  20170122  20170707  中国农业大学  A kind of wind power combination forecasting method based on climbing affair character identification 
CN107014451A (en) *  20170503  20170804  东南大学  The method of ultrasonic flow sensor coefficient is speculated based on generalized regression nerve networks 
CN107609697A (en) *  20170906  20180119  南京邮电大学  A kind of Wind power forecasting method 
CN107730044A (en) *  20171020  20180223  燕山大学  A kind of hybrid forecasting method of renewable energy power generation and load 
CN107944622A (en) *  20171121  20180420  华北电力大学  Wind power forecasting method based on continuous time cluster 
CN108549962A (en) *  20180604  20180918  中国农业大学  Wind power forecasting method based on history fragment sequence search and sequential rarefaction 
CN108615054A (en) *  20180418  20181002  清华大学  The overall target construction method that similitude is weighed between drainage pipeline networks node 
CN108832623A (en) *  20180629  20181116  国网山东省电力公司电力科学研究院  A kind of physicsstatistics mixing two stages wind power forecasting method 
CN109063939A (en) *  20181101  20181221  华中科技大学  A kind of wind speed forecasting method and system based on neighborhood door shot and long term memory network 
CN109146192A (en) *  20180903  20190104  贵州电网有限责任公司  A kind of wind power forecasting method considering running of wind generating set operating condition 
CN109687479A (en) *  20171019  20190426  中国南方电网有限责任公司  Power swing stabilizes method, system, storage medium and computer equipment 
CN110009037A (en) *  20190403  20190712  中南大学  A kind of engineering wind speed Forecasting Approach for Shortterm and system based on physical message coupling 
CN110210641A (en) *  20180228  20190906  北京金风科创风电设备有限公司  Wind direction prediction technique and device for wind power plant 
CN110276468A (en) *  20180314  20190924  北京金风科创风电设备有限公司  The prediction technique and equipment of the generated output of wind power generating set 
CN112257953A (en) *  20201103  20210122  上海电力大学  Data processing method based on polar region new energy power generation power prediction 
Citations (2)
Publication number  Priority date  Publication date  Assignee  Title 

CN104036073A (en) *  20140523  20140910  电子科技大学  Doublefed wind power plant dynamic equivalence modeling method suitable for active power characteristic analysis 
CN104299044A (en) *  20140701  20150121  沈阳工程学院  Clusteringanalysisbased wind power shortterm prediction system and prediction method 

2016
 20160107 CN CN201610007840.XA patent/CN105654207A/en active Pending
Patent Citations (2)
Publication number  Priority date  Publication date  Assignee  Title 

CN104036073A (en) *  20140523  20140910  电子科技大学  Doublefed wind power plant dynamic equivalence modeling method suitable for active power characteristic analysis 
CN104299044A (en) *  20140701  20150121  沈阳工程学院  Clusteringanalysisbased wind power shortterm prediction system and prediction method 
Cited By (23)
Publication number  Priority date  Publication date  Assignee  Title 

CN106779203B (en) *  20161208  20200915  贵州电网有限责任公司电力科学研究院  Plateau mountain area wind power prediction method based on different wind speed sections 
CN106779203A (en) *  20161208  20170531  贵州电网有限责任公司电力科学研究院  A kind of plateau mountain area wind power forecasting method based on different wind speed section 
CN106933778A (en) *  20170122  20170707  中国农业大学  A kind of wind power combination forecasting method based on climbing affair character identification 
CN106849066A (en) *  20170307  20170613  云南电网有限责任公司电力科学研究院  A kind of regional wind power prediction method 
CN107014451A (en) *  20170503  20170804  东南大学  The method of ultrasonic flow sensor coefficient is speculated based on generalized regression nerve networks 
CN107609697A (en) *  20170906  20180119  南京邮电大学  A kind of Wind power forecasting method 
CN109687479A (en) *  20171019  20190426  中国南方电网有限责任公司  Power swing stabilizes method, system, storage medium and computer equipment 
CN109687479B (en) *  20171019  20201225  中国南方电网有限责任公司  Power fluctuation stabilizing method, system, storage medium and computer device 
CN107730044A (en) *  20171020  20180223  燕山大学  A kind of hybrid forecasting method of renewable energy power generation and load 
CN107944622A (en) *  20171121  20180420  华北电力大学  Wind power forecasting method based on continuous time cluster 
CN110210641A (en) *  20180228  20190906  北京金风科创风电设备有限公司  Wind direction prediction technique and device for wind power plant 
CN110276468A (en) *  20180314  20190924  北京金风科创风电设备有限公司  The prediction technique and equipment of the generated output of wind power generating set 
CN108615054B (en) *  20180418  20200605  清华大学  Method for constructing comprehensive index for measuring similarity between drainage pipe network nodes 
CN108615054A (en) *  20180418  20181002  清华大学  The overall target construction method that similitude is weighed between drainage pipeline networks node 
CN108549962A (en) *  20180604  20180918  中国农业大学  Wind power forecasting method based on history fragment sequence search and sequential rarefaction 
CN108832623A (en) *  20180629  20181116  国网山东省电力公司电力科学研究院  A kind of physicsstatistics mixing two stages wind power forecasting method 
CN108832623B (en) *  20180629  20200804  国网山东省电力公司电力科学研究院  Physicalstatistical hybrid twostage wind power prediction method 
CN109146192A (en) *  20180903  20190104  贵州电网有限责任公司  A kind of wind power forecasting method considering running of wind generating set operating condition 
CN109063939B (en) *  20181101  20200818  华中科技大学  Wind speed prediction method and system based on neighborhood gate shortterm memory network 
CN109063939A (en) *  20181101  20181221  华中科技大学  A kind of wind speed forecasting method and system based on neighborhood door shot and long term memory network 
CN110009037A (en) *  20190403  20190712  中南大学  A kind of engineering wind speed Forecasting Approach for Shortterm and system based on physical message coupling 
CN110009037B (en) *  20190403  20201027  中南大学  Shortterm engineering wind speed prediction method and system based on physical information coupling 
CN112257953A (en) *  20201103  20210122  上海电力大学  Data processing method based on polar region new energy power generation power prediction 
Similar Documents
Publication  Publication Date  Title 

CN105654207A (en)  Wind power prediction method based on wind speed information and wind direction information  
Mabel et al.  Estimation of energy yield from wind farms using artificial neural networks  
CN102880810B (en)  Wind power prediction method based on time sequence and neural network method  
CN102184453A (en)  Wind power combination predicting method based on fuzzy neural network and support vector machine  
CN103268366A (en)  Combined wind power prediction method suitable for distributed wind power plant  
CN102496927A (en)  Wind power station power projection method based on error statistics modification  
CN103996079B (en)  Wind power weighting predication method based on conditional probability  
CN102509027B (en)  Wind powder combined predication method based on cross entropy theory  
CN105303250A (en)  Wind power combination prediction method based on optimal weight coefficient  
CN104978608B (en)  A kind of wind electric powder prediction device and prediction technique  
CN102749471B (en)  A kind of shortterm wind speed, wind power forecasting method  
CN105809293A (en)  Multimodel combined prediction method for shortterm power of wind farm  
CN103296701A (en)  Active power control method in wind power plant  
CN103473621A (en)  Wind power station shortterm power prediction method  
CN102938562B (en)  Prediction method of total wind electricity power in area  
Islam et al.  Vertical extrapolation of wind speed using artificial neural network hybrid system  
Mao et al.  A review of wind power forecasting & prediction  
CN103683274B (en)  Regional longterm wind power generation capacity probability prediction method  
CN105512766A (en)  Wind power plant power predication method  
CN107507097A (en)  A kind of shortterm wind power prediction method  
Yang et al.  Photovoltaic power forecasting with a rough set combination method  
CN104156885B (en)  Fast wind power capacity reliability calculating method based on reliability function  
CN106505631B (en)  Intelligent wind power wind power prediction system  
CN106875033A (en)  A kind of windpowered electricity generation cluster power forecasting method based on dynamic selfadapting  
Zhang et al.  Efficient energy planning with decompositionbased evolutionary neural networks 
Legal Events
Date  Code  Title  Description 

PB01  Publication  
C06  Publication  
SE01  Entry into force of request for substantive examination  
C10  Entry into substantive examination  
RJ01  Rejection of invention patent application after publication 
Application publication date: 20160608 

RJ01  Rejection of invention patent application after publication 