CN114865620B - Wind power plant generating capacity prediction method based on machine learning algorithm - Google Patents

Wind power plant generating capacity prediction method based on machine learning algorithm Download PDF

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CN114865620B
CN114865620B CN202210467269.5A CN202210467269A CN114865620B CN 114865620 B CN114865620 B CN 114865620B CN 202210467269 A CN202210467269 A CN 202210467269A CN 114865620 B CN114865620 B CN 114865620B
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綦方中
卓可翔
曹聪
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Abstract

The invention discloses a wind power plant generating capacity prediction method based on a machine learning algorithm, which comprises the following steps: obtaining and inputting meteorological historical data to obtain a vector capable of representing meteorological data characteristics, training and learning characteristics related to a time sequence among variables in a meteorological data characteristic sequence by using a circulating high-speed network, re-screening the characteristic vector in different dimensions by using a circulating high-speed path network encoder and a multi-layer space-time attention mechanism to respectively obtain a time dimension attention vector and a network layer dimension attention vector, obtaining and inputting wind power generation capacity historical data, obtaining a prediction result of wind power generation capacity by performing full link layer dimension matching through decoding operation of a circulating high-speed path network decoder, and calculating a confidence interval of the wind power generation capacity prediction result. The method not only effectively improves the wind power generation prediction precision, but also can provide confidence interval information of wind power generation prediction, and enriches the decision space of a power grid manager.

Description

Wind power plant generating capacity prediction method based on machine learning algorithm
Technical Field
The invention belongs to the technical field of wind power generation capacity prediction, and particularly relates to a wind power generation field power generation capacity prediction method based on a machine learning algorithm.
Background
The generated energy of the wind power plant has strong instability under the influence of meteorological conditions such as wind speed and air pressure. The prediction of the wind power generation amount in the unit of hours or days is significant for the comprehensive scheduling of a power grid and the operation or maintenance of a wind turbine generator, but has extremely high difficulty. The machine learning algorithm can effectively represent deep features of input data, and is widely applied to prediction of wind power generation. However, performance degradation is caused by the phenomena of input sequence information loss, gradient disappearance caused by network layer superposition and the like in machine learning, particularly in deep learning algorithm application, and therefore the power training efficiency and the prediction accuracy are influenced. Meanwhile, the existing prediction method does not provide confidence interval distribution information of a prediction result on the basis of more accurately predicting the wind power generation amount, and the method is more important for more efficient power grid decision.
Disclosure of Invention
Aiming at the problems in the prior art, the invention aims to provide a method for predicting the power generation amount of a wind power plant based on a machine learning algorithm.
In order to achieve the purpose, the method is based on a circulating high-speed path network and combines a multi-layer space-time attention mechanism to predict the wind power generation amount and further calculate the probability distribution of the wind power generation amount. The adopted technical scheme comprises the following steps:
step 1, acquiring and inputting meteorological historical data to obtain a vector capable of representing meteorological data characteristics:
step 1.1, acquiring meteorological historical data to obtain a meteorological historical data sequence which can be input into a convolutional neural network for feature extraction: (x) 1 ,x 1 ,…,x t ,…,x T-1 )
Wherein T is equal to {1,2,. Multidot., T-1}, x t ∈R n N-dimensional meteorological real vector data at the time T, wherein T is a time sequence of a target to be predicted;
step 1.2, inputting the sequences into a convolutional neural network, and performing convolutional operation to obtain a sequence capable of representing meteorological data characteristics:
(w 1 ,w 2 ,…,w t ,…,w T-1 )
wherein, w t ∈R m Representing meteorological characteristic real vector data for dimension m at t moment after processing;
step 2, training and learning the characteristics related to the time sequence among variables in the meteorological data characteristic sequence by using a circulating high-speed network: with S '= S' (W, W) S' )、t'＝T'(w,W T' ) And C '= C' (W, W) C' ) Respectively representing the outputs, w, after action conversion by nonlinear conversion tanh function S ', sigmoid function T' and sigmoid function C [t] For the meteorological feature vector input at the time t, the hidden state in the circulating high-speed path network is updated to
Figure GDA0003949367170000021
Wherein the content of the first and second substances,
Figure GDA0003949367170000022
the hidden state vector of the dimension I is output by the circulating expressway network unit at the time point t of the K layer, K belongs to {1, 2.. K }, and K is the number of network layers and is specified when K =0
Figure GDA0003949367170000023
Figure GDA0003949367170000024
Figure GDA0003949367170000025
Figure GDA0003949367170000026
Wherein, W S' ,W T' ,W C' ∈R l×m 、R S'k ,R C'k ,R T'k ∈R l×l And b Hk ,b C'k ,b T'k ∈R l A weight matrix and a bias unit respectively representing the S ', T ' and C ' conversion of the k-th layer, and an indicator function I {. Cndot. } representing a feature vector w [t] Only at layer 1 (k = 1) of the round robin highway network participates in the operation,
Figure GDA0003949367170000027
indicating that all of the original input information is retained,
Figure GDA0003949367170000028
representing conversion of all input information;
and 3, re-screening the feature vectors in different dimensions through the encoding operation of an encoder and a multi-layer space-time attention mechanism to respectively obtain a time dimension attention vector and a network hierarchy dimension attention vector:
step 3.1, screening different coding characteristics by adopting a space-time attention mechanism to obtain a time dimension attention vector:
let the decoder have a hidden state vector of the k-th layer at the time T-1
Figure GDA0003949367170000029
To D T-1 The Query vector Query1 is obtained after the deformation operation, and the Query vector Query and the hidden vector of the encoder at the kth layer at the T (T is more than 1 and less than or equal to T-1) time
Figure GDA00039493671700000210
The attention weight between is expressed as
Figure GDA00039493671700000211
Wherein, query1 belongs to R p ，V k ∈R l ，T' k ∈R l×p ，U k ∈R l×l ，V k 、T’ k And U k Respectively are k-th layer nonlinear transformation matrixes, p is a query vector dimension, and the attention weight after normalization processing is expressed as
Figure GDA0003949367170000031
The k-th layer time-dimension local attention vector can be obtained
Figure GDA0003949367170000032
The local attention vectors of each layer are spliced to obtain the attention vector of time dimension
Figure GDA0003949367170000033
And 3.2, screening different coding characteristics by adopting a space-time attention mechanism to obtain a network level dimension attention vector:
for the decoder at T-1Hidden state vector of etch/k layer
Figure GDA0003949367170000034
After reshape operation, the Query vector Query2 is obtained, which is obtained from the operation of reshape
Figure GDA0003949367170000035
The attention weight between is expressed as
Figure GDA0003949367170000036
Wherein Query2 ∈ R p ，V t ∈R l ，T' t ∈R l×p ，U t ∈R l×l ，V t 、T’ t And U t Respectively, the t-th time nonlinear transformation matrix is normalized and the attention weight is expressed as
Figure GDA0003949367170000037
The network hierarchy dimension local attention vector at the t moment can be obtained
Figure GDA0003949367170000038
The local attention vectors of all layers are spliced to obtain the network level dimension attention vector
Figure GDA0003949367170000039
And 4, acquiring and inputting historical data of the wind power generation capacity, and obtaining a prediction result of the wind power generation capacity through decoding operation of a decoder and dimension matching of a full link layer:
step 4.1, obtaining and inputting historical data of wind power generation amount to obtain a power generation amount data sequence:
(y 1 ,y 2 ,…,y t ,…,y T-1 )
step 4.2, performing dimension matching on the time dimension and the network dimension attention vector and the wind power generation capacity historical data through the full connection layer to obtain a characterization vector
Figure GDA0003949367170000041
Wherein the content of the first and second substances,
Figure GDA0003949367170000042
and
Figure GDA0003949367170000043
representing a full connection layer weight matrix, d being the weight matrix dimension,
Figure GDA0003949367170000044
is a bias unit;
and 4.3, updating the hidden state vector of the encoder:
subjecting the obtained mixture to
Figure GDA0003949367170000045
As an input to the k-th loop freeway network, an encoder state vector is updated to
Figure GDA0003949367170000046
Wherein the content of the first and second substances,
Figure GDA0003949367170000047
Figure GDA0003949367170000048
Figure GDA0003949367170000049
wherein, the first and the second end of the pipe are connected with each other,
Figure GDA00039493671700000410
respectively representing the weight matrices of different layers of the encoder stage cyclic highway network,
Figure GDA00039493671700000411
representing different bias units, q is the weight matrix dimension,
and 4.4, obtaining and outputting a prediction result of the wind power generation amount at the T moment:
Figure GDA00039493671700000412
wherein the content of the first and second substances,
Figure GDA00039493671700000413
a hidden state vector of a K-th layer of a decoder at the moment T-1 is represented by W, V and H, and b is a bias unit;
step 5, calculating a confidence interval of the wind power generation amount prediction result:
if the distribution function of the random variable Y satisfies F (Y) = P (Y ≦ Y), the τ -th quantile thereof may be defined as
Q(τ)＝inf{y:F(y)≥τ},τ∈(0,1)
The fractal regression model is optimized by using pinball loss function minimum as an index through a neural network back propagation method
Figure GDA0003949367170000051
Figure GDA0003949367170000052
Wherein N is the predicted horizontal number of quantiles, X i (i =1,2, \8230;, N) is a sample of the density function f (x),
Figure GDA0003949367170000053
the output values of different quantiles. The influence of the interpretation variables on the condition quantiles of the response variables at different quantiles can be measured by continuously adjusting the values of W and b in the learning process. Obtaining the optimal parameter vector
Figure GDA0003949367170000054
And
Figure GDA0003949367170000055
then, the optimum estimated value of Y is
Figure GDA0003949367170000056
The quantiles of the wind power generation capacity predicted value under different quantiles are used as input of a Gaussian kernel, and the probability distribution estimation value is obtained by selecting a proper window width
Figure GDA0003949367170000057
Wherein h is the window width, and the Gaussian kernel function K (-) is expressed as
Figure GDA0003949367170000058
The invention has the beneficial effects that: the machine learning, particularly the deep learning algorithm, has advantages in short-term prediction in unit of hour or day on the wind power generation amount with strong instability; in order to further improve the short-term prediction precision and the prediction performance of the wind power generation, a circulating high-speed path network and a multi-layer space-time attention mechanism are introduced, so that the characteristics of a learning data sequence are better represented, the loss of input sequence information is reduced, and the information selection and utilization capacity is improved; in order to provide more and more valuable decision information about the prediction result, a complete probability distribution interval of a short-term wind power generation capacity prediction value is obtained by combining quantile regression and a Gaussian kernel function method.
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FIG. 1 is a schematic flow chart of the present invention.
Detailed Description
The invention will be further described with reference to the drawings attached to the description, but the scope of the invention is not limited thereto.
Step 1, acquiring and inputting meteorological historical data to obtain a vector capable of representing meteorological data characteristics:
step 1.1, acquiring meteorological historical data to obtain a meteorological historical data sequence which can be input into a convolutional neural network for feature extraction: (x) 1 ,x 1 ,…,x t ,…,x T-1 )
Wherein, T is an element of {1,2,. Eta., T-1}, x t ∈R n N-dimensional meteorological real vector data at the moment T, wherein T is a time sequence of a target to be predicted;
the meteorological historical data mainly comprises 2-meter wind speed, 10-meter wind speed, 50-meter wind speed, roughness, ground level radiation, atmospheric level radiation, air temperature, air density, air pressure, on-shore wind speed profile, off-shore wind speed profile and the like, and the table 1 shows the meteorological historical data of each meteorological point per hour:
TABLE 1 weather History data sequence
Figure GDA0003949367170000061
Step 1.2, inputting the sequences into a convolutional neural network, and obtaining the sequences capable of representing meteorological data characteristics after convolution operation processing: (w) 1 ,w 2 ,…,w t ,…,w T-1 )
Wherein w t ∈R m Representing real vector data of meteorological features for m-dimension at t moment after processing;
after convolution operation, a sequence representing the meteorological historical data characteristics is obtained, and a table 2 shows the meteorological characteristic data sequence:
TABLE 2 weather characteristic data series
Figure GDA0003949367170000062
Figure GDA0003949367170000071
Step 2, training and learning the characteristics related to the time sequence among variables in the meteorological data characteristic sequence by using a circulating high-speed network: with S '= S' (W, W) S' )、t'＝T'(w,W T' ) And C '= C' (W, W) C' ) Respectively representing the outputs, w, after action conversion by nonlinear conversion tanh function S ', sigmoid function T' and sigmoid function C [t] For the meteorological characteristic vector input at the time t, the hidden state in the circulating high-speed path network is updated to
Figure GDA0003949367170000072
Wherein the content of the first and second substances,
Figure GDA0003949367170000073
the hidden state vector of the dimension I is output by the circulating expressway network unit at the time point t of the K layer, K belongs to {1, 2.. K }, and K is the number of network layers and is specified when K =0
Figure GDA0003949367170000074
Figure GDA0003949367170000075
Figure GDA0003949367170000076
Figure GDA0003949367170000077
Wherein, W S' ,W T' ,W C' ∈R l×m 、R S'k ,R C'k ,R T'k ∈R l×l And b Hk ,b C'k ,b T'k ∈R l A weight matrix and a bias unit respectively representing the conversion of the k-th layer S ', T ' and C ', and an indication function I {. DEG } representing a characteristic vector w [t] Only at layer 1 (k = 1) of the round robin highway network participates in the operation,
Figure GDA0003949367170000078
indicating that all of the original input information is retained,
Figure GDA0003949367170000079
representing conversion of all input information;
taking the data from 1 month and 1 day to 10 months and 17 days as the training set of the model, taking the data from 10 months and 18 days to 11 months and 23 days as the test set of the model, setting the relevant parameters, and obtaining the updated internal hidden state, as shown in table 3:
TABLE 3 updated hidden State vector List
Figure GDA00039493671700000710
Figure GDA0003949367170000081
And 3, re-screening the feature vectors in different dimensions through the encoding operation of an encoder and a multi-layer space-time attention mechanism to respectively obtain a time dimension attention vector and a network hierarchy dimension attention vector:
step 3.1, screening different coding characteristics by adopting a space-time attention mechanism to obtain a time dimension attention vector:
let the decoder hide the state of the k-th layer at time T-1Vector is
Figure GDA0003949367170000082
To D T-1 After reshape operation, the Query vector Query1 is obtained, which is associated with the hidden vector of the k-th layer at the T (T is more than 1 and less than or equal to T-1) time of the encoder
Figure GDA0003949367170000083
The attention weight between is expressed as
Figure GDA0003949367170000084
Wherein, query1 belongs to R p ，V k ∈R l ，T' k ∈R l×p ，U k ∈R l×l ，V k 、T’ k And U k Respectively, k-th layer nonlinear transformation matrix, p is query vector dimension, and after normalization treatment, attention weight is expressed as
Figure GDA0003949367170000085
The k-th layer time-dimension local attention vector can be obtained
Figure GDA0003949367170000086
The local attention vectors of each layer are spliced to obtain the attention vector of time dimension
Figure GDA0003949367170000087
Table 4 is the time-dimensional attention vector after the stitching process:
TABLE 4 time-dimensional attention vector
Figure GDA0003949367170000088
Figure GDA0003949367170000091
Step 3.2, screening different coding characteristics by adopting a space-time attention mechanism to obtain a network level dimension attention vector:
hidden state vector of k layer at T-1 time of decoder
Figure GDA0003949367170000092
Get Query vector Query2 after reshape operation, it and
Figure GDA0003949367170000093
the attention weight between is expressed as
Figure GDA0003949367170000094
Wherein Query2 ∈ R p ，V t ∈R l ，T' t ∈R l×p ，U t ∈R l×l ，V t 、T’ t And U t Respectively, the t-th time nonlinear transformation matrix, and the attention weight after normalization treatment is expressed as
Figure GDA0003949367170000095
The network hierarchy dimension local attention vector at the t moment can be obtained
Figure GDA0003949367170000096
The local attention vectors of all layers are spliced to obtain the network level dimension attention vector
Figure GDA0003949367170000097
Table 5 shows the attention vectors after the local attention items of each layer of network hierarchy are spliced:
TABLE 5 network hierarchy dimension attention vector
Index 0 1 2 3 4 …… 253 254 255
0 0.1864 0.1060 -0.1316 -0.0669 0.0009 …… 0.1204 0.0836 -0.0913
1 0.1980 0.0854 -0.1102 -0.0839 -0.0078 …… 0.1217 0.0739 -0.1037
…… …… …… …… …… …… …… …… …… ……
334 0.1997 0.0821 -0.1207 -0.0778 -0.0080 …… 0.1180 0.0839 -0.1028
335 0.2072 0.0967 -0.0942 -0.0817 -0.0165 …… 0.1171 0.0812 -0.1058
And 4, acquiring and inputting historical data of the wind power generation capacity, and obtaining a prediction result of the wind power generation capacity through decoding operation of a decoder and dimension matching of a full link layer:
step 4.1, obtaining and inputting historical data of wind power generation amount to obtain a power generation amount data sequence:
(y 1 ,y 2 ,…,y t ,…,y T-1 )
the historical data of the wind power generation amount is 8784 sample data in each hour at intervals and in a time span of one year, and the table 6 shows part of the sample data:
TABLE 6 historical data List of wind power generation
Figure GDA0003949367170000101
Step 4.2, performing dimension matching on the time dimension and the network dimension attention vector and the wind power generation capacity historical data through the full connection layer to obtain a characterization vector
Figure GDA0003949367170000102
Wherein, the first and the second end of the pipe are connected with each other,
Figure GDA0003949367170000103
and
Figure GDA0003949367170000104
representing the fully connected layer weight matrix, d is the weight matrix dimension,
Figure GDA0003949367170000105
is a bias unit;
and 4.3, updating the hidden state vector of the encoder:
subjecting the obtained product to
Figure GDA0003949367170000106
As an input to the k-th loop freeway network, an encoder state vector is updated to
Figure GDA0003949367170000107
Wherein the content of the first and second substances,
Figure GDA0003949367170000108
Figure GDA0003949367170000109
Figure GDA00039493671700001010
wherein, the first and the second end of the pipe are connected with each other,
Figure GDA00039493671700001011
respectively representing the weight matrices of different layers of the encoder stage circular high-speed path network,
Figure GDA00039493671700001012
representing different bias units, and q is a weight matrix dimension;
and 4.4, obtaining and outputting a prediction result of the wind power generation amount at the T moment:
Figure GDA00039493671700001013
wherein the content of the first and second substances,
Figure GDA00039493671700001014
a hidden state vector of a K-th layer of a decoder at the moment T-1 is represented by W, V and H, and b is a bias unit;
the prediction results of the prediction method at the time T and different network layers can be obtained by respectively adopting the average absolute percentage error MAPE and the root mean square error RMSE methods, the point prediction results are shown in the table 7, and the point prediction results are divided into the point prediction results shown in the table 8:
TABLE 7 prediction results
Figure GDA0003949367170000111
TABLE 8 Point prediction results
Figure GDA0003949367170000112
Step 5, calculating a confidence interval of the wind power generation amount prediction result:
if the distribution function of the random variable Y satisfies F (Y) = P (Y ≦ Y), the τ th quantile can be defined as
Q(τ)＝inf{y:F(y)≥τ},τ∈(0,1)
The fractal regression model is optimized by using pinball loss function minimum as an index through a neural network back propagation method
Figure GDA0003949367170000121
Figure GDA0003949367170000122
Wherein N is the predicted horizontal number of quantiles, X i (i =1,2, \8230;, N) is a sample of the density function f (x),
Figure GDA0003949367170000123
the output values of different quantiles. The influence of the interpretation variables on the condition quantiles of the response variables at different quantiles can be measured by continuously adjusting the values of W and b in the learning process. Obtaining the optimal parameter vector
Figure GDA0003949367170000124
And
Figure GDA0003949367170000125
then, the optimum estimate of Y is
Figure GDA0003949367170000126
The quantiles of the wind power generation capacity predicted value under different quantiles are used as the input of a Gaussian kernel, and the probability distribution estimation value can be obtained by selecting a proper window width
Figure GDA0003949367170000127
Wherein h is the window width, and the Gaussian kernel function K (-) is expressed as
Figure GDA0003949367170000128
Selecting interval coverage (PICP) and average width of Prediction Interval (PINAW) as evaluation indexes of interval prediction, predicting the wind power generation amount of a complete time interval from 11 months 3 days to 11 months 10 days in 1 week, respectively selecting 85%, 90% and 95% confidence levels to establish the prediction interval of the wind power generation amount, and the result is shown in Table 9:
TABLE 9 comparison of interval prediction indexes
Confidence level PICP PINAW
85％ 0.997 0.078
90％ 1 0.097
95％ 1 0.116
From table 9, it can be seen that the algorithm obtains a relatively high PICP and a relatively small PINAW at 85%, 90%, and 95% confidence levels, which indicates that the algorithm can provide interval distribution information of wind power generation pre-measurement in a relatively small range and a relatively large degree, and provides an optimization space for power grid decision.

Claims (6)

1. The wind power plant power generation amount prediction method based on the machine learning algorithm is characterized by comprising the following steps of:
step 1: acquiring and inputting meteorological historical data to obtain a vector capable of representing meteorological data characteristics;
and 2, step: training and learning characteristics related to a time sequence among variables in a meteorological data characteristic sequence by using a circulating high-speed network;
and step 3: re-screening the feature vectors in different dimensions through the encoding operation of a circulating high-speed path network encoder and a multi-layer space-time attention mechanism to respectively obtain a time dimension attention vector and a network hierarchy dimension attention vector;
and 4, step 4: acquiring and inputting historical data of wind power generation capacity, and acquiring a prediction result of the wind power generation capacity through decoding operation of a circulating high-speed path network decoder and dimension matching of a full link layer;
and 5: and calculating a confidence interval of the wind power generation amount prediction result.
2. A method for predicting the power production of a wind farm based on a machine learning algorithm according to claim 1, characterized in that the specific operating procedure of step 1 comprises the following steps:
step 1.1, acquiring meteorological historical data to obtain a meteorological historical data sequence which can be input into a convolutional neural network for feature extraction: (x) 1 ,x 1 ,…,x t ,…,x T-1 ) Wherein, T is an element {1,2, 1}, x t ∈R n N-dimensional meteorological real vector data at the time T, wherein T is a time sequence of a target to be predicted;
step 1.2, inputting the sequences into a convolutional neural network, and obtaining the sequences (w) capable of representing meteorological data characteristics after convolutional operation processing 1 ,w 2 ,…,w t ,…,w T-1 ) Wherein w is t ∈R m And representing real vector data of meteorological features for m-dimension at the t moment after processing.
3. The machine learning algorithm based wind farm energy production prediction method according to claim 2, characterized in that the specific operation of step 2 comprises the steps of: with S '= S' (W, W) S' )、t'＝T'(w,W T' ) And C '= C' (W, W) C' ) Respectively representing the outputs after action conversion by nonlinear conversion tanh function S ', sigmoid function T ' and sigmoid function C ', w [t] For the meteorological characteristic vector input at the time t, the hidden state in the circulating high-speed path network is updated to
Figure FDA0003949367160000011
Wherein the content of the first and second substances,
Figure FDA0003949367160000012
and the hidden state vector is expressed by a dimension I output by the circulating expressway network unit at the time point t of the K layer, K belongs to {1, 2.. K }, and K is the number of network layers and is specified when K =0
Figure FDA0003949367160000013
Figure FDA0003949367160000021
Figure FDA0003949367160000022
Figure FDA0003949367160000023
Wherein, W S' ,W T' ,W C' ∈R l×m 、R S'k ,R C'k ,R T'k ∈R l×l And b Hk ,b C'k ,b T'k ∈R l A weight matrix and a bias unit respectively representing the conversion of the k-th layer S ', T ' and C ', and an indication function I {. DEG } representing a characteristic vector w [t] Only at layer 1 of the round robin highway network,
Figure FDA0003949367160000024
indicating that all of the original input information is retained,
Figure FDA0003949367160000025
indicating that all input information is converted.
4. A method for predicting the power production of a wind farm based on a machine learning algorithm according to claim 3, characterized in that the specific operating procedure of step 3 comprises the following steps:
step 3.1, screening different coding characteristics by adopting a space-time attention mechanism to obtain a time dimension attention vector:
let the decoder at the T-1 time point and the hidden state vector of the k layer be
Figure FDA0003949367160000026
To D T-1 After the deformation operation, the Query vector Query1 is obtained, which is the hidden vector of the k layer at the t time point with the encoder
Figure FDA0003949367160000027
The attention weight between is expressed as
Figure FDA0003949367160000028
Wherein, query1 belongs to R p ，V k ∈R l ，T' k ∈R l×p ，U k ∈R l×l ，V k 、T’ k And U k Respectively are k-th layer nonlinear transformation matrixes, p is a query vector dimension, and the attention weight after normalization processing is expressed as
Figure FDA0003949367160000029
Get the k-th layer time-dimensional local attention vector as
Figure FDA00039493671600000210
The local attention vectors of each layer are spliced to obtain the attention vector with time dimension as
Figure FDA00039493671600000211
Step 3.2, screening different coding characteristics by adopting a space-time attention mechanism to obtain a network level dimension attention vector:
hidden state vector of k layer at time T-1 of decoder
Figure FDA0003949367160000031
After reshape operation, the Query vector Query2 is obtained, which is obtained from the operation of reshape
Figure FDA0003949367160000032
Is expressed as the attention weight between
Figure FDA0003949367160000033
Wherein Query2 ∈ R p ，V t ∈R l ，T' t ∈R l×p ，U t ∈R l×l ，V t 、T’ t And U t Respectively, the t-th time nonlinear transformation matrix is normalized and the attention weight is expressed as
Figure FDA0003949367160000034
The network hierarchy dimension local attention vector at the t-th moment can be obtained as
Figure FDA0003949367160000035
The local attention vectors of each layer are spliced to obtain the network level dimension attention vector of
Figure FDA0003949367160000036
5. A method for predicting the power production of a wind farm based on machine learning algorithms according to claim 4, characterized in that the specific operating procedure of step 4 comprises the following steps:
step 4.1, obtaining and inputting historical data of wind power generation capacity to obtain a power generation capacity data sequence:
(y 1 ,y 2 ,…,y t ,…,y T-1 )
step 4.2, performing dimension matching on the time dimension and the network dimension attention vector and the wind power generation capacity historical data through the full connection layer to obtain a characterization vector
Figure FDA0003949367160000037
Wherein the content of the first and second substances,
Figure FDA0003949367160000038
and
Figure FDA0003949367160000039
representing a full connection layer weight matrix, d being the weight matrix dimension,
Figure FDA00039493671600000310
is a bias unit;
and 4.3, updating the hidden state vector of the encoder:
subjecting the obtained product to
Figure FDA00039493671600000311
As an input to the k-th loop freeway network, the encoder state vector is updated to
Figure FDA00039493671600000312
Wherein, the first and the second end of the pipe are connected with each other,
Figure FDA0003949367160000041
Figure FDA0003949367160000042
Figure FDA0003949367160000043
wherein the content of the first and second substances,
Figure FDA0003949367160000044
respectively representing the weight matrices of different layers of the encoder stage cyclic highway network,
Figure FDA0003949367160000045
representing different bias units, q is the weight matrix dimension,
and 4.4, obtaining and outputting a prediction result of the wind power generation amount at the T moment:
Figure FDA0003949367160000046
wherein, the first and the second end of the pipe are connected with each other,
Figure FDA0003949367160000047
w, V and H represent weight matrixes which can be learned for a hidden state vector of a K-th layer of a decoder at the moment T-1, and b is a bias unit.
6. The machine learning algorithm based wind farm energy production prediction method according to claim 5, characterized in that the specific operation process of step 5 is that if the distribution function of the random variable Y satisfies F (Y) = P (Y ≦ Y), the τ th quantile thereof is defined as
Q(τ)＝inf{y:F(y)≥τ},τ∈(0,1)
Optimizing the quantile regression model by using the minimum pinball loss function as an index through a neural network back propagation method
Figure FDA0003949367160000048
Figure FDA0003949367160000049
Wherein N is the number of predicted quantile levels, X i Is a sample of the density function f (x), i =1,2, \8230;, N,
Figure FDA00039493671600000410
for the output values of different quantiles, the influence of the explanatory variables on the quantiles of the response variable condition at different quantiles is measured and calculated by continuously adjusting the values of W and b in the learning process to obtain the optimal parameter vector
Figure FDA00039493671600000411
And
Figure FDA00039493671600000412
then, the optimum estimated value of Y is
Figure FDA00039493671600000413
The quantiles of the wind power generation capacity predicted value under different quantiles are used as the input of a Gaussian kernel, and the probability distribution estimated as
Figure FDA0003949367160000051
Wherein h is the window width, and the Gaussian kernel function K (-) is expressed as
Figure FDA0003949367160000052
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