CN111461418B - Wind speed prediction method, system, electronic equipment and storage medium - Google Patents
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Abstract
The invention discloses a wind speed prediction method, a wind speed prediction system, electronic equipment and a storage medium. The wind speed prediction method comprises the following steps: acquiring an initial wind speed sequence comprising wind speeds acquired in N continuous periods; respectively constructing a wind speed derivative sequence according to each wind speed; training a wind speed prediction model by using an LSTM model based on an attention mechanism according to the wind speed derivative sequence; and outputting the predicted wind speed of the period to be predicted according to the wind speeds acquired by i periods before the period to be predicted and the wind speed prediction model obtained through training. According to the wind speed prediction model training method, the wind speed derivation sequence is constructed according to the initial wind speed sequence, expansion of input parameters required by deep learning can be achieved, the wind speed prediction model obtained by training the LSTM model based on the attention mechanism according to the wind speed derivation sequence has high prediction accuracy and good prediction effect, accurate prediction of short-time wind speed is facilitated, and further control of a fan control system is facilitated to improve maximum power generation capacity of a wind field.
Description
Technical Field
The present invention relates to the field of power technologies, and in particular, to a wind speed prediction method, a wind speed prediction system, an electronic device, and a storage medium.
Background
Based on the accurate prediction of the short-time wind speed of the environment where the wind field is located, the operation of the fan control system is facilitated to be changed to adjust the specific position (such as direction, angle and the like) of the fan in the wind field, so that the maximum power generation amount of the wind field is improved, however, the current wind speed prediction model has poor prediction effect.
Disclosure of Invention
The invention aims to overcome the defect that the prediction effect of a wind speed prediction model in the prior art needs to be further improved, and provides a wind speed prediction method, a system, electronic equipment and a storage medium.
The invention solves the technical problems by the following technical scheme:
a method of wind speed prediction, comprising:
acquiring an initial wind speed sequence, wherein the initial wind speed sequence comprises wind speeds acquired in N continuous periods;
respectively constructing a wind speed derivative sequence according to each wind speed in the initial wind speed sequence, wherein the wind speed derivative sequence comprises wind speeds and derivative data corresponding to the wind speeds;
training a wind speed prediction model by using an LSTM (Long Short Term Memory, long-short term memory network) model based on an attention mechanism according to the wind speed derivative sequence, wherein the wind speed prediction model is used for outputting the predicted wind speed of the Mth period according to the wind speed derivative sequence corresponding to the wind speeds acquired from the Mth period to the Mth period;
acquiring wind speeds acquired in i periods before a period to be predicted;
outputting the predicted wind speed of the period to be predicted according to the wind speeds acquired in i periods before the period to be predicted and a wind speed prediction model obtained through training;
wherein N is greater than or equal to M > i, and N, M and i are both positive integers.
Preferably, the step of constructing a wind speed derivative sequence from each wind speed in the initial wind speed sequence comprises:
decomposing wind speed to obtain a sub wind speed sequence, wherein the sub wind speed sequence comprises a plurality of sub wind speeds;
and splicing the sub-wind speed sequences corresponding to the wind speeds to obtain a wind speed derivative sequence.
Preferably, after the step of decomposing the wind speed to obtain the sub-wind speed sequence, the method further comprises:
training a SVR (Support Vactor Regerssion, support vector regression) model by taking a sub-wind speed sequence as input and the wind speed corresponding to the sub-wind speed sequence as output;
inputting a sub wind speed sequence into a SVR model obtained by training to output SVR wind speed corresponding to the sub wind speed sequence;
the step of splicing the sub-wind speed sequences corresponding to the wind speeds to obtain a wind speed derivative sequence comprises the following steps:
splicing wind speeds, sub-wind speed sequences corresponding to the wind speeds and SVR wind speeds corresponding to the sub-wind speed sequences to obtain wind speed derivative sequences;
or alternatively, the first and second heat exchangers may be,
the step of decomposing the wind speed to obtain a sub-wind speed sequence further comprises the following steps:
training an RF (Random Forest) model by taking a sub wind speed sequence as input and the wind speed corresponding to the sub wind speed sequence as output;
inputting a sub wind speed sequence into a trained RF model to output RF wind speed corresponding to the sub wind speed sequence;
the step of splicing the sub-wind speed sequences corresponding to the wind speeds to obtain a wind speed derivative sequence comprises the following steps:
splicing wind speeds, sub-wind speed sequences corresponding to the wind speeds and RF wind speeds corresponding to the sub-wind speed sequences to obtain wind speed derivative sequences;
or alternatively, the first and second heat exchangers may be,
the step of decomposing the wind speed to obtain a sub-wind speed sequence further comprises the following steps:
training an SVR model by taking a sub-wind speed sequence as input and the wind speed corresponding to the sub-wind speed sequence as output;
inputting a sub wind speed sequence into a SVR model obtained by training to output SVR wind speed corresponding to the sub wind speed sequence;
training an RF model by taking a sub wind speed sequence as input and the wind speed corresponding to the sub wind speed sequence as output;
inputting a sub wind speed sequence into a trained RF model to output RF wind speed corresponding to the sub wind speed sequence;
the step of splicing the sub-wind speed sequences corresponding to the wind speeds to obtain a wind speed derivative sequence comprises the following steps:
splicing wind speeds, sub wind speed sequences corresponding to the wind speeds, SVR wind speeds corresponding to the sub wind speed sequences and RF wind speeds corresponding to the sub wind speed sequences to obtain wind speed derivative sequences.
Preferably, the step of training a wind speed prediction model using an LSTM model based on an attention mechanism according to the initial wind speed sequence comprises:
parameters of the wind speed prediction model are optimized based on DE.
A wind speed prediction system, comprising:
the first acquisition module is used for acquiring an initial wind speed sequence, wherein the initial wind speed sequence comprises wind speeds acquired in N continuous periods;
the construction module is used for respectively constructing a wind speed derivative sequence according to each wind speed in the initial wind speed sequence, wherein the wind speed derivative sequence comprises wind speeds and derivative data corresponding to the wind speeds;
the training module is used for training a wind speed prediction model by utilizing an LSTM model based on an attention mechanism according to the wind speed derivative sequence, and the wind speed prediction model is used for outputting the predicted wind speed of the Mth period according to the wind speed derivative sequence corresponding to the wind speeds acquired from the Mth period to the Mth period;
the second acquisition module is used for acquiring the wind speeds acquired in i periods before the period to be predicted;
the prediction module is used for outputting the predicted wind speed of the period to be predicted according to the wind speeds acquired in i periods before the period to be predicted and the wind speed prediction model obtained through training;
wherein N is greater than or equal to M > i, and N, M and i are both positive integers.
Preferably, the construction module includes:
the decomposing unit is used for decomposing the wind speed to obtain a sub wind speed sequence, wherein the sub wind speed sequence comprises a plurality of sub wind speeds;
and the splicing unit is used for splicing the wind speed and the sub-wind speed sequence corresponding to the wind speed to obtain a wind speed derivative sequence.
Preferably, the construction module further comprises:
the first training unit is used for training an SVR model by taking a sub wind speed sequence as input and the wind speed corresponding to the sub wind speed sequence as output;
the first prediction unit is used for inputting the sub-wind speed sequence into the SVR model obtained through training so as to output SVR wind speed corresponding to the sub-wind speed sequence;
the splicing unit is specifically used for splicing wind speeds, sub-wind speed sequences corresponding to the wind speeds and SVR wind speeds corresponding to the sub-wind speed sequences to obtain wind speed derivative sequences;
or alternatively, the first and second heat exchangers may be,
the construction module includes:
the second training unit is used for taking the sub wind speed sequence as input and the wind speed corresponding to the sub wind speed sequence as output to train the RF model;
the second prediction unit is used for inputting the sub wind speed sequence into the trained RF model so as to output the RF wind speed corresponding to the sub wind speed sequence;
the splicing unit is specifically used for splicing wind speeds, sub-wind speed sequences corresponding to the wind speeds and RF wind speeds corresponding to the sub-wind speed sequences to obtain wind speed derivative sequences;
or alternatively, the first and second heat exchangers may be,
the construction module further comprises:
the first training unit is used for training an SVR model by taking a sub wind speed sequence as input and the wind speed corresponding to the sub wind speed sequence as output;
the first prediction unit is used for inputting the sub-wind speed sequence into the SVR model obtained through training so as to output SVR wind speed corresponding to the sub-wind speed sequence;
the second training unit is used for taking the sub wind speed sequence as input and the wind speed corresponding to the sub wind speed sequence as output to train the RF model;
the second prediction unit is used for inputting the sub wind speed sequence into the trained RF model so as to output the RF wind speed corresponding to the sub wind speed sequence;
the splicing unit is specifically used for splicing wind speed, a sub wind speed sequence corresponding to the wind speed, SVR wind speed corresponding to the sub wind speed sequence and RF wind speed corresponding to the sub wind speed sequence to obtain a wind speed derivative sequence.
Preferably, the training module includes:
and the optimizing unit is used for optimizing parameters of the wind speed prediction model based on the DE.
An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing any of the wind speed prediction methods described above when executing the computer program.
A computer readable storage medium having stored thereon a computer program which when executed by a processor performs the steps of any of the wind speed prediction methods described above.
The invention has the positive progress effects that: according to the invention, the wind speed derivative sequence is constructed according to the initial wind speed sequence, so that the expansion of input parameters required by deep learning can be realized, and further, the wind speed prediction model obtained by training the LSTM model based on the attention mechanism according to the wind speed derivative sequence has higher prediction precision and better prediction effect, thereby being beneficial to realizing the accurate prediction of short-time wind speed and further being beneficial to the control of a fan control system so as to promote the maximum power generation capacity of a wind field.
Drawings
FIG. 1 is a flowchart of a wind speed prediction method according to embodiment 1 of the present invention.
Fig. 2 is a flowchart of step S2 in the wind speed prediction method according to embodiment 1 of the present invention.
FIG. 3 is a logic diagram of the implementation of the LSTM model based on the attention mechanism in the wind speed prediction method according to embodiment 1 of the present invention.
Fig. 4 is a schematic structural diagram of LSTM model based on attention mechanism in the wind speed prediction method according to embodiment 1 of the present invention.
FIG. 5 is a block diagram of a wind speed prediction system according to embodiment 2 of the present invention.
Fig. 6 is a schematic structural diagram of an electronic device according to embodiment 3 of the present invention.
Detailed Description
The invention is further illustrated by means of the following examples, which are not intended to limit the scope of the invention.
Example 1
The present embodiment provides a wind speed prediction method, referring to fig. 1, the wind speed prediction method of the present embodiment includes:
s1, acquiring an initial wind speed sequence;
s2, respectively constructing a wind speed derivative sequence according to each wind speed in the initial wind speed sequence;
s3, training a wind speed prediction model by using an LSTM model based on an attention mechanism according to a wind speed derivative sequence;
s4, acquiring wind speeds acquired in i periods before a period to be predicted;
s5, outputting the predicted wind speed of the period to be predicted according to the wind speeds acquired in i periods before the period to be predicted and the wind speed prediction model obtained through training.
In this embodiment, the initial wind speed sequence may include wind speeds acquired in N consecutive periods, where N is a positive integer that may be set in a user-defined manner according to practical applications, for example, N may take on a value of 200, and the initial wind speed sequence may be represented as s= [ V ] 1 ,V 2 ,…,V N-1 ,V N ]. The wind speed prediction model is used for outputting the predicted wind speed of the Mth period according to the wind speeds acquired from the Mth period to the Mth period, wherein N is more than or equal to M>i and M and i are both positive integers, and the acquisition period can be set in a customized manner according to practical applications, for example, the acquisition period can be set to 30 minutes. In this embodiment, the wind speed derivative sequence respectively constructed according to each wind speed in the initial wind speed sequence may include a wind speed and derivative data correspondingly obtained after processing the wind speed.
Referring to fig. 2, step S2 in this embodiment may specifically include:
s21, decomposing the wind speed to obtain a sub wind speed sequence.
Specifically, in this embodiment, the sub-wind speed sequence is derived data of wind speed, where the sub-wind speed sequence may include a plurality of sub-wind speeds, and generation of the sub-wind speed sequence may implement expansion of input parameters required for deep learning, and it is assumed that the K-dimensional sub-wind speed sequence is obtained after wind speed decomposition, and wind speed V T The corresponding sub-wind speed sequence may be denoted as V TS ＝[V T1 ,V T2 ,…,V T(K-1) ,V TK ]Wherein T is 1.ltoreq.T.ltoreq.N and T is a positive integer. Further, in step S21, the sub-wind speed sequence may be obtained specifically based on the VMD (Variational Mode Decomposition, variational modal decomposition) decomposed wind speed, or may include obtaining the sub-wind speed sequence based on the EMD (Empirical Mode Decomposition, empirical modal decomposition) decomposed wind speed, where the VMD has an advantage over the EMD in that the dimension of the sub-wind speed sequence, that is, the number of included sub-wind speeds, may be customized according to the actual application.
In the present embodiment, it is preferable to obtain the sub wind speed sequence based on the VMD decomposed wind speed. In particular, the VMD may decompose the true signal f (t) (i.e., wind speed in this embodiment) into a series of modes u k (i.e., the sub-wind speeds in this embodiment), the VMD assumes each u k Can have an intermediate pulse signal w in the frequency domain k And a range defined by the upper and lower sidebands, wherein the implementation logic of the VMD may include:
u using Hilbert transform k Converting to a frequency domain;
removing the spectrum of the mode;
the demodulated sidebands are estimated by the gaussian equation:
s.t.∑ k u k ＝f(t)
where f (t) represents the main path of the decomposition, δ (t) represents the Dirac distribution, x represents the convolution, and in order to introduce the constraint, the lagrangian penalty factors λ and α are taken into account, so the optimization equation is modified as follows, and then the optimal solution calculation is performed:
referring to fig. 2, step S2 in this embodiment may further include:
s22, training an SVR model by taking a sub-wind speed sequence as input and the wind speed corresponding to the sub-wind speed sequence as output;
s23, inputting the sub-wind speed sequence into the SVR model obtained through training to output SVR wind speed corresponding to the sub-wind speed sequence.
Specifically, in the present embodiment, the training data of the SVR model may include [ V T ,V TS ]. After SVR model training is completed, the wind speed sequence V is used as a sub-wind speed sequence TS Output for input SVR wind speed V predicted by training derived SVR model T-SVR . Wherein, SVR wind speed V predicted by SVR model T-SVR And is also derived data of wind speed.
Referring to fig. 2, step S2 in this embodiment may further include:
s24, training an RF model by taking the sub wind speed sequence as input and the wind speed corresponding to the sub wind speed sequence as output;
s25, inputting the sub wind speed sequence into the trained RF model to output RF wind speed corresponding to the sub wind speed sequence.
Specifically, in the present embodiment, the training data of the RF model may include [ V T ,V TS ]. After the RF model training is completed, the wind speed sequence V is used as a sub-wind speed sequence TS Outputting for input the RF wind speed V predicted by the trained RF model T-RF . Wherein the RF wind speed V predicted by the RF model T-RF And is also derived data of wind speed.
Referring to fig. 2, step S2 in this embodiment may further include:
s26, splicing wind speeds, sub-wind speed sequences corresponding to the wind speeds, SVR wind speeds corresponding to the sub-wind speed sequences and RF wind speeds corresponding to the sub-wind speed sequences to obtain wind speed derivative sequences.
In the present embodiment, the spliced wind speed V is preferable T Wind speed V T Corresponding sub wind speed sequence V TS Sequence of sub wind speeds V TS Corresponding SVR wind speed V T-SVR Sub-wind speed sequence V TS Corresponding RF wind speed V T-RF To construct wind speed derivative sequences to achieve further expansion of the input parameters required for deep learning, where S can be used T ＝[V T ,V T1 ,V T2 ,…，V T(K-1) ，V TK ，V T-SVR ，V T-RF ]To represent wind speed V T A corresponding sequence of sub-wind speeds. In other embodiments of the invention, the wind speed derivative sequences may also incorporate SVR model predicted wind speeds V based on wind speeds and their corresponding sub-sequences T-SVR Or the wind speed V predicted by the RF model T-RF The structure is obtained.
In the present embodiment, training data [ V ] can be constructed M ，S M-i ，S M-i+1 ，…，S M-2 ，S M-1 ]For training a wind speed prediction model for deriving the sequence from i wind speeds S M-i ,S M-i+1 ,…,S M-2 ,S M-1 ]To output the predicted wind speed V of the Mth period M . Specifically, it can be based on the input S 1 ,S 2 ,…,S i-1 ,S i ]To predict wind speed V i+1 According to the input S 2 ,S 3 ,…,S i ,S i+1 ]To predict V i+2 … … according to the input [ S ] N-i ,S N-i+1 ,…,S N-2 ,S N-1 ]To predict wind speed V N 。
In step S5, i wind speed derivative sequences may be constructed according to the wind speeds acquired i periods before the period to be predicted, and then the constructed i wind speed derivative sequences may be input into a wind speed prediction model obtained by training to output the predicted wind speed of the period to be predicted.
Referring to FIG. 3, the implementation logic of the attention-based LSTM model for training a wind speed prediction model may include:
1) Forgetting gate, which is used to decide whether to discard the current cell state, the mathematical formula is: f (f) t ＝σ(W f X t +R f h t-1 +b f ) Wherein σ represents a sigmoid equation, h t-1 And X t Data respectively representing the previous layer and the current input layer, (W) f ,R f ,b f ) Representing the input weight, recursive weight, and bias of the forgetting gate.
2) An input gate for determining whether to store new data into the cell, the mathematical formula of which is: i.e t ＝σ(W i X t +R i h t-1 +b i ) Wherein, (W) i ,R i ,b i ) Representing the input weight, recursive weight, and bias of the input gate. In addition, a tanh layer is used to form new memories: g t ＝tanh(W g X t +R g h t-1 +b g ) Wherein, (W) g ,R g ,b g ) Representing the newly memorized input weights, recursive weights and biases.
3) The state of the cell will be updated as: c (C) t ＝C t-1 ×f f +g t ×i t 。
4) The output gate is used for deciding whether to transfer the information in the cell to the current hidden layer, and the mathematical formula is as follows: o (o) t ＝σ(W o X t +R o h t-1 +b o ) Wherein, (W) o ,R o ,b o ) Representing the output gate input weight, recursive weight, and bias. Finally, multiplying the output gate with the sigmoid gate: h is a t ＝o t ×tanh(C t )。
In this example, the softmax conversion is performed at the last output Layer LSTM Layer: p is p t ＝softxmax(o t ) Finally, the LSTM is convolved with softmax, e.g. y t ＝p t *o t ,. Referring to fig. 4, in the structure of the LSTM model based on the attention mechanism: input_1 represents the input layer, lstm_1 represents the last output layer, dense_1 represents the weight layer of softmax, dense_1 represents the inner product of input_1 and dense_1, and dense_2 represents the predicted wind speed of the output.
In this embodiment, compared with the normal LSTM model, the LSTM model based on the attention mechanism can avoid overlengthy memory chains, and the attention mechanism gives a weight to each neuron, specifically, gives a larger weight to neurons with accurate prediction results and gives smaller weight to neurons with inaccurate prediction results, so that the embodiment can analyze neurons with high weight without analyzing each neuron, and in addition, the weights can be automatically obtained without manual intervention when the model iterates.
Step S3 in this embodiment may further include a step of optimizing parameters of the wind speed prediction model based on DE, and for optimizing parameters of the LSTM model based on the attention mechanism, implementation logic may include:
initializing a search cluster, wherein the search cluster is an upper limit range and a lower limit range of a needed solving parameter, and can be set in a self-defining way according to practical application;
setting the MAE as the minimized objective function:
wherein A is t F for the actual wind speed at time t t For the predicted wind speed at this point, n is the calculated array;
parameters that determine DE, for example, optimized parameters may include, but are not limited to, batch_ size, neuro, look _back and dropout;
transferring the parameters to an LSTM model based on an attention mechanism to calculate MAE;
and if the result is converged, the optimization parameters are obtained.
Further, in this embodiment, parameters of the SVR model may be optimized based on the DE, and the optimized parameters may include, but are not limited to, gamma and cost, and parameters of the RF model may be optimized based on the DE. The prediction effect of the model can be further improved through optimizing the model parameters.
In this embodiment, the initial wind speed sequence may be normalized, or training data for training a model may be divided into a training set and a test set.
According to the embodiment, a wind speed derivative sequence is constructed according to the initial wind speed sequence, wherein the wind speed derivative sequence further comprises SVR wind speed predicted based on a SVR model obtained through training and RF wind speed predicted based on an RF model obtained through training, expansion of input parameters required by deep learning is achieved, further, a wind speed prediction model obtained through training by utilizing an LSTM model based on an attention mechanism according to the wind speed derivative sequence has higher prediction precision and shows a better prediction effect, accurate prediction of short-time wind speed is facilitated, and further control of a fan control system is facilitated to improve maximum power generation of a wind field.
Example 2
The present embodiment provides a wind speed prediction system, referring to fig. 5, the wind speed prediction system of the present embodiment includes:
the first acquisition module 1 is used for acquiring an initial wind speed sequence;
a construction module 2 for constructing a wind speed derivative sequence according to each wind speed in the initial wind speed sequence;
the training module 3 is used for training a wind speed prediction model by utilizing an LSTM model based on an attention mechanism according to a wind speed derivative sequence;
the second acquisition module 4 is used for acquiring the wind speeds acquired by i periods before the period to be predicted;
and the prediction module 5 is used for outputting the predicted wind speed of the period to be predicted according to the wind speeds acquired by i periods before the period to be predicted and the wind speed prediction model obtained by training.
In this embodiment, the initial wind speed sequence may include wind speeds acquired in N consecutive periods, where N is a positive integer that may be set in a user-defined manner according to practical applications, for example, N may take on a value of 200, and the initial wind speed sequence may be represented as s= [ V ] 1 ,V 2 ,…,V N-1 ,V N ]. The wind speed prediction model is used for outputting the predicted wind speed of the Mth period according to the wind speeds acquired from the Mth period to the Mth period, wherein N is more than or equal to M>i and M and i are positive integers, and the acquisition period can beTo customize the settings according to the actual application, for example, the acquisition period may be set to 30 minutes. In this embodiment, the wind speed derivative sequence respectively constructed according to each wind speed in the initial wind speed sequence may include a wind speed and derivative data correspondingly obtained after processing the wind speed.
Referring to fig. 5, the construction module 2 in this embodiment may specifically include:
and a decomposing unit 21 for decomposing the wind speed to obtain a sub-wind speed sequence.
Specifically, in this embodiment, the sub-wind speed sequence is derived data of wind speed, where the sub-wind speed sequence may include a plurality of sub-wind speeds, and generation of the sub-wind speed sequence may implement expansion of input parameters required for deep learning, and it is assumed that the K-dimensional sub-wind speed sequence is obtained after wind speed decomposition, and wind speed V T The corresponding sub-wind speed sequence may be denoted as V TS ＝[V T1 ,V T2 ,…,V T(K-1) ,V TK ]Wherein T is 1.ltoreq.T.ltoreq.N and T is a positive integer. Further, the decomposition unit 21 may specifically obtain the sub wind speed sequence based on VMD (Variational Mode Decomposition, variational modal decomposition) decomposition wind speed, or may include obtaining the sub wind speed sequence based on EMD (Empirical Mode Decomposition, empirical modal decomposition) decomposition wind speed, wherein VMD has an advantage over EMD in that the dimension of the sub wind speed sequence, that is, the number of included sub wind speeds, may be custom set according to practical applications. In the present embodiment, it is preferable to obtain the sub wind speed sequence based on the VMD decomposed wind speed. In particular, the VMD may decompose the true signal f (t) (i.e., wind speed in this embodiment) into a series of modes u k (i.e., the sub-wind speeds in this embodiment), the VMD assumes each u k Can have an intermediate pulse signal w in the frequency domain k And a range defined by the upper and lower sidebands, wherein the implementation logic of the VMD may include:
u using Hilbert transform k Converting to a frequency domain;
removing the spectrum of the mode;
the demodulated sidebands are estimated by the gaussian equation:
s.t.Σ k u k ＝f(t)
where f (t) represents the main path of the decomposition, δ (t) represents the Dirac distribution, x represents the convolution, and in order to introduce the constraint, the lagrangian penalty factors λ and α are taken into account, so the optimization equation is modified as follows, and then the optimal solution calculation is performed:
referring to fig. 5, the construction module 2 in this embodiment may further include:
a first training unit 22, configured to train the SVR model with the sub-wind speed sequence as input and the wind speed corresponding to the sub-wind speed sequence as output;
the first prediction unit 23 is configured to input the sub-wind speed sequence into the trained SVR model, so as to output an SVR wind speed corresponding to the sub-wind speed sequence.
Specifically, in the present embodiment, the training data of the SVR model may include [ V T ,V TS ]. After SVR model training is completed, the wind speed sequence V is used as a sub-wind speed sequence TS Output for input SVR wind speed V predicted by training derived SVR model T-SVR . Wherein, SVR wind speed V predicted by SVR model T-SVR And is also derived data of wind speed.
Referring to fig. 5, the construction module 2 in this embodiment may further include:
a second training unit 24, configured to train the RF model with the sub wind speed sequence as input and the wind speed corresponding to the sub wind speed sequence as output;
the second prediction unit 25 is configured to input the sub-wind speed sequence into the trained RF model, so as to output an RF wind speed corresponding to the sub-wind speed sequence.
Specifically, in the present embodiment, the training data of the RF model may include [ V T ,V TS ]. After the RF model training is completed, the wind speed sequence V is used as a sub-wind speed sequence TS Output training-derived for inputRF wind speed V predicted by RF model T-RF . Wherein the RF wind speed V predicted by the RF model T-RF And is also derived data of wind speed.
Referring to fig. 5, the construction module 2 in this embodiment may further include:
the splicing unit 26 is configured to splice the wind speed, the sub-wind speed sequence corresponding to the wind speed, the SVR wind speed corresponding to the sub-wind speed sequence, and the RF wind speed corresponding to the sub-wind speed sequence to obtain a wind speed derivative sequence.
In the present embodiment, the spliced wind speed V is preferable T Wind speed V T Corresponding sub wind speed sequence V TS Sequence of sub wind speeds V TS Corresponding SVR wind speed V T-SVR Sub-wind speed sequence V TS Corresponding RF wind speed V T-RF To construct wind speed derivative sequences to achieve further expansion of the input parameters required for deep learning, where S can be used T ＝[V T ,V T1 ,V T2 ,…,V T(K-1) ,V TK ,V T-SVR ,V T-RF ]To represent wind speed V T A corresponding sequence of sub-wind speeds. In other embodiments of the present invention, the splicing unit 26 may also combine the SVR model predicted wind speed V based on the spliced wind speed and the corresponding subsequence T-SVR Or the wind speed V predicted by the RF model T-RF Constructing a wind speed derivative sequence.
In the present embodiment, training data [ V ] can be constructed M ,S M-i ,S M-i+1 ,…,S M-2 ,S M-1 ]For training a wind speed prediction model for deriving the sequence from i wind speeds S M-i ,S M-i+1 ,…,S M-2 ,S M-1 ]To output the predicted wind speed V of the Mth period M . Specifically, it can be based on the input S 1 ,S 2 ,…,S i-1 ,S i ]To predict wind speed V i+1 According to the input S 2 ,S 3 ,…,S i ,S i+1 ]To predict V i+2 … … according to the input [ S ] N-i ,S N-i+1 ,…,S N-2 ,S N-1 ]To predict wind speed V N 。
In this embodiment, the prediction module 5 may construct i wind speed derivative sequences according to the wind speeds acquired i periods before the period to be predicted, and then may input the constructed i wind speed derivative sequences into a wind speed prediction model obtained by training to output the predicted wind speed of the period to be predicted.
Referring to FIG. 3, the implementation logic of the attention-based LSTM model for training a wind speed prediction model may include:
1) Forgetting gate, which is used to decide whether to discard the current cell state, the mathematical formula is: f (f) t ＝σ(W f X t +R f h t-1 +b f ) Wherein σ represents a sigmoid equation, h t-1 And X t Data respectively representing the previous layer and the current input layer, (W) f ,R f ,b f ) Representing the input weight, recursive weight, and bias of the forgetting gate.
2) An input gate for determining whether to store new data into the cell, the mathematical formula of which is: i.e t ＝σ(W i X t +R i h t-1 +b i ) Wherein, (W) i ,R i ,b i ) Representing the input weight, recursive weight, and bias of the input gate. In addition, a tanh layer is used to form new memories: g t ＝tanh(W g X t +R g h t-1 +b g ) Wherein, (W) g ,R g ,b g ) Representing the newly memorized input weights, recursive weights and biases.
3) The state of the cell will be updated as: c (C) t ＝C t-1 ×f f +g t ×i t 。
4) The output gate is used for deciding whether to transfer the information in the cell to the current hidden layer, and the mathematical formula is as follows: o (o) t ＝σ(W o X t +R o h t-1 +b o ) Wherein, (W) o ,R o ,b o ) Representing the output gate input weight, recursive weight, and bias. Finally, multiplying the output gate with the sigmoid gate: h is a t ＝o t ×tanh(C t )。
In the present embodiment, at the last output layer LSTM Layer performs softmax conversion: p is p t ＝softxmax(o t ) Finally, the LSTM is convolved with softmax, e.g. y t ＝p t *o t ,. Referring to fig. 4, in the structure of the LSTM model based on the attention mechanism: input _1 represents the input layer, lstm _1 represents the last output layer, dense _1 represents the weight layer of softmax, concate_1 represents the inner product of input_1 and dense_1, and dense_2 represents the output predicted wind speed.
In this embodiment, compared with the normal LSTM model, the LSTM model based on the attention mechanism can avoid overlengthy memory chains, and the attention mechanism gives a weight to each neuron, specifically, gives a larger weight to neurons with accurate prediction results and gives smaller weight to neurons with inaccurate prediction results, so that the embodiment can analyze neurons with high weight without analyzing each neuron, and in addition, the weights can be automatically obtained without manual intervention when the model iterates.
The training module 3 in this embodiment may further include a first optimizing unit for optimizing parameters of the wind speed prediction model based on DE, and in particular, the first optimizing unit may be configured to optimize parameters of the LSTM model based on the attention mechanism, and the implementation logic may include:
initializing a search cluster, wherein the search cluster is an upper limit range and a lower limit range of a needed solving parameter, and can be set in a self-defining way according to practical application;
setting the MAE as the minimized objective function:
wherein A is t F for the actual wind speed at time t t For the predicted wind speed at this point, n is the calculated array;
parameters that determine DE, for example, optimized parameters may include, but are not limited to, batch_ size, neuro, look _back and dropout;
transferring the parameters to an LSTM model based on an attention mechanism to calculate MAE;
and if the result is converged, the optimization parameters are obtained.
Further, in this embodiment, the training module 3 may further include a second optimizing unit for optimizing parameters of the SVR model based on the DE, where the optimized parameters may include, but are not limited to, gamma and cost, and may further include a third optimizing unit for optimizing parameters of the RF model based on the DE. The prediction effect of the model can be further improved through optimizing the model parameters.
In this embodiment, the wind speed training device may further include a preprocessing module for normalizing the initial wind speed sequence, and may further include a dividing module for dividing training data for training the model into a training set and a test set.
According to the embodiment, a wind speed derivative sequence is constructed according to the initial wind speed sequence, wherein the wind speed derivative sequence further comprises SVR wind speed predicted based on a SVR model obtained through training and RF wind speed predicted based on an RF model obtained through training, expansion of input parameters required by deep learning is achieved, further, a wind speed prediction model obtained through training by utilizing an LSTM model based on an attention mechanism according to the wind speed derivative sequence has higher prediction precision and shows a better prediction effect, accurate prediction of short-time wind speed is facilitated, and further control of a fan control system is facilitated to improve maximum power generation of a wind field.
Example 3
The present embodiment provides an electronic device, which may be expressed in the form of a computing device (for example, may be a server device), including a memory, a processor, and a computer program stored on the memory and executable on the processor, where the processor may implement the wind speed prediction method provided in embodiment 1 when executing the computer program.
Fig. 6 shows a schematic diagram of the hardware structure of the present embodiment, and as shown in fig. 6, the electronic device 9 specifically includes:
at least one processor 91, at least one memory 92, and a bus 93 for connecting the different system components (including the processor 91 and the memory 92), wherein:
the bus 93 includes a data bus, an address bus, and a control bus.
The memory 92 includes volatile memory such as Random Access Memory (RAM) 921 and/or cache memory 922, and may further include Read Only Memory (ROM) 923.
Memory 92 also includes a program/utility 925 having a set (at least one) of program modules 924, such program modules 924 including, but not limited to: an operating system, one or more application programs, other program modules, and program data, each or some combination of which may include an implementation of a network environment.
The processor 91 executes various functional applications and data processing, such as the wind speed prediction method provided in embodiment 1 of the present invention, by running a computer program stored in the memory 92.
The electronic device 9 may further communicate with one or more external devices 94 (e.g., keyboard, pointing device, etc.). Such communication may occur through an input/output (I/O) interface 95. Also, the electronic device 9 may communicate with one or more networks such as a Local Area Network (LAN), a Wide Area Network (WAN) and/or a public network, such as the Internet, through a network adapter 96. The network adapter 96 communicates with other modules of the electronic device 9 via the bus 93. It should be appreciated that although not shown in the figures, other hardware and/or software modules may be used in connection with the electronic device 9, including but not limited to: microcode, device drivers, redundant processors, external disk drive arrays, RAID (disk array) systems, tape drives, data backup storage systems, and the like.
It should be noted that although several units/modules or sub-units/modules of an electronic device are mentioned in the above detailed description, such a division is merely exemplary and not mandatory. Indeed, the features and functionality of two or more units/modules described above may be embodied in one unit/module according to embodiments of the present application. Conversely, the features and functions of one unit/module described above may be further divided into ones that are embodied by a plurality of units/modules.
Example 4
The present embodiment provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the wind speed prediction method provided by embodiment 1.
More specifically, among others, readable storage media may be employed including, but not limited to: portable disk, hard disk, random access memory, read only memory, erasable programmable read only memory, optical storage device, magnetic storage device, or any suitable combination of the foregoing.
In a possible embodiment, the invention may also be implemented in the form of a program product comprising program code for causing a terminal device to carry out the steps of implementing the wind speed prediction method as described in example 1, when said program product is run on the terminal device.
Wherein the program code for carrying out the invention may be written in any combination of one or more programming languages, which program code may execute entirely on the user device, partly on the user device, as a stand-alone software package, partly on the user device and partly on the remote device or entirely on the remote device.
While specific embodiments of the invention have been described above, it will be appreciated by those skilled in the art that this is by way of example only, and the scope of the invention is defined by the appended claims. Various changes and modifications to these embodiments may be made by those skilled in the art without departing from the principles and spirit of the invention, but such changes and modifications fall within the scope of the invention.
Claims (8)
1. A method of wind speed prediction, comprising:
acquiring an initial wind speed sequence, wherein the initial wind speed sequence comprises wind speeds acquired in N continuous periods;
respectively constructing a wind speed derivative sequence according to each wind speed in the initial wind speed sequence, wherein the wind speed derivative sequence comprises wind speeds and derivative data corresponding to the wind speeds;
training a wind speed prediction model by using an LSTM model based on an attention mechanism according to the wind speed derivative sequence, wherein the wind speed prediction model is used for outputting the predicted wind speed of the M th period according to the wind speed derivative sequence corresponding to the wind speeds collected from the M-i th period to the M-1 th period;
acquiring wind speeds acquired in i periods before a period to be predicted;
outputting the predicted wind speed of the period to be predicted according to the wind speeds acquired in i periods before the period to be predicted and a wind speed prediction model obtained through training;
wherein N is more than or equal to M > i, and N, M and i are both positive integers;
the step of constructing a wind speed derivative sequence from each wind speed in the initial wind speed sequence comprises:
decomposing wind speed to obtain a sub wind speed sequence, wherein the sub wind speed sequence comprises a plurality of sub wind speeds;
and splicing the sub-wind speed sequences corresponding to the wind speeds to obtain a wind speed derivative sequence.
2. The method of predicting wind speed of claim 1, further comprising, after the step of decomposing the wind speed to obtain a sequence of sub-wind speeds:
training an SVR model by taking a sub-wind speed sequence as input and the wind speed corresponding to the sub-wind speed sequence as output;
inputting a sub wind speed sequence into a SVR model obtained by training to output SVR wind speed corresponding to the sub wind speed sequence;
the step of splicing the sub-wind speed sequences corresponding to the wind speeds to obtain a wind speed derivative sequence comprises the following steps:
splicing wind speeds, sub-wind speed sequences corresponding to the wind speeds and SVR wind speeds corresponding to the sub-wind speed sequences to obtain wind speed derivative sequences;
or alternatively, the first and second heat exchangers may be,
the step of decomposing the wind speed to obtain a sub-wind speed sequence further comprises the following steps:
training an RF model by taking a sub wind speed sequence as input and the wind speed corresponding to the sub wind speed sequence as output;
inputting a sub wind speed sequence into a trained RF model to output RF wind speed corresponding to the sub wind speed sequence;
the step of splicing the sub-wind speed sequences corresponding to the wind speeds to obtain a wind speed derivative sequence comprises the following steps:
splicing wind speeds, sub-wind speed sequences corresponding to the wind speeds and RF wind speeds corresponding to the sub-wind speed sequences to obtain wind speed derivative sequences;
or alternatively, the first and second heat exchangers may be,
the step of decomposing the wind speed to obtain a sub-wind speed sequence further comprises the following steps:
training an SVR model by taking a sub-wind speed sequence as input and the wind speed corresponding to the sub-wind speed sequence as output;
inputting a sub wind speed sequence into a SVR model obtained by training to output SVR wind speed corresponding to the sub wind speed sequence;
training an RF model by taking a sub wind speed sequence as input and the wind speed corresponding to the sub wind speed sequence as output;
inputting a sub wind speed sequence into a trained RF model to output RF wind speed corresponding to the sub wind speed sequence;
the step of splicing the sub-wind speed sequences corresponding to the wind speeds to obtain a wind speed derivative sequence comprises the following steps:
splicing wind speeds, sub wind speed sequences corresponding to the wind speeds, SVR wind speeds corresponding to the sub wind speed sequences and RF wind speeds corresponding to the sub wind speed sequences to obtain wind speed derivative sequences.
3. The method of wind speed prediction according to claim 1, wherein the step of training a wind speed prediction model using an LSTM model based on an attention mechanism from the wind speed derivative sequence comprises:
parameters of the wind speed prediction model are optimized based on DE.
4. A system for predicting the wind speed, which comprises a wind speed sensor, characterized by comprising the following steps:
the first acquisition module is used for acquiring an initial wind speed sequence, wherein the initial wind speed sequence comprises wind speeds acquired in N continuous periods;
the construction module is used for respectively constructing a wind speed derivative sequence according to each wind speed in the initial wind speed sequence, wherein the wind speed derivative sequence comprises wind speeds and derivative data corresponding to the wind speeds;
the training module is used for training a wind speed prediction model by utilizing an LSTM model based on an attention mechanism according to the wind speed derivative sequence, and the wind speed prediction model is used for outputting the predicted wind speed of the Mth period according to the wind speed derivative sequence corresponding to the wind speeds acquired from the Mth period to the Mth period;
the second acquisition module is used for acquiring the wind speeds acquired in i periods before the period to be predicted;
the prediction module is used for outputting the predicted wind speed of the period to be predicted according to the wind speeds acquired in i periods before the period to be predicted and the wind speed prediction model obtained through training;
wherein N is more than or equal to M > i, and N, M and i are both positive integers;
the construction module includes:
the decomposing unit is used for decomposing the wind speed to obtain a sub wind speed sequence, wherein the sub wind speed sequence comprises a plurality of sub wind speeds;
and the splicing unit is used for splicing the wind speed and the sub-wind speed sequence corresponding to the wind speed to obtain a wind speed derivative sequence.
5. The wind speed prediction system of claim 4, wherein the configuration module further comprises:
the first training unit is used for training an SVR model by taking a sub wind speed sequence as input and the wind speed corresponding to the sub wind speed sequence as output;
the first prediction unit is used for inputting the sub-wind speed sequence into the SVR model obtained through training so as to output SVR wind speed corresponding to the sub-wind speed sequence;
the splicing unit is specifically used for splicing wind speeds, sub-wind speed sequences corresponding to the wind speeds and SVR wind speeds corresponding to the sub-wind speed sequences to obtain wind speed derivative sequences;
or alternatively, the first and second heat exchangers may be,
the construction module further comprises:
the second training unit is used for taking the sub wind speed sequence as input and the wind speed corresponding to the sub wind speed sequence as output to train the RF model;
the second prediction unit is used for inputting the sub wind speed sequence into the trained RF model so as to output the RF wind speed corresponding to the sub wind speed sequence;
the splicing unit is specifically used for splicing wind speeds, sub-wind speed sequences corresponding to the wind speeds and RF wind speeds corresponding to the sub-wind speed sequences to obtain wind speed derivative sequences;
or (b) the process comprises,
the construction module further comprises:
the first training unit is used for training an SVR model by taking a sub wind speed sequence as input and the wind speed corresponding to the sub wind speed sequence as output;
the first prediction unit is used for inputting the sub-wind speed sequence into the SVR model obtained through training so as to output SVR wind speed corresponding to the sub-wind speed sequence;
the second training unit is used for taking the sub wind speed sequence as input and the wind speed corresponding to the sub wind speed sequence as output to train the RF model;
the second prediction unit is used for inputting the sub wind speed sequence into the trained RF model so as to output the RF wind speed corresponding to the sub wind speed sequence;
the splicing unit is specifically used for splicing wind speed, a sub wind speed sequence corresponding to the wind speed, SVR wind speed corresponding to the sub wind speed sequence and RF wind speed corresponding to the sub wind speed sequence to obtain a wind speed derivative sequence.
6. The wind speed prediction system of claim 4, wherein the training module comprises:
and the optimizing unit is used for optimizing parameters of the wind speed prediction model based on the DE.
7. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements a wind speed prediction method as claimed in any one of claims 1 to 3 when the computer program is executed by the processor.
8. A computer readable storage medium, on which a computer program is stored, which computer program, when being executed by a processor, implements the steps of the wind speed prediction method according to any one of claims 1 to 3.
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