CN113780636A - Solar radiation prediction method based on EMD-GRU-Attention - Google Patents

Solar radiation prediction method based on EMD-GRU-Attention Download PDF

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CN113780636A
CN113780636A CN202110988946.3A CN202110988946A CN113780636A CN 113780636 A CN113780636 A CN 113780636A CN 202110988946 A CN202110988946 A CN 202110988946A CN 113780636 A CN113780636 A CN 113780636A
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孔祥飞
杜欣瑜
薛桂香
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Abstract

The invention relates to a solar radiation prediction method based on EMD-GRU-Attention, which comprises the steps of S1, inputting a layer; step S2, an EMD layer; step S3, a GRU layer; step S4, an Attention mechanism layer; and step S5, outputting the layer. According to the invention, signals with different frequencies are gradually decomposed according to the characteristics of data per se through an EMD algorithm, prediction models are respectively established for the decomposed subsequences, then the output of each prediction model is linearly weighted to obtain a final prediction result, when the input time sequence in solar radiation prediction is too long, the GRU network is easy to have the problems of information loss and difficult modeling, which can affect the prediction precision of the models, and an Attention mechanism distributes weight for the output of a hidden layer of the GRU network, so that the hidden layer of the GRU can highlight key information in prediction, a solar radiation value of one to six hours in the future is finally obtained, and the prediction precision and efficiency of the solar radiation multi-step prediction model are improved.

Description

Solar radiation prediction method based on EMD-GRU-Attention
Technical Field
The invention relates to the technical field of solar radiation prediction, in particular to a solar radiation prediction method based on EMD-GRU-Attention.
Background
Solar energy is widely applied to a heat energy system and an electric power system, the solar energy is directly from the sun in a radiation mode, but the solar radiation degree is easily influenced by atmospheric changes, so that the problems of mismatch with the heat load of a building and the like exist in a heat supply system, the problems of intermittence, uncertainty and the like exist in photovoltaic power generation power, and therefore, a high-precision radiation prediction model is researched, a reasonable optimization regulation and control plan is made for the heat supply system and the electric power system, and the solar energy utilization and development significance is achieved.
Predictive models for solar radiation can be generally divided into three categories: the first type is a physical prediction model, the second type is a statistical prediction model, and the third type is a hybrid prediction model; the deep learning models such as LSTM and GRU are concerned with the capability of deeply mining complex data, the GRU and the LSTM are similar in structure, but the GRU reduces the number of internal hidden states and thresholds, the problem of gradient explosion or disappearance is solved, the nonlinear time sequence data features are effectively extracted, and the training time of the models is shortened.
The invention provides a high-precision hybrid prediction model of GRU (generalized regression decomposition) based on EMD (empirical mode decomposition), which can realize solar radiation prediction for 6h in the future, calculate the weight of features by introducing an Attention mechanism and allocate more Attention to key data, thereby improving the prediction precision.
Disclosure of Invention
Therefore, the invention provides a solar radiation prediction method based on EMD-GRU-Attention, which is used for overcoming the problem of insufficient solar radiation prediction precision in the prior art.
To achieve the above objects, the present invention provides a solar radiation prediction method based on EMD-GRU-Attention, comprising,
step S1, inputting a layer, acquiring solar radiation data and related meteorological data, and preprocessing the solar radiation data and the related meteorological data;
step S2, an EMD layer decomposes the preprocessed solar radiation time-series data into a plurality of IMF components and a residual RES by utilizing EMD;
step S3, the GRU layer respectively establishes corresponding GRU deep learning prediction models aiming at each IMF component and the residual error RES, and the input of the GRU deep learning prediction models is the relevant meteorological data and the corresponding historical IMFs or residual error RES;
step S4, an Attention mechanism layer, wherein the input of the Attention mechanism layer is the output vector of the GRU layer, the probabilities corresponding to different eigenvectors are calculated according to the weight distribution principle, and a weight parameter matrix is updated and iterated to obtain a new vector;
and step S5, outputting the layer, and inputting the new vector into the full-connection layer to obtain a final prediction result.
Further, in step S1, performing filtering and denoising on the correlated meteorological data by using kalman filtering to correct measurement errors and human errors, where the kalman filtering estimates the current state by using a previous estimated value and a latest observed value of the current state under a linear unbiased minimum variance estimation criterion, determines an updated estimate of a new state according to the previous estimated value and the observed value of the current state, and adds interference of an external environment, where the kalman filtering is used to describe a linear random difference equation between the previous state and the current state and an observation equation of the current state are respectively,
xk＝Axk-1+Buk-1+ωk
zk＝Hxk-1+vk
wherein x is a state value, k is a current time state, k-1 is a previous time state, A is a transition matrix that linearly maps the previous state to the current state, B is a transition matrix that controls inputs to the current state, u is a control input, ω is a control valuekFor the system input random noise during state updating, z is the measured value, H is the conversion matrix from the current state variable to the observation variable, vkTo observe the noise vector.
Further, the kalman filter algorithm is performed recursively through a time update equation and a measurement update equation, the time update equation projects the current state variable as a priori estimate to the measurement update equation in time forward, the time update equation extrapolates the state variable and the error covariance from the time k-1 forward to the time k, the specific expression is,
Figure BDA0003231640610000021
Figure BDA0003231640610000022
wherein the content of the first and second substances,
Figure BDA0003231640610000023
is an a posteriori estimate of the time k,
Figure BDA0003231640610000024
is a priori estimate of the time of k,
Figure BDA0003231640610000025
covariance is estimated a priori at time k, and Q is process noise covariance.
Further, the measurement update equation requires the calculation of the kalman gain, and then the a priori estimate and the new measured variable are combined to construct an improved a posteriori estimate, specifically expressed as,
Figure BDA0003231640610000031
Figure BDA0003231640610000032
Figure BDA0003231640610000033
wherein, PkEstimating covariance for a posteriori at time k, ZkAnd (4) for the posterior state estimation of the k step, after the calculation of the time updating equation and the measurement updating equation is completed, iterating the whole process again to obtain the denoising result of the related meteorological data.
Further, in the step S2, including,
s2.1, calculating all local maximum value points and minimum value points for the original time sequence x (t) of the solar radiation data, and obtaining an upper envelope e by adopting cubic spline function interpolation fittingmax(t) and lower envelope emin(t) sequence, and calculating the mean value sequence m of the upper and lower envelope sequences1(t)，
Figure BDA0003231640610000034
S2.2, subtracting the mean value sequence m from the original time sequence1(t) obtaining a new data sequence h1(t)，h1(t)＝x(t)-m1(t)；
S2.3, when the new sequence h1(t) if there are also a negative local maximum and a positive local minimum, it is determined that the IMF component is not present, and x (t) is set to h1(t) repeating the processes from S2.1 to S2.2 until the IMF condition is satisfied, to obtain a first IMF component;
s2.4, a sequence of remainders r from which the high-frequency components have been removedk(t) is obtained by the following formula,
rk(t)＝x(t)-hk(t)
s2.5, corresponding the obtained residual items to x (t), repeating the steps from S2.1 to S2.4 to obtain the rest n IMF components IMF1(t)，IMF2(t)，…，IMFn(t) up to sequence rn(t) is a monotonic function, the original sequence is represented as,
Figure BDA0003231640610000035
the EMD decomposition is now complete.
Further, in said step S3, the input of the model is selected using the pearson correlation coefficient, which is defined as the quotient of the covariance and the standard deviation between two variables, reflecting the degree of correlation between the two variables, and the specific expression is,
Figure BDA0003231640610000041
correlation coefficient rhoX，YThe absolute value of which is less than 0.2, and finally 4 meteorological parameters are selected as the input of the predicted irradiance amplitude, namely the temperature, the relative humidity, the solar altitude and the wind speed.
Further, in step S3, the EMD layer output data is divided into a training set and a test set, the training set is input into the GRU deep learning prediction model, the GRU deep learning prediction model learns the extracted features to capture the internal change rule thereof, the GRU deep learning prediction model calculates to obtain an output vector, the specific expression is,
rt＝σ(W(r)xt+U(r)ht-1)#
zt＝σ(W(z)xt+U(z)ht-1)#
Figure BDA0003231640610000042
Figure BDA0003231640610000043
wherein x istFor input at time t, htHidden state at time t, ht-1Hidden state at time t-1, ztAnd rtRespectively an update gate and a reset gate,
Figure BDA0003231640610000044
is input xtAnd the previous moment ht-1A collection of hidden states, σ and tanh being activation functions, W(r)And U(r)Training parameter matrix for resetting gates, W(z)And U(z)For updating the training parameter matrix of the door, U and W are
Figure BDA0003231640610000045
And (3) obtaining a training parameter matrix when the matrix is obtained, wherein x is the number multiplication of the matrix.
Further, in the step S5, the predicted value of the solar radiation output by the output layer is
Figure BDA0003231640610000046
Comprehensively evaluating the performance of the prediction method, and using RMSE and NSE with specific expressions,
Figure BDA0003231640610000047
Figure BDA0003231640610000048
wherein, yiIs the true value of the solar radiation,
Figure BDA0003231640610000049
for the predicted value of solar radiation, n is the total number of samples, the performance of the prediction method increases with decreasing RMSE and with increasing NSE.
Compared with the prior art, the method has the advantages that the EMD algorithm can gradually decompose signals with different frequencies according to the characteristics of data, the prediction models are respectively established for the decomposed subsequences, then the output linear weighting of each prediction model is carried out to obtain the final prediction result, when the input time sequence in the solar radiation prediction is too long, the GRU network easily has the problems of information loss and difficulty in modeling, the prediction accuracy of the model can be influenced, and the Attention mechanism distributes weight for the GRU neural network hidden layer output, so that the GRU hidden layer can highlight the key information in prediction;
furthermore, the forecasting method utilizes a gated recursion unit to capture dynamic changes of solar radiation data and a hidden nonlinear relation between meteorological factors, models the time dependence, decomposes an original solar radiation sequence, establishes a model for each subsequence, and utilizes a linear weighting method to remarkably improve the forecasting precision and stability of the mixed model;
further, the prediction method predicts the solar radiation 1-6 hours in advance, and the EMD-AGRU model is in a stable state under different prediction levels, which shows that the EMD-AGRU model not only can realize short-term prediction, but also can be used for long-term solar radiation prediction tasks.
Drawings
FIG. 1 is a flow chart of the solar radiation prediction method based on EMD-GRU-Attention according to the present invention;
FIG. 2 is a Kalman filtering denoising result diagram of the solar radiation prediction method based on EMD-GRU-Attention according to the present invention;
FIG. 3 is a diagram illustrating the decomposition result of the EMD layer of the solar radiation prediction method based on EMD-GRU-Attention according to the present invention;
FIG. 4 is a correlation thermodynamic diagram of the EMD-GRU-Attention based solar radiation prediction method of the present invention;
FIG. 5 is a network structure diagram of a GRU deep learning prediction model of the solar radiation prediction method based on EMD-GRU-Attention according to the present invention;
FIG. 6 is an RMSE evaluation index chart of the solar radiation prediction method based on EMD-GRU-Attention according to the present invention;
FIG. 7 is an NSE evaluation index chart of the solar radiation prediction method based on EMD-GRU-Attention according to the present invention.
Detailed Description
In order that the objects and advantages of the invention will be more clearly understood, the invention is further described below with reference to examples; it should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are only for explaining the technical principle of the present invention, and do not limit the scope of the present invention.
It should be noted that in the description of the present invention, the terms of direction or positional relationship indicated by the terms "upper", "lower", "left", "right", "inner", "outer", etc. are based on the directions or positional relationships shown in the drawings, which are only for convenience of description, and do not indicate or imply that the device or element must have a specific orientation, be constructed in a specific orientation, and be operated, and thus, should not be construed as limiting the present invention.
Furthermore, it should be noted that, in the description of the present invention, unless otherwise explicitly specified or limited, the terms "mounted," "connected," and "connected" are to be construed broadly, and may be, for example, fixedly connected, detachably connected, or integrally connected; can be mechanically or electrically connected; they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meanings of the above terms in the present invention can be understood by those skilled in the art according to specific situations.
Referring to fig. 1, which is a flowchart illustrating a solar radiation prediction method based on EMD-GRU-Attention according to the present invention, the present invention provides a solar radiation prediction method based on EMD-GRU-Attention, including,
step S1, inputting a layer, acquiring solar radiation data and related meteorological data, and preprocessing the solar radiation data and the related meteorological data;
step S2, an EMD layer decomposes the preprocessed solar radiation time-series data into a plurality of IMF components and a residual RES by utilizing EMD;
step S3, the GRU layer respectively establishes corresponding GRU deep learning prediction models aiming at each IMF component and the residual error RES, and the input of the GRU deep learning prediction models is the relevant meteorological data and the corresponding historical IMFs or residual error RES;
step S4, an Attention mechanism layer, wherein the input of the Attention mechanism layer is the output vector of the GRU layer, the probabilities corresponding to different eigenvectors are calculated according to the weight distribution principle, and a weight parameter matrix is updated and iterated to obtain a new vector;
and step S5, outputting the layer, and inputting the new vector into the full-connection layer to obtain a final prediction result.
Please refer to fig. 2, which is a graph of kalman filtering denoising results of the EMD-GRU-Attention-based solar radiation prediction method of the present invention,
specifically, in step S1, a kalman filter is used to filter and denoise the correlated meteorological data, correct measurement errors and human errors, the kalman filter uses a previous estimated value and a latest observed value of a current state to estimate the current state under a linear unbiased minimum variance estimation criterion, and determines an updated estimate of a new state according to the previous estimated value and the observed value of the current state, and adds interference of an external environment, wherein the kalman filter is used to describe a linear random difference equation between a previous state and a next state and an observation equation of the current state are respectively,
xk＝Axk-1+Buk-1+ωk
zk＝Hxk-1+vk
wherein x is a state value, k is a current time state, k-1 is a previous time state, A is a transition matrix that linearly maps the previous state to the current state, B is a transition matrix that controls inputs to the current state, u is a control input, ω is a control valuekFor the system input random noise during state updating, z is the measured value, H is the conversion matrix from the current state variable to the observation variable, vkTo observe the noise vector.
Specifically, the kalman filtering algorithm is performed recursively through a time update equation and a measurement update equation, the time update equation projects the current state variable as a priori estimate to the measurement update equation in time and forward, the time update equation calculates the state variable and the error covariance from the time k-1 forward to the time k, and the specific expression is,
Figure BDA0003231640610000071
Figure BDA0003231640610000072
wherein the content of the first and second substances,
Figure BDA0003231640610000073
is an a posteriori estimate of the time k,
Figure BDA0003231640610000074
is a priori estimate of the time of k,
Figure BDA0003231640610000075
covariance is estimated a priori at time k, and Q is process noise covariance.
Specifically, the measurement update equation requires the calculation of the kalman gain, and then the a priori estimate and the new measured variable are combined to construct an improved a posteriori estimate, specifically expressed as,
Figure BDA0003231640610000076
Figure BDA0003231640610000077
Figure BDA0003231640610000078
wherein, PkEstimating covariance for a posteriori at time k, ZkAnd (4) for the posterior state estimation of the k step, after the calculation of the time updating equation and the measurement updating equation is completed, iterating the whole process again to obtain the denoising result of the related meteorological data.
Please refer to fig. 3, which is a diagram illustrating the decomposition result of the EMD layer of the solar radiation prediction method based on EMD-GRU-Attention according to the present invention,
specifically, in the step S2, including,
s2.1, calculating all local maximum value points and minimum value points for the original time sequence x (t) of the solar radiation data, and obtaining an upper envelope e by adopting cubic spline function interpolation fittingmax(t) and lower envelope emin(t) sequence, and calculating the mean value sequence m of the upper and lower envelope sequences1(t)，
Figure BDA0003231640610000081
S2.2, subtracting the mean value sequence m from the original time sequence1(t) obtaining a new data sequence h1(t)，h1(t)＝x(t)-m1(t)；
S2.3, when the new sequence h1(t) if there are also a negative local maximum and a positive local minimum, it is determined that the IMF component is not present, and x (t) is set to h1(t) repeating the processes from S2.1 to S2.2 until the IMF condition is satisfied, to obtain a first IMF component;
s2.4, a sequence of remainders r from which the high-frequency components have been removedk(t) is obtained by the following formula,
rk(t)＝x(t)-hk(t)
s2.5, corresponding the obtained residual items to x (t), repeating the steps from S2.1 to S2.4 to obtain the rest n IMF components IMF1(t)，IMF2(t)，…，IMFn(t) up to sequence rn(t) is a monotonic function, the original sequence is represented as,
Figure BDA0003231640610000082
the EMD decomposition is now complete.
In step S2, the complex signal is decomposed by EMD and then composed of several independent Intrinsic Mode Function (IMF) components with different scale features and a remainder RES, and the solar radiation sequence is a time sequence with nonlinear and non-stationary characteristics, so that the solar radiation data is decomposed by empirical mode decomposition, thereby realizing individual prediction of different scale features, achieving an effect of non-interference, reducing the prediction difficulty, and improving the prediction accuracy.
Please continue to refer to fig. 4, which is a related thermodynamic diagram of the EMD-GRU-Attention based solar radiation prediction method of the present invention,
specifically, in the step S3, the input of the model is selected using the pearson correlation coefficient, which is defined as the quotient of the covariance and the standard deviation between two variables, reflecting the degree of correlation between the two variables, and the specific expression is,
Figure BDA0003231640610000091
correlation coefficient rhoX，YAnd removing parameters with the absolute value less than 0.2, and finally selecting 4 meteorological parameters as the input of the predicted irradiance amplitude, wherein the parameters are temperature, relative humidity, solar altitude and wind speed, and finally establishing an optimal solar total radiation prediction model with high accuracy and strong reliability.
Please refer to fig. 5, which is a diagram of a network structure of a GRU deep learning prediction model according to the solar radiation prediction method based on EMD-GRU-Attention of the present invention;
specifically, in step S3, the EMD layer output data is divided into a training set and a test set, the training set is input into the GRU deep learning prediction model, the GRU deep learning prediction model learns the extracted features to capture the internal change rule thereof, the GRU deep learning prediction model calculates to obtain an output vector, specifically, the expression is,
rt＝σ(W(r)xt+U(r)ht-1)#
zt＝σ(W(z)xt+U(z)ht-1)#
Figure BDA0003231640610000092
Figure BDA0003231640610000093
wherein x istFor input at time t, htHidden state at time t, ht-1Hidden state at time t-1, ztAnd rtRespectively an update gate and a reset gate,
Figure BDA0003231640610000094
is input xtAnd the previous moment ht-1A collection of hidden states, σ and tanh being activation functions, W(t)And U(t)Training parameter matrix for resetting gates, W(z)And U(z)For updating the training parameter matrix of the door, U and W are
Figure BDA0003231640610000095
And (3) obtaining a training parameter matrix when the matrix is obtained, wherein x is the number multiplication of the matrix.
In the step S3, the GRU and the LSTM have similar structures, but the GRU reduces the number of internal hidden states and thresholds, solves the problem of gradient explosion or disappearance caused by inherent memory in the conventional RNN, can effectively extract the nonlinear time series data feature, and shortens the training time of the model.
Please refer to fig. 6 and 7, wherein fig. 6 is an RMSE evaluation index chart of the EMD-GRU-Attention based solar radiation prediction method of the present invention, fig. 7 is an NSE evaluation index chart of the EMD-GRU-Attention based solar radiation prediction method of the present invention,
in step S5, the predicted value of the solar radiation output by the output layer is
Figure BDA0003231640610000096
Comprehensively evaluating the performance of the prediction method, and using RMSE and NSE with specific expressions,
Figure BDA0003231640610000101
Figure BDA0003231640610000102
wherein, yiIs the true value of the solar radiation,
Figure BDA0003231640610000103
the predicted value of the solar radiation is shown, and n is the total number of samples;
please refer to table 1 and table 2, wherein table 1 shows the evaluation and comparison results of the solar radiation prediction method based on EMD-GRU-Attention and other single model prediction methods by NSE, table 2 shows the evaluation and comparison results of the solar radiation prediction method based on EMD-GRU-Attention and other single model prediction methods by RMSE,
the performance of the prediction method increases with decreasing RMSE and with increasing NSE.
Figure BDA0003231640610000104
TABLE 1
Figure BDA0003231640610000105
TABLE 2
So far, the technical solutions of the present invention have been described in connection with the preferred embodiments shown in the drawings, but it is easily understood by those skilled in the art that the scope of the present invention is obviously not limited to these specific embodiments. Equivalent changes or substitutions of related technical features can be made by those skilled in the art without departing from the principle of the invention, and the technical scheme after the changes or substitutions can fall into the protection scope of the invention.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention; various modifications and alterations to this invention will become apparent to those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims (8)

1. A solar radiation prediction method based on EMD-GRU-Attention is characterized by comprising the following steps,
step S1, inputting a layer, acquiring solar radiation data and related meteorological data, and preprocessing the solar radiation data and the related meteorological data;
step S2, an EMD layer decomposes the preprocessed solar radiation time-series data into a plurality of IMF components and a residual RES by utilizing EMD;
step S3, the GRU layer respectively establishes corresponding GRU deep learning prediction models aiming at each IMF component and the residual error RES, and the input of the GRU deep learning prediction models is the relevant meteorological data and the corresponding historical IMFs or residual error RES;
step S4, an Attention mechanism layer, wherein the input of the Attention mechanism layer is the output vector of the GRU layer, the probabilities corresponding to different eigenvectors are calculated according to the weight distribution principle, and a weight parameter matrix is updated and iterated to obtain a new vector;
and step S5, outputting the layer, and inputting the new vector into the full-connection layer to obtain a final prediction result.
2. The method for solar radiation prediction based on EMD-GRU-Attention of claim 1, wherein in step S1, the relevant meteorological data is filtered and denoised by Kalman filtering to correct measurement errors and human errors, the Kalman filtering uses the previous estimation value and the latest observation value of the current state to estimate the current state under the linear unbiased minimum variance estimation criterion, the updated estimation of the new state is determined according to the previous estimation value and the observation value of the current state, and the interference of the external environment is added, wherein, the Kalman filtering is used to describe the linear random difference equation between the previous state and the current state and the observation equation of the current state are respectively,
xk＝Axk-1+Buk-1+ωk
zk＝Hxk-1+vk
wherein x is a state value, k is a current time state, k-1 is a previous time state, A is a transition matrix that linearly maps the previous state to the current state, B is a transition matrix that controls inputs to the current state, u is a control input, ω is a control valuekFor the system input random noise during state updating, z is the measured value, H is the conversion matrix from the current state variable to the observation variable, vkTo observe the noise vector.
3. The EMD-GRU-Attention-based solar radiation prediction method of claim 2, characterized in that the kalman filtering algorithm is performed recursively through a time update equation and a measurement update equation, the time update equation projecting the current state variable as a priori estimate and forward in time to the measurement update equation, the time update equation extrapolating forward from the time k-1 to the state variable and error covariance of the time k, the specific expression being,
Figure FDA0003231640600000021
Figure FDA0003231640600000022
wherein the content of the first and second substances,
Figure FDA0003231640600000023
is an a posteriori estimate of the time k,
Figure FDA0003231640600000024
is a priori estimate of the time of k,
Figure FDA0003231640600000025
covariance is estimated a priori at time k, and Q is process noise covariance.
4. The method of claim 3, wherein the measurement update equation requires Kalman gain calculations, and then the a priori estimates are combined with new measurement variables to construct an improved a posteriori estimate, as expressed in,
Figure FDA0003231640600000026
Figure FDA0003231640600000027
Figure FDA0003231640600000028
wherein, PkEstimating covariance for a posteriori at time k, ZkAnd (4) for the posterior state estimation of the k step, after the calculation of the time updating equation and the measurement updating equation is completed, iterating the whole process again to obtain the denoising result of the related meteorological data.
5. The method for solar radiation prediction based on EMD-GRU-Attention of claim 1, wherein in the step S2, comprising,
s2.1, calculating all local maximum value points and minimum value points for the original time sequence x (t) of the solar radiation data, and obtaining an upper envelope e by adopting cubic spline function interpolation fittingmax(t) and lower envelope emin(t) sequence, and calculating the mean value sequence m of the upper and lower envelope sequences1(t)，
Figure FDA0003231640600000029
S2.2, subtracting the mean value sequence m from the original time sequence1(t) obtaining a new data sequence h1(t)，h1(t)＝x(t)-m1(t)；
S2.3, when the new sequence h1(t) if there are also a negative local maximum and a positive local minimum, it is determined that the IMF component is not present, and x (t) is set to h1(t) repeating the processes from S2.1 to S2.2 until the IMF condition is satisfied, to obtain a first IMF component;
s2.4, a sequence of remainders r from which the high-frequency components have been removedk(t) is obtained by the following formula,
rk(t)＝x(t)-hk(t)
s2.5, corresponding the obtained residual items to x (t), repeating the steps from S2.1 to S2.4 to obtain the rest n IMF components IMF1(t)，IMF2(t)，…，IMFn(t) up to sequence rn(t) is a monotonic function, the original sequence is represented as,
Figure FDA0003231640600000031
the EMD decomposition is now complete.
6. The EMD-GRU-Attention-based solar radiation prediction method of claim 1, wherein in the step S3, an input of a model is selected using a pearson correlation coefficient, which is defined as a quotient of a covariance and a standard deviation between two variables reflecting a degree of correlation between the two variables, and the specific expression is,
Figure FDA0003231640600000032
correlation coefficient rhoX，YThe absolute value of less than 0.2, and finally 4 meteorological parameters are selected as the input of the predicted irradiance amplitude, namely the temperature, the relative humidity and the solar altitudeAngle and wind speed.
7. The method of claim 6, wherein in step S3, the EMD layer output data is divided into a training set and a testing set, the training set is inputted into the GRU deep learning prediction model, the GRU deep learning prediction model learns the extracted features to capture the internal variation rule thereof, the GRU deep learning prediction model calculates an output vector, specifically expressed as,
rt＝σ(W(r)xt+U(r)ht-1)#
zt＝σ(W(z)xt+U(z)ht-1)#
Figure FDA0003231640600000033
Figure FDA0003231640600000034
wherein x istFor input at time t, htHidden state at time t, ht-1Hidden state at time t-1, ztAnd rtRespectively an update gate and a reset gate,
Figure FDA0003231640600000035
is input xtAnd the previous moment ht-1A collection of hidden states, σ and tanh being activation functions, W(r)And U(r)Training parameter matrix for resetting gates, W(z)And U(z)For updating the training parameter matrix of the door, U and W are
Figure FDA0003231640600000037
And (3) obtaining a training parameter matrix when the matrix is obtained, wherein x is the number multiplication of the matrix.
8. The method for predicting EMD-GRU-Attention-based solar radiation according to claim 1, wherein in the step S5, the predicted value of the solar radiation output by the output layer is
Figure FDA0003231640600000036
Comprehensively evaluating the performance of the prediction method, and using RMSE and NSE with specific expressions,
Figure FDA0003231640600000041
Figure FDA0003231640600000042
wherein, yiIs the true value of the solar radiation,
Figure FDA0003231640600000043
for the predicted value of solar radiation, n is the total number of samples, the performance of the prediction method increases with decreasing RMSE and with increasing NSE.
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