CN113642255A - Photovoltaic power generation power prediction method based on multi-scale convolution cyclic neural network - Google Patents
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Abstract
The invention discloses a photovoltaic power generation power prediction method based on a multi-scale convolution cyclic neural network, which comprises the following steps of: constructing a photovoltaic power generation power prediction model based on a multi-scale convolution cyclic neural network; training the photovoltaic power generation power prediction model by using training set data; and inputting the test set data into the trained photovoltaic power generation power prediction model, and calculating to obtain a predicted value of the photovoltaic power generation power. The method introduces the multi-scale convolutional layer into the recurrent neural network, and attention mechanisms are distributed from different scales, so that the model can acquire historical information from different scales; the bidirectional GRU layer can more fully acquire context information of sequence data, the whole model adopts a convolution structure to fuse identification outputs of attention mechanisms of different scales, and the convolution connection is used for screening and identifying the outputs of different scales, so that the prediction of the photovoltaic power generation power can be more accurately acquired.
Description
Technical Field
The invention belongs to the technical field of photovoltaic power generation power prediction, and particularly relates to a photovoltaic power generation power prediction method based on a multi-scale convolution cyclic neural network.
Background
The output of the photovoltaic power generation power has obvious intermittent fluctuation characteristics, and the large-scale photovoltaic power generation access can bring certain impact to the safe and stable operation of a power grid. With the increase of the proportion of renewable energy sources such as wind power, photovoltaic and the like in various regions, the phenomena of wind abandonment and light abandonment are further increased.
The photovoltaic power generation power prediction is one of key technologies for solving the problem, and the development of a photovoltaic power station power generation power prediction method and system research has important academic and application values. Therefore, how to accurately predict the power of the photovoltaic power station becomes a research hotspot in recent years, and is popular among domestic and foreign scholars.
The prediction accuracy of the photovoltaic power generation power is still insufficient at present. The traditional machine learning method is difficult to capture long-term dependency relationship, and the time sequence importance among all factors is not well acquired. Recently, learners adopt a deep learning method to predict the photovoltaic power generation power, so that a good effect is achieved. However, both the conventional machine learning method and the deep learning method which is popular in recent years do not capture the correlation characteristics between the elements from the time sequence, and the prediction accuracy is not ideal.
Disclosure of Invention
In view of the above, in order to solve the problem of accurate prediction of photovoltaic power generation power, the present invention aims to provide a method for predicting photovoltaic power generation power based on a multi-scale convolution cyclic neural network, which provides a method with higher prediction accuracy and precision by using meteorological data such as irradiation intensity, temperature, humidity, air pressure, altitude, etc., so as to predict the photovoltaic power generation power.
Based on the purpose, the photovoltaic power generation power prediction method based on the multi-scale convolution cyclic neural network comprises the following steps:
and 3, inputting the test set data into the trained photovoltaic power generation power prediction model, and calculating to obtain a predicted value of the photovoltaic power generation power.
Specifically, the photovoltaic power generation power prediction model comprises a first convolution layer, a first bidirectional GRU layer, a first multi-scale convolution layer, a second bidirectional GRU layer, a second multi-scale convolution layer, a first full-connection layer and a second full-connection layer, wherein the layers are sequentially connected, the output of the first convolution layer and the output of the first bidirectional GRU layer are connected and then simultaneously used as the input of the first multi-scale convolution layer and the second multi-scale convolution layer, the bidirectional GRU layer is formed by connecting a forward GRU model and a backward GRU model in parallel to form a bidirectional structure, the bidirectional GRU layer outputs two combined GRU signals, the output layer of the first full connection layer is 100, the output layer of the second full connection layer is 1, the input of the first convolution layer in the photovoltaic power generation power prediction model is an influence factor data sequence, and the output of the second full-connection layer is a photovoltaic power generation power value.
Specifically, the photovoltaic power generation power prediction model isx0,...,xTTo influence the factor sequence data, (y)0,...,yK),K<T is a known photovoltaic power generation power value, (y)K+1,...,yT) In order to require a predicted photovoltaic power generation power value,for the corresponding estimated value, the input is x0,...,xT,y0,...,yKAnd variables are sequentially input into the photovoltaic power generation power prediction model to start training, the loss function adopts standard normalized MSE, and the activation function adopts Relu function.
Specifically, the analytical expression of the photovoltaic power generation power prediction model is as follows:
C4 t=η2([C2 t,C3 t])
wherein x istIs an input to the model at time t, η1(. eta.) and eta2(. is) two convolution operations, [, ]]For merge join operations, mutiscalaconv (·, Scale1) and mutiscalaconv (·, Scale2) are two multi-Scale convolution operations with scales Scale1 and Scale2, respectively, and the specific process of fusion convolution is as follows:
first convolution layer η1(xt) Accepting sequence data xtIs transported byIn and out areSimultaneously as inputs to the first multi-scale convolutional layer and the second multi-scale convolutional layer;
is the output of the first bi-directional GRU layer,indicating the output to be GRU in forward directionAnd backward GRU outputCarrying out merging connection;is to multiply the first bidirectional GRU layer by a weight vectorAnd adding the offset vectorThe result of (1);
will be provided withAnd η1(xt) Output of (2)Are combined into Pt 1As input to the first multi-scale convolutional layer;
is the output of the first multi-Scale convolution layer with Scale1Out, connected to a second bidirectional GRU layer;
is the output of the second bidirectional GRU layer,indicating the output of the GRU in forward direction thereinAnd backward GRU outputCarrying out merging connection;is to multiply the second bidirectional GRU layer by a weight vectorAnd adding the offset vectorThe result of (1);
by analogy, the expression is obtainedBy a convolution operation with Scale2, for [ C2 t,C3 t]Extracting to reserve the more important scale information of the target and obtain outputThen obtaining output O through full connection operationt;
Specifically, the bidirectional GRU layer is a bidirectional structure formed by connecting two GRU models of a forward GRU and a backward GRU, and the first layer of the forward GRU is forgotten to be output by a gate: f. of1 t=σ(W1 f[H1 t-1,x”t]+B1 f),σ(x)=1/(1+e-x) In the forward GRU update gate, the first output is: z is a radical of1 t=σ(W1 z[H1 t-1,x”t]+B1 z) And the second output is:similarly, the corresponding first output to the GRU is: z is a radical of2 t=σ(W2 z[H2 t-1,x”t]+B2 z) And a second output:intermediate output of forward GRUAnd backward GRU intermediate outputObtaining an output by an aggregation operation on the intermediate output Indicating the output to be GRU in forward directionAnd backward GRU outputPerforming merged connection as output of bidirectional GRU layerFor the input of the bidirectional GRU layer, [ W ]1 f,B1 f], [W1 Z,B1 Z],[W1 h,B1 h]For forward GRU model parameters, [ W ]2 f,B2 f],[W2 Z,B2 Z],[W2 h,B2 h]For the inverse GRU model parameters, [ W ]12 o,B12 o]Are output layer parameters.The output of the first bidirectional GRU layer, correspondingly,is the output of the second bidirectional GRU layer.
Preferably, the convolutional layer is a 1-dimensional convolutional network.
Specifically, the influencing factor data comprises irradiation intensity, temperature, humidity and air pressure.
The photovoltaic power generation power prediction model in the method mainly comprises a multi-scale convolution layer, a bidirectional GRU layer and a full-connection layer, the multi-scale convolution layer is introduced into a recurrent neural network, a plurality of improved attention units are connected in series, attention mechanisms are distributed on different scales, therefore, the model can collect historical information from different scales, distinguish the influence of different input elements on a prediction result, the bidirectional GRU layer can more fully acquire the context information of sequence data on the basis of GRUs, the whole model adopts a convolution structure to fuse the recognition output of attention mechanisms of different scales, and the output is screened and identified by different scales through convolution connection, so that the scale information which is more important to the target can be reserved, and the prediction of the photovoltaic power generation power obtains better accuracy.
Drawings
FIG. 1 is a schematic flow diagram of the process of the present invention;
FIG. 2 is a data processing flow diagram of the method of the present invention;
FIG. 3 is a schematic diagram of a 1-dimensional convolutional network in the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
As shown in fig. 1, a photovoltaic power generation power prediction method based on a multi-scale convolution cyclic neural network is provided, which includes the following steps:
and 3, inputting the test set data into the trained photovoltaic power generation power prediction model, and calculating to obtain a predicted value of the photovoltaic power generation power.
Specifically, the photovoltaic power generation power prediction model comprises a first convolution layer, a first bidirectional GRU layer, a first multi-scale convolution layer, a second bidirectional GRU layer, a second multi-scale convolution layer, a first full-connection layer and a second full-connection layer, wherein the layers are sequentially connected, the output of the first convolution layer and the output of the first bidirectional GRU layer are connected and then simultaneously used as the input of the first multi-scale convolution layer and the second multi-scale convolution layer, the bidirectional GRU layer is formed by connecting a forward GRU model and a backward GRU model in parallel to form a bidirectional structure, the bidirectional GRU layer outputs two combined GRU signals, the output layer of the first full connection layer is 100, the output layer of the second full connection layer is 1, the input of the first convolution layer in the photovoltaic power generation power prediction model is an influence factor data sequence, and the output of the full-connection layer is a photovoltaic power generation power value.
Specifically, the photovoltaic power generation power prediction model isx0,...,xTTo influence the factor sequence data, (y)0,...,yK),K<T is a known photovoltaic power generation power value, (y)K+1,...,yT) In order to require a predicted photovoltaic power generation power value,for the corresponding estimated value, the input is x0,...,xT,y0,...,yKAnd variables are sequentially input into the photovoltaic power generation power prediction model to start training, the loss function adopts standard normalized MSE, and the activation function adopts Relu function.
Specifically, the analytical expression of the photovoltaic power generation power prediction model is as follows:
C4 t=η2([C2 t,C3 t])
wherein x istIs an input to the model at time t, η1(. eta.) and eta2(. is) two convolution operations, [, ]]For merge join operations, mutiscalaconv (·, Scale1) and mutiscalaconv (·, Scale2) are two multi-Scale convolution operations with scales Scale1 and Scale2, respectively, and the specific process of fusion convolution is as follows:
first convolution layer η1(xt) Accepting sequence data xtInput and output ofSimultaneously as inputs to the first multi-scale convolutional layer and the second multi-scale convolutional layer;
is the output of the first bi-directional GRU layer,indicating the output to be GRU in forward directionAnd backward GRU outputCarrying out merging connection;is to multiply the first bidirectional GRU layer by a weight vectorAnd adding the offset vectorThe result of (1);
will be provided withAnd η1(xt) Output of (2)Are combined into Pt 1As input to the first multi-scale convolutional layer;
is the output of the first multi-Scale convolutional layer with Scale1, connected to the second bidirectional GRU layer;
is the output of the second bidirectional GRU layer,indicating the output of the GRU in forward direction thereinAnd backward GRU outputCarrying out merging connection;is to multiply the second bidirectional GRU layer by a weight vectorAnd adding the offset vectorThe result of (1);
by analogy, the expression is obtainedBy a convolution operation with Scale2, for [ C2 t,C3 t]Extracting to reserve the more important scale information of the target and obtain outputThen obtaining output O through full connection operationt(ii) a The data processing flow of the method of the invention is shown in FIG. 2;
Specifically, the bidirectional GRU layer is a bidirectional structure formed by connecting two GRU models of a forward GRU and a backward GRU, and the first layer of the forward GRU is forgotten to be output by a gate: f. of1 t=σ(W1 f[H1 t-1,x”t]+B1 f),σ(x)=1/(1+e-x) In the forward GRU update gate, the first output is: z is a radical of1 t=σ(W1 z[H1 t-1,x”t]+B1 z) And the second output is:similarly, the corresponding first output to the GRU is: z is a radical of2 t=σ(W2 z[H2 t-1,x”t]+B2 z) And a second output:intermediate output of forward GRUAnd an intermediate output H to the GRU2 t=(1-z2 t)·h2 i-1+h2 t·z2 tObtaining an output by an aggregation operation on the intermediate output Indicating the output to be GRU in forward directionAnd backward GRU outputPerforming merged connection as output of bidirectional GRU layerx”tFor the input of the bidirectional GRU layer, [ W ]1 f,B1 f], [W1 Z,B1 Z],[W1 h,B1 h]For forward GRU model parameters, [ W ]2 f,B2 f],[W2 Z,B2 Z],[W2 h,B2 h]For the inverse GRU model parameters, [ W ]12 o,B12 o]Are output layer parameters.The output of the first bidirectional GRU layer, correspondingly,is the output of the second bidirectional GRU layer.
Preferably, the convolutional layer is a 1-dimensional convolutional network.
Specifically, the influencing factor data comprises irradiation intensity, temperature, humidity and air pressure.
Preferably, the convolutional layer is a 1-dimensional convolutional network. Convolutional neural networks generally include 1-dimensional convolution, 2-dimensional convolution, and 3-dimensional convolution networks. The one-dimensional convolution network is mainly used for sequence data such as audio data, equipment maintenance sampling data and the like, the two-dimensional convolution is mainly used for image processing such as image classification, target recognition, image segmentation and the like, and the three-dimensional convolution network is mainly used for video processing such as medical image video, motion detection and the like. In this embodiment, the time series data is mainly analyzed, and a 1-dimensional convolution network result is adopted. A typical 1-dimensional convolutional network results are shown in fig. 3. This includes a one-dimensional convolution kernel vector with a filter size k of 4. Convolution factors (convolution factors) d is 1.
For the element s that currently needs to be convolved, the mathematical expression of the one-dimensional convolution operation is:
wherein f (i) represents a convolution kernel, Xs-d·iIndicating that sample values at interval d are taken forward.
In order to better show the details of the present embodiment, the present embodiment provides an experimental effect description. The data adopted by the invention is actually measured data from a certain foreign photovoltaic power generation company. Data was collected once every hour and ten hours per day. The experimental data of this example included 3000 sets of data for 300 days, of which 2900 sets were subjected to classification training, 100 sets of data were used as test data,
the experimental background used in this example is: the computer is mainly configured as follows: pentium (R) Dual-core 3.06 CPU, 8G RAM memory.
And (3) effect evaluation: parameters adopted for performance evaluation of the method in this embodiment include RMSE, MAE, MAPE, and CC:
RMSE (Root Mean Square Error) is calculated as:
MAE (Mean absolute Error) is calculated as:
MAPE (Mean absolute percent Error) was calculated as:
r2 (coeffient of Determination), determining the coefficient by the following calculation method:
it should be noted that RMSE, MAE and MAPE are measures of prediction errors, and the smaller the value is, the more accurate the value is, and the R2 parameter represents the coefficient for determining the number of two sequences, and the larger the value is, the more relevant the two sequence data is, the better the prediction effect is.
Experiments are carried out on the data, and the prediction accuracy of the photovoltaic power generation power can be predicted as shown in table 1.
Table 1: predicted result values of different algorithms
The result shows that the method has better precision compared with other recurrent neural network models.
According to the invention content and the embodiment, the photovoltaic power generation power prediction model in the method mainly comprises a multi-scale convolution layer, a bidirectional GRU layer and a full connection layer, the multi-scale convolution layer is introduced into a recurrent neural network, a plurality of improved attention units are connected in a series connection mode and a jump connection mode, attention mechanisms are distributed on different scales, therefore, the model can collect historical information from different scales, distinguish the influence of different input elements on a prediction result, the bidirectional GRU layer can more fully acquire the context information of sequence data on the basis of GRUs, the whole model adopts a convolution structure to fuse the recognition output of attention mechanisms of different scales, and the output is screened and identified by different scales through convolution connection, so that the scale information which is more important to the target can be reserved, and the prediction of the photovoltaic power generation power obtains better accuracy.
Claims (6)
1. The photovoltaic power generation power prediction method based on the multi-scale convolution cyclic neural network is characterized by comprising the following steps of:
step 1, constructing a photovoltaic power generation power prediction model based on a multi-scale convolution cyclic neural network;
step 2, training the photovoltaic power generation power prediction model by using training set data, wherein the training set data comprises influence factor data and known photovoltaic power generation power data;
step 3, inputting the test set data into the trained photovoltaic power generation power prediction model, and calculating to obtain a predicted value of the photovoltaic power generation power;
the photovoltaic power generation power prediction model comprises a first convolution layer, a first bidirectional GRU layer, a first multi-scale convolution layer, a second bidirectional GRU layer, a second multi-scale convolution layer, a first full-connection layer and a second full-connection layer, wherein the first convolution layer, the second bidirectional GRU layer, the second multi-scale convolution layer, the first full-connection layer and the second full-connection layer are sequentially connected, the output of the first convolution layer and the output of the first bidirectional GRU layer are connected and then simultaneously used as the input of the first multi-scale convolution layer and the input of the second multi-scale convolution layer, the bidirectional GRU layer is in a bidirectional structure formed by connecting a forward GRU model and a backward GRU model in parallel, the bidirectional GRU layer outputs two combined GRU signals, the output layer of the first full-connection layer is 100, the output layer of the second full-connection layer is 1, the input of the first convolution in the photovoltaic power generation power prediction model is an influence factor data sequence, and the output of the second full-connection layer is a photovoltaic power generation value.
2. The method according to claim 1, wherein the photovoltaic power generation power prediction model isx0,...,xTTo influence the factor sequence data, (y)0,...,yK),K<T is a known photovoltaic power generation power value, (y)K+1,...,yT) In order to require a predicted photovoltaic power generation power value,for the corresponding estimated value, the input is x0,...,xT,y0,...,yKAnd variables are sequentially input into the photovoltaic power generation power prediction model to start training, the loss function adopts standard normalized MSE, and the activation function adopts Relu function.
3. The method according to claim 1, wherein the photovoltaic power generation prediction model has the following analytical expression:
C4 t=η2([C2 t,C3 t])
wherein x istIs an input to the model at time t, η1(. eta.) and eta2(. is) two convolution operations, [, ]]For merge join operations, mutiscalaconv (·, Scale1) and mutiscalaconv (·, Scale2) are two multi-Scale convolution operations with scales Scale1 and Scale2, respectively, and the specific process of fusion convolution is as follows:
first convolution layer η1(xt) Accepting sequence data xtInput and output ofSimultaneously as inputs to the first multi-scale convolutional layer and the second multi-scale convolutional layer;
is firstThe output of the bi-directional GRU layer,indicating the output to be GRU in forward directionAnd backward GRU outputCarrying out merging connection;is to multiply the first bidirectional GRU layer by a weight vectorAnd adding the offset vectorThe result of (1);
will be provided withAnd η1(xt) Output of (2)Are combined intoAs an input to the first multi-scale convolutional layer;
is the output of the first multi-Scale convolutional layer with Scale1, connected to the second bidirectional GRU layer;
is the output of the second bidirectional GRU layer,indicating the output of the GRU in forward direction thereinAnd backward GRU outputCarrying out merging connection;is to multiply the second bidirectional GRU layer by a weight vectorAnd adding the offset vectorThe result of (1);
by analogy, the expression is obtainedBy a convolution operation with Scale2, for [ C2 t,C3 t]Extracting to reserve the more important scale information of the target and obtain outputThen obtaining output O through full connection operationt;
4. The multi-scale convolution cyclic neural network-based photovoltaic power generation power prediction method according to claim 1 or 3, wherein the bidirectional GRU layer is a bidirectional structure formed by connecting two GRU models, namely a forward GRU model and a backward GRU model, and a first layer in the forward GRU model is output through a forgetting gate: f. of1 t=σ(W1 f[H1 t-1,x”t]+B1 f),σ(x)=1/(1+e-x) In the forward GRU update gate, the first output is: z is a radical of1 t=σ(W1 z[H1 t-1,x”t]+B1 z) And the second output is:similarly, the corresponding first output to the GRU is: z is a radical of2 t=σ(W2 z[H2 t-1,x”t]+B2 z) And a second output:intermediate output of forward GRUAnd backward GRU intermediate outputObtaining an output by an aggregation operation on the intermediate outputIndicating the output to be GRU in forward directionAnd backward GRU outputPerforming merged connection as output of bidirectional GRU layerx”tFor the input of the bidirectional GRU layer, [ W ]1 f,B1 f],[W1 Z,B1 Z],[W1 h,B1 h]For forward GRU model parameters, [ W ]2 f,B2 f],[W2 Z,B2 Z],[W2 h,B2 h]For the inverse GRU model parameters, [ W ]12 o,B12 o]Are output layer parameters.The output of the first bidirectional GRU layer, correspondingly,is the output of the second bidirectional GRU layer.
5. The multi-scale convolution cyclic neural network-based photovoltaic power generation power prediction method according to claim 4, wherein the convolution layer is a 1-dimensional convolution network.
6. The method according to claim 1, wherein the data of the influencing factors comprise irradiation intensity, temperature, humidity and air pressure.
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CN114123200A (en) * | 2022-01-24 | 2022-03-01 | 国网山西省电力公司晋城供电公司 | Photovoltaic power station dynamic modeling method based on data driving and storage device |
CN116205666A (en) * | 2022-12-22 | 2023-06-02 | 国网湖北省电力有限公司宜昌供电公司 | RACNet-based multivariable power load prediction method |
CN117556715A (en) * | 2024-01-12 | 2024-02-13 | 湖南大学 | Method and system for analyzing degradation of intelligent ammeter in typical environment based on information fusion |
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CN114123200A (en) * | 2022-01-24 | 2022-03-01 | 国网山西省电力公司晋城供电公司 | Photovoltaic power station dynamic modeling method based on data driving and storage device |
CN116205666A (en) * | 2022-12-22 | 2023-06-02 | 国网湖北省电力有限公司宜昌供电公司 | RACNet-based multivariable power load prediction method |
CN117556715A (en) * | 2024-01-12 | 2024-02-13 | 湖南大学 | Method and system for analyzing degradation of intelligent ammeter in typical environment based on information fusion |
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