CN110110915A - A kind of integrated prediction technique of the load based on CNN-SVR model - Google Patents
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Abstract
The present invention relates to a kind of loads based on CNN-SVR model to integrate prediction technique, this method first initializes the daily load data of all users in the influence factor of somewhere influence load, somewhere, calculate Pearson correlation coefficient, then data are subjected to extreme value normalization and cluster, obtain different classes of user data label, according to step packet label, user data is merged, as training input data；Secondly model training being carried out after building CNN-SVR load forecasting model and input data being pre-processed, data are input in the CNN-SVR model after training and are predicted, obtain prediction result, by result renormalization, obtain final multiple groups prediction load；Finally multiple groups load is summed, somewhere is obtained and finally predicts load daily, if prediction somewhere month load, load is monthly summed.Compared with prior art, the present invention a large number of users in somewhere can be carried out Ensemble classifier, have many advantages, such as independently to extract data characteristics, precision of prediction it is high.
Description
Technical field
The present invention relates to Load Prediction In Power Systems fields, more particularly, to a kind of load collection based on CNN-SVR model
At prediction technique.
Background technique
Traditional Methods of electric load forecasting have regression analysis, wavelet analysis method, decision tree, random forest, support to
Amount machine, neural network scheduling algorithm, wherein support vector regression is often used in clock synchronization as the mutation of algorithm of support vector machine
Between sequence data prediction, be a kind of algorithms most in use with preferable quasi- precision of prediction, but since its precision of prediction is joined by itself
It is several and input feature vector data to be affected, therefore the step of input data is frequently necessary to by feature extraction.Neural network is calculated
Method is popular algorithm in recent years, has the autonomous ability for extracting nonlinear characteristic well, can be very good to carry out non-linear
Fitting.But there are many hyper parameter of neural network algorithm, model training process complexity is cumbersome.
With the rapid development of China's economic level, when the river rises the boat goes up for demand of the China to electricity consumption, and electricity consumption increases continuously and healthily
It is long.In this case, reinforce demand side management, analyze the electricity consumption behavior of user, the accuracy of load prediction is improved, to enhancing
The economy of electric system, stabilization, intelligent operation have realistic meaning.The behind of electricity consumption sustainable growth is that the electricity consumption of flood tide is used
Family faces a large amount of electricity consumption user when carrying out regional load prediction, and most methods are only to divide user's cluster
Class, but how load prediction is carried out to it after not analyzing user's classification；Or it proposes only for some user or a certain whole
Load forecasting method, and the load prediction situation of the user of a large amount of different electricity consumption behaviors is not analyzed.Therefore, being badly in need of one kind being capable of needle
The method that regional load is predicted in the case where there is a large number of users to a certain area.
Summary of the invention
It is an object of the present invention to overcome the above-mentioned drawbacks of the prior art and provide one kind can be to somewhere
A large number of users carry out Ensemble classifier, can independently extract data characteristics and with preferable precision of prediction based on CNN-SVR mould
The load of type integrates prediction technique.
The purpose of the present invention can be achieved through the following technical solutions:
A kind of integrated prediction technique of the load based on CNN-SVR model, comprising the following steps:
Step 1: on every daily load of all users in the influence factor of somewhere influence power system load, somewhere
Data are initialized.
Step 2: calculate each user data the moon load, moon load mean value, median, standard deviation and influence factor with
The Pearson correlation coefficient of load data, calculation formula are as follows:
In formula, γmIndicate the Pearson correlation coefficient of different influence factors, aiIndicate load number of certain user at i-th day
According to,Indicate the m influence factors such as temperature, weather, date type.
Step 3: the data in step 1 and step 2 are carried out extreme value normalized according to the following formula:
In formula, x ' is the data after normalization, and x is input data, xminIt is input data minimum value, xmaxIt is input data
Maximum value.
Step 4: the data after normalization are clustered, and obtain different classes of user data label.
Step 5: merged the data of user in step 1 according to the packet label in step 4, it is defeated as training
Enter data.
Step 6: building CNN-SVR load forecasting model (convolutional neural networks-support vector regression load prediction mould
Type), which is made of two layers of convolutional layer and two layers of full articulamentum.
Step 7: according to the packet data of step 4, station work CNN-SVR load forecasting model, specific training process
Are as follows:
1) dummy variable is constructed to the training data in step 5.
2) dummy variable data are subjected to matrixing processing.
Matrixing is that the one-dimensional vector for the m 1 × n that will be originally inputted is converted into mMatrix form.If
It is not integer, then to former vector data zero padding, until n can be by radication of integer.
3) data of step 2) are passed sequentially through into two layers of convolutional layer and two layers of full articulamentum carries out convolution algorithm.
4) CNN (Convolutional Neural Networks, convolutional neural networks) model is trained, training
Number is not less than ten times, and after training, the data of the first full articulamentum are input to SVR (Support Vector
Machine, support vector regression) it is trained in model.
Preferably, CNN model training number is 10 times, and learning rate is set as selecting RBF kernel function in 0.01, SVR model
(Radial Basis Function, radial basis function) is as full articulamentum and ReLU layers of primary function, hyper parameter punishment system
Number C takes 50, γ to take 0.01.
Step 8: building prediction input data, i.e., will need the weather, temperature and date type of forecast date to normalize,
Construct dummy variable, row matrix of going forward side by side.
It is predicted Step 9: the data in step 8 are input in trained CNN-SVR model, obtains prediction knot
Fruit simultaneously saves.
Step 10: the result in step 9 is carried out renormalization, final multiple groups prediction load is obtained.Renormalization is public
Formula are as follows:
yre=ypre(rmax-rmin)+rmin
In formula: yreIt is that load, y are predicted obtained by renormalizationpreIt is model prediction as a result, rminFor the minimum in input data
Value, rmaxFor the maximum value of input data.
Step 11: multiple groups load is summed, obtains somewhere and finally predict load daily.If predicting somewhere month load,
Then load is monthly summed.
Compared with prior art, the invention has the following advantages that
1) the method for the present invention creates convolutional neural networks-support vector regression load characteristics clustering integrated predictive model, right
Cnn-svr model is reused after data progress clustering ensemble to be predicted, can be grouped integrated number of users effectively to user grouping
According to rear, user data greatly reduces, therefore model quickly can carry out load prediction to the area for having a large number of users, quickly high
Effect；
2) the CNN-SVR model that the present invention creates combines the advantages of CNN and SVR model, when convolutional neural networks are trained
Between it is long, difficulty is big, support vector regression requires initial data and hyper parameter high, and the present invention uses shallow-layer convolutional neural networks
It is fast that requirement and shallow Model training speed of the support vector regression to data are met as data characteristics extractor, are used
The support vector regression of RBF kernel function is to load prediction, and training is simple, predetermined speed is fast, improves the overall load of model
Precision of prediction；
3) decision-tree model based on the clustering ensemble load forecasting method of CNN-SVR model relative to conventional method, branch
Holding vector machine model, convolutional neural networks model, shot and long term memory models has higher load prediction precision, high reliablity.
Detailed description of the invention
Fig. 1 is the flow diagram of the method for the present invention；
Fig. 2 is the training flow chart of CNN-SVR model in the method for the present invention.
Specific embodiment
The present invention is described in detail with specific embodiment below in conjunction with the accompanying drawings.Obviously, described embodiment is this
A part of the embodiment of invention, rather than whole embodiments.Based on the embodiments of the present invention, those of ordinary skill in the art exist
Every other embodiment obtained under the premise of creative work is not made, all should belong to the scope of protection of the invention.
As shown in Figure 1, the present invention relates to a kind of loads based on CNN-SVR model to integrate prediction technique, specifically include down
Column step:
S1. the daily of all users in the influence factors such as daily height temperature, weather, date type and somewhere is initialized to bear
Lotus data, if the number of users in somewhere is K.
S2. calculate each user data the moon load, moon load mean value, median, standard deviation and influence factor and load
The Pearson correlation coefficient of data.The formula of Pearson correlation coefficient is as follows:
In formula, γmIndicate the Pearson correlation coefficient of different influence factors.aiIndicate load number of certain user at i-th day
According to,Indicate the m influence factors such as temperature, weather, date type.
S3. the data in step S1 and S2 are subjected to extreme value normalized, normalization formula is as follows:
In formula, x ' is the data after normalization, and x is input data, xminIt is input data minimum value, xmaxIt is input data
Maximum value.
S4. the data after normalization are clustered, setting clustering method is the hierarchical clustering method using ward connection, is gathered
Class group range is 22~26, and the selected cluster group of the present embodiment is 24, and end of clustering obtains 24 groups of different classes of users
Data label.
S5. according to the packet label in S4, the data integration of script K group user in S1 is merged into 24 groups of data as instruction
Practice input data.
S6. convolutional neural networks-support vector regression load forecasting model is constructed, model is complete by level 2 volume lamination and 2 layers
Articulamentum composition.
S7. dummy variable is constructed to the training data in S5.
S8. the data in S7 are subjected to matrixing processing.Using the load data of the one day of some user as a training
Sample, data are the one-dimensional vector form of 1 × n.Matrixing is exactly that the one-dimensional vector for the m 1 × n that will be originally inputted is converted into m
It is aMatrix form.IfIt is not integer, then to former vector data zero padding, until n can be by radication of integer.
S9. station work CNN-SVR load forecasting model, training process are shown in Fig. 2.Wherein CNN model training number is 10
Secondary, learning rate is set as selecting RBF kernel function (Radial Basis Function, radial basis function) in 0.01, SVR model, surpasses
Parameter penalty coefficient C takes 50, γ to take 0.01.
S10. building prediction input data, i.e., will need the weather, temperature and date type of forecast date to normalize, and construct
Dummy variable, row matrix of going forward side by side.
S11. load prediction is carried out, the data in S10 are input to train and are predicted in 24 groups of CNN-SVR models,
It obtains 24 groups of prediction results and saves.
S12. the result in S11 is subjected to renormalization, obtains 24 groups of final prediction loads.Renormalization formula is such as
Under:
yre=ypre(rmax-rmin)+rmin
In formula: yreIt is that load, y are predicted obtained by renormalizationpreIt is model prediction as a result, rminFor the minimum in input data
Value, rmaxFor the maximum value of input data.
S13. 24 groups of loads are summed, obtains somewhere and finally predicts load daily.If predicting somewhere month load, will bear
Lotus is monthly summed.
For the validity and reliability for proving CNN-SVR model in the method for the present invention, the present embodiment takes real data pair
Decision-tree model, supporting vector machine model (Support Vector Machine, SVM), convolutional neural networks model
(Convolutional Neural Networks, CNN), shot and long term memory models (Long Short Term Memory,
LSTM) prediction with CNN-SVR model in the method for the present invention is compared, and test result is as shown in table 1.
Table 1 uses the test result of distinct methods
By table 1 it is found that relative to the decision-tree model of conventional method, supporting vector machine model, convolutional neural networks mould
Type, shot and long term memory models, clustering ensemble load forecasting method predetermined speed based on CNN-SVR model is fast, rapidly and efficiently, tool
There are higher load prediction precision, high reliablity.
The above description is merely a specific embodiment, but scope of protection of the present invention is not limited thereto, any
The staff for being familiar with the art in the technical scope disclosed by the present invention, can readily occur in various equivalent modifications or replace
It changes, these modifications or substitutions should be covered by the protection scope of the present invention.Therefore, protection scope of the present invention should be with right
It is required that protection scope subject to.
Claims (7)
1. a kind of load based on CNN-SVR model integrates prediction technique, which is characterized in that this method includes the following steps:
1) the daily load data of all users in the influence factor of somewhere influence power system load, somewhere is carried out just
Beginningization；
2) calculate each user data the moon load, moon load mean value, median, standard deviation and influence factor and load data
Pearson correlation coefficient；
3) data in step 1) and step 2) are subjected to extreme value normalized, and the data after normalization are clustered,
Obtain different classes of user data label；
4) according to the packet label in step 3), the data of user in step 1) are merged, as training input data；
5) CNN-SVR load forecasting model is constructed, and according to the packet data in step 3), station work CNN-SVR load is pre-
Survey model；
6) prediction input data is built, dummy variable, row matrix of going forward side by side are constructed；
7) data in step 6) are input in the CNN-SVR model after training and are predicted, obtain prediction result and saved；
8) result in step 7), is subjected to renormalization, obtains final multiple groups prediction load；
9) multiple groups load is summed, obtains somewhere and finally predicts load daily, if prediction somewhere month load, monthly by load
Summation.
2. a kind of load based on CNN-SVR model according to claim 1 integrates prediction technique, which is characterized in that step
It is rapid 2) in, each user data the moon load, moon load mean value, median, standard deviation and influence factor and load data skin
The expression formula of your inferior related coefficient are as follows:
In formula, γmFor the Pearson correlation coefficient of different influence factors, aiLoad data for certain user at i-th day,For
The m influence factor such as temperature, weather, date type.
3. a kind of load based on CNN-SVR model according to claim 1 integrates prediction technique, which is characterized in that step
It is rapid 3) in, the expression formula of extreme value normalized are as follows:
In formula, x ' is the data after normalization, and x is input data, xminFor input data minimum value, xmaxFor input data maximum
Value.
4. a kind of load based on CNN-SVR model according to claim 3 integrates prediction technique, which is characterized in that step
It is rapid 3) in, the data after normalization are clustered using the hierarchical clustering method of ward connection.
5. a kind of load based on CNN-SVR model according to claim 1 integrates prediction technique, which is characterized in that institute
The CNN-SVR load forecasting model stated is made of two layers of convolutional layer and two layers of full articulamentum.
6. a kind of load based on CNN-SVR model according to claim 5 integrates prediction technique, which is characterized in that
The training process of CNN-SVR load forecasting model specifically includes the following steps:
A1 dummy variable) is constructed to the training data in step 4)；
A2 dummy variable data) are subjected to matrixing processing；
A3) by the data of step a2) pass sequentially through CNN-SVR load forecasting model two layers of convolutional layer and two layers of full articulamentum into
Row convolution algorithm；
A4) CNN model is trained, frequency of training is not less than ten times, after training, the data of the first full articulamentum are defeated
Enter into SVR model and is trained.
7. a kind of load based on CNN-SVR model according to claim 1 integrates prediction technique, which is characterized in that step
It is rapid 8) in, the calculating formula of renormalization are as follows:
yre=ypre(rmax-rmin)+rmin
In formula: yreTo predict load, y obtained by renormalizationpreFor model prediction as a result, rm i nFor the minimum value in input data,
rmaxFor the maximum value of input data.
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| CN110956306A (en) * | 2019-10-23 | 2020-04-03 | 广东电网有限责任公司 | Load prediction method based on load clustering |
| CN111461462A (en) * | 2020-04-29 | 2020-07-28 | 南京工程学院 | Daily load prediction method based on TrellisNet-L STM |
| CN111581581A (en) * | 2020-04-23 | 2020-08-25 | 大唐环境产业集团股份有限公司 | Method and system for detecting NOx concentration at SCR inlet under multi-boundary condition |
| CN113067383A (en) * | 2021-03-31 | 2021-07-02 | 广东博力威科技股份有限公司 | Method, apparatus, electronic device and medium for optimizing user battery charging habits |
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Application publication date: 20190809 |