CN110222727A - A kind of short-term load forecasting method and device based on deep neural network - Google Patents
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Abstract
The invention discloses a kind of short-term load forecasting methods based on deep neural network, comprising: obtains historical data and characteristic to be predicted；Calculate the similarity measurement of the historical data and the characteristic；Several data samples are picked out from the historical data according to the similarity measurement, and assign training weight to each data sample, obtain sample training collection；Deep neural network model is trained using the sample training collection, obtains prediction model；Prediction result is calculated by the prediction model in the characteristic.The present invention enables the prediction model based on deep neural network targetedly to learn to training sample, to promote the accuracy and precision of short-term load forecasting by screening and assigning sample different training weights to sample.
Description
Technical field
The present invention relates to Techniques for Prediction of Electric Loads fields, more particularly, to a kind of short-term negative based on deep neural network
Lotus prediction technique and device.
Background technique
Short-term load forecasting is the basis of power system security economical operation, continuous complete with China's electricity market system
Kind, the effect that short-term load forecasting plays is increasingly important.Currently, experts and scholars carry out in terms of short-term load forecasting both at home and abroad
Numerous studies, and a series of intelligent Forecasting methods are proposed based on machine learning, wherein deep neural network (Deep
Neural network, DNN) it is one of most common machine learning algorithm.But currently based on the short-term load forecasting of DNN
Method causes prediction result inaccurate since the training of machine learning algorithm does not reach requirement also.
Summary of the invention
In view of the above technical problems, the present invention provides a kind of short-term load forecasting method based on deep neural network and
Device, by being screened and being assigned sample different training weights to sample, so that the prediction mould based on deep neural network
Type can targetedly learn training sample, to promote the accuracy and precision of short-term load forecasting.The skill
Art scheme is as follows:
In a first aspect, the embodiment of the invention provides a kind of short-term load forecasting method based on deep neural network, packet
It includes:
Obtain historical data and characteristic to be predicted；
Calculate the similarity measurement of the historical data and the characteristic；
Several data samples are picked out from the historical data according to the similarity measurement and to each data sample
Training weight is assigned, sample training collection is obtained；
Deep neural network model is trained using the sample training collection, obtains prediction model；
Prediction result is calculated by the prediction model in the characteristic.
In a first possible implementation of the first aspect of the invention, the calculating historical data and the spy
The similarity measurement of data is levied, specifically:
It parses the historical data and obtains sampling feature vectors；
It calculates each sampling feature vectors and predicts the Euclidean distance of day character vector.
In a second possible implementation of the first aspect of the invention, described to assign training power to each data sample
Weight, specifically:
The Euclidean distance of a certain data sample of correspondence is compared with preset distance threshold, when the Euclidean distance is small
The weight coefficient of the data sample is set as constant 1 when the distance threshold；Described in being more than or equal to when the Euclidean distance
The weight coefficient of the data sample is set as to the ratio of the distance threshold Yu the Euclidean distance when distance threshold.
It is described based on the short-term of deep neural network in the third possible implementation of first aspect present invention
Load forecasting method, further includes: the prediction day character vector and sampling feature vectors that get are normalized, and
Anti-normalization processing is carried out to the prediction result of output.
Second aspect, the embodiment of the invention provides a kind of short-term load forecasting device based on deep neural network, packet
It includes:
Data acquisition module, for obtaining historical data and characteristic to be predicted；
Similarity measurement computing module, for calculating the similarity measurement of the historical data and the characteristic；
Training set generation module, for picking out several data samples from the historical data according to the similarity measurement
This, and training weight is assigned to each data sample, obtain sample training collection；
Model training module is obtained pre- for being trained using the sample training collection to deep neural network model
Survey model；
As a result prediction result is calculated by the prediction model for the characteristic in output module.
In a first possible implementation of the second aspect of the invention, the similarity measurement computing module, is also wrapped
It includes:
Data resolution module obtains sampling feature vectors for parsing the historical data；
Distance calculation module, for calculating each sampling feature vectors and predicting the Euclidean distance of day character vector.
In second of possible implementation of second aspect of the present invention, the training set generation module, further includes:
Weight calculation module, for carrying out pair the Euclidean distance and preset distance threshold that correspond to a certain data sample
Than the weight coefficient of the data sample is set as constant 1 when the Euclidean distance is less than the distance threshold；When the Europe
The weight coefficient of the data sample is set as the distance threshold and the Europe when family name's distance is more than or equal to the distance threshold
The ratio of family name's distance.
It is described based on the short-term of deep neural network in the third possible implementation of second aspect of the present invention
Load prediction device, further includes:
Normalized module, for place to be normalized to the prediction day character vector and sampling feature vectors that get
Reason, and anti-normalization processing is carried out to the prediction result of output.
Compared with the prior art, the embodiment of the present invention has the following beneficial effects:
A kind of short-term load forecasting method based on deep neural network of the invention has been recorded short-term negative by acquisition
Characteristic to be predicted needed for the historical data and short-term load forecasting of lotus prediction, and calculate the historical data and institute
The similarity measurement for stating characteristic, with the similitude between the similarity measurement characterize data.It is filtered out according to similitude
More targeted data are used for model training, to improve the quality of training sample, the data for rejecting interference model training are same
When reduce training data volume to mitigate calculating work load；Further, data sample is assigned according to similarity measurement and is instructed
Practice weight, is conducive to Optimized model training process, to keep deep neural network more intelligent, it is short-term to have more professional processing
The ability of load prediction work, and the prediction result for exporting prediction model are closer practical, and then it is pre- to promote short term
The accuracy and precision of survey.
Detailed description of the invention
Fig. 1 is the basic step of short-term load forecasting method of one of the embodiment of the present invention based on deep neural network
Flow chart；
Fig. 2 is the specific steps of short-term load forecasting method of one of the embodiment of the present invention based on deep neural network
Flow chart；
Fig. 3 is the structure chart of short-term load forecasting device of one of the embodiment of the present invention based on deep neural network.
Specific embodiment
Following will be combined with the drawings in the embodiments of the present invention, and technical solution in the embodiment of the present invention carries out clear, complete
Site preparation description, it is clear that described embodiments are only a part of the embodiments of the present invention, instead of all the embodiments.It is based on
Embodiment in the present invention, it is obtained by those of ordinary skill in the art without making creative efforts every other
Embodiment shall fall within the protection scope of the present invention.
Please refer to Fig. 1 it illustrates an illustrative embodiment of the invention provide it is a kind of based on the short of deep neural network
Phase load forecasting method, step include:
S101, historical data and characteristic to be predicted are obtained；Wherein, the historical data include have detected that it is negative
Charge values, historical forecast load value, history short-term load forecasting accuracy value etc.；The characteristic includes obtaining before this is predicted
Route variable, weather variable etc..
S102, the similarity measurement for calculating the historical data and the characteristic；
Wherein, it usually needs to estimate the similarity measurement between different samples when doing and classifying, the side at this moment generallyd use
Method is exactly " distance " between calculating sample, therefore calculates similarity measurement and can use these methods: Euclidean distance, Manhattan away from
With a distance from, Chebyshev, Minkowski Distance, standardization Euclidean distance, mahalanobis distance, included angle cosine, Hamming distance, outstanding card
Moral distance and Jie Kade similarity factor, related coefficient and correlation distance, comentropy.
S103, several data samples are picked out from the historical data according to the similarity measurement and to each data
Sample assigns training weight, obtains sample training collection；
S104, deep neural network model is trained using the sample training collection, obtains prediction model；Wherein,
The deep neural network model is the primary mold constructed, due to not having the ability of load prediction, it is therefore desirable to pass through
Special training is trained model and is conducive to improve load prediction precision.
Prediction result is calculated by the prediction model in S105, the characteristic.
A kind of short-term load forecasting method based on deep neural network of the embodiment of the present invention has been recorded by acquisition
Characteristic to be predicted needed for the historical data and short-term load forecasting of short-term load forecasting, and calculate the history number
According to the similarity measurement with the characteristic, with the similitude between the similarity measurement characterize data.According to similitude
More targeted data are filtered out for model training, to improve the quality of training sample, reject interference model training
Data reduce trained data volume simultaneously to mitigate calculating work load；Further, according to similarity measurement to data sample
Training weight is assigned, is conducive to Optimized model training process, to keep deep neural network more intelligent, has more professional place
The ability of short-term load forecasting work is managed, and the prediction result for exporting prediction model is closer practical, and then is promoted short-term
The accuracy and precision of load prediction.
Preferably, the similarity measurement for calculating the historical data and the characteristic, specifically:
It parses the historical data and obtains sampling feature vectors；
It calculates each sampling feature vectors and predicts the Euclidean distance of day character vector.
Wherein, the prediction day character vector and the sampling feature vectors select day meteorology as eigen vector, feature
Variable in vector comprising temperature, wind speed, humidity etc. with different dimensions.
It is understood that these meteorological index of the temperature of one day, wind speed, humidity are the feature of this day, if two days
Characteristic is more similar, then the level of this two days electric loads is also more similar.
And after being normalized, calculates each sampling feature vectors and predict the Euclidean distance of day character vector, use
Similarity degree between two features of measurement.Euclidean distance is a kind of method for calculating and clustering between two points in n-dimensional space,
Calculation formula is as follows:
Wherein, x=(x1,x2,…,xn) and y=(y1,y2,…,yn) it is corresponding be two n dimension sampling feature vectors with
Predict day character vector.
It is understood that the Euclidean distance between two points is closer, show that the similarity of two points is higher.According to above formula
Euclidean distance calculated is as a result, pick out with highest 150 data samples of day eigen vector similarity to be predicted as instruction
Practice sample, to guarantee that training sample and the degree of correlation for predicting day data are higher, and guarantees certain training samples number.
It is preferably, described to assign training weight to each data sample, specifically:
The Euclidean distance of a certain data sample of correspondence is compared with preset distance threshold, when the Euclidean distance is small
The weight coefficient of the data sample is set as constant 1 when the distance threshold；Described in being more than or equal to when the Euclidean distance
The weight coefficient of the data sample is set as to the ratio of the distance threshold Yu the Euclidean distance when distance threshold.
In the present embodiment, to the sample selected, conventional training method is that all samples are uniformly treated, and in fact,
The higher training sample with prediction day sample similarity degree, algorithm more answers selective learning, in view of considerations above, to the sample selected
Assign different weights.According to the bigger principle of the smaller weight of Euclidean distance, formulate following Weight Algorithm: Euclidean distance is less than c
Sample weight coefficient ωs=1；The weight coefficient ω of sample of the Euclidean distance more than or equal to cs=c/ds.Wherein, c is distance
Threshold value, dsIt is the Euclidean distance of s-th of sample data and prediction day data.
Preferably, the short-term load forecasting method based on deep neural network, further includes: to the prediction day got
Feature vector and sampling feature vectors are normalized, and carry out anti-normalization processing to the prediction result of output.
Wherein, place is normalized to sampling feature vectors and prediction day character vector using z-score method for normalizing
Reason, calculation formula are as follows:
In formula (2), x*For the feature vector after normalization, x is to normalized feature vector, and μ is sample average, and σ is
Sample standard deviation.
Meanwhile in order to make output quantity revert to actual value, need to carry out anti-normalization processing, meter to the load data of output
It is as follows to calculate formula:
X=x*σ+μ (3)
In formula (3), x*For the normalization predicted value of model output, x is to normalized sample value, and μ is sample average, σ
For sample standard deviation.
Please refer to Fig. 2 it illustrates an illustrative embodiment of the invention provide it is a kind of based on the short of deep neural network
Phase load forecasting method, specific steps are as follows:
Historical data and prediction day character vector are obtained, and the historical data is parsed to obtain sampling feature vectors；
The prediction day character vector described in each sample characteristics vector sum is normalized；
Calculate the Euclidean distance that day character vector is predicted described in each sample characteristics vector sum；
Pick out the smallest data sample of Euclidean distance of 150 sampling feature vectors and the prediction day character vector；
The weight that each data sample is calculated according to the Euclidean distance obtains the sample training collection with sample weights；
Deep neural network model is trained using the sample training collection, obtains prediction model；
Tentative prediction result is calculated by the prediction model in prediction day character vector after normalized；
Anti-normalization processing is carried out to the tentative prediction result, obtains final prediction result.
In the present embodiment, normalized can be improved precision, this is being related to the algorithm timeliness of some distance calculating
Fruit is significant, for example algorithm will calculate Euclidean distance.In the multiple criteria system, since the property of each evaluation index is different, lead to
Often with having different dimension and the order of magnitude.When the level between each index differs greatly, if directly carried out with original index value
Analysis, will protrude effect of the higher index of numerical value in comprehensive analysis, the opposite effect for weakening the horizontal lower index of numerical value.
Therefore, it in order to guarantee the reliability of result, needs that original index data are normalized.From empirically saying, normalize
Processing is that allow the feature between different dimensions numerically to have certain comparative, can greatly improve in deep neural network and classify
The accuracy of device, while normalized is conducive to the convergence rate and precision of lift scheme, and in deep learning, data
Normalization can prevent model gradient from exploding.
Please refer to Fig. 3 it illustrates an illustrative embodiment of the invention provide it is a kind of based on the short of deep neural network
Phase load prediction device, comprising:
Data acquisition module 201, for obtaining historical data and characteristic to be predicted；
Similarity measurement computing module 202, for calculating the similarity measurement of the historical data and the characteristic；
Training set generation module 203, for picking out several numbers from the historical data according to the similarity measurement
Training weight is assigned according to sample, and to each data sample, obtains sample training collection；
Model training module 204 is obtained for being trained using the sample training collection to deep neural network model
Prediction model；
As a result prediction result is calculated by the prediction model for the characteristic in output module 205.
Preferably, the similarity measurement computing module, further includes:
Data resolution module obtains sampling feature vectors for parsing the historical data；
Distance calculation module, for calculating each sampling feature vectors and predicting the Euclidean distance of day character vector.
Preferably, the training set generation module, further includes:
Weight calculation module, for carrying out pair the Euclidean distance and preset distance threshold that correspond to a certain data sample
Than the weight coefficient of the data sample is set as constant 1 when the Euclidean distance is less than the distance threshold；When the Europe
The weight coefficient of the data sample is set as the distance threshold and the Europe when family name's distance is more than or equal to the distance threshold
The ratio of family name's distance.
Preferably, the short-term load forecasting device based on deep neural network, further includes:
Normalized module, for place to be normalized to the prediction day character vector and sampling feature vectors that get
Reason, and anti-normalization processing is carried out to the prediction result of output.
The above is a preferred embodiment of the present invention, it is noted that for those skilled in the art
For, various improvements and modifications may be made without departing from the principle of the present invention, these improvements and modifications are also considered as
Protection scope of the present invention.
Those of ordinary skill in the art will appreciate that realizing all or part of the process in above-described embodiment method, being can be with
Relevant hardware is instructed to complete by computer program, the program can be stored in a computer-readable storage medium
In, the program is when being executed, it may include such as the process of the embodiment of above-mentioned each method.Wherein, the storage medium can be magnetic
Dish, CD, read-only memory (Read-Only Memory, ROM) or random access memory (Random Access
Memory, RAM) etc..
Claims (8)
1. a kind of short-term load forecasting method based on deep neural network characterized by comprising
Obtain historical data and characteristic to be predicted；
Calculate the similarity measurement of the historical data and the characteristic；
Several data samples are picked out from the historical data according to the similarity measurement and each data sample is assigned
Training weight, obtains sample training collection；
Deep neural network model is trained using the sample training collection, obtains prediction model；
Prediction result is calculated by the prediction model in the characteristic.
2. as described in claim 1 based on the short-term load forecasting method of deep neural network, which is characterized in that the calculating
The similarity measurement of the historical data and the characteristic, specifically:
It parses the historical data and obtains sampling feature vectors；
It calculates each sampling feature vectors and predicts the Euclidean distance of day character vector.
3. as claimed in claim 2 based on the short-term load forecasting method of deep neural network, which is characterized in that described to every
One data sample assigns training weight, specifically:
The Euclidean distance of a certain data sample of correspondence is compared with preset distance threshold, when the Euclidean distance is less than institute
The weight coefficient of the data sample is set as constant 1 when stating distance threshold；When the Euclidean distance is more than or equal to the distance
The weight coefficient of the data sample is set as to the ratio of the distance threshold Yu the Euclidean distance when threshold value.
4. as claimed in claim 2 based on the short-term load forecasting method of deep neural network, which is characterized in that further include:
The prediction day character vector and sampling feature vectors that get are normalized, and the prediction result of output is carried out
Anti-normalization processing.
5. a kind of short-term load forecasting device based on deep neural network characterized by comprising
Data acquisition module, for obtaining historical data and characteristic to be predicted；
Similarity measurement computing module, for calculating the similarity measurement of the historical data and the characteristic；
Training set generation module, for picking out several data samples from the historical data according to the similarity measurement,
And training weight is assigned to each data sample, obtains sample training collection；
Model training module obtains prediction mould for being trained using the sample training collection to deep neural network model
Type；
As a result prediction result is calculated by the prediction model for the characteristic in output module.
6. the short-term load forecasting device based on deep neural network as claimed in claim 5, which is characterized in that described similar
Property metric calculation module, further includes:
Data resolution module obtains sampling feature vectors for parsing the historical data；
Distance calculation module, for calculating each sampling feature vectors and predicting the Euclidean distance of day character vector.
7. the short-term load forecasting device based on deep neural network as claimed in claim 6, which is characterized in that the training
Collect generation module, further includes:
Weight calculation module, for the Euclidean distance for corresponding to a certain data sample to be compared with preset distance threshold, when
The weight coefficient of the data sample is set as constant 1 when the Euclidean distance is less than the distance threshold；When the Euclidean away from
From when being more than or equal to the distance threshold by the weight coefficient of the data sample be set as the distance threshold and the Euclidean away from
From ratio.
8. the short-term load forecasting device based on deep neural network as claimed in claim 6, which is characterized in that further include:
Normalized module, for the prediction day character vector and sampling feature vectors that get to be normalized,
And anti-normalization processing is carried out to the prediction result of output.
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