CN106778841A - The method for building up of abnormal electricity consumption detection model - Google Patents
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Abstract
The present invention relates to a kind of method for building up of abnormal electricity consumption detection model, the power information to training set user carries out the extraction of validity feature, sets up the initial training data set for deep learning；In initial training data set, by the data without the whether abnormal electricity consumption of demarcation, as the input of the abnormal electricity consumption detection model, the successively unsupervised learning from bottom to top layer is carried out in depth noise reduction autoencoder network, obtain the parameter of each layer coder of network and decoder；Network top is provided with Gaussian process grader, whether the data of abnormal electricity consumption exercise supervision study by having demarcated, and the transmission error from top layer to bottom, the parameter to each layer coder of network and decoder is adjusted.The present invention is analyzed with reference to artificial intelligence field cutting edge technology to multi-platform electricity consumption data, and depth excavates the user power utilization behavior pattern hidden in mass data, and the abnormal electricity consumption suspicion user of positioning makes abnormal electricity consumption monitoring more intelligent, more efficient.
Description
Technical field
The present invention relates to a kind of method for building up of abnormal electricity consumption detection model.
Background technology
Abnormal electricity consumption（Stealing, metering device exception）It is the key factor for causing line loss abnormal, can pacifies to electricity consumption and power network
Bring hidden danger entirely.But at present, there is following problem in exception electro-detection：It was found that difficult, the abnormal power consumption of difficult, evidence obtaining measures difficult.It is special
It is not low-voltage platform area, user is more and disperses, it is difficult to effective detection, this brings difficulty to Low-voltage Line Loss management, compromises power supply enterprise
Industry interests.
Specifically, under the overall background of battalion's auxiliary tone, electric power information degree is improved constantly, with electricity consumption data volume
It is swift and violent therewith to increase, power information acquisition system, sales service system, line loss become more meticulous the systems such as platform have accumulated largely,
Abundant, complete user data, but lack effective and reasonable means of numerical analysis.Electricity consumption data is carried out by technical staff currently
Manual analysis, is compared using electricity mutation threshold value or shallow-layer learns, and carries out exception electro-detection research, less effective.And threshold value
Need to be manually set, with larger uncertainty.
The line loss unusual fluctuation that abnormal electricity consumption causes is a key factor for influenceing line losses indices, and with low-voltage power supply, user is
Example, its power consumption occupies the vast scale of power consumption always.However, at present line loss level of the low-voltage customer caused by abnormal electricity consumption compared with
Greatly, local line loss level is even up to more than 50%, and these low pressure are distributed in old city residential block and business management more in the line loss platform area high
Concentration zones, all exist power utilization environment complexity be difficult grasp, lack effective Prevention Stealing Electricity Technology means situations such as, it has also become influence
The severely afflicated area of line losses indices, lacks effective line loss unusual fluctuation monitoring and abnormal electricity consumption location technology at present.
There is following difficult point in current exception electro-detection：
Abnormal electricity consumption finds difficult：The low pressure electricity consumption information of opposing electricity-stealing mostlys come from reports and is generally investigated with business, and reports are very
Hardly possible all area's electricity consumption situations of covering are uncertain big；Business generaI investigation cost manpower and materials are huge, and efficiency is low, it is impossible to normalization
Carry out, and the problem that power supply enterprise's generally existing power customer is more, power utility check personnel amount is few, stealing discovery is difficult to have turned into mesh
The greatest problem that preceding work of electricity anti-stealing is present.
Abnormal electricity consumption evidence obtaining is difficult：Because electric energy is different from other commodity, its production, transport and sale are completed simultaneously, thus
Electric energy is stolen stolen different from other commodity, there is the performance of its uniqueness：1. stealing scene is difficult to keep, and electricity filching person completely can be with fast
Speed destroys stealing instrument, does not leave trace；2. the stealing time do not fix, stealing evidence is difficult to be caught.
Abnormal power consumption metering is difficult：Because of the particularity of electric energy, even if exception electricity consumption at present is found, specific electricity is also difficult
Accurately to calculate, can only by inquiry inquire and determine energy data again after collecting relevant information.《Power supply and business rules》Regulation,
When that cannot find out the stealing time, stealing number of days was at least calculated by 180 days.But this computational methods is simple and crude, easily meets with and query.
Result is：The loss of power supply enterprise can not by faster, in full recover；Electricity filching person does not obtain due sanction.It is more tight
Weight, electricity filching behavior is increasingly serious, the electricity consumption order of the whole society is brought and is had a strong impact on.
The content of the invention
It is an object of the invention to provide a kind of method for building up of abnormal electricity consumption detection model, before artificial intelligence field
Multi-platform electricity consumption data is analyzed along technology, depth excavates the user power utilization behavior pattern hidden in mass data, positioning
Abnormal electricity consumption suspicion user, makes abnormal electricity consumption monitoring more intelligent, more efficient.
In order to achieve the above object, the technical scheme is that providing a kind of foundation side of abnormal electricity consumption detection model
Method, the power information to training set user carries out the extraction of validity feature, sets up the initial training data set for deep learning；
In initial training data set, by the data without the whether abnormal electricity consumption of demarcation, as the input of the abnormal electricity consumption detection model,
The successively unsupervised learning from bottom to top layer is carried out in depth noise reduction autoencoder network, each layer coder of network and decoding is obtained
The parameter of device；Network top is provided with Gaussian process grader, whether the data of abnormal electricity consumption exercise supervision by having demarcated
Study, error is transmitted from top layer to bottom, and the parameter to each layer coder of network and decoder is adjusted.
Preferably, when the input of abnormal electricity consumption detection model is power information-frequency feature.
Preferably, extract power information when-frequency feature, comprising in user profile single index time series difference
When carrying out overall experience mode decomposition and small echo-frequency division solution.
Preferably, the single index includes one or more following：Daily power consumption, platform area line loss, user's property, work(
Rate factor, contract hold proportion, zero power, day electricity mutation, moon electricity mutation, typical industry user's index, history power supply service
Index, electricity, electric energy meter dead electricity record, electric energy meter cover opening record before and after day of checking meter.
Preferably, the abnormal electricity consumption detection model output abnormality degree suspicion coefficient, and according to abnormality degree suspicion coefficient pair
The doubtful probability of abnormality degree of user is ranked up；The doubtful probability of abnormality degree of the bigger user of abnormality degree suspicion coefficient is higher.
Preferably, according to low pressure resident and the power information of the non-resident user of low pressure, the electricity consumption of corresponding exception is set up
Detection model.
Preferably, it is input into the power information of the abnormal electricity consumption detection model, comprising user's information about power, user property
And meter event information.
Preferably, by or many in power information acquisition system, sales service system, line loss fine-grained management platform
It is individual, provide the power information to the abnormal electricity consumption detection model.
The method for building up of abnormal electricity consumption detection model of the present invention, the advantage is that：
The present invention is performed based on overall experience mode decomposition-wavelet transformation-entropy by the analysis to abnormal electricity consumption behavioral mechanism
The extraction of abnormal user behavioural characteristic, cancelling noise component of signal obtains the useful feature of source signal.The present invention considers
Noise, deep learning and Gaussian process, establish the abnormal electricity consumption behavior based on depth noise reduction autoencoder network-Gaussian process
Identification model.Model training is carried out by mass data, recycles model to automatically analyze user according to user power utilization information data
Electricity consumption behavior, automatic discrimination multiplexing electric abnormality suspicion user reduces power utility check scope.The input of model is the electricity of mass users
Information, user property and meter event information, are output as abnormal electricity consumption suspicion user list.The present invention is based on exception electro-detection
Model, is that abnormal electricity consumption monitoring sets up step testing mechanism with positioning, and first examination suspicion user again targetedly monitor by collection,
Power utility check scope can be effectively reduced, the manpower and materials of field monitoring are saved, company operation cost is reduced.
Brief description of the drawings
Fig. 1 is a kind of principle schematic of autocoder；
Fig. 2 a are traditional neural network structure schematic diagrames；
Fig. 2 b are the schematic diagrames of deep learning structure；
Fig. 3 is exception electro-detection modeling procedure；
Fig. 4 a, Fig. 4 b refer respectively to mark EEMD mode decompositions and Hilbert that power factor (PF) is obtained by EEMD-HT Time-frequency Decompositions
Huang energy spectrum schematic diagram；
Fig. 5 is the Wavelet time-frequency decomposing schematic representation of index power factor (PF)；
Fig. 6 a, Fig. 6 b refer respectively to indicate EEMD mode decompositions and Martin Hilb that the total electric energy of work(is obtained by EEMD-HT Time-frequency Decompositions
Special Huang energy spectrum schematic diagram；
Fig. 7 refers to the Wavelet time-frequency decomposing schematic representation for indicating the total electric energy of work(.
Specific embodiment
Abnormal electricity consumption detection model of the present invention, is applied to a kind of abnormal electricity consumption monitoring and localization process system.From
Power information acquisition system, sales service system, line loss become more meticulous platform etc., mass users power information are obtained, as different
The input of conventional electro-detection model, is automatically extracted validity feature by model and is classified, and exports the list of suspicion abnormal user；So as to
Power utility check personnel carry out site inspection to the suspicion user for filtering out, and abnormal user is investigated and prosecuted.Wherein, using artificial intelligence
Can field cutting edge technology --- deep learning（Deep Learning, hereinafter referred to as DL）Algorithm, builds of the present invention being based on
The artificial intelligence exception electricity consumption detection model of depth noise reduction autoencoder network-Gaussian process.
It is as shown in Figure 1 a kind of autocoder（Auto-Encoder, AE）Realization principle, input signal（input）
Send an encoder to（encoder）Obtain representing the corresponding encoded of the input signal（code）, one is connected afterwards
Decoder（decoder）A reconfiguration information is obtained from code conversion（reconstruction）If, reconfiguration information with input
Signal like（Ideally both are the same）, then have reason to believe that this coding is reliable.Therefore, by adjusting volume
The parameter of code device and decoder so that reconstructed error is minimum, has at this time just obtained first expression of input signal, i.e., foregoing
Coding information.Input be without label data when, the source of error is exactly to be obtained compared with original input after directly reconstruct.
On the basis of autocoder shown in Fig. 1, a certain proportion of ambient noise is added in input signal, then existed
During noise is removed in study, study to input vector is most essential, most stable of feature representation, so as to obtain a kind of noise reduction
Autocoder（Denoise Auto-Encoder, DAE）.After training is completed, noise reduction autoencoder network containing having powerful connections to making an uproar
The input data of sound has stronger adaptability, the expression for having more robust to input signal.
Depth noise reduction autoencoder network of the present invention（Deep Denoise Auto-Encoder Network,
DDAEN）It is made up of several noise reduction autocoders DAE, initial data is successively trained as the input of ground floor DAE, will
The output of L-1 layers for training is used as L layers of input.When successively training, training criterion is to minimize reconstructed error, and is instructed
Neutral net can be expanded into encoder and decoder when practicing, that is, expand into three layers of neutral net, propagated by direction of error
Algorithm changes weights.The encoder for obtaining by this method and decoder weights, in the absence of the restriction relation of transposition.Successively
Complex distributions in training process since initial data in feature space, low-dimensional, smooth distribution are successively transformed into, that is, ignored
Fall some small noises, remove the redundancy of initial data so that data distribution is simpler.Instruction is gone by gradient descent algorithm
Practice DDAEN, multilayer study can be carried out to the appraisement system index of abnormal electricity consumption detection model, and then obtain exception electro-detection
The evaluation target abstract characteristics of model.
Specifically, the process of deep learning training is carried out in the present invention, comprising：
1）From the unsupervised learning of lower rising（Since bottom, past top layer training in layer）, using whether different without demarcating
The data hierarchy for commonly using electricity trains each layer parameter, and this step can be regarded as a unsupervised training process, be and traditional neural
Network distinguishes the best part：Specifically, first training ground floor with without nominal data, the parameter of ground floor is first learnt during training
（This layer can be regarded as obtaining a hidden layer for causing to export and be input into the minimum three-layer neural network of difference）, due to model
Capacity（capacity）Limitation and sparsity constraints so that the model for obtaining can learn the structure to data in itself, from
And obtain the feature than input with more expression ability；After study obtains (n-1)th layer, using n-1 layers of output as n-th layer
Input, trains n-th layer, thus respectively obtains the parameter of each layer.
2）Top-down supervised learning（It is trained by the data of tape label, the top-down transmission of error, to network
It is finely adjusted）；Based on the 1st）Each layer parameter that step is obtained further is adjusted（fine-tune）The parameter of whole multilayered model, this
One step is a Training process；The random initializtion initial value process of the similar neutral net of the first step, due to the first of DL
Step is not random initializtion, but by learning what the structure of input data was obtained, thus this initial value is closer to global optimum,
So as to obtain more preferable effect；So modelling effect well largely gives the credit to the feature learning of the first step（feature
learning）Process.The comparing of traditional neural network and deep learning DL structures, referring to shown in Fig. 2 a, Fig. 2 b.
As shown in figure 3, being exception electro-detection workflow.The course of work bag of abnormal electricity consumption detection model in the present invention
Include training and test two parts.Training process is abnormal electricity consumption detection model and sets up process, and test process is to be according to foundation
Model carry out user power utilization abnormality degree prediction.
A training process：
A1）The information such as electricity, ammeter event index to training set mass users, when carrying out-frequency feature extraction, set up depth
The initial training data set of habit；
A2）For the abnormal electricity consumption detection model based on depth noise reduction autoencoder network-Gaussian process, by the initial instruction without label
Practice data set input model, the network initial weight for more optimizing is obtained by unsupervised Level by level learning；
A3）In the Gaussian process grader of the additional supervised learning of network top（It is trained by the data of tape label, error
Transmitted to bottom from top layer；Whether tag representation is abnormal electricity consumption user）, the overall network parameter of model is carried out further thin
Fine adjustment, it is final to obtain abnormal electricity consumption detection model.
It is lift scheme forecasting accuracy, different models can be set up for different types of user, is directed in invention
Low pressure resident and the non-resident user of low pressure model respectively.
B, test process：
1）During the information such as the tested user's electricity of extraction, ammeter event index-frequency feature；
2）The feature that will be extracted, input to abnormal electricity consumption detection model, the tested user's exception suspicion coefficient of model output.Suspicion system
Number is bigger, and abnormal electricity consumption possibility is bigger.
Deep learning algorithm is based in the present invention, is held with daily power consumption, platform area line loss, user's property, power factor (PF), contract
Proportion, zero power, day electricity mutation, moon electricity mutation, typical industry user's index, history power supply service index, check meter a few days ago
Afterwards electricity, electric energy meter dead electricity record, electric energy meter cover opening record etc. as exception electricity consumption detection model single index, to these lists
When the time series of index carries out overall experience mode decomposition and small echo respectively-frequency division solution, time and frequency domain characteristics are extracted respectively to be made
It is the original input data collection of deep learning model（Fig. 4 a and Fig. 4 b, Fig. 5 provide an overall experience mode for index point respectively
Solution and wavelet decomposition spectrogram；Fig. 6 a and Fig. 6 b, Fig. 7 provide the overall experience mode decomposition and Wavelet time-frequency of another index respectively
Exploded view）.
On this basis, commenting for the abnormal electricity consumption detection model based on depth noise reduction autoencoder network-Gaussian process is set up
Valency system, using the time-frequency characteristics of the bottom single index of appraisement system as input vector project training sample, sets up depth drop
Autoencoder network structure, selection Gaussian process model make an uproar as top-level categories precaution device.Application training sample is to deep learning net
Network is trained, and finally using abnormality degree suspicion coefficient as output, the user power utilization situation big by analyzing suspicion coefficient is found out
User property feature and judgment rule with abnormal electricity consumption behavior.
The present invention studies mass data in depth by depth noise reduction autoencoder network-Gaussian process model, quickly from difference
Dimension extracts the validity feature of data, and provides the doubtful probability sorting of abnormality degree.Abnormality degree sequence need to be only monitored using this model
Forward a few users, you can find most of abnormal user, solve abnormal electricity consumption and find difficult problem.
Although present disclosure is discussed in detail by above preferred embodiment, but it should be appreciated that above-mentioned
Description is not considered as limitation of the present invention.After those skilled in the art have read the above, for of the invention
Various modifications and substitutions all will be apparent.Therefore, protection scope of the present invention should be limited to the appended claims.
Claims (8)
1. a kind of method for building up of abnormal electricity consumption detection model, it is characterised in that
Power information to training set user carries out the extraction of validity feature, sets up the initial training data for deep learning
Collection；
In initial training data set, by the data without the whether abnormal electricity consumption of demarcation, as the abnormal electricity consumption detection model
Input, the successively unsupervised learning from bottom to top layer is carried out in depth noise reduction autoencoder network, obtains each layer coder of network
With the parameter of decoder；
Network top is provided with Gaussian process grader, whether the data of abnormal electricity consumption exercise supervision study by having demarcated,
Error is transmitted from top layer to bottom, the parameter to each layer coder of network and decoder is adjusted.
2. the method for building up of exception electricity consumption detection model as claimed in claim 1, it is characterised in that
When the input of the abnormal electricity consumption detection model is power information-frequency feature.
3. the method for building up of exception electricity consumption detection model as claimed in claim 2, it is characterised in that
Extract power information when-frequency feature, carry out overall warp respectively comprising the time series to single index in user profile
When testing mode decomposition and small echo-frequency division solution.
4. the method for building up of exception electricity consumption detection model as claimed in claim 3, it is characterised in that
The single index includes one or more following：Daily power consumption, platform area line loss, user's property, power factor (PF), contract
Hold proportion, zero power, day electricity mutation, moon electricity mutation, typical industry user's index, history power supply service index, check meter day
Front and rear electricity, electric energy meter dead electricity record, electric energy meter cover opening record.
5. the method for building up of exception electricity consumption detection model as claimed in claim 1, it is characterised in that
The abnormal electricity consumption detection model output abnormality degree suspicion coefficient, and according to abnormality degree suspicion coefficient to the abnormality degree of user
Doubtful probability is ranked up；The doubtful probability of abnormality degree of the bigger user of abnormality degree suspicion coefficient is higher.
6. the method for building up of exception electricity consumption detection model as claimed in claim 3, it is characterised in that
According to low pressure resident and the power information of the non-resident user of low pressure, corresponding exception electricity consumption detection model is set up.
7. as described in claim 1 or 3 abnormal electricity consumption detection model method for building up, it is characterised in that
It is input into the power information of the abnormal electricity consumption detection model, comprising user's information about power, user property and meter event
Information.
8. the method for building up of exception electricity consumption detection model as claimed in claim 7, it is characterised in that
By one or more in power information acquisition system, sales service system, line loss fine-grained management platform, to described different
Conventional electro-detection model provides the power information.
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