CN110490329A - A kind of extensive electricity exception data detection method and system based on machine learning - Google Patents
A kind of extensive electricity exception data detection method and system based on machine learning Download PDFInfo
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Abstract
The invention discloses a kind of extensive electricity exception data detection method and system based on machine learning.Extensive electricity exception data detection method of the invention comprising step: 1) user data record to be detected is inputted；2) correlated characteristic of user data is extracted；3) feature is inputted into Multilayer perceptron network model；4) detection probability is exported, and adjusts Multilayer perceptron network model parameter based on loss function is minimized.The present invention is using the Multilayer perceptron network model that can detect electricity exception data, the acquisition data containing exception information based on magnanimity, there are causal elements with data generation acquisition abnormity for excavation, the abnormality of electric power acquisition data is detected, can the data exception to universal class effectively detected.
Description
Technical field
The present invention relates to electric power data detection field, especially a kind of extensive electricity exception data based on machine learning
Detection method and system.
Background technique
The basis of smart grid and artificial intelligence collision is data, in this regard, as the acquisition real-time electricity consumption of user
The basic platform of information, the power information acquisition system with power grid user data " all standing " for construction object provide adequately
Data guarantee.However in data acquisition, during device talk due to loss of data etc., inevitably send out
Phenomena such as raw acquired error in data or missing.Thus, the validity and accuracy of acquisition data are judged and handled, at
For a step of most critical in gage work.Only the stabilization of guaranteed acquisition data could be supported with reliably to a certain extent
Based on the upper layer application of acquisition big data, the electricity consumption situation of power customer is such as reasonably analyzed and understood.
Therefore, the acquisition data of mistake are detected, carrying out completion to the data of missing has important practical significance.
Such as " electric energy measurement data anomaly analysis in power information acquisition ", Liu Xiaoxiang, Shao Qiang, Chen Yingxin, Wang Zhixin, " science and technology and enterprise
Industry ", the 17th phase in 2015；" power information acquisition system electric energy measurement data Analysis on Abnormal ", Wang Dengxue, " science and technology passes
Broadcast ", o. 11th in 2016；And " for the discussion of abnormal data elimination method in metrology and measurement ", Sun Fei, Ding Cheng, " Heilungkiang
Scientific and technological information ", the 32nd phase in 2015；The reason of these literature research cause electric energy acquisition data exception such as includes all kinds of things
Electricity caused by part fluctuates, data volume sharply increases, data are transmitted and processing speed is accelerated etc., and is directed to voltage and current, power
The different types of data such as factor, switching signal propose detection method.However, on the one hand these method majorities rest on it is qualitative
On the other hand the not quantitative stage has the method that work fails to propose effectively to detect the data exception of universal class.
Summary of the invention
The technical problem to be solved by the present invention is to overcome the problems of the above-mentioned prior art, provide a kind of based on machine
The extensive electricity exception data detection method of study, by extracting various features as input, sensitive more of utilization cost
Layer Perceptrons Network model is acquired the differentiation of abnormal data, is effectively examined with the data exception to universal class
It surveys.
For this purpose, the present invention adopts the following technical scheme that: a kind of extensive electricity exception data inspection based on machine learning
Survey method comprising step:
1) user data record to be detected is inputted；
2) correlated characteristic of user data is extracted；
3) feature is inputted into Multilayer perceptron network model；
4) detection probability is exported, and adjusts Multilayer perceptron network model parameter based on loss function is minimized.
Further, in step 2), the feature includes when daily electricity, more daily electricity average value/variance, electricity are stablized
Value and electric quantity balancing value.
Further, the particular content of step 3) is as follows:
Firstly, given training data X1,X2,…,Xi…,Xn, wherein XiIndicate that i-th records corresponding feature and right
Answer abnormal label: y1,y2,…,yi…,yn, wherein yi∈ { 0,1 }, yiIndicate abnormality, 1 indicates abnormal, and 0 indicates normal,
Learn mapping function f (X)=p (y=1 | X) by the method for supervised learning, indicating a given data record X, it is judged to
It is set to the probability of exceptional sample；Its minimum is made by using optimization loss function:
Min (loss (y, f (X))),
Wherein loss function is called loss function, it is used to the difference between two values of measurement；
Have the characteristics that its own for acquisition abnormity, gives normal weights different with exceptional sample, loss function is by again
Is defined as:
Loss=Cploss(yp,f(Xp))+Cnloss(yn,f(Xn))
In above formula, CpWith CnFor exceptional sample and the respective weight of normal sample, by give exceptional sample greater weight come
Its accuracy is improved, in verifying, is taken
Further, in step 3), if f (Xi) > 0.5, it is predicted as exception.
A kind of technical solution that the present invention also uses are as follows: extensive electricity exception Data Detection system based on machine learning
System comprising:
Data input cell, for inputting user data record to be detected；
Data characteristics extraction unit, for extracting the correlated characteristic of user data；
Feature input unit, for feature to be inputted Multilayer perceptron network model；
Detection probability output unit, for exporting detection probability；
Model parameter adjustment unit adjusts Multilayer perceptron network model parameter based on loss function is minimized.
The device have the advantages that as follows: the present invention is using the multi-layer perception (MLP) that can detect electricity exception data
Neural network model, the acquisition data containing exception information based on magnanimity, there are causes and effects with data generation acquisition abnormity for excavation
The element of relationship detects the abnormality of electric power acquisition data, can carry out to the data exception of universal class effective
Detection.
Detailed description of the invention
Fig. 1 is the flow chart of detection method；
Fig. 2 is the functional block diagram of detection system of the present invention.
Specific embodiment
The invention will be further described with specific embodiment with reference to the accompanying drawings of the specification.
Embodiment 1
The present embodiment provides a kind of extensive electricity exception data detection method based on machine learning, as shown in Figure 1, its
Steps are as follows:
1) user data record to be detected is inputted；
2) correlated characteristic of user data is extracted；
3) feature is inputted into Multilayer perceptron network model；
4) detection probability is exported, and adjusts Multilayer perceptron network model parameter based on loss function is minimized.
In step 2), the feature includes working as daily electricity, more daily electricity average value/variance, electricity stationary value and electricity
Equilibrium valve.
Acquisition abnormity data have two big characteristics --- low frequency and extreme.On the basis of data observation, it is proposed that
Feature below:
The particular content of step 3) is as follows:
Firstly, given training data X1,X2,…,Xi…,Xn, wherein XiIndicate that i-th records corresponding feature and right
Answer abnormal label: y1,y2,…,yi…,yn, wherein yi∈ { 0,1 }, yiIndicate abnormality, 1 indicates abnormal, and 0 indicates normal,
Learn mapping function f (X)=p (y=1 | X) by the method for supervised learning, indicating a given data record X, it is judged to
It is set to the probability of exceptional sample；Its minimum is made by using optimization loss function:
Min (loss (y, f (X))),
Wherein loss function is called loss function, it is used to the difference between two values of measurement；
Have the characteristics that its own for acquisition abnormity, gives normal weights different with exceptional sample, loss function is by again
Is defined as:
Loss=Cploss(yp,f(Xp))+Cnloss(yn,f(Xn))
In above formula, CpWith CnFor exceptional sample and the respective weight of normal sample, by give exceptional sample greater weight come
Its accuracy is improved, in verifying, is takenIf f (Xi) > 0.5, it is predicted as exception.
Embodiment 2
The present embodiment provides a kind of extensive electricity exception data detection system based on machine learning comprising:
Data input cell, for inputting user data record to be detected；
Data characteristics extraction unit, for extracting the correlated characteristic of user data；
Feature input unit, for feature to be inputted Multilayer perceptron network model；
Detection probability output unit, for exporting detection probability；
Model parameter adjustment unit adjusts Multilayer perceptron network model parameter based on loss function is minimized.
Experimental verification
1.1 data source
Lishui annual user power utilization records in 2017 and abnormal work order.
1.2 sampling rules
1,000,000 normal recordings of metering exception record and stochastical sampling that sampling 5W item is marked by abnormal work order.
1.3 verification mode
Data set is divided into training set and test set according to the ratio of 3:1.
1.4 measurement index
Accuracy=being judged as abnormal practical for abnormal number of samples/is judged as abnormal number of samples.
Recall rate=it is abnormal number of samples that it is practical, which to be judged as exception ,/is actually abnormal number.
1.5 benchmark algorithmic descriptions
Logistics recurrence is common linear classification model；
Random forest+down-sampling is the Integrated Decision Tree Classifier based on down-sampling, specifically, each decision tree classification
The training sample of device includes the equal number of normal sample of all exceptional samples and sampling；
Easyensemble is a kind of uneven classification method of integrated AdaBoost classifier.
1.6 experimental result
| Method | Accuracy rate | Recall rate | F1 |
| Multilayer perceptron network | 0.99 | 0.88 | 0.93 |
| Logistics is returned | 0.97 | 0.09 | 0.16 |
| Random forest+down-sampling | 0.87 | 0.68 | 0.76 |
| Easyensemble | 0.84 | 0.92 | 0.88 |
From experimental result it can be found that current algorithm (detection method i.e. of the invention) can preferably be detected daily
Electricity record in abnormal conditions, and in F1 value be better than other methods.
The above description is only an embodiment of the present invention, is not intended to limit the scope of the invention, all to utilize this hair
Equivalent structure or equivalent flow shift made by bright specification and accompanying drawing content is applied directly or indirectly in other relevant skills
Art field, is included within the scope of the present invention.
Claims (5)
1. a kind of extensive electricity exception data detection method based on machine learning, which is characterized in that comprising steps of
1) user data record to be detected is inputted；
2) correlated characteristic of user data is extracted；
3) feature is inputted into Multilayer perceptron network model；
4) detection probability is exported, and adjusts Multilayer perceptron network model parameter based on loss function is minimized.
2. the extensive electricity exception data detection method according to claim 1 based on machine learning, which is characterized in that
In step 2), the feature includes working as daily electricity, more daily electricity average value/variance, electricity stationary value and electric quantity balancing value.
3. the extensive electricity exception data detection method according to claim 1 or 2 based on machine learning, feature exist
In the particular content of step 3) is as follows:
Firstly, given training data X1, X2..., Xi..., Xn, wherein XiIndicate that i-th records corresponding feature, and corresponding
Abnormal label: y1, y2..., yi..., yn, wherein yi∈ { 0,1 }, yiIndicate abnormality, 1 indicates abnormal, and 0 indicates normal,
Learn mapping function f (X)=p (y=1 | X) by the method for supervised learning, indicating a given data record X, it is judged to
It is set to the probability of exceptional sample；Its minimum is made by using optimization loss function:
Min (loss (y, f (X))),
Wherein loss function is called loss function, it is used to the difference between two values of measurement；
Have the characteristics that its own for acquisition abnormity, give normal weights different with exceptional sample, loss function is redefined
Are as follows:
Loss=Cploss(yp, f (Xp))+Cnloss(yn, f (Xn))
In above formula, CpWith CnFor exceptional sample and the respective weight of normal sample, improved by giving exceptional sample greater weight
Its accuracy takes in verifying
4. the extensive electricity exception data detection method according to claim 3 based on machine learning, which is characterized in that
In step 3), if f (Xi) > 0.5, it is predicted as exception.
5. a kind of extensive electricity exception data detection system based on machine learning characterized by comprising
Data input cell, for inputting user data record to be detected；
Data characteristics extraction unit, for extracting the correlated characteristic of user data；
Feature input unit, for feature to be inputted Multilayer perceptron network model；
Detection probability output unit, for exporting detection probability；
Model parameter adjustment unit adjusts Multilayer perceptron network model parameter based on loss function is minimized.
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| CN111241056A (en) * | 2019-12-31 | 2020-06-05 | 国网浙江省电力有限公司电力科学研究院 | Power energy consumption data storage optimization method based on decision tree model |
| CN112329895A (en) * | 2021-01-05 | 2021-02-05 | 国网江西综合能源服务有限公司 | Method and device for identifying user with suspicion of electricity stealing |
| CN115759236A (en) * | 2022-12-30 | 2023-03-07 | 北京德风新征程科技有限公司 | Model training method, information sending method, device, equipment and medium |
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