CN113344346A - Power utilization abnormity detection method and system based on non-invasive load decomposition - Google Patents

Power utilization abnormity detection method and system based on non-invasive load decomposition Download PDF

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CN113344346A
CN113344346A CN202110570582.7A CN202110570582A CN113344346A CN 113344346 A CN113344346 A CN 113344346A CN 202110570582 A CN202110570582 A CN 202110570582A CN 113344346 A CN113344346 A CN 113344346A
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power consumption
user
behavior
load
abnormal electricity
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CN113344346B (en
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周开乐
周晨
周炯
叶林海
焦建玲
李兰兰
梁昌勇
余本功
丁涛
段飞
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Jizhong Energy Technology Service Shanghai Co ltd
Hefei University of Technology
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Hefei University of Technology
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    • G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
    • G—PHYSICS
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    • G06F18/00—Pattern recognition
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    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/23—Clustering techniques
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    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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Abstract

The invention provides a power utilization abnormity detection method and system based on non-invasive load decomposition, and relates to the technical field of abnormal power utilization behavior detection of users. The technical scheme is that firstly, the total power consumption of a user is decomposed based on a non-invasive load decomposition technology to obtain the power consumption of each device; mining user behavior characteristics including power utilization time characteristics and load characteristics based on the power utilization power of each device; and finally, detecting abnormal electricity utilization behaviors by using a density peak value clustering algorithm based on the user behavior characteristics. According to the technical scheme, the user behavior characteristics are extracted by using a non-invasive load decomposition technology, clustering is realized based on the user behavior characteristics, so that the detection result of the abnormal electricity utilization behavior of the user is obtained, the accuracy rate of the abnormal electricity utilization detection is improved, and economic benefits are brought to electricity selling companies and energy service providers.

Description

Power utilization abnormity detection method and system based on non-invasive load decomposition
Technical Field
The invention relates to the technical field of user power utilization abnormity detection, in particular to a power utilization abnormity detection method and system based on non-invasive load decomposition.
Background
Abnormal electricity utilization behaviors of users, such as electricity stealing and the like, can cause non-technical loss of power transmission and distribution in the operation of a power grid, so that the benefits of electricity selling companies, energy service providers and the like are damaged. Along with the development of the smart power grid, the popularity of the smart power meter is continuously increased, meanwhile, the electricity stealing means of the user is changed from destroying the traditional power meter or private power line and the like into attacking the smart power meter through a digital storage technology and a network communication technology, and the electricity consumption at the corresponding moment is reduced or directly zeroed through data tampering. The tradition relies on technical staff to carry out artifical screening or relies on camera, unmanned aerial vehicle control in order to prevent that more manpower and material resources are wasted to the method that the electricity of stealing takes place. Under an advanced measurement system, the load data of the user is collected and analyzed in real time, a new effective way is provided for power utilization abnormity detection, the cost of power utilization abnormity detection can be greatly reduced, and the abnormity detection efficiency and accuracy are improved.
At present, data driving methods for the field of user abnormal electricity utilization behavior detection mainly include methods based on classification, regression and clustering, and several methods have respective advantages and disadvantages, but the methods are more commonly used and are also based on clustering. The clustering-based method is used for extracting the electricity utilization characteristics of the users and finding out a few abnormal users which do not accord with the electricity utilization behavior characteristics of most users, so that the abnormal detection is realized.
However, the existing clustering-based abnormal electricity consumption behavior detection excessively focuses on the improvement of the clustering algorithm, the extraction research on the electricity consumption characteristics of the user is less, the adopted characteristics are mostly the electrical characteristics of a time domain, a frequency domain and the like, and the user behavior characteristics which have important effects on the abnormal electricity consumption behavior detection are often ignored or less focused, so that the abnormal electricity consumption behavior detection result of the user is inaccurate.
Disclosure of Invention
Technical problem to be solved
Aiming at the defects of the prior art, the invention provides a power utilization abnormity detection method and system based on non-invasive load decomposition, and solves the problem that the power utilization abnormity detection result is inaccurate because the user behavior characteristics are ignored or less concerned in the prior art.
(II) technical scheme
In order to achieve the purpose, the invention is realized by the following technical scheme:
in a first aspect, the present invention first provides a power utilization anomaly detection method based on non-intrusive load decomposition, where the method includes:
decomposing the total power consumption of a user based on a non-invasive load decomposition technology to obtain the power consumption of each device;
acquiring user behavior characteristics based on the power consumption of each device;
and detecting abnormal electricity utilization behaviors by using a density peak value clustering algorithm based on the user behavior characteristics.
Preferably, the decomposing the total power consumption of the user based on the non-intrusive load decomposition technology to obtain the power consumption of each device includes:
s11, establishing a mapping relation between the decomposed total power consumption and the working modes of the electric equipment based on a non-invasive load decomposition model, and obtaining codes of state codes corresponding to the total power consumption of all the electric equipment based on the combination of the working modes of all the electric equipment;
s12, training an RNN-based seq2seq model based on the state code;
and S13, performing load decomposition by using the trained seq2seq model based on the RNN to obtain the power consumption of each device.
Preferably, the user behavior characteristics include power consumption time characteristics and load characteristics, wherein the power consumption time characteristics include common operation periods, daily operation frequencies, common off periods and daily off frequencies of different devices; the load characteristics comprise daily average power consumption of equipment of different users, daily maximum power consumption of the equipment, maximum load hours of the equipment and daily average usage of the equipment.
Preferably, the detecting abnormal electricity utilization behavior by using a density peak clustering algorithm based on the user behavior characteristics includes:
s31, primarily classifying the users by adopting a density clustering algorithm based on the electricity utilization time characteristics to obtain different classes of users;
and S32, aiming at the different classes of users, realizing abnormal electricity utilization behavior detection by adopting a KNN-based fast density peak value clustering algorithm according to load characteristics.
Preferably, the determination condition for detecting the abnormal electricity consumption behavior is: when the local density of a certain point
Figure BDA0003082441730000031
Distance between two adjacent plates
Figure BDA0003082441730000032
If so, determining that the user is a user with abnormal electricity utilization behavior;
wherein the local density threshold
Figure BDA0003082441730000033
Is defined as:
Figure BDA0003082441730000034
distance threshold
Figure BDA0003082441730000035
Is defined as:
Figure BDA0003082441730000036
where b represents the device and b ∈ {1,2, 3.. eta., M }, a represents a sample in the dataset and a ∈ {1,2, 3.. eta., N }, γ }ρAnd gammaδAre empirical parameters.
In a second aspect, the present invention further provides a power consumption anomaly detection system based on non-invasive load decomposition, the system including:
the load decomposition module is used for decomposing the total power consumption of the user based on a non-invasive load decomposition technology to obtain the power consumption of each device;
the user behavior feature mining module is used for acquiring user behavior features based on the power consumption of each device;
and the abnormal electricity utilization behavior detection module is used for realizing the abnormal electricity utilization behavior detection by utilizing a density peak value clustering algorithm based on the user behavior characteristics.
Preferably, the load decomposition module decomposing the total power consumption of the user based on a non-intrusive load decomposition technique to obtain the power consumption of each device includes:
s11, establishing a mapping relation between the decomposed total power consumption and the working modes of the electric equipment based on a non-invasive load decomposition model, and obtaining codes of state codes corresponding to the total power consumption of all the electric equipment based on the combination of the working modes of all the electric equipment;
s12, training an RNN-based seq2seq model based on the state code;
and S13, performing load decomposition by using the trained seq2seq model based on the RNN to obtain the power consumption of each device.
Preferably, the user behavior characteristics acquired by the user behavior characteristic mining module include power consumption time characteristics and load characteristics, wherein the power consumption time characteristics include common operation periods, daily operation frequencies, common shutdown periods and daily shutdown frequencies of different devices; the load characteristics comprise daily average power consumption of equipment of different users, daily maximum power consumption of the equipment, maximum load hours of the equipment and daily average usage of the equipment.
Preferably, the detecting module of abnormal electricity consumption behavior based on the user behavior characteristics and using a density peak clustering algorithm to detect abnormal electricity consumption behavior includes:
s31, primarily classifying the users by adopting a density clustering algorithm based on the electricity utilization time characteristics to obtain different classes of users;
and S32, aiming at the different classes of users, detecting abnormal electricity utilization behaviors by adopting a KNN-based density peak clustering algorithm according to load characteristics.
Preferably, the condition that the abnormal electricity consumption behavior detection module judges the abnormal electricity consumption behavior is as follows: when the local density of a certain point
Figure BDA0003082441730000041
Distance between two adjacent plates
Figure BDA0003082441730000042
If so, determining that the user is a user with abnormal electricity utilization behavior;
wherein the local density threshold
Figure BDA0003082441730000043
Is defined as:
Figure BDA0003082441730000044
distance threshold
Figure BDA0003082441730000045
Is defined as:
Figure BDA0003082441730000046
where b represents the device and b ∈ {1,2, 3.. eta., M }, a represents a sample in the dataset and a ∈ {1,2, 3.. eta., N }, γ }ρAnd gammaδAre empirical parameters.
(III) advantageous effects
The invention provides a power utilization abnormity detection method and system based on non-invasive load decomposition. Compared with the prior art, the method has the following beneficial effects:
1. according to the technical scheme, total power consumption is decomposed on the basis of a non-invasive load decomposition technology to obtain power consumption of each device, user behavior characteristics are mined according to the power consumption of each device, and clustering is performed twice in sequence by using a density peak clustering algorithm and a KNN-based rapid density peak clustering algorithm on the basis of the user behavior characteristics, so that abnormal power consumption behaviors are detected. According to the technical scheme, the user behavior characteristics are extracted by using a non-invasive load decomposition technology, clustering is realized based on the user behavior characteristics, so that the detection result of the abnormal electricity utilization behavior of the user is obtained, the accuracy rate of the detection of the abnormal electricity utilization behavior is improved, and economic benefits are brought to electricity selling companies and energy service providers;
2. according to the technical scheme, the total power utilization of the electric equipment is decomposed by using a non-invasive load decomposition technology to obtain the power utilization of each equipment, and then user behavior characteristics including power utilization time characteristics and load characteristics are extracted from the power utilization of each equipment, so that the user behavior characteristics which can more accurately represent the power utilization behavior of the user can be obtained;
3. according to the technical scheme, the density peak clustering algorithm and the KNN-based rapid density peak clustering algorithm are utilized to cluster the power utilization time characteristic and the load characteristic of the user behavior characteristic for two times, the power utilization behavior recognition results of the users on different equipment sets are weighted when the final clustering result is obtained, and finally the abnormal power utilization behavior of the users is detected.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a flow chart of a method for detecting power consumption anomalies based on non-intrusive load decomposition in accordance with an embodiment of the present invention;
FIG. 2 is a block diagram of a Seq2Seq network architecture in an embodiment of the present invention;
fig. 3 is a flowchart illustrating an embodiment of detecting a power consumption abnormality of a user.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention are clearly and completely described, and it is obvious that the described embodiments are a part of the embodiments of the present invention, but 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.
By providing the power consumption abnormity detection method and system based on non-invasive load decomposition, the problem that in the prior art, the detection result of the abnormal power consumption behavior of the user is inaccurate due to neglect or less attention to the behavior characteristics of the user is solved, and the purpose of accurately monitoring the abnormal power consumption behavior of the user is achieved.
In order to solve the technical problems, the general idea of the embodiment of the application is as follows:
in order to obtain an accurate user abnormal electricity consumption behavior detection result, the technical scheme firstly decomposes the total electricity consumption power of the electric equipment based on a non-invasive load decomposition technology to obtain the electricity consumption power of each equipment, then mines user behavior characteristics including electricity consumption time characteristics and load characteristics according to the electricity consumption power of each equipment, finally carries out twice clustering successively by utilizing a density peak value clustering algorithm and a KNN-based rapid density peak value clustering algorithm based on the user behavior characteristics, and weights the clustering results, so that the accurate detection of the abnormal electricity consumption behavior is realized.
In order to better understand the technical solution, the technical solution will be described in detail with reference to the drawings and the specific embodiments.
Example 1:
in a first aspect, referring to fig. 1, the present invention first proposes a power utilization anomaly detection method based on non-invasive load decomposition, the method including:
s1, decomposing the total power consumption of the user based on a non-invasive load decomposition technology to obtain the power consumption of each device;
s2, mining user behavior characteristics based on the power consumption of each device;
and S3, detecting abnormal electricity utilization behaviors by using a density peak value clustering algorithm based on the user behavior characteristics.
According to the technical scheme, the total power consumption is decomposed on the basis of a non-invasive load decomposition technology to obtain the power consumption of each device, then the user behavior characteristics are mined according to the power consumption of each device, and finally clustering is performed twice in sequence by using a density peak value clustering algorithm and a KNN-based rapid density peak value clustering algorithm on the basis of the user behavior characteristics, so that the abnormal power consumption behavior is detected. According to the technical scheme, the user behavior characteristics are extracted by using a non-invasive load decomposition technology, clustering is realized based on the user behavior characteristics, so that the detection result of the abnormal electricity utilization behavior of the user is obtained, the accuracy rate of the detection of the abnormal electricity utilization behavior is improved, and economic benefits are brought to electricity selling companies and energy service providers.
In the above method according to an embodiment of the present invention, in order to avoid cost consumption caused by methods such as intrusive load decomposition when decomposing a load, the present technical solution decomposes the total power consumption of the electrical equipment by using a non-intrusive load decomposition technique, and at this time, a preferred processing manner is that decomposing the total power consumption of the user based on the non-intrusive load decomposition technique to obtain the power consumption of each device includes:
s11, establishing a mapping relation between the decomposed total power consumption and the working modes of the electric equipment based on a non-invasive load decomposition model, and obtaining codes of state codes corresponding to the total power consumption of all the electric equipment based on the combination of the working modes of all the electric equipment;
s12, training an RNN-based seq2seq model based on the state code;
and S13, performing load decomposition by using the trained seq2seq model based on the RNN to obtain the power consumption of each device.
In practice, in order to obtain more accurate mining user behavior characteristics, based on consideration of actual user electricity utilization real conditions, a better processing mode is that the user behavior characteristics include electricity utilization time characteristics and load characteristics, wherein the electricity utilization time characteristics include common operation periods, daily operation frequencies, common shutdown periods and daily shutdown frequencies of different devices; the load characteristics comprise daily average power consumption of equipment of different users, daily maximum power consumption of the equipment, maximum load hours of the equipment and daily average usage of the equipment.
In addition, in order to accurately obtain a determination result of the abnormal electricity consumption behavior of the user, a preferred processing manner is that the detecting of the abnormal electricity consumption behavior by using a density peak value clustering algorithm based on the user behavior characteristics includes:
s31, primarily classifying the users by adopting a density clustering algorithm based on the electricity utilization time characteristics to obtain different classes of users;
and S32, aiming at the different classes of users, realizing abnormal electricity utilization behavior detection by adopting a KNN-based fast density peak value clustering algorithm according to load characteristics.
Meanwhile, when the abnormal electricity utilization behavior of the user is judged, two quantities, namely local density and distance, are selected for judgment, and specifically, the judgment condition of the abnormal electricity utilization behavior detection is as follows: when the local density of a certain point
Figure BDA0003082441730000081
Distance between two adjacent plates
Figure BDA0003082441730000082
If so, determining that the user is a user with abnormal electricity utilization behavior;
wherein the local density threshold
Figure BDA0003082441730000083
Is defined as:
Figure BDA0003082441730000084
distance threshold
Figure BDA0003082441730000085
Is defined as:
Figure BDA0003082441730000086
where b represents the device and b ∈ {1,2, 3.. eta., M }, a represents a sample in the dataset and a ∈ {1,2, 3.. eta., N }, γ }ρAnd gammaδAre empirical parameters.
The following describes the implementation of an embodiment of the present invention in detail with reference to the explanation of specific steps.
And S1, decomposing the total power consumption of the user based on a non-invasive load decomposition technology to obtain the power consumption of each device.
The electric appliance in this embodiment is a household electric appliance, and it is known that in practice, the states of electric appliances of household electric appliances are not only simple to turn on and off, but also can be divided into four types, namely, a switch type, a multi-state type, a continuous change type and a permanent operation type, according to the use, internal structure and operating principle of each electric appliance. Wherein, the switch type only has two states of on and off, such as a lamp, a water dispenser and the like; the multi-state type device has a finite number of operational states including not only on and off states, but also other states, ratiosFor example, an air conditioner is a multi-state type device, which includes various states such as on, off, cooling, heating, etc.; the power of the continuous variation type equipment can be continuously changed, and no obvious conversion signal exists when the state is changed, such as a dimming lamp with continuously adjustable brightness; permanently operating devices, such as routers, television set-top boxes, etc., which maintain a constant power consumption over a long monitoring period, are relatively low powered. Each state of these devices corresponds to a power, for example, for an air conditioner, the power of the air conditioner is a when the air conditioner is turned on1The power of the air conditioning equipment during refrigeration is a2The power of the air conditioning equipment is a during heating3The power is 0 when the air conditioner is turned off.
Because the working modes of the household appliance are limited and the appliance has rated power in each working mode, the traditional non-intrusive load decomposition model can be reduced to the mapping relation of the decomposed power value to each appliance working mode, and the formula is as follows:
[Yt 1,Yt 2,…,Yt M]＝f(Xt)
Figure BDA0003082441730000091
wherein, XtRepresents the total power at time t;
Yt ithe power value of the ith electric appliance obtained by decomposition at the t moment is represented;
m represents a total of M electrical devices;
Figure BDA0003082441730000092
the working mode of the ith electric appliance at the t-th moment is shown;
the f-function represents a decomposition mapping function of Non-intrusive load monitoring (NILM).
Consider further, the formula
Figure BDA0003082441730000093
The number of the combination is finite, each combination case is expressed by a state code to realize dimension reduction, and the formula can be expressed as follows:
y＝[S1,S2,…,SM],Si∈{0,1,…,ni-1},i∈{1,2,3,...,M}
wherein y represents a status code; siIndicating the working mode of the ith electric appliance at a certain moment; n isiIndicates the number of operation modes the i-th electrical appliance has.
Assuming that M devices are to be decomposed, the total number N of state codes can be expressed as:
Figure BDA0003082441730000094
wherein N represents the total number of status codes of the M electrical devices.
In conjunction with the above equations, the conventional non-intrusive load detection problem can be transformed into the following equation:
y'＝f(Xt),y'∈{0,1,…,N-1}
wherein, XtRepresents the total power of all appliances; y' represents the code of the state code corresponding to the total power at the current moment; n represents the number of state codes possessed by the electric appliance; f denotes the NILM decomposition function.
Adopting a Seq2Seq model based on RNN, referring to FIG. 2, which is a structural diagram of a Seq2Seq network structure, an encoder (encoder) selects a bidirectional single-layer LSTM, a decoder (decoder) selects a single-layer LSTM, and a hidden state { h) corresponding to each moment of an input time sequence obtained in the encoding process is utilized1,h2,…,hlL represents the length of the input sequence, and an Attention mechanism is introduced to calculate to obtain a dynamic semantic vector CtAs part of the input to participate in the operation of the decoding process.
Then, the seq2seq model based on the RNN is trained, and the model training process is judged by taking the model loss function as a judgment standard, so as to obtain the model meeting the expected requirements. Specifically, the model loss function may adopt a cross entropy loss function, and the smaller the value of the cross entropy loss function is, the better the model training effect is represented. When the value of the cross entropy loss function no longer changes, it can be determined that training of the model can be stopped.
Network parameters are adjusted in a back propagation mode, so that the model is improved in training. In order to prevent the risk of the model falling into overfitting, methods such as Dropout and L2 regularization are introduced in the model training process.
And finally, carrying out load decomposition by using the trained seq2seq model based on the RNN to obtain power information of each electric device.
And S2, mining the user behavior characteristics based on the power consumption of each device.
And mining the user behavior characteristics based on the result of the non-invasive load decomposition, namely the power value of each electric device. Wherein the user behavior characteristics include four power consumption time characteristics and four load characteristics, for a total of eight specific characteristics, specifically,
acquiring electricity consumption time characteristics: and obtaining the electricity utilization time characteristic by considering the behavior habits of users based on the power value of each device obtained by non-invasive load decomposition. The electricity consumption time characteristics specifically include common operation periods, daily operation frequencies, common off periods, and daily off frequencies of different devices.
Acquiring load characteristics: and obtaining load characteristics by combining the power consumption of different electrical equipment and the power consumption based on the power value of each equipment obtained by non-invasive load decomposition. The load characteristics specifically include daily average power consumption of the devices of different users, daily maximum power consumption of the devices, maximum load hours of the devices, and daily average usage of the devices.
And S3, detecting abnormal electricity utilization behaviors by using a density peak value clustering algorithm based on the user behavior characteristics.
Referring to fig. 3, firstly, according to four characteristics of the electricity consumption time characteristics in the user behavior characteristics, the density clustering algorithm is adopted to preliminarily classify the users to obtain users of different categories, and then the KNN-based fast density peak clustering algorithm is adopted to realize abnormal electricity consumption behavior detection according to the four characteristics of the load characteristics in the user behavior characteristics for the users of different categories. The specific flow of the algorithm is as follows:
calculating any sample x in different equipment data sets of various usersaWith other samples xbEuclidean distance d (x) betweena,xb) And arranging the calculation results in ascending order, and recording the sample corresponding to the kth distance as Nk(xa) Then xaThe k nearest neighbors of (c) are:
N(xa)＝{xb∈X|d(xa,xb)≤d(xa,Nk(xa))}
with N (x)a) To calculate the local density
Figure BDA0003082441730000111
Figure BDA0003082441730000112
Wherein K is determined by the parameter p, and K is pNP is the percentage of the number of samples N, the local density
Figure BDA0003082441730000121
The larger the value of (A), the larger is xaThe greater the density of (a).
The distance between the KNN-based and the high-density point is given on the basis of the formula
Figure BDA0003082441730000122
Is defined as:
Figure BDA0003082441730000123
calculating the local density of each sample and the distance between the local density and the high-density point according to a formula
Figure BDA0003082441730000124
Based on samples obtained by the preceding step
Figure BDA0003082441730000125
And determining the users with abnormal electricity utilization behaviors.
The technical scheme is that clustering is carried out on different electric equipment sets, so that the performance of users on different electric equipment sets needs to be comprehensively considered in a weighting mode when abnormal values are detected, clustering can be more consistent with actual conditions, clustering results are more accurate, namely, the electric equipment sets obtained through decomposition are respectively calculated on different electric equipment sets
Figure BDA0003082441730000126
And then, carrying out average weighting on the values on the electric equipment sets, and judging the outlier by using the weighted values. Due to calculated
Figure BDA0003082441730000127
The value of (1) is corresponding to each user using the electric equipment, and finally, the user with abnormal electricity utilization behavior can be found according to the outlier. Specifically, the judgment conditions of the outliers are as follows:
when the local density of a certain point
Figure BDA0003082441730000128
Distance between two adjacent plates
Figure BDA0003082441730000129
Then the point is an outlier;
wherein the local density threshold
Figure BDA00030824417300001210
Is defined as:
Figure BDA00030824417300001211
distance threshold
Figure BDA00030824417300001212
Is defined as:
Figure BDA00030824417300001213
where b represents the device and b ∈ {1,2, 3.. eta., M }, a and represents the sample in the dataset, a ∈ {1,2, 3.. eta., N }, γ }ρAnd gammaδAre empirical parameters.
And finally, judging the user corresponding to the obtained outlier as the user with abnormal electricity utilization behavior.
Therefore, the whole process of the power utilization abnormity detection method based on non-invasive load decomposition is completed.
Example 2:
in a second aspect, the present invention further provides a power utilization anomaly detection system based on non-invasive load decomposition, including:
the load decomposition module is used for decomposing the total power consumption of the user based on a non-invasive load decomposition technology to obtain the power consumption of each device;
the user behavior feature mining module is used for acquiring user behavior features based on the power consumption of each device;
and the abnormal electricity utilization behavior detection module is used for realizing the abnormal electricity utilization behavior detection by utilizing a density peak value clustering algorithm based on the user behavior characteristics.
Preferably, the load decomposition module decomposing the total power consumption of the user based on a non-intrusive load decomposition technique to obtain the power consumption of each device includes:
s11, establishing a mapping relation between the decomposed total power consumption and the working modes of the electric equipment based on a non-invasive load decomposition model, and obtaining codes of state codes corresponding to the total power consumption of all the electric equipment based on the combination of the working modes of all the electric equipment;
s12, training an RNN-based seq2seq model based on the state code;
and S13, performing load decomposition by using the trained seq2seq model based on the RNN to obtain the power consumption of each device.
Preferably, the user behavior characteristics acquired by the user behavior characteristic mining module include power consumption time characteristics and load characteristics, wherein the power consumption time characteristics include common operation periods, daily operation frequencies, common shutdown periods and daily shutdown frequencies of different devices; the load characteristics comprise daily average power consumption of equipment of different users, daily maximum power consumption of the equipment, maximum load hours of the equipment and daily average usage of the equipment.
Preferably, the detecting module of abnormal electricity consumption behavior based on the user behavior characteristics and using a density peak clustering algorithm to detect abnormal electricity consumption behavior includes:
s31, primarily classifying the users by adopting a density clustering algorithm based on the electricity utilization time characteristics to obtain different classes of users;
and S32, aiming at the different classes of users, detecting abnormal electricity utilization behaviors by adopting a KNN-based density peak clustering algorithm according to load characteristics.
Preferably, the condition that the abnormal electricity consumption behavior detection module judges the abnormal electricity consumption behavior is as follows: when the local density of a certain point
Figure BDA0003082441730000141
Distance between two adjacent plates
Figure BDA0003082441730000142
If so, determining that the user is a user with abnormal electricity utilization behavior;
wherein the local density threshold
Figure BDA0003082441730000143
Is defined as:
Figure BDA0003082441730000144
distance threshold
Figure BDA0003082441730000145
Is defined as:
Figure BDA0003082441730000146
where b represents the device and b ∈ {1,2, 3.. eta., M }, a represents a sample in the dataset and a ∈ {1,2, 3.. eta., N }, γ }ρAnd γ δ are empirical parameters.
It can be understood that the power consumption anomaly detection system based on non-invasive load decomposition provided in the embodiment of the present invention corresponds to the above power consumption anomaly detection method based on non-invasive load decomposition, and the explanations, examples, and beneficial effects of the relevant contents thereof may refer to the corresponding contents in the power consumption anomaly detection method based on non-invasive load decomposition, and are not repeated herein.
In summary, compared with the prior art, the method has the following beneficial effects:
1. according to the technical scheme, total power consumption is decomposed on the basis of a non-invasive load decomposition technology to obtain power consumption of each device, user behavior characteristics are mined according to the power consumption of each device, and clustering is performed twice in sequence by using a density peak clustering algorithm and a KNN-based rapid density peak clustering algorithm on the basis of the user behavior characteristics, so that abnormal power consumption behaviors are detected. According to the technical scheme, the user behavior characteristics are extracted by using a non-invasive load decomposition technology, clustering is realized based on the user behavior characteristics, so that the detection result of the abnormal electricity utilization behavior of the user is obtained, the accuracy rate of the detection of the abnormal electricity utilization behavior is improved, and economic benefits are brought to electricity selling companies and energy service providers;
2. according to the technical scheme, the total power utilization of the electric equipment is decomposed by using a non-invasive load decomposition technology to obtain the power utilization of each equipment, and then user behavior characteristics including power utilization time characteristics and load characteristics are extracted from the power utilization of each equipment, so that the user behavior characteristics which can more accurately represent the power utilization behavior of the user can be obtained;
3. according to the technical scheme, the density peak clustering algorithm and the KNN-based rapid density peak clustering algorithm are utilized to cluster the power utilization time characteristic and the load characteristic of the user behavior characteristic for two times, the power utilization behavior recognition results of the users on different equipment sets are weighted when the final clustering result is obtained, and finally the abnormal power utilization behavior of the users is detected.
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
The above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.

Claims (10)

1. A method for detecting power consumption anomaly based on non-intrusive load decomposition, the method comprising:
decomposing the total power consumption of a user based on a non-invasive load decomposition technology to obtain the power consumption of each device;
acquiring user behavior characteristics based on the power consumption of each device;
and detecting abnormal electricity utilization behaviors by using a density peak value clustering algorithm based on the user behavior characteristics.
2. The method of claim 1, wherein the decomposing the total power usage of the user based on the non-intrusive load decomposition technique to obtain the power usage of each device comprises:
s11, establishing a mapping relation between the decomposed total power consumption and the working modes of the electric equipment based on a non-invasive load decomposition model, and obtaining codes of state codes corresponding to the total power consumption of all the electric equipment based on the combination of the working modes of all the electric equipment;
s12, training an RNN-based seq2seq model based on the state code;
and S13, performing load decomposition by using the trained seq2seq model based on the RNN to obtain the power consumption of each device.
3. The method of claim 1, wherein the user behavior characteristics include power usage time characteristics and load characteristics, wherein the power usage time characteristics include common operating periods, daily operating frequencies, common off periods, and daily off frequencies of different devices; the load characteristics comprise daily average power consumption of equipment of different users, daily maximum power consumption of the equipment, maximum load hours of the equipment and daily average usage of the equipment.
4. The method of claim 3, wherein the utilizing a density peak clustering algorithm to implement abnormal electricity usage behavior detection based on the user behavior feature comprises:
s31, primarily classifying the users by adopting a density clustering algorithm based on the electricity utilization time characteristics to obtain different classes of users;
and S32, aiming at the different classes of users, realizing abnormal electricity utilization behavior detection by adopting a KNN-based fast density peak value clustering algorithm according to load characteristics.
5. As claimed in claim 4The method is characterized in that the judgment condition for detecting the abnormal electricity consumption behavior is as follows: when the local density of a certain point
Figure FDA0003082441720000021
Distance between two adjacent plates
Figure FDA0003082441720000022
If so, determining that the user is a user with abnormal electricity utilization behavior;
wherein the local density threshold
Figure FDA0003082441720000023
Is defined as:
Figure FDA0003082441720000024
distance threshold
Figure FDA0003082441720000025
Is defined as:
Figure FDA0003082441720000026
where b denotes the device, b ∈ {1,2, 3.. eta., M }, a denotes the sample in the dataset, a ∈ {1,2, 3.. eta., N }, γ ∈ {1,2, 3.. eta., N }, γ ∈ρAnd gammaδAre empirical parameters.
6. An electricity usage anomaly detection system based on non-intrusive load shedding, the system comprising:
the load decomposition module is used for decomposing the total power consumption of the user based on a non-invasive load decomposition technology to obtain the power consumption of each device;
the user behavior feature mining module is used for acquiring user behavior features based on the power consumption of each device;
and the abnormal electricity utilization behavior detection module is used for realizing the abnormal electricity utilization behavior detection by utilizing a density peak value clustering algorithm based on the user behavior characteristics.
7. The system of claim 6, wherein the load decomposition module decomposing the total power usage of the user to obtain the power usage of each device based on a non-intrusive load decomposition technique comprises:
s11, establishing a mapping relation between the decomposed total power consumption and the working modes of the electric equipment based on a non-invasive load decomposition model, and obtaining codes of state codes corresponding to the total power consumption of all the electric equipment based on the combination of the working modes of all the electric equipment;
s12, training an RNN-based seq2seq model based on the state code;
and S13, performing load decomposition by using the trained seq2seq model based on the RNN to obtain the power consumption of each device.
8. The system of claim 6, wherein the user behavior characteristics obtained by the user behavior characteristics mining module comprise power consumption time characteristics and load characteristics, wherein the power consumption time characteristics comprise common operation periods, daily operation frequencies, common shutdown periods, and daily shutdown frequencies of different devices; the load characteristics comprise daily average power consumption of equipment of different users, daily maximum power consumption of the equipment, maximum load hours of the equipment and daily average usage of the equipment.
9. The system of claim 8, wherein the abnormal electricity usage behavior detection module implementing abnormal electricity usage behavior detection using a density peak clustering algorithm based on the user behavior characteristics comprises:
s31, primarily classifying the users by adopting a density clustering algorithm based on the electricity utilization time characteristics to obtain different classes of users;
and S32, aiming at the different classes of users, detecting abnormal electricity utilization behaviors by adopting a KNN-based density peak clustering algorithm according to load characteristics.
10. The system of claim 9, wherein the abnormal electricity usage behavior detection module determines the abnormal electricity usage behavior conditional on: when the local density of a certain point
Figure FDA0003082441720000031
Distance between two adjacent plates
Figure FDA0003082441720000032
If so, determining that the user is a user with abnormal electricity utilization behavior;
wherein the local density threshold
Figure FDA0003082441720000033
Is defined as:
Figure FDA0003082441720000034
distance threshold
Figure FDA0003082441720000035
Is defined as:
Figure FDA0003082441720000036
where b denotes the device, b ∈ {1,2, 3.. eta., M }, a denotes the sample in the dataset, a ∈ {1,2, 3.. eta., N }, γ ∈ {1,2, 3.. eta., N }, γ ∈ρAnd gammaδAre empirical parameters.
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