CN113344346B - Power utilization abnormity detection method and system based on non-intrusive load decomposition - Google Patents

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

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CN113344346B
CN113344346B CN202110570582.7A CN202110570582A CN113344346B CN 113344346 B CN113344346 B CN 113344346B CN 202110570582 A CN202110570582 A CN 202110570582A CN 113344346 B CN113344346 B CN 113344346B
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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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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-intrusive 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 a user is changed from destroying the traditional power meter or a 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 power utilization characteristics of the users and finding out a few abnormal users which do not accord with the power 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 working modes of all electric equipment based on a non-invasive load decomposition model, and obtaining codes of state codes corresponding to the total power consumption of all electric equipment based on the combination of the working modes of all electric equipment;
s12, training a seq2seq model based on RNN based on the coding of the state code;
and S13, carrying out 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 the average daily power consumption of equipment of different users, the maximum daily power consumption of the equipment, the maximum load hours of the equipment and the average daily consumption of the equipment.
Preferably, the detecting abnormal electricity consumption 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, detecting abnormal electricity utilization behaviors 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 follows:
Figure BDA0003082441730000034
distance threshold
Figure BDA0003082441730000035
Is defined as:
Figure BDA0003082441730000036
where b represents the device, and b ∈ {1,2, 3., M }, a represents the sample in the dataset, and a ∈ {1,2, 3., N }, γ ∈ {1,2, 3., N } ρ And gamma δ Are empirical parameters.
In a second aspect, the present invention further provides a power consumption anomaly detection system based on non-intrusive load decomposition, where the system includes:
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 working modes of all electric equipment based on a non-invasive load decomposition model, and obtaining codes of state codes corresponding to the total power consumption of all electric equipment based on the combination of the working modes of all electric equipment;
s12, training a seq2seq model based on RNN based on the coding of 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 obtained by the user behavior characteristic mining module include power utilization time characteristics and load characteristics, wherein the power utilization time characteristics include common operation periods, daily operation frequencies, common shutdown periods and daily shutdown frequencies of different devices; the load characteristics comprise the average daily power consumption of equipment of different users, the maximum daily power consumption of the equipment, the maximum load hours of the equipment and the average daily consumption 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 value 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 devices
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., M }, a represents the sample in the dataset, and a ∈ {1,2, 3., N }, γ ∈ {1,2, 3., N } ρ And gamma δ Are 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-intrusive 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 the user behavior characteristics including the power utilization time characteristics and the 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 embodiments or the prior art descriptions will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and other drawings can be obtained by those skilled in the art 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 according to 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
To make the objects, technical solutions and advantages of the embodiments of the present invention clearer and more complete description of the technical solutions in the embodiments of the present invention, it is obvious that the described embodiments are some, but not all, embodiments of the present invention. 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 digs 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 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 result, thereby realizing the accurate detection of the abnormal electricity consumption behavior.
In order to better understand the technical scheme, the technical scheme is described in detail in the following with reference to the attached drawings of the specification and 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 a 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 utilization is decomposed on the basis of a non-intrusive load decomposition technology to obtain the power utilization of each device, then the user behavior characteristics are mined according to the power utilization of each device, and finally clustering is performed twice on the basis of the user behavior characteristics by using a density peak clustering algorithm and a KNN-based rapid density peak clustering algorithm, so that the abnormal power utilization 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 method according to the embodiment of the present invention, in order to avoid cost consumption caused by methods such as intrusive load decomposition when decomposing a load, in the present technical solution, the total power consumption of the electrical equipment is decomposed by using a non-intrusive load decomposition technique, and at this time, a preferred processing manner is that the decomposing the total power consumption of the user based on the non-intrusive load decomposition technique to obtain the power consumption of each equipment includes:
s11, establishing a mapping relation between the decomposed total power consumption and working modes of all electric equipment based on a non-invasive load decomposition model, and obtaining codes of state codes corresponding to the total power consumption of all electric equipment based on the combination of the working modes of all electric equipment;
s12, training a seq2seq model based on RNN based on the coding of 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, detecting abnormal electricity utilization behaviors 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 of local density and distance are selected for judgment, and specifically, the abnormal electricity utilization behaviorThe judgment conditions for detection are as follows: when the local density of a certain point
Figure BDA0003082441730000081
Distance between two adjacent devices
Figure BDA0003082441730000082
If so, determining that the user is a user with abnormal electricity utilization behavior;
wherein the local density threshold value
Figure BDA0003082441730000083
Is defined as:
Figure BDA0003082441730000084
distance threshold
Figure BDA0003082441730000085
Is defined as follows:
Figure BDA0003082441730000086
where b represents the device, and b ∈ {1,2, 3., M }, a represents the sample in the dataset, and a ∈ {1,2, 3., N }, γ ∈ {1,2, 3., N } ρ And gamma δ Are empirical parameters.
The following describes the implementation of an embodiment of the present invention in detail with reference to the explanation of specific steps.
S1, decomposing the total power consumption of a 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 has only two states of on and off, such as lamp and drinking waterMachines, etc.; the multi-state type device has a limited number of operating states, which include not only on and off states, but also other states, for example, an air conditioner is a multi-state type device, which includes on, off, cooling, heating, and other states; 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 running devices, such as routers, television set-top boxes, etc., which have relatively low power consumption, maintain constant power consumption for long monitoring periods. 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 on 1 The power of the air conditioning equipment during refrigeration is a 2 The power of the air conditioning equipment is a when heating 3 The 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:
[Y t 1 ,Y t 2 ,…,Y t M ]＝f(X t )
Figure BDA0003082441730000091
wherein, X t Represents the total power at time t;
Y t i the 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＝[S 1 ,S 2 ,…,S M ],S i ∈{0,1,…,n i -1},i∈{1,2,3,...,M}
wherein y represents a status code; s i Indicating the working mode of the ith electric appliance at a certain moment; n is a radical of an alkyl radical i Indicates 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(X t ),y'∈{0,1,…,N-1}
wherein X t Represents 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 of 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 utilized 1 ,h 2 ,…,h l L represents the length of the input sequence, and an Attention mechanism is introduced to calculate to obtain a dynamic semantic vector C t As 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 a 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 over-fitting the model, methods such as Dropout and L2 regularization are introduced in the model training process.
And finally, performing load decomposition by using the trained seq2seq model based on the RNN to obtain power information of each electric device.
And S2, mining 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-intrusive 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, a density clustering algorithm is adopted to preliminarily classify the users to obtain users of different categories, and then a 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 users a With other samples x b Euclidean distance d (x) therebetween a ,x b ) And arranging the calculation results in ascending order, and recording the sample corresponding to the kth distance as N k (x a ) Then x a The k nearest neighbors of (c) are:
N(x a )＝{x b ∈X|d(x a ,x b )≤d(x a ,N k (x a ))}
with N (x) a ) To calculate the local density
Figure BDA0003082441730000111
Figure BDA0003082441730000112
Wherein K is defined by the parameter p, K = p N P is the percentage of the number of samples N, the local density
Figure BDA0003082441730000121
The larger the value of (A), the larger is x a The greater the density of (a).
The distance between the KNN-based point and the high-density point is given on the basis of the formula
Figure BDA0003082441730000122
Is defined as follows:
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 better accord with the actual condition, clustering results are more accurate, namely, for the electric equipment sets obtained through decomposition, the clustering results 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 calculation of
Figure BDA0003082441730000127
The value of (2) 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 devices
Figure BDA0003082441730000129
Then the point is an outlier;
wherein the local density threshold value
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., M }, a and represents the sample in the dataset, and a ∈ {1,2, 3., N }, γ ∈ {1,2, 3., N } ρ And gamma δ Are empirical parameters.
And finally, judging the user corresponding to the obtained outlier as a 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 consumption anomaly detection system based on non-intrusive 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 working modes of all electric equipment based on a non-invasive load decomposition model, and obtaining codes of state codes corresponding to the total power consumption of all electric equipment based on the combination of the working modes of all electric equipment;
s12, training a seq2seq model based on RNN based on the coding of 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 obtained by the user behavior characteristic mining module include power utilization time characteristics and load characteristics, wherein the power 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.
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 power 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 value
Figure BDA0003082441730000143
Is defined as follows:
Figure BDA0003082441730000144
distance threshold
Figure BDA0003082441730000145
Is defined as:
Figure BDA0003082441730000146
where b represents the device, and b ∈ {1,2, 3., M }, a represents the sample in the dataset, and a ∈ {1,2, 3., N }, γ ∈ {1,2, 3., N } ρ And 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-intrusive 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 behavior detection is achieved. According to the technical scheme, the user behavior characteristics are extracted by using a non-intrusive 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 phrases "comprising a," "8230," "8230," or "comprising" does not exclude the presence of additional like 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 (4)

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;
detecting abnormal electricity utilization behaviors by using a density peak value clustering algorithm based on the user behavior characteristics;
the user behavior characteristics comprise power utilization time characteristics and load characteristics, wherein the power utilization time characteristics comprise common operation periods, daily operation frequencies, common closing periods and daily closing 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 consumption of the equipment;
the method for realizing abnormal electricity utilization behavior detection by using a density peak value clustering algorithm based on the user behavior characteristics comprises the following steps:
s31, primarily classifying the users by adopting a density clustering algorithm based on the electricity utilization time characteristics to obtain different classes of users;
s32, aiming at the different classes of users, detecting abnormal electricity utilization behaviors by adopting a KNN-based fast density peak value clustering algorithm according to load characteristics;
the S31 and S32 include: calculating any sample x in different equipment data sets of various users a With other samples x b Euclidean distance d (x) between a ,x b ) And arranging the calculation results in ascending order, and recording the sample corresponding to the kth distance as N k (x a ) Then x a The k nearest neighbors of (c) are:
N(x a )＝{x b ∈X|d(x a ,x b )≤d(x a ,N k (x a ))}
with N (x) a ) To calculate the local density
Figure FDA0003796969960000011
Figure FDA0003796969960000012
Where K is determined by the parameter p, K = pN, p being the percentage of the number of samples N;
the distance between the KNN-based and the high-density point is given on the basis of the formula
Figure FDA0003796969960000013
Is defined as follows:
Figure FDA0003796969960000021
calculating the local density of each sample and the distance between the local density and the high-density point according to a formula
Figure FDA0003796969960000022
Based on samples obtained by the preceding calculation
Figure FDA0003796969960000023
Determining a user with abnormal electricity utilization behaviors;
for the electric equipment sets obtained through the decomposition, the calculation is respectively carried out on different electric equipment sets
Figure FDA0003796969960000024
The values on the power utilization equipment sets are weighted averagely, then outliers are judged by using the weighted values, and users with abnormal power utilization behaviors are detected according to the outliers;
the judgment conditions for detecting the abnormal electricity utilization behavior are as follows: when the local density of a certain point
Figure FDA0003796969960000025
Distance between two adjacent plates
Figure FDA0003796969960000026
If so, determining that the user is a user with abnormal electricity utilization behavior;
wherein the local density threshold
Figure FDA0003796969960000027
Is defined as:
Figure FDA0003796969960000028
distance threshold
Figure FDA0003796969960000029
Is defined as:
Figure FDA00037969699600000210
where b represents the device, b is in {1,2, 3.., M }, a represents the sample in the dataset, a is in {1,2, 3.., N }, γ is in the dataset ρ And gamma δ Are empirical parameters.
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 working modes of all electric equipment based on a non-invasive load decomposition model, and obtaining codes of state codes corresponding to the total power consumption of all electric equipment based on the combination of the working modes of all electric equipment;
s12, training a seq2seq model based on RNN based on the coding of the state code;
and S13, carrying out load decomposition by using the trained seq2seq model based on the RNN to obtain the power consumption of each device.
3. A system for detecting power usage anomalies based on non-intrusive load splitting, 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;
the abnormal electricity utilization behavior detection module is used for realizing abnormal electricity utilization behavior detection by utilizing a density peak value clustering algorithm based on the user behavior characteristics;
the user behavior characteristics comprise power utilization time characteristics and load characteristics, wherein the power utilization time characteristics comprise common operation periods, daily operation frequencies, common closing periods and daily closing 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 consumption of the equipment;
the method for realizing abnormal electricity utilization behavior detection by using a density peak value clustering algorithm based on the user behavior characteristics comprises the following steps:
s31, primarily classifying the users by adopting a density clustering algorithm based on the electricity utilization time characteristics to obtain different classes of users;
s32, aiming at the different classes of users, detecting abnormal electricity utilization behaviors by adopting a KNN-based fast density peak value clustering algorithm according to load characteristics;
the S31 and S32 include: calculating any sample x in different equipment data sets of various users a With other samples x b Euclidean distance d (x) therebetween a ,x b ) And arranging the calculation results in ascending order, and recording the sample corresponding to the kth distance as N k (x a ) Then x a The k nearest neighbors of (c) are:
N(x a )＝{x b ∈X|d(x a ,x b )≤d(x a ,N k (x a ))}
with N (x) a ) To calculate the local density
Figure FDA0003796969960000031
Figure FDA0003796969960000032
Where K is determined by the parameter p, K = pN, p being the percentage of the number of samples N;
the distance between the KNN-based point and the high-density point is given on the basis of the formula
Figure FDA0003796969960000041
Is defined as:
Figure FDA0003796969960000042
calculating the local density of each sample and the distance between the local density and the high-density point according to a formula
Figure FDA0003796969960000043
Based on samples obtained by the preceding calculation
Figure FDA0003796969960000044
Determining a user with abnormal electricity utilization behavior; for the electric equipment sets obtained through the decomposition, the calculation is respectively carried out on different electric equipment sets
Figure FDA0003796969960000045
The values on the power utilization equipment sets are weighted averagely, then outliers are judged by using the weighted values, and users with abnormal power utilization behaviors are detected according to the outliers;
the judgment conditions for detecting the abnormal electricity utilization behavior are as follows: when the local density of a certain point
Figure FDA0003796969960000046
Distance between two adjacent plates
Figure FDA0003796969960000047
If so, determining that the user is a user with abnormal electricity utilization behavior;
wherein the local density threshold
Figure FDA0003796969960000048
Is defined as:
Figure FDA0003796969960000049
distance threshold
Figure FDA00037969699600000410
Is defined as:
Figure FDA00037969699600000411
where b represents the device, b is in {1,2, 3.., M }, a represents the sample in the dataset, a is in {1,2, 3.., N }, γ is in the dataset ρ And gamma δ Are empirical parameters.
4. The system of claim 3, 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 decomposed total power utilization and working modes of all electric equipment based on a non-invasive load decomposition model, and obtaining codes of state codes corresponding to the total power utilization of all electric equipment based on the combination of the working modes of all electric equipment;
s12, training a seq2seq model based on RNN based on the coding of 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.
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