CN110378371B - Energy consumption abnormity detection method based on average neighbor distance abnormity factor - Google Patents
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Abstract
The invention provides an energy consumption abnormity detection method based on an average neighbor distance abnormity factor, which comprises the following steps: acquiring energy consumption data and converting the energy consumption data into alternating data; defining a time sequence characteristic value of the energy consumption data, and dividing the time sequence into subsequences which are respectively mapped to a four-dimensional characteristic space; respectively calculating average neighbor distance abnormal factors of the time subsequences in a four-dimensional feature space; processing the average neighbor distance abnormal factor of the subsequence to obtain an average neighbor distance abnormal factor of the time sequence; and calculating an average neighbor distance abnormal threshold according to the average neighbor distance abnormal factor, and judging whether the mode is abnormal or not. The energy consumption abnormity detection method based on the average neighbor distance abnormity factor eliminates the interference of mode abnormity detection, effectively improves the precision of mode abnormity detection, accurately positions the abnormity position, and effectively detects the abnormal data appearing in step-type and alternating performance consumption data.
Description
Technical Field
The invention relates to the technical field of energy consumption data anomaly detection and energy consumption state monitoring, in particular to an energy consumption anomaly detection method based on an average neighbor distance anomaly factor.
Background
With the development of industrial information technology, many high energy consuming enterprises have set up energy management systems to realize real-time acquisition of energy consumption data, with the purpose of optimizing energy operation and saving production cost. Therefore, high energy consumption enterprises also put higher requirements on real-time detection and monitoring of energy consumption data, and detection technologies with real-time performance and intelligence are needed. The production condition of the high energy consumption machine is complex, the long-term full load operation has high probability of abnormal energy consumption [1], [2], thereby generating a large amount of abnormal energy consumption data. In the production process, data of a plurality of high-energy consumption machines (such as a hydraulic machine and a polysilicon reduction furnace) has the characteristics of step, alternation, periodicity and the like. The abnormal energy consumption of the machine is accompanied by a great amount of energy loss and energy efficiency reduction, and even can cause shutdown and safety accidents which cannot be calculated, thereby influencing the normal production of the whole production line. Therefore, it is very important to develop a reliable, fast and automatic abnormal energy consumption detection technology. By using the new methods, production enterprises can monitor and process high-energy-consumption machines, avoid energy consumption loss and improve the energy efficiency of the machines. The pattern anomaly is a typical anomaly form in the energy consumption anomaly data, and the current challenge is to develop a reliable, rapid and automatic pattern anomaly detection technology.
At present, the abnormality detection is generally realized by manually inspecting and recording meter data, which has great hysteresis. And the energy consumption alarm threshold set through manual experience is a fixed and unchangeable threshold and cannot adapt to the requirement of real-time change of energy consumption data. The time series data abnormality detection method includes: distance-based anomaly detection, prediction-based anomaly detection, cluster-based anomaly detection, and the like [3] [4] [5]. The conventional anomaly detection methods are mainly classified into a local anomaly detection method and a global anomaly detection method.
The local anomaly detection method excessively emphasizes small local change, so that higher false detection rate and lower expandability are caused; the global anomaly detection method neglects local slight anomaly, so that the report missing rate is high. Their detection efficiency is low and adaptability is poor due to lack of proper optimization [6]. Therefore, the prior art cannot effectively detect abnormal data occurring in the step-wise and alternating performance consumption data.
Disclosure of Invention
The invention provides an energy consumption abnormity detection method based on an average neighbor distance abnormity factor, aiming at overcoming the technical defect that the existing energy consumption abnormity detection method cannot effectively detect abnormal data appearing in jump-type and alternating performance consumption data.
In order to solve the technical problems, the technical scheme of the invention is as follows:
an energy consumption anomaly detection method based on average neighbor distance anomaly factors comprises the following steps:
s1: acquiring energy consumption data, and converting the energy consumption data into alternating data by a rain flow counting method;
s2: defining a time sequence characteristic value of the energy consumption data, and dividing the time sequence into subsequences which are respectively mapped to a four-dimensional characteristic space;
s3: respectively calculating average neighbor distance abnormal factors of the time subsequences in a four-dimensional feature space;
s4: processing the average neighbor distance abnormal factor of the subsequence to obtain an average neighbor distance abnormal factor of the time sequence;
s5: and calculating an average neighbor distance abnormal threshold according to the average neighbor distance abnormal factor, and judging whether the mode is abnormal or not.
Wherein, the step S1 specifically includes the following steps:
s11: collecting energy consumption data to obtain a time sequence X = (X) of the energy consumption data 1 ,x 2 ,...x n ) The period length CL, the length m of the sliding window and the weight factor lambda of the abnormal characteristic are calculated;
s12: converting the energy consumption data into alternating data by a rain flow counting method, wherein the specific calculation formula is as follows:
(X i -X i-1 )(X i -X i+1 )<0i＝2,3,4,...,M-1；
wherein X i Energy consumption data collected on site; and the end points of the data segment are peak-valley points, filtering is carried out according to the standard of a peak-valley sequence, namely a PV sequence, all non-peak-valley points are deleted, and the interference data of an ascending slope and a descending slope are deleted to obtain alternating data.
Wherein, the step S2 specifically includes the following steps:
s21: time series X = (X) according to sliding window length m 1 ,x 2 ,...x n ) Intercepting into a plurality of subsequences;
s22: defining four characteristic values of the time subsequence, specifically including:
the subsequence height h is defined as:
h＝x max -x min ；
subsequence mean value
Is defined as:
variance σ of the subsequence 2 Is defined as:
the maximum adjacent distance m of a subsequence is defined as:
m＝max(|x i -x i-1 |),(i＝2,3,4...n)；
s23: mapping temporal subsequences to four-dimensional feature space
In (1).
In the step S3, energy data are clustered into 2 types by adopting a K-Means clustering method, and then average neighbor distance abnormal factors of the 2 types of time sequences are respectively calculated by adopting an average neighbor distance abnormal factor MNNDAF algorithm.
The MNNDAF algorithm specifically comprises the following steps:
s31: setting point
And point
Into a four-dimensional feature space
At an arbitrary point of (2), the Euclidean distance of the feature spaceExpressed as:
s32: defining the average nearest neighbor distance MNND as the average value of the distances between the point p and all the adjacent points, and recording the k-nearest neighbor distance of the point p as k-dist (p); let k be an element of N +,
the average nearest neighbor distance MNND for point p is defined as:
s33: is provided with
The eigenvalue subspaces of the four eigenvalues of point C are each C h (h 1 ,h 2 ,...,h n ),
C σ (σ 1 ,σ 2 ,...,σ n ),C m (m 1 ,m 2 ,...,m n ) Separately calculating the point c in the four-dimensional feature space
And the average nearest neighbor distance in the feature value subspace, MNND, MNNDh (c),
MNND σ (c) and MNNDm (c), so far, the mean neighbor distance anomaly factor for point c is:
MNNDAF(c)＝MNND(c)+max{X}；
wherein,
λ is a weighting factor for the anomaly characteristic.
Wherein, the step S4 specifically includes the following steps:
s41: interpolating the average neighbor distance anomaly factor value of the subsequence in the 2 types of energy data;
s42: normalizing the interpolated energy data;
s43: and reordering the energy data after the normalization processing according to the sequence of the original data to obtain the average neighbor distance abnormal factor of the time sequence.
The specific process of the normalization processing in step S42 is:
establishing a mapping from c to c' such that L of c 2 Norm 1, then:
wherein the eigenvalue vector c (c) 1 ,c 2 ,...c n ) L of 2 Norm of
Is a normalized coefficient.
In step S5, a specific calculation formula of the average neighbor distance anomaly threshold δ is:
wherein alpha is a mode abnormity coefficient set according to actual conditions, and if MNNDAF is larger than delta, the time series of the energy consumption data is judged to be mode abnormity.
Compared with the prior art, the technical scheme of the invention has the beneficial effects that:
the invention provides an energy consumption anomaly detection method based on average neighbor distance anomaly factors, which adopts a rain flow counting method to eliminate the interference of mode anomaly detection; improving the accuracy of mode anomaly detection by adopting a K-Means clustering method, and accurately positioning the anomaly position; the MNNDAF algorithm is adopted to accurately detect the mode abnormity of the energy data time sequence, and the abnormal data appearing in the step-type and alternating performance consumption data are effectively detected by the method.
Drawings
FIG. 1 is a schematic flow diagram of the process;
FIG. 2 is a schematic time series diagram of raw energy data;
FIG. 3 is a schematic diagram of a time series of energy data processed by a rain flow counting method;
fig. 4 is a schematic diagram of a pattern abnormality detection result.
Detailed Description
The drawings are for illustrative purposes only and are not to be construed as limiting the patent;
for the purpose of better illustrating the present embodiments, certain elements of the drawings may be omitted, enlarged or reduced, and do not represent the size of an actual product;
it will be understood by those skilled in the art that certain well-known structures in the drawings and descriptions thereof may be omitted.
The technical solution of the present invention is further described with reference to the drawings and the embodiments.
Example 1
As shown in fig. 1, an energy consumption anomaly detection method based on an average neighbor distance anomaly factor includes the following steps:
s1: acquiring energy consumption data, and converting the energy consumption data into alternating data by a rain flow counting method;
s2: defining a time sequence characteristic value of the energy consumption data, and dividing the time sequence into subsequences which are respectively mapped to a four-dimensional characteristic space;
s3: respectively calculating average neighbor distance abnormal factors of the time subsequences in a four-dimensional feature space;
s4: processing the average neighbor distance abnormal factor of the subsequence to obtain an average neighbor distance abnormal factor of the time sequence;
s5: and calculating an average neighbor distance abnormal threshold according to the average neighbor distance abnormal factor, and judging whether the mode is abnormal.
More specifically, the step S1 specifically includes the following steps:
s11: acquiring energy consumption data to obtain a time series X = (X) of the energy consumption data 1 ,x 2 ,...x n ) The period length CL, the length m of the sliding window and the weight factor lambda of the abnormal characteristic are calculated;
s12: converting the energy consumption data into alternating data by a rain flow counting method, wherein the specific calculation formula is as follows:
(X i -X i-1 )(X i -X i+1 )<0i＝2,3,4,...,M-1；
wherein, X i Energy consumption data collected on site; and the end points of the data segment are peak-valley points, filtering is carried out according to a peak-valley sequence, namely the standard of a PV sequence, all non-peak-valley points are deleted, and the interference data of an ascending slope and a descending slope are deleted to obtain alternating data.
More specifically, the step S2 specifically includes the following steps:
s21: time series X = (X) according to sliding window length m 1 ,x 2 ,...x n ) Intercepting into a plurality of subsequences;
s22: defining four characteristic values of the time subsequence, specifically including:
the subsequence height h is defined as:
h＝x max -x min ；
subsequence mean value
Is defined as:
variance σ of the subsequence 2 Is defined as:
the maximum neighbor distance m of a subsequence is defined as:
m＝max(|x i -x i-1 |),(i＝2,3,4...n)；
s23: mapping temporal subsequences to four-dimensional feature space
In (1).
More specifically, in the step S3, the energy data are clustered into 2 classes by using a K-Means clustering method, and then the average neighbor distance anomaly factors of the 2 classes of time series are respectively calculated by using an average neighbor distance anomaly factor mnnda algorithm.
More specifically, the mnnda algorithm specifically includes the following steps:
s31: setting point
And point
In a four-dimensional feature space
The euclidean distance of the feature space is then expressed as:
s32: defining the average nearest neighbor distance MNND as the average value of the distances between the point p and all the near neighbor points, and recording the k-nearest neighbor distance of the point p as k-dist (p); let k be equal to N + ,
The average nearest neighbor distance MNND for point p is defined as:
s33: is provided with
The feature value subspaces of the four feature values of point C are C h (h 1 ,h 2 ,...,h n ),
C σ (σ 1 ,σ 2 ,...,σ n ),C m (m 1 ,m 2 ,...,m n ) Separately computing the point c in a four-dimensional feature space
And the average nearest neighbor distance in the feature value subspace, MNND, MNNDh (c),
MNND σ (c) and MNNDm (c), so far, the average neighbor distance anomaly factor for point c is:
MNNDAF(c)＝MNND(c)+max{X}；
wherein,
λ is a weighting factor for the anomaly characteristic.
More specifically, the step S4 specifically includes the following steps:
s41: interpolating the average neighbor distance anomaly factor value of the subsequence in the 2 types of energy data;
s42: normalizing the interpolated energy data;
s43: and reordering the energy data after the normalization processing according to the sequence of the original data to obtain the average neighbor distance abnormal factor of the time sequence.
More specifically, the specific process of the normalization process in step S42 is:
establishing a mapping from c to c' such that L of c 2 Norm 1, then:
wherein the eigenvalue vector c (c) 1 ,c 2 ,...c n ) L of 2 Norm is
Is a normalized coefficient.
More specifically, in step S5, the specific calculation formula of the average neighbor distance anomaly threshold δ is as follows:
wherein alpha is a mode abnormity coefficient set according to actual conditions, and if MNNDAF is larger than delta, the time series of the energy consumption data is judged to be mode abnormity.
In the specific implementation process, the method adopts a rain flow counting method to eliminate the interference of mode anomaly detection; improving the accuracy of mode anomaly detection by adopting a K-Means clustering method, and accurately positioning the anomaly position; the MNNDAF algorithm is adopted to accurately detect the mode abnormity of the energy data time sequence, and the abnormal data appearing in the step-type and alternating performance consumption data are effectively detected by the method.
Example 2
More specifically, on the basis of embodiment 1, the proposed anomaly detection method is implemented by MATLAB programming, and the compiling tool is: MATLAB R2018a, runtime environment: windows 7 or above, hardware: client computer CPU3.3G, and memory 4,0M.
In the specific implementation process, firstly, the collected energy data is imported, and relevant parameters are adjusted, specifically including: cycle length CL =100; setting a classification number, namely the number of the centers of the initial clusters n =2; sliding window length m =5; normalized coefficient
The mode anomaly coefficient α =1.4.
As shown in fig. 2, there are 5 periods in the original energy data time series diagram. The black oval marks the three mode anomalies that need to be detected. Data at the positions of the 'climbing' and the 'descending' and data of 'trial extrusion' before the 'climbing' are interference data, and the interference data need to be cleared before detection.
The time sequence of the energy data processed by the rain flow counting method is shown in fig. 3, and it can be seen that interference data at the position of the trial extrusion before the climbing, the downhill and the climbing are removed by the processing of the rain flow counting method; the data segment of the load forward is translated upwards by a distance d, so that the distinguishing degree of no-load operation and load operation is effectively improved, and a K-Means clustering method is used for classification; as can be seen from fig. 4, the anomaly detection method provided by the present invention correctly detects the mode anomalies at the "no-load operation" and the "load operation".
It should be understood that the above-described embodiments of the present invention are merely examples for clearly illustrating the present invention and are not intended to limit the embodiments of the present invention. Other variations and modifications will be apparent to persons skilled in the art in light of the above description. And are neither required nor exhaustive of all embodiments. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention should be included in the protection scope of the claims of the present invention.
[1]Li L,Huang H,Zhao F,et al.Operation scheduling of multi–hydraulic press system for energy consumption reduction[J].Journal of Cleaner Production,2017,165.
[2]Gao M,Li X,Huang H,et al.Energy–saving Methods for Hydraulic Presses Based on Energy Dissipation Analysis[J].Procedia Cirp,2016,48:331–335.
[3]Ahmed M,Mahmood A N,Maher M J.Heart Disease Diagnosis Using Co–clustering[M]//Scalable Information Systems.Springer International Publishing,2014:61–70.
[4]Cecílio I M,Ottewill J R,Pretlove J,et al.Nearest neighbors method for detecting transient disturbances in process and electromechanical systems[J].Journal of Process Control,2014,24(9):1382–1393.
[5]Drugman T.Using mutual information in supervised temporal event detection:Application to cough detection[J].Biomedical Signal Processing&Control,2014,10(1):50–57.
[6]Ren H,Ye Z,Li Z.Anomaly detection based on a dynamic Markov model[J].Information Sciences,2017,411.
Claims (4)
1. An energy consumption anomaly detection method based on an average neighbor distance anomaly factor is characterized by comprising the following steps:
s1: acquiring energy consumption data, and converting the energy consumption data into alternating data by a rain flow counting method;
s2: defining a time sequence characteristic value of the energy consumption data, and dividing the time sequence into subsequences which are respectively mapped to a four-dimensional characteristic space;
s3: respectively calculating average neighbor distance abnormal factors of the time subsequences in a four-dimensional feature space;
s4: processing the average neighbor distance abnormal factor of the subsequence to obtain an average neighbor distance abnormal factor of the time sequence;
s5: calculating an average neighbor distance abnormal threshold according to the average neighbor distance abnormal factor, and judging whether a mode is abnormal or not;
the step S1 specifically includes the steps of:
s11: acquiring energy consumption data to obtain a time series X = (X) of the energy consumption data 1 ,x 2 ,...x n ) The period length CL, the length m of the sliding window and the weight factor lambda of the abnormal characteristic are calculated;
s12: converting the energy consumption data into alternating data by a rain flow counting method, wherein the specific calculation formula is as follows:
(X i -X i-1 )(X i -X i+1 )＜0
wherein i =2,3,4., M-1,X i Energy consumption data collected on site; the end points of the data segments are peak-valley points, filtering is carried out according to the standard of a peak-valley sequence, namely a PV sequence, all non-peak-valley points are deleted, and interference data of an ascending slope and a descending slope are deleted to obtain alternating data;
the step S2 specifically includes the following steps:
s21: time series X = (X) according to sliding window length m 1 ,x 2 ,...x n ) Intercepting a plurality of subsequences;
s22: defining four characteristic values of the time subsequence, specifically including:
the subsequence height h is defined as:
h＝x max -x min ；
subsequence mean value
Is defined as follows:
variance σ of the subsequence 2 Is defined as:
the maximum adjacent distance m of a subsequence is defined as:
m＝max(|x i -x i-1 |),(i＝2,3,4...n)；
s23: mapping temporal subsequences to four-dimensional feature space
The preparation method comprises the following steps of (1) performing;
s3, clustering the energy data into 2 types by adopting a K-Means clustering method, and calculating average neighbor distance abnormal factors of 2 types of time sequences respectively by adopting an average neighbor distance abnormal factor MNNDAF algorithm;
the MNNDAF algorithm specifically comprises the following steps:
s31: setting point
And point
Into a four-dimensional feature space
The euclidean distance of the feature space is then expressed as:
s32: defining the average nearest neighbor distance MNND as the average value of the distances between the point p and all the near neighbor points, and recording the k-nearest neighbor distance of the point p as k-dist (p); let k be an element of N + ,
The average nearest neighbor distance MNND for point p is defined as:
s33: is provided with
The feature value subspaces of the four feature values of point C are C h (h 1 ,h 2 ,...,h n ),
C σ (σ 1 ,σ 2 ,...,σ n ),C m (m 1 ,m 2 ,...,m n ) Separately calculating the point c in the four-dimensional feature space
And the average nearest neighbor distance in the feature value subspace, MNND, MNNDh (c),
MNND σ (c) and MNNDm (c), so far, the mean neighbor distance anomaly factor for point c is:
MNNDAF(c)＝MNND(c)+max{X}；
wherein,
λ is a weighting factor for the anomaly characteristic.
2. The method according to claim 1, wherein the step S4 specifically includes the following steps:
s41: interpolating the average neighbor distance anomaly factor value of the subsequence in the 2 types of energy data;
s42: normalizing the interpolated energy data;
s43: and reordering the energy data after the normalization processing according to the sequence of the original data to obtain the average neighbor distance abnormal factor of the time sequence.
3. The method according to claim 2, wherein the normalization in step S42 is performed by:
establishing a mapping from c to c' such that L of c 2 Norm 1, then:
wherein the eigenvalue vector c (c) 1 ,c 2 ,...c n ) L of 2 Norm of
Is a normalized coefficient.
4. The method for detecting energy consumption anomaly based on average nearest neighbor distance anomaly factor according to claim 3, wherein in said step S5, the specific calculation formula of the average nearest neighbor distance anomaly threshold δ is:
wherein α is a mode abnormality coefficient set according to an actual condition, and if MNNDAF > δ, the time series of the energy consumption data is determined as a mode abnormality.
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