CN107248086A - Advertisement putting aided analysis method based on user power utilization behavioural analysis - Google Patents
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Abstract
The invention discloses a kind of advertisement putting aided analysis method based on user power utilization behavioural analysis, it is characterized in that：Including Data Collection；The data collected, sequence of operations is carried out to the noise of data；Normalized；K means are clustered；Obtained result is clustered according to K means, the user power utilization flow characteristic of each classification is analyzed, it is determined that per the power consumption characteristics of class user；According to the result of cluster, the different electricity consumption behavioural characteristics of each classification Electricity customers of comparative analysis, the business delivered with regard to auxiliary ad-vertisement is promoted.The present invention provides different zones consumption of resident Capability Measurement, consumer goods concern direction prediction for enterprise customer, and advertisement orientation is carried out by its product of auxiliary enterprises and is delivered.
Description
Technical field
The present invention relates to a kind of advertisement putting aided analysis method based on user power utilization behavioural analysis.
Background technology
Current State Grid Corporation of China applies marketing information system, the progressively development of power information acquisition system, have accumulated each
Each sea of retribution amount power information of row, and with the development of intelligent power grid technology, intelligent gateway, smart jack equipment progressively popularize so that
User power utilization Data acquisition and storage becomes more convenient, and these data have that data volume is big, data type is various, data are real-time
Property high big data essential characteristic, Guo Wang intra-companies sales service aid decision is mainly used at present, to government, enterprise visitor
The data analysis at family is served by being extremely limited.
Strengthened research with " smart city " project in each experimental city is carried out, it is desirable to inter-trade, region, association of department
Make, break the obstacle of Information application, realize interconnecting for information, thus new demand is proposed to the application of power network big data, i.e.,
By customer electricity information calmodulin binding domain CaM historical data be government, corporate client offer customize scene analysis service, these determine
System analysis scene consigns to client by way of analysis report, data visualization, formulation service, is that State Grid Corporation of China creates
Economic value.
Customer electricity information based on power information acquisition system, all electric quantity datas, one in being related to according to preresearch estimates
3 years historical datas of individual province (city) company are about 50T, every year newly-increased about 20T, meet big data scale greatly, data type is various, number
According to the ageing high feature of processing.These electricity consumption datas except meet interior business lifting with improvement in terms of decision support in addition to,
Custom analysis service can be equally provided with Government, enterprise customer, such as Government provides region residential building vacancy rate point
Analysis, the every making policies of prediction economic development trend service direction government；Towards corporate client provide trade investment addressing,
Advertisement orientation delivers measuring and calculating service auxiliary enterprises and carries out trade investment decision-making.
The content of the invention
It is an object of the invention to provide one kind by analyzing different zones residential electricity consumption behavior, calmodulin binding domain CaM commercial consumption
Related many external datas, are modeled using clustering, segment residential electricity consumption consumption feature, excavate each region consumption of resident custom,
So as to provide different zones consumption of resident Capability Measurement, consumer goods concern direction prediction for enterprise customer, using auxiliary enterprises as it
Product carries out the advertisement putting aided analysis method based on user power utilization behavioural analysis that advertisement orientation is delivered.
The present invention technical solution be：
A kind of advertisement putting aided analysis method based on user power utilization behavioural analysis, it is characterized in that：Comprise the following steps：
(1) Data Collection, with reference to business demand, collects the power consumption data of user；
(2) data collected, sequence of operations is carried out to the noise of data：
Outlier processing：Exceptional value judges that Main Basiss method has two kinds, and one kind is Pauta Criterion, and method is simply easy
In operation, this method is that the most frequently used exceptional value judges to meet the number of totality x Normal Distributions for data with rejecting criterion
According to having：
P(|x‐μ|>3σ)≤0.003
Wherein, μ and σ represent the mathematic expectaion and standard deviation of normal population respectively, and x is actual observed value, and P is that observation exists
Probability outside the standard deviation of 3 times of the left and right of average；
Now occur being more than the σ of μ+3 or the data probability very little less than μ -3 σ in data, therefore can be picked as exceptional value
Except the partial data；
Another is standardized value method, the data Normal Distribution after Z score standardization；This method can recognize different
Constant value, data of the Z score less than -3 or higher than 3 are exceptional value；Criterion score (Z score) formula is：
Z=(x- μ)/σ
Wherein μ and σ represent the mathematic expectaion and standard deviation of normal population respectively；
KNN algorithms fill missing values：Missing Data Filling method based on k nearest neighbor, allows for the characteristic of electric power data, leads to
Cross to select and close on data near how many missing values, to calculate missing values, to select the data of K arest neighbors, and to not
The data setting weights of same distance, then determine missing values according to corresponding weighted average；Mean value method fills missing values,
After outlier processing the average value of index as missing values filling；
(3) normalized
The electricity consumption behavioural characteristic of user, primarily to user is distinguished in electricity consumption Long-term change trend situation not in the same time, by
Wide in Electricity customers coverage rate, the power consumption of different user differs greatly, to avoid the influence that electricity consumption data magnitude is brought,
The present invention is specially expert to having done normalized, and formula is：
Wherein i represents i-th of user, and t is a certain moment in one day 96 moment；Above-mentioned formula is to use each performance number
Subtract the minimum value of current line power, then divided by current line changed power scope, that is, the numerical value after being normalized；
(4) K-means is clustered
Before cluster is started, the operation of a Data Dimensionality Reduction is also done, is with the data that data are collected into one day are adopted
96 points, but this 96 points are more or less the same in 4 adjacent points, the power data then rounded a little is representative, is dropped
The power data of each user after dimension；
This step will be carried out category division to user, comprised the following steps that on the basis of above-mentioned processing procedure：
S1. optimal cluster number N is determined according to silhouette coefficient method；
S2. N number of user is randomly selected from above-mentioned data as barycenter；
S3. it is measured to the distance of each barycenter from remaining each user, and it is grouped into the classification of nearest barycenter；
S4. according to Euclidean distance method, the barycenter of each classification is recalculated；
S5. iteration S3-S4, until new barycenter is equal with the protoplasm heart or less than specified threshold, algorithm terminates；
(5) obtained result is clustered according to step (4) K-means, the user power utilization flow characteristic of each classification is divided
Analysis, it is determined that per the power consumption characteristics of class user；
(6) according to the result of above-mentioned cluster, the different electricity consumption behavioural characteristics of each classification Electricity customers of comparative analysis are just auxiliary
The business of advertisement putting is helped to be promoted；
S1. cluster analysis result is illustrated；
S2. different classes of corresponding user behavior feature is analyzed, including with electrical characteristics, the distribution situation of peak valley；
S3. the promoting service that auxiliary ad-vertisement is delivered：
Summary is analyzed the behavioural characteristic of different classes of user, and the major focus of advertisement putting is done and returned
Class.
The present invention is by analyzing different zones residential electricity consumption behavior, the related many external datas of calmodulin binding domain CaM commercial consumption, profit
Modeled with clustering, segment residential electricity consumption consumption feature, each region consumption of resident custom is excavated, so as to be provided for enterprise customer
Different zones consumption of resident Capability Measurement, consumer goods concern direction prediction, advertisement orientation is carried out by its product of auxiliary enterprises and is thrown
Put.
The present invention main innovation be to propose a kind of applied analysis framework based on user power utilization behavioural analysis result,
Effectively apply the result of user power utilization behavioural analysis.The present invention can solve the problem that user power utilization behavior validity, applicability, efficiently
Property and element set for higher-dimension the aspect of autgmentability four the problem of.
Validity refers to, that is, excavates all mode for meeting practical business requirement, and without omission, it is necessary to ensure to wait
Lectotype can cover all Electricity customers, and the pattern in result set is also necessarily satisfying for definition.
Applicability refers to, due to the number of some points in the data of sequence pattern in possible noise data itself, sequence
According to being probably mistake, if strict carries out pattern search according to sequence pattern order, the potential mould in part may be missed
Formula, these patterns may meet definition in itself, but be due to include noise in sequence data, cause these patterns can not
Directly excavated and obtained from sequence data in the way of strictly matching.So, the present invention needs to introduce other constraints, improves algorithm
The applicability for the pattern excavated.
High efficiency refers to, because in order to ensure the validity of result, the present invention needs to ensure the validity of arithmetic result, calculated
Method needs to travel through full search space, and this can cause for larger data set.So the present invention is carried out based on Hadoop
Realize, improve the operational efficiency of algorithm, i.e. high efficiency.
Element set refers to for the autgmentability of higher-dimension, with reference to different user power utilization behaviors, gathers the actual industry of various dimensions
Business, with the change increase of the practical business of element set, the electricity consumption behavioural characteristic of user is capable of the reform of auxiliary activities with carrying
Rise, this will cause obtained user power utilization behavioural analysis to have good autgmentability.
Brief description of the drawings
The degree present invention is described further with reference to the accompanying drawings and examples.
Fig. 1 is the general frame of the inventive method.
Fig. 2 is the total body display figure of cluster result.
Fig. 3 is all kinds of comprising number of users schematic diagram.
Fig. 4 is the 1st class user power utilization characteristic pattern.
Fig. 5 is the 2nd class user power utilization characteristic pattern.
Fig. 6 is the 3rd class user power utilization characteristic pattern.
Fig. 7 is the 4th class user power utilization characteristic pattern.
Fig. 8 is the 5th class user power utilization characteristic pattern.
Fig. 9 is the 6th class user power utilization characteristic pattern.
Figure 10 is the 7th class user power utilization characteristic pattern.
Figure 11 is the 8th class user power utilization characteristic pattern.
Figure 12 is the 9th class user power utilization characteristic pattern.
Figure 13 is the 10th class user power utilization characteristic pattern.
Embodiment
The date usually deposits in column form in the power consumption information of collection table area user, the data being collected into, according to
The realization approach of above-mentioned technical proposal, is first according to the mode that the date draws near and is arranged, and will be converted to row mark the date.
From with data are adopted, by denoising, duplicate removal, the data cleansing housekeeping such as sky is gone, then is normalized,
The clustering for being based ultimately upon electricity consumption data goes user power utilization behavior to be classified, and finds out inhomogeneity another characteristic, and be directed to
Property carry out analysis and advertisement putting decision-making.
1st, Data Collection, with reference to business demand, collects the power consumption data of user, such as following table：
The raw data table of table 1
Wherein, Pjt i(i=1,2 ..., m；J=1,2 ..., n；T=1,2 ..., 96) user i jth day is represented at t-th
The realtime power data at quarter.idi(i=1,2 ..., m) represent user i unique mark.datej(j=1,2 ..., m) represent jth
It date.
2nd, the data that step 1 is collected, have for sky, it is having probably due to external interference or itself
Mechanical disorder causes data acquisition not normal, it is necessary to carry out sequence of operations to the noise of data.
Outlier processing, exceptional value judges that Main Basiss method has two kinds, and one kind is Pauta Criterion, and method is simply easy
In operation, this method is that the most frequently used exceptional value judges to meet the number of totality x Normal Distributions for data with rejecting criterion
According to having：
P(|x‐μ|>3σ)≤0.003
Wherein, μ and σ represent the mathematic expectaion and standard deviation of normal population respectively, and x is actual observed value, and P is that observation exists
Probability outside the standard deviation of 3 times of the left and right of average.
Now occur being more than the σ of μ+3 or the data probability very little less than μ -3 σ in data, therefore can be picked as exceptional value
Except the partial data.One kind is standardized value (Z-score) method, the data Normal Distribution after Z score standardization.Therefore
This method can recognize exceptional value, it is proposed that data of the Z score less than -3 or higher than 3 are exceptional value.Criterion score (Z score) is public
Formula is：
Z=(x- μ)/σ
Wherein μ and σ represent the mathematic expectaion and standard deviation of normal population respectively.In addition statistic law, classification and Furthest Neighbor
It can also be applied according to the problem of different.
KNN algorithms fill missing values, and the Missing Data Filling method based on k nearest neighbor allows for the characteristic of electric power data, lead to
Cross to select and close on data near how many missing values, to calculate missing values, to select the data of K arest neighbors, and to not
The data setting weights of same distance, then determine missing values according to corresponding weighted average.Mean value method fills missing values,
The average value of index is as the filling of missing values after outlier processing, and this method is more convenient, the side of Main Basiss mathematical statistics
Method.
Data such as table 2 below after processing：
Table in the middle of the data processing of table 2
Wherein,Represent per day power datas of the user i k-th of moment.
3rd, normalized
The electricity consumption behavioural characteristic of user, primarily to user is distinguished in electricity consumption Long-term change trend situation not in the same time, by
Wide in Electricity customers coverage rate, the power consumption of different user differs greatly, to avoid the influence that electricity consumption data magnitude is brought,
The present invention is specially expert to having done normalized, and formula is：
(wherein i represents i-th of user, and t is a certain moment in one day 96 moment.Subtracted currently with each performance number
Capable minimum value, then divided by current line changed power scope, that is, the numerical value after being normalized)
The normalization data table of table 3
Wherein, idi(i=1,2 ..., m) represent user i unique mark, Pi k(i=1,2,3 ... 96, k=1,2,3 ... is m)
For the power data of each user after normalization
4th, K-means is clustered
Before cluster is started, the operation of a Data Dimensionality Reduction is also done, is with the data that data are collected into one day are adopted
96 points, but this 96 points are more or less the same in 4 adjacent points, the power data then rounded a little is representative, by returning
Data after changing form table 4 below：
The dimensionality reduction tables of data of table 4
Wherein, idi(i=1,2 ..., m) represent user i unique mark,
Pi k(the power datas of i=1,5,9 ... 93, k=1,2,3 ... m) for each user after dimensionality reduction
This step will be carried out category division to user, comprised the following steps that on the basis of above-mentioned processing procedure：
S1. optimal cluster number N is determined according to silhouette coefficient method；
S2. N number of user is randomly selected from above-mentioned data as barycenter；
S3. it is measured to the distance of each barycenter from remaining each user, and it is grouped into the classification of nearest barycenter；
S4. according to Euclidean distance method, the barycenter of each classification is recalculated；
S5. iteration S3~S4, until new barycenter is equal with the protoplasm heart or less than specified threshold, algorithm terminates.
1. clustering obtained result according to step 4 K-means, the user power utilization flow characteristic of each classification is analyzed,
It is determined that per the power consumption characteristics of class user.
2. practical business is combined, service hoisting quality of service.
, can be with the different electricity consumption behavioural characteristics of each classification Electricity customers of comparative analysis according to the result of above-mentioned cluster.This
The business that invention is delivered with regard to auxiliary ad-vertisement is promoted.
S1. cluster analysis result is illustrated
Based on above-mentioned analysis process, the cluster result such as Fig. 2 can be obtained.
(in Fig. 2, abscissa P1, P2 ... P24 are the time point of 24 hours one day, and ordinate is power data)
Abscissa is cluster out each vectorial classification of different transforming factors in Fig. 3, and ordinate is all kinds of use included
Amount.
From figure 3, it can be seen that electricity consumption behavior has larger difference between different classes of user.It is below that classification statement is each
The electricity consumption behavioural characteristic of class user.
S2. different classes of corresponding user behavior feature is analyzed, including with electrical characteristics, distribution situation of peak valley etc.；
In Fig. 4, abscissa P1, P2 ... P24 are the time point of 24 hours one day, and ordinate is power data.
Analyze being found with electrical feature for such user：Such user respectively has a small peak in the morning, afternoon and evening, and evening peak is substantially high
In morning peak, the noon peak it is minimum, and usually electricity consumption is general, thus it is speculated that such user is the common working clan for having old man in family, or
There are the child gone to school, the class family that only child is at noon in family.
In Fig. 5, abscissa P1, P2 ... P24 are the time point of 24 hours one day, and ordinate is power data.
Analyze being found with electrical feature for such user：Such user power consumption straight line after late 9 points declines, and early nine
Point is universal higher to 9 power consumptions of evening, and power consumption can be declined slightly at 13, thus it is speculated that such user is to belong to class processing
Factory, belongs to machinery operation.
In Fig. 6, abscissa P1, P2 ... P24 are the time point of 24 hours one day, and ordinate is power data.
Analyze being found with electrical feature for such user：The electricity consumption of such user fluctuates near 0.525 always, and floating
Dynamic value is no more than 0.1, and daily power consumption is more steady, thus it is speculated that such user is continual processing factory user round the clock.
In Fig. 7, abscissa P1, P2 ... P24 are the time point of 24 hours one day, and ordinate is power data.
Analyze being found with electrical feature for such user：Such user belongs to low power consumption at ten eight points from early 8 points to evening,
And early 6 points of late 8 points to second day are constantly in peak times of power consumption, thus it is speculated that such user belongs to the processing factory used electricity in off-peak hours,
Belong to machinery operation.
In Fig. 8, abscissa P1, P2 ... P24 are the time point of 24 hours one day, and ordinate is power data.
Analyze being found with electrical feature for such user：Such user is 8：00-12：00 He
14:00-18:Belong to peak of power consumption in 00 interval, noon power consumption is decreased obviously, and remaining period power consumption is relatively low,
It is the general office space such as office building to speculate such user.
In Fig. 9, abscissa P1, P2 ... P24 are the time point of 24 hours one day, and ordinate is power data.
Analyze being found with electrical feature for such user：The use electrical feature of such user and the user power utilization feature class of classification 5
Seemingly, bimodal class Electricity customers are belonged to, but afternoon, power consumption differed larger with morning power consumption, illustrated such user easily by gas
The influence of the other factorses such as temperature so that power consumption is decreased obviously.
In Figure 10, abscissa P1, P2 ... P24 are the time point of 24 hours one day, and ordinate is power data.
Analyze being found with electrical feature for such user：The power consumption of such user is relatively low, and most power consumption is all small
In 0.03, and overall electricity consumption is more steady, illustrates that the time that such user is typically in is few, thus it is speculated that such user is zero load.
In Figure 11, abscissa P1, P2 ... P24 are the time point of 24 hours one day, and ordinate is power data.
Analyze being found with electrical feature for such user：The use electrical feature and classification 4 of such user is somewhat like, electricity consumption on daytime
Amount is few and evening power consumption is higher, and there be a small peak of electricity consumption at ten two points at noon, and it is electricity consumption of cooking at noon to infer it, is pushed away
On the basis of such user is surveyed for comprehensive 4th class user, some is used for the user cooked at noon.
In Figure 12, abscissa P1, P2 ... P24 are the time point of 24 hours one day, and ordinate is power data.
Analyze being found with electrical feature for such user:It is high that such user only just steps into electricity consumption after ten nine points
, there is a small peak of electricity consumption at peak with noon in the morning, thus it is speculated that such user is young working clan user, and stay out at noon in the morning,
Go home the more type of electricity consumption in the evening.
In Figure 13, abscissa P1, P2 ... P24 are the time point of 24 hours one day, and ordinate is power data.
Analyze being found with electrical feature for such user：Some similar, electricity consumption trend of the use electrical feature of such user and classification 1
It is similar, but the electricity consumption of such user is characterized as that electricity consumption in morning is higher than evening, thus it is speculated that and such user is that housework activity etc. was liked in morning
One class user of upper progress.
S3. the promoting service that auxiliary ad-vertisement is delivered.
Summary is analyzed the behavioural characteristic of different classes of user, and the major focus of advertisement putting is done into as follows
Sort out：
Above category division belongs to a kind of division of frame-type, if to be specifically divided in the commercial paper of different time sections
Type, then need to combine more customer profile datas；Meanwhile, the advertisement for distinguishing festivals or holidays and non-festivals or holidays is divided, and being also can be with
The direction that further auxiliary ad-vertisement is delivered.
Claims (6)
1. a kind of advertisement putting aided analysis method based on user power utilization behavioural analysis, it is characterized in that：Comprise the following steps：
(1) Data Collection, with reference to business demand, collects the power consumption data of user；
(2) data collected, sequence of operations is carried out to the noise of data：
Outlier processing：Exceptional value judges that Main Basiss method has two kinds, and one kind is Pauta Criterion, and method is simply easy to behaviour
Make, this method is that the most frequently used exceptional value judges to meet the data of totality x Normal Distributions for data with rejecting criterion,
Have：
P(|x‐μ|>3σ)≤0.003
Wherein, μ and σ represent the mathematic expectaion and standard deviation of normal population respectively, and x is actual observed value, and P is observation in average
3 times of left and right standard deviation outside probability；
Now occur being more than the σ of μ+3 or the data probability very little less than μ -3 σ in data, therefore can be somebody's turn to do as abnormality value removing
Partial data；
Another is standardized value method, the data Normal Distribution after Z score standardization；This method can recognize exception
Value, data of the Z score less than -3 or higher than 3 are exceptional value；Criterion score (Z score) formula is：
Z=(x- μ)/σ
Wherein μ and σ represent the mathematic expectaion and standard deviation of normal population respectively；
KNN algorithms fill missing values：Missing Data Filling method based on k nearest neighbor, allows for the characteristic of electric power data, passes through choosing
Close on data near fixed how many missing values, to calculate missing values, to select the data of K arest neighbors, and to difference away from
From data setting weights, missing values are then determined according to corresponding weighted average；Mean value method fills missing values, abnormal
Value processing after index average value as missing values filling；
(3) normalized
The electricity consumption behavioural characteristic of user, primarily to distinguish user in electricity consumption Long-term change trend situation not in the same time, due to
Electric client's coverage rate is wide, and the power consumption of different user differs greatly, to avoid the influence that electricity consumption data magnitude is brought, this hair
Bright to be specially expert to having done normalized, formula is：
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<mi>t</mi>
<mi>i</mi>
</msubsup>
<mo>&OverBar;</mo>
</mover>
<mo>-</mo>
<munder>
<mrow>
<mi>m</mi>
<mi>i</mi>
<mi>n</mi>
</mrow>
<mi>t</mi>
</munder>
<mover>
<msubsup>
<mi>P</mi>
<mi>t</mi>
<mi>i</mi>
</msubsup>
<mo>&OverBar;</mo>
</mover>
</mrow>
</mfrac>
<mo>&Element;</mo>
<mo>&lsqb;</mo>
<mn>0</mn>
<mo>,</mo>
<mn>1</mn>
<mo>&rsqb;</mo>
</mrow>
Wherein i represents i-th of user, and t is a certain moment in one day 96 moment；Above-mentioned formula is subtracted with each performance number
The minimum value of current line power, then divided by current line changed power scope, that is, the numerical value after being normalized；
(4) K-means is clustered
Before cluster is started, the operation of a Data Dimensionality Reduction is also done, is 96 with the data that data are collected into one day are adopted
Point, but this 96 points are more or less the same in 4 adjacent points, the power data then rounded a little is representative, is obtained after dimensionality reduction
Each user power data；
This step will be carried out category division to user, comprised the following steps that on the basis of above-mentioned processing procedure：
S1. optimal cluster number N is determined according to silhouette coefficient method；
S2. N number of user is randomly selected from above-mentioned data as barycenter；
S3. it is measured to the distance of each barycenter from remaining each user, and it is grouped into the classification of nearest barycenter；
S4. according to Euclidean distance method, the barycenter of each classification is recalculated；
S5. iteration S3-S4, until new barycenter is equal with the protoplasm heart or less than specified threshold, algorithm terminates；
(5) obtained result is clustered according to step (4) K-means, the user power utilization flow characteristic of each classification is analyzed, really
The fixed power consumption characteristics per class user；
(6) according to the result of above-mentioned cluster, the different electricity consumption behavioural characteristics of each classification Electricity customers of comparative analysis are just aided in wide
The business delivered is accused to be promoted；
S1. cluster analysis result is illustrated；
S2. different classes of corresponding user behavior feature is analyzed, including with electrical characteristics, the distribution situation of peak valley；
S3. the promoting service that auxiliary ad-vertisement is delivered：
Summary is analyzed the behavioural characteristic of different classes of user, and the major focus of advertisement putting is done and sorted out.
2. the advertisement putting aided analysis method according to claim 1 based on user power utilization behavioural analysis, it is characterized in that：
From with data are adopted, by denoising, duplicate removal, sky data cleansing housekeeping is gone, then is normalized, use is based ultimately upon
The clustering of electric data goes user power utilization behavior to be classified, and finds out inhomogeneity another characteristic, and targetedly divided
Analysis and advertisement putting decision-making.
3. the advertisement putting aided analysis method according to claim 1 based on user power utilization behavioural analysis, it is characterized in that：
If to be specifically divided in the adline of different time sections, need to combine more customer profile datas.
4. the advertisement putting aided analysis method according to claim 1 based on user power utilization behavioural analysis, it is characterized in that：
Divided by the advertisement for distinguishing festivals or holidays and non-festivals or holidays, the direction that further auxiliary ad-vertisement is delivered.
5. the advertisement putting aided analysis method according to claim 1 based on user power utilization behavioural analysis, it is characterized in that：
The different classes of user include family's class, normal commercial class, uninterrupted industrial class, industrial class of avoiding the peak hour, working class, unloaded class,
Industry+family, young working class, family's class.
6. the advertisement putting aided analysis method according to claim 1 based on user power utilization behavioural analysis, it is characterized in that：
The date deposits in column form in the power consumption information of collection table area user, the data being collected into, according to above-mentioned technical proposal
Realization approach, is first according to the mode that the date draws near and is arranged, and will be converted to row mark the date.
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