CN106096805A - A kind of residential electricity consumption load classification method based on entropy assessment feature selection - Google Patents

A kind of residential electricity consumption load classification method based on entropy assessment feature selection Download PDF

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CN106096805A
CN106096805A CN201610303136.9A CN201610303136A CN106096805A CN 106096805 A CN106096805 A CN 106096805A CN 201610303136 A CN201610303136 A CN 201610303136A CN 106096805 A CN106096805 A CN 106096805A
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electricity consumption
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陆俊
朱炎平
张旭
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North China Electric Power University
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Abstract

The invention discloses a kind of residential electricity consumption load classification method based on entropy assessment feature selection, comprise the following steps: obtain user power utilization data；Selection sort influence factor, i.e. for the feature of classification；Feature is carried out preferably by the comentropy and the respective weights that calculate each feature；User data is trained by the feature after using preferably, is clustered user by clustering algorithm；Sample after training is identified.The present invention preferably further dimensionality reduction by user data feature, effectively reduce the amount of calculation of cluster process, consider each feature clustering effect when computed range, each feature to be weighted simultaneously, improve the accuracy of cluster, thus be load prediction, flood peak staggered regulation, the intelligent power consumption strategy such as demand response lays the first stone.

Description

A kind of residential electricity consumption load classification method based on entropy assessment feature selection
Technical field
The invention belongs to intelligent power technical field, particularly relate to a kind of residential electricity consumption based on entropy assessment feature selection and bear Lotus sorting technique.
Background technology
Along with the in-depth of power system reform, enterprise implement dsm alleviates the nervous situation of operation of power networks and excellent Change and utilize resource, therefore analyze customer charge characteristic and improve Power System Reliability important in inhibiting for more preferable, based on negative The cluster analysis research of lotus characteristic is also more widely used.
In the face of colony is more and more huger, electricity consumption user power utilization behavior the most flexibly, implementing Demand Side Response measure Before, it is not necessary that it is also impossible to each user is gone one by one Modeling Research, but by the method for cluster, by them with The typical user's classification confirmed compares, and goes out their use electrical characteristics by that analogy, thus back-up needs responds project implementation, It is greatly improved work efficiency.
In cluster process, when user data is extracted feature, choosing of feature is all artificial subjective selection, wherein selected The feature taken the most effectively does not has an objective appraisal, therefore existing characteristics too much causes calculating the problem increased, therefore have must The feature of subjective selection is carried out preferably.
Summary of the invention
Based on this, the invention provides a kind of residential electricity consumption load classification method based on entropy assessment feature selection.
This based on entropy assessment feature selection the residential electricity consumption load classification method that the present invention provides specifically includes following Step:
Step 1: obtain the user power utilization data of required classification；
Step 2: select the feature of user power utilization load classification, user data is carried out feature extraction；
Step 3: use entropy assessment to calculate comentropy and the entropy weight of user power utilization load characteristic；
Step 4: user power utilization load characteristic is carried out preferably according to entropy weight；
Step 5: the user power utilization load characteristic after using preferably carries out cluster training to user data；
Step 6: the electricity consumption behavior to the typical user after training is identified.
Compared with general technology, present invention residential electricity consumption based on entropy assessment feature selection load classification method, except master See the feature selected for classification, also feature has been carried out objective screening, further user data has been carried out dimensionality reduction, effectively The amount of calculation reducing cluster process, simultaneously because get rid of during preferably is the poor feature of effect, so row Except rear less on Clustering Effect impact, substantially can guarantee that the accuracy of cluster, and it is special to have considered each in cluster process Levy Clustering Effect, thus each feature can be weighted when computed range, thus improve Clustering Effect.
Advantageously reduce the amount of calculation of user power utilization load classification, and make the precision of analysis of user power utilization behavior higher, Thus for load prediction, flood peak staggered regulation, the intelligent power consumption strategy such as demand response lays the first stone.
Accompanying drawing explanation
Fig. 1 is the inventive method overall flow figure.
Fig. 2 is the inventive method comentropy and weight calculation flow chart.
Fig. 3 is the inventive method feature preferred flow charts.
Fig. 4 is the typical user's curve synoptic diagram after the inventive method.
Detailed description of the invention
By further illustrating the technological means and the effect of acquirement that the present invention taked, below in conjunction with the accompanying drawings and the most real Execute example, to technical scheme, carry out clear and complete description.
As it is shown in figure 1, a kind of residential electricity consumption load classification method based on entropy assessment feature selection comprises the following steps:
Step 1: obtain user power utilization data；
Extracted the load curve of electricity consumption user by power information acquisition system, this load curve is generally intelligent electric meter every 15 Minute gather 1 point, one day 96 point, power information acquisition system is by being calculated the power information of ammeter collection 24 hours load curves of user.
Step 2: select the feature of residential electricity consumption load classification, user data is carried out feature extraction；
Selecting key feature pointedly according to classification purpose, it includes but not limited to electricity consumption total amount, and during peak, consumption rate (is defined as Peak period power consumption with the ratio of total electricity consumption), paddy electrostrictive coefficient (is defined as the ratio of low-valley interval power consumption and total electricity consumption), flat Section electricity consumption percentage ratio (being defined as the ratio of flat section power consumption and total electricity consumption), peak-valley difference, rate of load condensate (is defined as user's average load Ratio with peak load) etc. load characteristic.
Step 3: use entropy assessment to calculate comentropy and the entropy weight thereof of residential electricity consumption load characteristic；
As in figure 2 it is shown, calculate comentropy and the entropy weight thereof of residential electricity consumption load characteristic according to following steps:
To be treated that preferred feature forms following state matrix R', wherein r' by the n of m userjiRepresent that i-th user is in jth Eigenvalue in individual feature,
According to below equation, R' is standardized process and obtains standardized state matrix R, wherein r'jiRepresent through standard The i-th user that change processes eigenvalue in jth feature, its value is all between 0 ~ 1,
The proportion P of the user power utilization behavior characteristics of each object is calculated according to below equationji,
According to proportion PjiEntropy corresponding as follows,
Wherein as p=0, PjilnPji=0, then counted entropy all meets 0≤eijThe constraints of≤1；
The feature weight w for judging is calculated according to below equation according to comentropyj,
。
Step 4: residential electricity consumption load characteristic is carried out preferably according to entropy weight；
As it is shown on figure 3, feature is carried out preferably according to calculating gained entropy weight according to following steps:
First according to formulaCalculate preferred threshold value, wherein NFNumber for feature selected in step 2；Then select Select maximum feature corresponding to weights in feature weight set and, from selecting proposition characteristic set, put into preferred feature set；? Calculate the weight sum that the feature in preferred feature set is corresponding, it is judged that whether it is more than threshold value, if more than threshold value, preferably tying Bundle, exports preferred feature set, if less than threshold value, repeats selection course, until weight and more than threshold value, exports preferred feature Set.
Step 5: user data is trained by the feature after using preferably, is clustered user by clustering algorithm；
With set of preferred features cooperation for input data, given FUZZY WEIGHTED index, cluster classification number, iteration stopping threshold value is set, Initialize cluster centre, iteration count is set, use fuzzy C-mean algorithm (FCM) algorithm that it is clustered.Cluster process is fallen into a trap Below equation is used to be weighted when calculating distance,
Wherein d (Xm,Xn) represent the distance between sample and sample, wjRepresent the comentropy weight that user power utilization behavior characteristics is corresponding, xmjAnd xnjRepresent the feature of sample.
Step 6: the electricity consumption behavior to the typical resident after training is identified.
According to sorted class of subscriber, the electricity consumption behavior to all types of user is analyzed, identify its use power mode.
The present invention is described in detail below by a specific embodiment.
The day power load curve of 800 residents in certain city is extracted, as example, according to this by information acquisition system Invent described method to classify.
First it is to obtain user power utilization data, as a example by 24 hours power loads of a user, its load such as table 1 below institute Show,
24 hours power load statistics of table 1 user
Time 1 2 3 4 5 6 7 8
Power consumption 0.958 0.2396 0.2673 0.4506 0.478 0.4629 0.7966 0.8744
Time 9 10 11 12 13 14 15 16
Power consumption 0.5548 0.9877 1.6138 0.8411 1.143 0.3751 1.0529 0.5595
Time 17 18 19 20 21 22 23 24
Power consumption 0.9121 0.2622 0.4524 0.201 0.1533 0.1685 0.1714 0.1963
Then according to user power utilization curve, extract power load feature according to, by with power consumption when selecting peak as a example by 1 user Rate, paddy electrostrictive coefficient, flat section electricity consumption percentage ratio, rate of load condensate four kinds represents the key feature of user power utilization load fluctuation situation, accordingly Can be calculated this user power utilization load characteristic as shown in table 2 below,
Table 2 power load eigenvalue
Consumption rate during peak Paddy electrostrictive coefficient Flat section electricity consumption percentage ratio Rate of load condensate
0.3838 0.2716 0.3446 0.3659
Again user characteristics set is normalized, the user power utilization load characteristic as a example by 10 users, after normalization It is as shown in the table,
Table 3 certain customers normalization load characteristic
User's sequence number 1 2 3 4 5 6 7 8 9 10
Consumption rate during peak 0.32 0.70 0.21 0.35 0.00 0.22 0.51 1.00 0.08 0.45
Paddy electrostrictive coefficient 0.67 0.34 0.52 0.48 0.96 0.54 0.41 0.27 0.83 0.38
Flat section electricity consumption percentage ratio 0.47 0.37 0.74 0.62 0.52 0.71 0.52 0.12 0.56 0.62
Rate of load condensate 0.98 0.28 0.30 0.59 0.57 0.47 0.48 0.04 0.52 0.57
And calculate the entropy of each feature, result is as shown in the table,
Table 4 user power utilization load characteristic entropy
Consumption rate during peak Paddy electrostrictive coefficient Flat section electricity consumption percentage ratio Rate of load condensate
0.9360 0.9477 0.9751 0.9714
Gained weight is calculated as shown in the table according to entropy
Table 5 user power utilization load characteristic comentropy weight
Consumption rate during peak Paddy electrostrictive coefficient Flat section electricity consumption percentage ratio Rate of load condensate
0.3768 0.3079 0.1467 0.1686
Carrying out preferably according to the method for the invention, original feature has carried out dimensionality reduction, last gained preferred feature is peak Time consumption rate, paddy electrostrictive coefficient, rate of load condensate.
Then with these three user power utilization load characteristic carry out cluster training, clusters number is set to 5, initial cluster center with Machine is chosen, and FUZZY WEIGHTED index is set to, and iteration stopping threshold value is set to 10-6, use FCM algorithm that user is clustered, cluster knot All kinds of typical curves in Guo are as shown in Figure 4.It is as shown in the table with traditional algorithm comparing result, and the present invention can be to user The further dimensionality reduction of power load feature, and the accuracy having lifted clustering algorithm can be improved simultaneously.
Table 6 algorithm performance contrasts
Algorithm Characteristic Number Accuracy
Traditional F CM algorithm 4 83.25%
The present invention provides algorithm 3 95.75%
Result shown in 4 with reference to the accompanying drawings, being identified with power mode user, it is seen that first kind user's whole day power consumption one Directly it is in high levels；And the 2nd class user can in the afternoon 3 to electricity consumptions a large amount of between morning；3rd class user power utilization is less, but still Exist rise and fall, its by day electricity consumption apparently higher than evening；4th class user's electricity consumption in the morning is more, and morning, electricity consumption was less, remaining period Electricity consumption is mild；There is obvious peak of power consumption in the 5th class user, its electricity consumption is concentrated mainly on morning 7 and mid-afternoon.
The above, the only present invention preferably detailed description of the invention, but protection scope of the present invention is not limited thereto, Any those familiar with the art in the technical scope that the invention discloses, the change that can readily occur in or replacement, All should contain within protection scope of the present invention；Therefore, protection scope of the present invention should be with scope of the claims It is as the criterion.

Claims (5)

1. a distribution line information monitoring service communication method for predicting, it is characterised in that comprise the following steps:
A kind of residential electricity consumption load classification method based on entropy assessment feature selection, it is characterised in that comprise the following steps:
Step 1: obtain the user power utilization data of required classification；
Step 2: select the feature of user power utilization load classification, user data is carried out feature extraction；
Step 3: use entropy assessment to calculate comentropy and the entropy weight of user power utilization load characteristic；
Step 4: user power utilization load characteristic is carried out preferably according to entropy weight；
Step 5: the user power utilization load characteristic after using preferably carries out cluster training to user data；
Step 6: the electricity consumption behavior to the typical user after training is identified.
2., according to the residential electricity consumption load classification method based on entropy assessment feature selection described in claim 1, its feature exists In, use entropy assessment to calculate comentropy and the entropy weight thereof of resident's power load feature, it is as follows that it calculates process:
First the proportion of the residential electricity consumption load characteristic of each object is calculated,
According to proportion PjiEntropy corresponding as follows,
The feature weight w for judging is calculated according to below equation according to comentropyj,
。
3., according to the residential electricity consumption load classification method based on entropy assessment feature selection described in claim 1, its feature exists In, its described preferred process is as follows, first calculates comentropy and the respective weights of feature, resets for preferred threshold value p, Then select in feature weight set the feature corresponding to maximum weight to put into preferred feature set, calculate preferred feature set The weight sum that middle feature is corresponding, it is judged that whether it is more than threshold value, if more than threshold value, exporting preferred feature set, if less than threshold Value then repeats selection course, until weight and more than threshold value, exports preferred feature set.
Residential electricity consumption load classification method based on entropy assessment feature selection the most according to claim 3, it is characterised in that root Preferred threshold value is calculated according to below equationp,, wherein NFNumber for selected feature.
5., according to the residential electricity consumption load classification method based on entropy assessment feature selection described in claim 1, its feature exists In, cluster process is calculated in available preferred process sample distance calculating is weighted by weight.
CN201610303136.9A 2016-05-10 2016-05-10 A kind of residential electricity consumption load classification method based on entropy assessment feature selection Withdrawn CN106096805A (en)

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Cited By (18)

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CN107229602A (en) * 2017-05-22 2017-10-03 湘潭大学 A kind of recognition methods of intelligent building microgrid electricity consumption behavior
CN107703854A (en) * 2017-10-26 2018-02-16 国网黑龙江省电力有限公司信息通信公司 power load monitoring system and method
CN107729455A (en) * 2017-09-25 2018-02-23 山东科技大学 A kind of social network opinion leader sort algorithm based on multidimensional characteristic analysis
CN108960657A (en) * 2018-07-13 2018-12-07 国网上海市电力公司 One kind being based on the preferred building Load Characteristic Analysis method of feature
CN109193674A (en) * 2018-08-30 2019-01-11 中国南方电网有限责任公司 Substation's load different degree online evaluation method based on entropy weight
CN109241991A (en) * 2018-07-23 2019-01-18 南昌大学 A kind of data clusters integrated approach based on comentropy weight incremental learning strategy
CN109492667A (en) * 2018-10-08 2019-03-19 国网天津市电力公司电力科学研究院 A kind of feature selecting discrimination method for non-intrusive electrical load monitoring
CN109685552A (en) * 2018-12-06 2019-04-26 国网山东省电力公司青岛供电公司 The analysis of non-intrusion type residential electricity consumption efficiency and method of servicing
CN109816034A (en) * 2019-01-31 2019-05-28 清华大学 Signal characteristic combines choosing method, device, computer equipment and storage medium
CN110009231A (en) * 2019-04-04 2019-07-12 上海电力学院 A kind of power load recognition methods and device based on repeatability and entropy weight distinction
WO2019184131A1 (en) * 2018-03-29 2019-10-03 清华大学 Entropy method and density clustering method integrated power stealing detection method and apparatus
CN110659699A (en) * 2019-09-30 2020-01-07 重庆大学 Distributed double-layer clustering analysis method based on feature index dimension reduction
CN110766032A (en) * 2018-07-27 2020-02-07 国网江西省电力有限公司九江供电分公司 Power distribution network data clustering integration method based on hierarchical progressive strategy
CN110826641A (en) * 2019-11-13 2020-02-21 上海积成能源科技有限公司 System and method for classifying electricity consumption condition of residents based on cluster analysis
CN110825723A (en) * 2019-10-09 2020-02-21 上海电力大学 Residential user classification method based on power load analysis
CN111144447A (en) * 2019-12-09 2020-05-12 国网新疆电力有限公司电力科学研究院 Power grid peak-valley time interval division method for preventing peak regulation risk caused by new energy output
CN111612275A (en) * 2020-05-29 2020-09-01 云南电网有限责任公司 Method and device for predicting load of regional user
CN111915116A (en) * 2019-05-10 2020-11-10 国网能源研究院有限公司 Electric power resident user classification method based on K-means clustering

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CN107229602A (en) * 2017-05-22 2017-10-03 湘潭大学 A kind of recognition methods of intelligent building microgrid electricity consumption behavior
CN107729455A (en) * 2017-09-25 2018-02-23 山东科技大学 A kind of social network opinion leader sort algorithm based on multidimensional characteristic analysis
CN107703854A (en) * 2017-10-26 2018-02-16 国网黑龙江省电力有限公司信息通信公司 power load monitoring system and method
WO2019184131A1 (en) * 2018-03-29 2019-10-03 清华大学 Entropy method and density clustering method integrated power stealing detection method and apparatus
CN108960657A (en) * 2018-07-13 2018-12-07 国网上海市电力公司 One kind being based on the preferred building Load Characteristic Analysis method of feature
CN108960657B (en) * 2018-07-13 2021-11-12 国网上海市电力公司 Building load characteristic analysis method based on feature optimization
CN109241991A (en) * 2018-07-23 2019-01-18 南昌大学 A kind of data clusters integrated approach based on comentropy weight incremental learning strategy
CN110766032A (en) * 2018-07-27 2020-02-07 国网江西省电力有限公司九江供电分公司 Power distribution network data clustering integration method based on hierarchical progressive strategy
CN109193674A (en) * 2018-08-30 2019-01-11 中国南方电网有限责任公司 Substation's load different degree online evaluation method based on entropy weight
CN109492667A (en) * 2018-10-08 2019-03-19 国网天津市电力公司电力科学研究院 A kind of feature selecting discrimination method for non-intrusive electrical load monitoring
CN109685552A (en) * 2018-12-06 2019-04-26 国网山东省电力公司青岛供电公司 The analysis of non-intrusion type residential electricity consumption efficiency and method of servicing
CN109816034A (en) * 2019-01-31 2019-05-28 清华大学 Signal characteristic combines choosing method, device, computer equipment and storage medium
CN109816034B (en) * 2019-01-31 2021-08-27 清华大学 Signal characteristic combination selection method and device, computer equipment and storage medium
CN110009231A (en) * 2019-04-04 2019-07-12 上海电力学院 A kind of power load recognition methods and device based on repeatability and entropy weight distinction
CN111915116A (en) * 2019-05-10 2020-11-10 国网能源研究院有限公司 Electric power resident user classification method based on K-means clustering
CN110659699A (en) * 2019-09-30 2020-01-07 重庆大学 Distributed double-layer clustering analysis method based on feature index dimension reduction
CN110825723A (en) * 2019-10-09 2020-02-21 上海电力大学 Residential user classification method based on power load analysis
CN110825723B (en) * 2019-10-09 2023-04-25 上海电力大学 Resident user classification method based on electricity load analysis
CN110826641A (en) * 2019-11-13 2020-02-21 上海积成能源科技有限公司 System and method for classifying electricity consumption condition of residents based on cluster analysis
CN111144447A (en) * 2019-12-09 2020-05-12 国网新疆电力有限公司电力科学研究院 Power grid peak-valley time interval division method for preventing peak regulation risk caused by new energy output
CN111144447B (en) * 2019-12-09 2022-05-31 国网新疆电力有限公司电力科学研究院 Power grid peak-valley time interval division method for preventing peak regulation risk caused by new energy output
CN111612275A (en) * 2020-05-29 2020-09-01 云南电网有限责任公司 Method and device for predicting load of regional user
CN111612275B (en) * 2020-05-29 2022-04-01 云南电网有限责任公司 Method and device for predicting load of regional user

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Application publication date: 20161109