CN106779341A - A kind of method and system of power consumer electricity consumption situation measures of effectiveness - Google Patents
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Abstract
The invention discloses the method and system of power consumer electricity consumption situation measures of effectiveness, the method is using the subjective weight for improving G1 Algorithm for Solving first class index weights and the corresponding two-level index of each first class index；Using the amendment weight for correcting intuitionistic fuzzy entropy weight method and subjective weight calculation two-level index；The measures of effectiveness fraction of the TOPSIS method first order calculation indexs based on intuitionistic Fuzzy Sets is utilized according to amendment weight；The electricity consumption situation measures of effectiveness value of power consumer is obtained according to measures of effectiveness fraction and first class index weight calculation；The method is based on passing rank comprehensive estimation method, first class index weight is solved by improving G1 methods, two-level index weight is calculated using amendment intuitionistic fuzzy entropy weight method, substantially it is that desired values at different levels have respectively been carried out with weighted comprehensive twice, reduce the single influence for assigning power to energy efficiency evaluation result, and organically combine expertise and objective data, embody the science and reasonability of main, objective evaluation information assurance measures of effectiveness.
Description
Technical field
The present invention relates to technical field of electricity, the method for more particularly to a kind of power consumer electricity consumption situation measures of effectiveness and it is
System.
Background technology
During social development, the energy plays important role always, and it is the power of social progress and development, is
Maintain the guarantee of social being.At present, the world is but generally faced with the crisis of energy deficiency, is but on the other hand due to management
The improper mass energy for causing is wasted.How the energy is more reasonably utilized, it has also become worldwide research theme.
The core of electric energy efficiency assessment is its reliable assessment algorithm, objective comprehensive, the state in order that assessment result is tried one's best
Inside and outside scholars propose various appraisal procedures, to qualitative index quantification, index system is turned into computable, and pass through
Computer software is realized.Mainly have currently for the conventional method in power consumer energy efficiency evaluation：It is analytic hierarchy process (AHP), fuzzy
Comprehensive evaluation, PCA and artificial intelligence approach.
But index system used needs the support of expert system in above-mentioned various methods, if the index for being given is not
The reasonable then result that obtains is also just inaccurate；The weight of index is mostly artificial determination, random with larger subjectivity, and
And when index set number is larger, in weight vectors and under 1 constraint, fuzzy set relative defects weight coefficient is often inclined
Small, weight vectors are mismatched with fuzzy matrix, and super blooming as a result occurs, resolution ratio is very poor, it is impossible to whose is distinguished and is subordinate to
Du Genggao, or even cause to judge failure；Policymaker generally requires to provide its preference information significance level between index.Due to receiving
The influence of the factors such as the complexity of objective environment, the structure of knowledge of policymaker and professional standards, policymaker tends not to carry
For accurate preference information.And the membership function value of traditional fuzzy set is only a single value, in actual applications,
It can not simultaneously represent support (affirmative), oppose the evidence of (negative) and hesitation (uncertain), it is impossible to which intactly expression is studied
The full detail of problem.
Therefore, how measures of effectiveness to be carried out to power consumer exactly, is the technology that those skilled in the art need to solve
Problem.
The content of the invention
It is an object of the invention to provide a kind of method and system of power consumer electricity consumption situation measures of effectiveness, to indexs at different levels
Value has respectively carried out weighted comprehensive twice, reduce it is single assign influence of the power to energy efficiency evaluation result, and by expertise and
Objective data is organically combined, it is ensured that the science and reasonability of result.
In order to solve the above technical problems, the present invention provides a kind of method of power consumer electricity consumption situation measures of effectiveness, it is described
Method includes：
Using the subjectivity for improving G1 Algorithm for Solving first class index weights and the corresponding two-level index of each first class index
Weight；
Using intuitionistic fuzzy entropy weight method and the subjective weight is corrected, the corresponding two-level index of each first class index is calculated
Amendment weight；
According to the amendment weight, the efficiency for calculating the first class index using the TOPSIS methods based on intuitionistic Fuzzy Sets is commented
Estimate fraction；
Measures of effectiveness fraction and the first class index weight according to the first class index, are calculated the use of power consumer
Electric situation measures of effectiveness value.
Optionally, using intuitionistic fuzzy entropy weight method and the subjective weight is corrected, each first class index is calculated corresponding
The amendment weight of two-level index, including：
Determine the membership function and non-affiliated degree function of the corresponding two-level index of each first class index；
According to the membership function and the non-affiliated degree function, intuitionistic Fuzzy Sets decision matrix is built；
According to the intuitionistic Fuzzy Sets decision matrix, the objective power of the corresponding two-level index of each first class index is calculated
Weight；
According to the objective weight and the subjective weight, the amendment of the corresponding two-level index of each first class index is calculated
Weight.
Optionally, according to the amendment weight, the first class index is calculated using the TOPSIS methods based on intuitionistic Fuzzy Sets
Measures of effectiveness fraction, including：
Build weighting intuitionistic Fuzzy Sets decision matrix；
The corresponding positive ideal solution of each first class index and negative reason are determined according to the weighting intuitionistic Fuzzy Sets decision matrix
Think solution；
Power consumer correspondence under each first class index is calculated according to the positive ideal solution and the minus ideal result
Positive ideal solution distance and minus ideal result distance；
The power consumer is calculated according to the positive ideal solution distance and minus ideal result distance to refer in each one-level
Corresponding relative proximities under mark；
The corresponding measures of effectiveness fraction of each first class index is calculated according to the relative proximities.
Optionally, according to the first class index measures of effectiveness fraction and the first class index weight, are calculated electric power
The electricity consumption situation measures of effectiveness value of user, including：
Using formulaCalculate power consumer electricity consumption situation measures of effectiveness value M；
Wherein, liIt is the corresponding measures of effectiveness fraction of i-th first class index, ωiIt is the corresponding one-level of i-th first class index
Index weights, n is the quantity of first class index.
Optionally, according to the first class index measures of effectiveness fraction and the first class index weight, are calculated electric power
After the electricity consumption situation measures of effectiveness value of user, also include：
By the measures of effectiveness fraction of the corresponding first class index of each power consumer and electricity consumption situation measures of effectiveness value with radar
The form output of figure.
Optionally, the method also includes：
Measures of effectiveness fraction and electricity consumption situation measures of effectiveness value according to the corresponding first class index of power consumer are analyzed,
Determine that power consumer improves the strategy protocol of electricity consumption situation efficiency.
The present invention also provides a kind of system of power consumer electricity consumption situation measures of effectiveness, including：
First weight determination module, G1 Algorithm for Solving first class index weights and each first class index are improved for utilizing
The subjective weight of corresponding two-level index；
Second weight determination module, for using intuitionistic fuzzy entropy weight method and the subjective weight is corrected, calculating each described
The amendment weight of the corresponding two-level index of first class index；
First class index efficiency fraction determining module, for according to the amendment weight, using based on intuitionistic Fuzzy Sets
TOPSIS methods calculate the measures of effectiveness fraction of the first class index；
Electricity consumption situation efficiency determining module, for measures of effectiveness fraction and the first class index according to the first class index
Weight, is calculated the electricity consumption situation measures of effectiveness value of power consumer.
Optionally, the system also includes：
Output module, for by the measures of effectiveness fraction of the corresponding first class index of each power consumer and electricity consumption situation efficiency
Assessed value is exported in the form of radar map.
Optionally, the system also includes：
Analysis module, comments for the measures of effectiveness fraction according to the corresponding first class index of power consumer and electricity consumption situation efficiency
Valuation is analyzed, and determines that power consumer improves the strategy protocol of electricity consumption situation efficiency.
A kind of method of power consumer electricity consumption situation measures of effectiveness provided by the present invention, methods described includes：Using changing
Enter the subjective weight of G1 Algorithm for Solving first class index weights and the corresponding two-level index of each first class index；It is straight using amendment
Feel Based on Entropy method and the subjective weight, calculate the amendment weight of the corresponding two-level index of each first class index；According to institute
Amendment weight is stated, the measures of effectiveness fraction of the first class index is calculated using the TOPSIS methods based on intuitionistic Fuzzy Sets；According to institute
The measures of effectiveness fraction and the first class index weight of first class index are stated, the electricity consumption situation measures of effectiveness of power consumer is calculated
Value.
It can be seen that, the method is based on passing rank comprehensive estimation method, first class index weight is solved by improving G1 methods, using amendment
Intuitionistic fuzzy entropy weight method calculates two-level index weight, is substantially that desired values at different levels have respectively been carried out with weighted comprehensive twice,
Reduce it is single assign influence of the power to energy efficiency evaluation result, while expertise and objective data are organically combined, embody it is main,
Objective evaluation information, it is ensured that the science and reasonability of the electricity consumption situation measures of effectiveness of power consumer；The present invention also provides one
The system for planting power consumer electricity consumption situation measures of effectiveness, with above-mentioned beneficial effect, will not be repeated here.
Brief description of the drawings
In order to illustrate more clearly about the embodiment of the present invention or technical scheme of the prior art, below will be to embodiment or existing
The accompanying drawing to be used needed for having technology description is briefly described, it should be apparent that, drawings in the following description are only this
Inventive embodiment, for those of ordinary skill in the art, on the premise of not paying creative work, can also basis
The accompanying drawing of offer obtains other accompanying drawings.
The flow chart of the method for the power consumer electricity consumption situation measures of effectiveness that Fig. 1 is provided by the embodiment of the present invention；
The flow chart of the calculating amendment weight that Fig. 2 is provided by the embodiment of the present invention；
The flow chart that the measures of effectiveness fraction of the first class index that Fig. 3 is provided by the embodiment of the present invention is calculated；
Fig. 4 is by the schematic diagram of the embodiment of the present invention relative proximities c for providing and the relation for assessing fraction M；
The schematic flow sheet of the method for the power consumer electricity consumption situation measures of effectiveness that Fig. 5 is provided by the embodiment of the present invention；
The structured flowchart of the system of the power consumer electricity consumption situation measures of effectiveness that Fig. 6 is provided by the embodiment of the present invention.
Specific embodiment
Core of the invention is to provide a kind of method and system of power consumer electricity consumption situation measures of effectiveness, to indexs at different levels
Value has respectively carried out weighted comprehensive twice, reduce it is single assign influence of the power to energy efficiency evaluation result, and by expertise and
Objective data is organically combined, it is ensured that the science and reasonability of result.
To make the purpose, technical scheme and advantage of the embodiment of the present invention clearer, below in conjunction with the embodiment of the present invention
In accompanying drawing, the technical scheme in the embodiment of the present invention is clearly and completely described, it is clear that described embodiment is
A part of embodiment of the present invention, rather than whole embodiments.Based on the embodiment in the present invention, those of ordinary skill in the art
The every other embodiment obtained under the premise of creative work is not made, belongs to the scope of protection of the invention.
Because each electricity consumption situation efficiency estimation method can not intactly express whole letters of measures of effectiveness in the prior art
Breath, therefore the present embodiment is using consideration degree of membership simultaneously, this 3 intuitionistic Fuzzy Sets of aspect information of non-affiliated degree and hesitation degree,
Making up traditional fuzzy analysis method cannot show affirmative, negative or the deficiency between the hesitation certainly and between negative, more carefully
The ambiguity essence of objective world is described and portrayed greasyly, realizes flexibility and the practicality of the assessment of electricity consumption situation effectiveness synthesis.
Please specifically refer to Fig. 1, the flow of the method for the power consumer electricity consumption situation measures of effectiveness that Fig. 1 is provided by the embodiment of the present invention
Figure；The method can include：
S100, using improving G1 Algorithm for Solving first class index weights and the corresponding two-level index of each first class index
Subjective weight；
Wherein, here first class index and the corresponding two-level index of each first class index can be determined by user.
User can determine that its first class index selected and corresponding two grades of each first class index refer to according to itself actual electricity consumption situation
Mark.For example first class index can include economic index, energy information index, user power utilization situation index, distributed power generation and
Energy storage situation index etc..Each first class index has its corresponding two-level index, and corresponding two grades of such as energy information index refers to
Mark can include voltage deviation, frequency departure, voltage tri-phase unbalance factor, flickering, power factor etc..And subsequent user carries out one
The measures of effectiveness fraction of level index when calculating can also only calculating section first class index measures of effectiveness fraction.Such as first class index
Economic index, energy information index, user power utilization situation index, distributed power generation and energy storage situation index can be included, but
It is only to calculate the corresponding measures of effectiveness fraction of energy information index.Therefore, the present embodiment is not to first class index and each described one
The particular content of the corresponding two-level index of level index is defined.
Specifically, because traditional G1 methods are present, some are not enough, and such as subjectivity is big, cannot pull open the numerical difference of index weights
Not etc., based on this, the present embodiment carries out tax power using G1 methods are improved to index, index is compared two-by-two and is changed to expert estimation.Change
Entering G1 Algorithm for Solving step can include：
1st, expert analysis mode
Assuming that p expert scores index importance, standards of grading are as shown in table 1.
The expert analysis mode standard of table 1
Wherein, remember q (q=1,2 ... p) expert to index Si(i=1,2 ... the number that n) scores is xSiq.Then expert
To index SiScored number is as shown in table 2.
The expert analysis mode table of table 2
2nd, index importance sequence
According to q expert score it is several index is resequenced, work as index SiFraction xSiqMore than (being not less than)
Index SkFraction xSkq, it is designated as Si＞ Sk.So just can obtain the new sequence that index importance is commented by q expert.
3rd, index importance sequence
The ratio of weight between the adjacent index of above-mentioned sequence is calculated by index score, then index S(i-1)With SiWeight ratio is remembered
For：
Wherein, ωSiqFor the index S that q expert tries to achieveiWeight, i=n, n-1 ..., 2, n be index number.
4th, parameter weight
Based on the scoring of expert, according to weight ratio, the index S that q expert tries to achieve is calculatednWeight,
Formula is：
Other index weights can be obtained by formula (1) recursion.
After p expert scores index, index SiFinal weight ωSiTried to achieve by formula (3)：
I.e. the present embodiment solves selected first class index weight and each institute using above-mentioned 4 steps (improving G1 algorithms)
The weight of the corresponding two-level index of first class index is stated as subjective weight.
Can be multiple further for computational efficiency power consumer here is improved.For example select 4 power consumers same
When solve the weight of 4 corresponding first class index weights of power consumer and the corresponding two-level index of each first class index.
S110, using intuitionistic fuzzy entropy weight method and the subjective weight is corrected, calculate each first class index corresponding two
The amendment weight of level index；
Wherein, intuitionistic Fuzzy Sets (Intuitionistic fuzzy set) related notion is earliest by Bulgarian scholar
Atanassov proposes that it includes degree of membership, three aspect information of expense degree of membership and hesitation degree, compared to traditional fuzzy collection more
The ambiguity essence of objective world can be depicted.Intuitionistic Fuzzy Entropy is a concept in intuitionistic Fuzzy Sets theory, can be as
Quantizating index reflects fog-level and uncertainty degree.Wherein, the correlation theory of intuitionistic Fuzzy Sets is as follows：
If X is a nonempty set, claim A={ (x, μA(x),νA(x)) | x ∈ X } it is the intuitionistic Fuzzy Sets on X, (μA(x),
νA(x)) it is Intuitionistic Fuzzy Numbers, wherein μA(x):X → [0,1] and νA(x):X → [0,1] represents the membership function of A and non-respectively
Membership function, and 0≤μA(x)+νA(x)≤1。
The collection of all intuitionistic Fuzzy Sets on note X is combined into IFS (X).To arbitrary intuitionistic Fuzzy Sets A ∈ IFS (X), claim πA
(x)=1- μA(x)-νAX () is the hesitation degree or uncertainty of x in A, claim θA(x)=1- | μA(x)-νA(x) | in A x it is fuzzy
Degree.Obvious πA(x) ∈ [0,1], θA(x)∈[0,1]。
If X={ x1,x2,…,xnIt is nonempty set, A ∈ IFS (X), definition
Then E (A) is the Intuitionistic Fuzzy Entropy of A.
It is to be understood that Intuitionistic Fuzzy Numbers can simultaneously describe " support ", " opposition " and " neither support nor anti-
It is right " 3 kinds of evidence degree, efficiently solve policymaker to scheme there is a problem of hesitate.The assessment of scheme depends on multiple
The good and bad degree of index, the present embodiment is overall as domain using individual event two-level index parameter, optimal relative to index to each scheme
The degree of membership and non-affiliated degree assignment of value, with this description scheme to leveling off to the satisfaction degree of optimal solution.
Specifically, refer to Fig. 2, using intuitionistic fuzzy entropy weight method and the subjective weight is corrected, each one-level is calculated
The detailed process of the amendment weight of the corresponding two-level index of index can be as follows：
S200, the membership function and non-affiliated degree function that determine the corresponding two-level index of each first class index；
Specifically, the step is in order to determine the membership function and non-affiliated degree function of two-level index, main process is as follows：
Individual event two-level index can be divided into cost type and the type of profit evaluation model index 2.The smaller the better index of measured value is
Cost type index, on the contrary it is then profit evaluation model index.Cost type index in index system is subordinate to using as formula (4) describes it
Degree function muij：
In formula, fijIt is the measured value of index to be assessed；fjmax、fjminIt is the maximum and minimum of all measured values of index
Value.
In view of non-affiliated degree function is difficult to directly determination, therefore desirable hesitation degree πij=0.1, then non-affiliated degree function νij
Can be described with formula (5)：
Wherein, because 0≤μij≤ 1, there is νij=1- μij-πij=0.9- μij∈ [- 0.1,0.9], here when obtaining -0.1
≤νijDuring ＜ 0, ν is takenij=0 so that degree of membership is satisfied by definition with non-affiliated degree.
Its membership function μ is represented using such as formula (6) to profit evaluation model indexij：
Take πij=0.1, the non-affiliated degree function ν of profit evaluation model indexijRepresented by formula (7)：
Wherein, ibid, when obtaining -0.1≤νijDuring ＜ 0, ν is takenij=0.
S210, according to the membership function and the non-affiliated degree function, build intuitionistic Fuzzy Sets decision matrix；
Specifically, construction intuitionistic Fuzzy Sets decision matrix F is specific as follows：
Wherein, i=1,2 ..., m, m are assessment object number；J=1,2 ..., n, n refer to for lower two grades of a certain first class index
Target number.
S220, according to the intuitionistic Fuzzy Sets decision matrix, calculate the visitor of the corresponding two-level index of each first class index
See weight；
Specifically, asking for two-level index S first with formula (9)ijIntuitionistic Fuzzy Entropy Eij：
Wherein, πij(x)=1- μij(x)-νij(x),θij(x)=1- | μij(x)-νij(x)|；I=1,2 ..., m, m are to comment
Estimate object number；J=1,2 ..., n, n are the number of two-level index under a certain first class index.
Secondly object to be assessed is solved in two-level index S using formula (10)ijThe irrelevance of making policy decision information
Finally two-level index S is solved using formula (11)ijObjective weight：
S230, according to the objective weight and the subjective weight, calculate the corresponding two-level index of each first class index
Amendment weight.
Specifically, the present embodiment considers the preference of policymaker, subjective weight η is calculated by improving G1 methods1,η2,…,ηn,
And then it is λ to obtain the amendment weight based on Intuitionistic Fuzzy Entropyij：
Using intuitionistic fuzzy entropy weight method and the subjective weight is corrected in the present embodiment, each first class index correspondence is calculated
Two-level index amendment weight, can be when policymaker has lack of knowledge to the preference information of scheme or has certain hesitation
When spending, " support ", " opposition " and " hesitation " can be simultaneously represented, be applied each two in power consumer electricity consumption situation energy efficiency evaluation
The determination of level index weights, by more objectivity and reasonability.
S120, according to the amendment weight, calculate the first class index using the TOPSIS methods based on intuitionistic Fuzzy Sets
Measures of effectiveness fraction；
Wherein, the key of traditional TOPSIS methods is to find out positive ideal solution and minus ideal result, Jin Erxuan in from normalized number
Select a scheme nearest from positive ideal solution, farthest from minus ideal result.The present embodiment is expanded to TOPSIS is based on intuition
The TOPSIS methods of fuzzy set, by index parameter with intuitionistic fuzzy set representations, are weighed with the difference degree between intuitionistic Fuzzy Sets
Each assessment object and positive ideal solution and the distance of minus ideal result, in this, as the foundation for evaluating each scheme quality.
Specifically, Fig. 3 is refer to, according to the amendment weight in the present embodiment, using based on intuitionistic Fuzzy Sets
The step of TOPSIS methods calculate the measures of effectiveness fraction of the first class index can include：
S300, structure weighting intuitionistic Fuzzy Sets decision matrix；
Specifically, weighting intuitionistic Fuzzy Sets decision matrix is
From definition, intuitionistic Fuzzy Sets decision matrix F is Standard Process, and each two grades are determined by above-mentioned formula (12)
Index weights vector λ=(λ1,λ2,…,λn), wherein λ1+λ2+…+λn=1, then can be obtained by fuzzy operation rule
S310, according to it is described weighting intuitionistic Fuzzy Sets decision matrix determine the corresponding positive ideal solution A of each first class index+
With minus ideal result A-；
Specifically, being solved using formula (14)
Wherein,
S320, the power consumer is calculated according to the positive ideal solution and the minus ideal result under each first class index
Corresponding positive ideal solution is apart from D+With minus ideal result apart from D-；
Specifically, each power consumer is (15) to positive ideal solution distance, each power consumer to minus ideal result distance is (16),
Wherein, i=1,2 ..., m；πij +=1- μij +-νij +, πij -=1- μij --νij -。
S330, the power consumer is calculated each described one according to the positive ideal solution distance and minus ideal result distance
Corresponding relative proximities c under level index；
Specifically, calculating the relative proximities of assessment object and positive ideal solutionI.e. formula (17) is relatively close to
C is bigger for degree, shows to assess optimal case of the object closer to selected sample.
That is power consumer corresponding relative proximities c under certain first class index is bigger, then the corresponding side of the first class index
Case is more excellent.User can keep according to this value corresponding scheme of power consumer big to the relative proximities numerical value, to this
The corresponding scheme of the small power consumer of relative proximities numerical value is improved, and electrical efficiency is used with improve power consumer.
S340, the corresponding measures of effectiveness fraction of each first class index is calculated according to the relative proximities.
Specifically, can be referred in the hope of each one-level according to the conversion relation between relative proximities and measures of effectiveness fraction
Mark corresponding measures of effectiveness fraction.The present embodiment is not defined to specific conversion relation.Conversion can certainly be closed
System is considered as 1 i.e. directly using relative proximity value as measures of effectiveness fraction.
Table 3 is refer to, the corresponding relation provided between a kind of relative proximities, evaluation grade and assessment fraction is illustrated i.e.
Conversion relation.The corresponding relation can be determined and be changed by user.
Table 3 assesses score graph
Further for assessment fraction more intuitively, is visually drawn, the corresponding relation of table 3 can be expressed as Fig. 4.
Can also be immediately arrived at according to Fig. 4 shown in the function such as formula (18) of above-mentioned corresponding relation, can be solved by formula (18)
The measures of effectiveness fraction of each first class index.
The corresponding relative proximity value of first class index directly can be converted directly into by measures of effectiveness point according to formula (18)
Number, the quality of the power program of the corresponding power consumer of the first class index can be intuitively found out according to the measures of effectiveness fraction
Property.
S130, the measures of effectiveness fraction according to the first class index and the first class index weight, are calculated electric power use
The electricity consumption situation measures of effectiveness value at family.
Specifically, using formulaI.e. formula (19) calculates power consumer electricity consumption situation measures of effectiveness value M；
Wherein, liIt is the corresponding measures of effectiveness fraction of i-th first class index, ωiIt is the corresponding one-level of i-th first class index
Index weights, n is the quantity of first class index.M represents electricity consumption situation measures of effectiveness value in the formula, in above-mentioned steps S340
Measures of effectiveness fraction M be designated as li。
The present embodiment is one kind by amendment intuitionistic fuzzy entropy weight method (Improved Intuitionistic Fuzzy
Entropy Weight Method), and improve G1 methods (Improved G1) and the good and bad solution based on intuitionistic Fuzzy Sets away from
From method (technique for order preference by similarity to an ideal solution,
TOPSIS) the electricity consumption shape passed rank appraisal procedure, that is, correct Intuitionistic Fuzzy Entropy-improvement G1- intuitionistic fuzzies TOPSIS synthesis of combination
Condition efficiency estimation method.The method evaluates multiple schemes parameter intuitionistic fuzzy set representations simultaneously, and recycling sets
The hundred-mark system standards of grading put are given a mark to assessment result and are obtained final electricity consumption situation measures of effectiveness value.Detailed effect
Energy evaluation process may be referred to shown in Fig. 5.
I.e. the embodiment solves the weight of first class index by improving G1 methods, and two are calculated using amendment intuitionistic fuzzy entropy weight method
The amendment weight of level index, finally carries out overall merit with the TOPSIS methods based on intuitionistic Fuzzy Sets to user's efficiency, and it can
Fellow peers' evaluation is carried out with to multiple assessment object (i.e. multiple power consumers), is that the electric energy efficiency of user improves and formulate economize on electricity
Scheme provides effective reference.
Further it is by each individual event two-level index because two-level index is determined by Intuitionistic Fuzzy Numbers in the embodiment
Membership function and non-affiliated degree function determine, by Intuitionistic Fuzzy Entropy and improve G1 methods and calculate two-level index amendment weight,
For user Intuitionistic Fuzzy Decision matrix weights and carry out good and bad solution distance and calculate, the efficiency level of active user is improved and is provided
Decision-making foundation.I.e. based on rank comprehensive estimation method is passed, first class index weight is solved by improving G1 methods, using amendment intuitionistic fuzzy
Entropy assessment calculates two-level index weight, is substantially that desired values at different levels have respectively been carried out with weighted comprehensive twice, reduces list
One assigns influence of the power to energy efficiency evaluation result, while expertise and objective data are organically combined, embodies main, objective evaluation
Information, it is ensured that the science and reasonability of assessment models.
Based on above-mentioned technical proposal, the method for the power consumer electricity consumption situation measures of effectiveness that the present embodiment is provided, to work
Industry enterprise power consumer carries out electric energy efficiency assessment, can be from economy, energy information, user power utilization situation and distributed hair
Electricity and energy storage situation carry out comprehensive analysis, are consumed energy with by energy efficiency evaluation measure reduction Demand-side, for energy-saving and emission-reduction work is provided
Use for reference and help.
Based on above-described embodiment, can be by order that related personnel can in time obtain the result of calculation, in the present embodiment
The measures of effectiveness fraction and electricity consumption situation measures of effectiveness value of the first class index being calculated are exported.Its output form can be
In numeral output to user's designated equipment, or voice output, can also be images outputting.The present embodiment is not to knot
Fruit output form is defined.Because each power consumer for calculating may be a lot, in order to save the interpretation of result time, improve
The readability of output result, iconicity can in a graphical form be exported output result.That is this implementation can also be wrapped
Include：
By the measures of effectiveness fraction of the corresponding first class index of each power consumer and electricity consumption situation measures of effectiveness value with radar
The form output of figure.
Specifically, the electricity consumption situation measures of effectiveness value of such as each power consumer can be exported in the form of radar map, use
The electricity consumption situation measures of effectiveness value that family can well compare that power consumer by area is best.
Based on above-mentioned any embodiment, the present embodiment uses feelings by analyzing the electric energy of demand side system or power consumer
Condition, improves energy use efficiency to a certain extent, and further excavates the energy-saving potential of energy consumption system.Scientific and effective comprehensive energy
Effect assessment, not only can use energy situation and main energy consumption problem, additionally it is possible to root with concentrated expression power distribution network or the overall of power consumer
According to assessment result, energy-saving scheme is targetedly worked out, be that energy-saving and emission-reduction work is offered reference and helped, with extremely important
Realistic meaning.I.e. the method can also include：
Measures of effectiveness fraction and electricity consumption situation measures of effectiveness value according to the corresponding first class index of power consumer are analyzed,
Determine that power consumer improves the strategy protocol of electricity consumption situation efficiency.
Specifically, the corresponding measures of effectiveness fraction of some first class index of such as user is especially low, then can be according to being
The corresponding preferably strategy protocol of the first class index for setting of uniting recommends user, the scheme for alloing it to improve this respect, from
And improve the overall efficiency of the user.I.e. its assessment result can not only reflect the comprehensive energy efficiency of power consumer, and can be just
The efficiency level (i.e. first class index efficiency) of certain one side is analyzed, and so as to find out efficiency weak link, targetedly makes
Make energy-saving scheme；Multiple users can also be carried out with fellow peers' evaluation simultaneously, the reference of preferable scheme is comprehensively comprehensively carried out, from
And lift overall user use electrical efficiency.Reach the purpose of preferable energy-saving and emission-reduction.
The embodiment is exemplified below implements process：
By taking 6 power consumers as an example, its electricity consumption situation energy efficiency state is commented with passing rank comprehensive estimation method and carry out efficiency
Estimate.
First：Determine first class index weight
To weaken the subjectivity of evaluation process, 4 experts are chosen altogether marking is estimated to 4 first class index.4 experts
Scored number is as shown in table 4.
The expert estimation table of table 4
Then 4 expert institute agriculture products importance rankings and adjacent index weights ratio γkAs shown in table 5.
The importance ranking of table 5 and weight ratio
Each expert's index weights are calculated by formula (1) and formula (2), result of calculation is as shown in table 6.
64 expert's index weights result of calculations of table
Formula (3) first order calculation index final weight is pressed again, is shown in Table 7.
The weight of the first class index relative target of table 7 layer
Second：Determine two-level index weight
So that the two-level index weight of energy information determines as an example, according to each power consumer energy information data that investigation is obtained
As shown in table 8.
The energy information tables of data of table 8
The weight for trying to achieve each two-level index in energy information using intuitionistic fuzzy entropy weight method is as shown in table 9.
Two-level index weight in the energy information of table 9
Similarly, can show that the weight of each two-level index versus primary index is shown in Table 10.
The two-level index weight of table 10
3rd：Solve first class index assessment fraction
The corresponding two-level index of each first class index is carried out respectively calculating finally give first class index assessment fraction, now with
As a example by energy information and its index are calculated, calculating process is as follows：
(1) by the data in table 8, Intuitionistic Fuzzy Decision matrix F is constructed according to formula (4)~(8).
It is a specification battle array to be apparent from this.
(2) according to formula (13) construction weighting intuitionistic Fuzzy Sets decision matrix, by table 9 know weight vectors for λ=(0.1811,
0.1993,0.1721,0.0903,0.1772,0.1800), then have
(3) according to formula (14), positive ideal solution is：
A+=[<0.1473,0.8111><0.1554,0.8011><0.1312,0.8313><0.1075,0.8581><
0.1606,0.7942><0.1242,0.8396>]
Minus ideal result is：
A-=[<0.0924,0.8773><0.1055,0.8608><0.0954,0.8743><0.0297,0.9572><
0.0792,0.8929><0.1106,0.8559>]
(4) distance according to formula (15)~(18) parameter actual value to positive ideal solution and minus ideal result, relative connect
Recency and assessment fraction.Then the evaluating data result of each user's first class index energy information is as shown in table 11.
The assessment result of table 11
4th：Solve each scheme overall evaluation fraction
Each scheme overall evaluation fraction is drawn according to formula (19), as shown in table 12.
Each scheme evaluation score graph of table 12
Then the electric energy efficiency situation of 6 users can be compared using radar map, area it is big show its final assessment
Result is better than other users.Can cause that result of calculation is more directly perceived by figure, image.
5th：Interpretation of result
Be can be seen that according to above-mentioned example：
1st, the assessment fraction of comprehensive each user, user 1 (49.86 points), user 2 (72.92 points), user 3 (70.18 points),
User 4 (64.87 points), user 5 (51.84 points) and user 6 (43.67 points)；The assessed value highest of user 2, the assessment of user 6
Value is minimum；User 2, and 3 efficiency situations belong to medium, and user 4 is qualified and user 1, and 5,6 is unqualified.
2nd, the analysis underproof reason of fraction of user 5 can show that the fraction of its energy information is low compared with other users
Much, further analyze, many index data in its energy information each user relatively in be worst, and energy information
The weight highest of index, is the main cause for causing the total score compared with other users of user 5 low, should focus on improvement energy information and ask
Topic；And user 1 and the underproof main cause of user 6 are then embodied in economy and electricity consumption situation, have compared with other users
It is obvious to improve space.
3rd, example shows, set forth herein assessment models be based on passing the thought of rank, combine the subjective warp in evaluation process
Testing and objective data, and can assess object by each carries out Comparative result, draws the efficiency situation of local user, more
Tool is presented in face of policymaker as ground.It is further, the weakness ring of efficiency present in each user power utilization process can be accurately positioned
Section, has certain directive significance to guiding user's reasonable energy, and formulating power saving scheme for user's science provides reference.
In sum：In the above example, it is known that the technical method in the present embodiment substantially has more than other conventional methods
There are practicality, persuasion property and operability.
The system to power consumer electricity consumption situation measures of effectiveness provided in an embodiment of the present invention is introduced below, hereafter retouches
The system of the power consumer electricity consumption situation measures of effectiveness stated and the method for above-described power consumer electricity consumption situation measures of effectiveness
Can be mutually to should refer to.
Refer to Fig. 6, the knot of the system of the power consumer electricity consumption situation measures of effectiveness that Fig. 6 is provided by the embodiment of the present invention
Structure block diagram；The system can include：
First weight determination module 100, G1 Algorithm for Solving first class index weights and each one-level are improved for utilizing
The subjective weight of the corresponding two-level index of index；
Second weight determination module 200, for using intuitionistic fuzzy entropy weight method and the subjective weight is corrected, calculating each institute
State the amendment weight of the corresponding two-level index of first class index；
First class index efficiency fraction determining module 300, for according to the amendment weight, using based on intuitionistic Fuzzy Sets
TOPSIS methods calculate the measures of effectiveness fraction of the first class index；
Electricity consumption situation efficiency determining module 400, for measures of effectiveness fraction and the one-level according to the first class index
Index weights, are calculated the electricity consumption situation measures of effectiveness value of power consumer.
Based on above-described embodiment, the system can also include：
Output module, for by the measures of effectiveness fraction of the corresponding first class index of each power consumer and electricity consumption situation efficiency
Assessed value is exported in the form of radar map.
Example is arbitrarily applied based on above-mentioned reality, the system can also include：
Analysis module, comments for the measures of effectiveness fraction according to the corresponding first class index of power consumer and electricity consumption situation efficiency
Valuation is analyzed, and determines that power consumer improves the strategy protocol of electricity consumption situation efficiency.
Each embodiment is described by the way of progressive in specification, and what each embodiment was stressed is and other realities
Apply the difference of example, between each embodiment identical similar portion mutually referring to.For system disclosed in embodiment
Speech, because it is corresponded to the method disclosed in Example, so describing fairly simple, related part is referring to method part illustration
.
Professional further appreciates that, with reference to the unit of each example of the embodiments described herein description
And algorithm steps, can be realized with electronic hardware, computer software or the combination of the two, in order to clearly demonstrate hardware and
The interchangeability of software, generally describes the composition and step of each example according to function in the above description.These
Function is performed with hardware or software mode actually, depending on the application-specific and design constraint of technical scheme.Specialty
Technical staff can realize described function to each specific application using distinct methods, but this realization should not
Think beyond the scope of this invention.
The step of method or algorithm for being described with reference to the embodiments described herein, directly can be held with hardware, processor
Capable software module, or the two combination is implemented.Software module can be placed in random access memory (RAM), internal memory, read-only deposit
Reservoir (ROM), electrically programmable ROM, electrically erasable ROM, register, hard disk, moveable magnetic disc, CD-ROM or technology
In field in known any other form of storage medium.
The method and system to power consumer electricity consumption situation measures of effectiveness provided by the present invention have carried out detailed Jie above
Continue.Specific case used herein is set forth to principle of the invention and implementation method, and the explanation of above example is only
It is to be used to help understand the method for the present invention and its core concept.It should be pointed out that for those skilled in the art
For, under the premise without departing from the principles of the invention, some improvement and modification can also be carried out to the present invention, these improve and repair
Decorations are also fallen into the protection domain of the claims in the present invention.
Claims (9)
1. a kind of method of power consumer electricity consumption situation measures of effectiveness, it is characterised in that methods described includes：
Using the subjective weight for improving G1 Algorithm for Solving first class index weights and the corresponding two-level index of each first class index；
Using intuitionistic fuzzy entropy weight method and the subjective weight is corrected, repairing for each corresponding two-level index of first class index is calculated
Positive weights；
According to the amendment weight, the measures of effectiveness point of the first class index is calculated using the TOPSIS methods based on intuitionistic Fuzzy Sets
Number；
Measures of effectiveness fraction and the first class index weight according to the first class index, are calculated the electricity consumption shape of power consumer
Condition measures of effectiveness value.
2. method according to claim 1, it is characterised in that using correcting intuitionistic fuzzy entropy weight method and the subjective power
Weight, calculates the amendment weight of the corresponding two-level index of each first class index, including：
Determine the membership function and non-affiliated degree function of the corresponding two-level index of each first class index；
According to the membership function and the non-affiliated degree function, intuitionistic Fuzzy Sets decision matrix is built；
According to the intuitionistic Fuzzy Sets decision matrix, the objective weight of the corresponding two-level index of each first class index is calculated；
According to the objective weight and the subjective weight, the amendment power of the corresponding two-level index of each first class index is calculated
Weight.
3. method according to claim 2, it is characterised in that according to the amendment weight, using based on intuitionistic Fuzzy Sets
TOPSIS methods calculate the measures of effectiveness fraction of the first class index, including：
Build weighting intuitionistic Fuzzy Sets decision matrix；
The corresponding positive ideal solution of each first class index and minus ideal result are determined according to the weighting intuitionistic Fuzzy Sets decision matrix；
According to the positive ideal solution and the minus ideal result calculate the power consumer under each first class index it is corresponding just
Ideal solution distance and minus ideal result distance；
According to the positive ideal solution distance and the minus ideal result distance calculating power consumer under each first class index
Corresponding relative proximities；
The corresponding measures of effectiveness fraction of each first class index is calculated according to the relative proximities.
4. method according to claim 3, it is characterised in that measures of effectiveness fraction according to the first class index and described
First class index weight, is calculated the electricity consumption situation measures of effectiveness value of power consumer, including：
Using formulaCalculate power consumer electricity consumption situation measures of effectiveness value M；
Wherein, liIt is the corresponding measures of effectiveness fraction of i-th first class index, ωiIt is the corresponding first class index of i-th first class index
Weight, n is the quantity of first class index.
5. method according to claim 4, it is characterised in that measures of effectiveness fraction according to the first class index and described
First class index weight, is calculated after the electricity consumption situation measures of effectiveness value of power consumer, also includes：
By the measures of effectiveness fraction of the corresponding first class index of each power consumer and electricity consumption situation measures of effectiveness value with radar map
Form is exported.
6. the method according to claim any one of 1-5, it is characterised in that also include：
Measures of effectiveness fraction and electricity consumption situation measures of effectiveness value according to the corresponding first class index of power consumer are analyzed, it is determined that
Power consumer improves the strategy protocol of electricity consumption situation efficiency.
7. a kind of system of power consumer electricity consumption situation measures of effectiveness, it is characterised in that including：
First weight determination module, G1 Algorithm for Solving first class index weights and each first class index correspondence are improved for utilizing
Two-level index subjective weight；
Second weight determination module, for using intuitionistic fuzzy entropy weight method and the subjective weight is corrected, calculating each one-level
The amendment weight of the corresponding two-level index of index；
First class index efficiency fraction determining module, for according to the amendment weight, using the TOPSIS based on intuitionistic Fuzzy Sets
Method calculates the measures of effectiveness fraction of the first class index；
Electricity consumption situation efficiency determining module, weighs for the measures of effectiveness fraction according to the first class index and the first class index
Weight, is calculated the electricity consumption situation measures of effectiveness value of power consumer.
8. system according to claim 7, it is characterised in that also include：
Output module, for by the measures of effectiveness fraction of the corresponding first class index of each power consumer and electricity consumption situation measures of effectiveness
Value is exported in the form of radar map.
9. the system according to claim 7 or 8, it is characterised in that also include：
Analysis module, for the measures of effectiveness fraction according to the corresponding first class index of power consumer and electricity consumption situation measures of effectiveness value
It is analyzed, determines that power consumer improves the strategy protocol of electricity consumption situation efficiency.
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Application publication date: 20170531 |
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