CN114881445A - Comprehensive energy system evaluation method and device based on analytic hierarchy process and rough set - Google Patents
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Abstract
The invention discloses a comprehensive energy system evaluation method and a comprehensive energy system evaluation device based on an analytic hierarchy process and a rough set, wherein the method comprises the following steps: s101, establishing a hierarchical structure model; s102, constructing a group evaluation decision matrix; s103, constructing a group evaluation decision rough number matrix; s104, acquiring a rough evaluation weight range; s105, obtaining an evaluation weight; s106, obtaining an evaluation result: and accumulating and multiplying the evaluation weight values after the normalization processing by the first-level, second-level and third-level indexes according to the branches to obtain the global evaluation weight of each third-level index. According to the method, on the basis of using the analytic hierarchy process, a rough set analysis method is assisted, and the rough evaluation weight range is secondarily determined to obtain the evaluation weight, so that the error and subjectivity of the evaluation result are reduced, the evaluation result has higher accuracy, objectivity and comprehensiveness, and a decision maker is further assisted to make reasonable index weight.
Description
Technical Field
The invention relates to the field of comprehensive energy systems and computers, in particular to a comprehensive energy system evaluation method and device based on an analytic hierarchy process and a rough set.
Background
With the development of society and economy, the energy demand is increasing and becoming complex, and the problem of coordinated development of energy and environment has become an important bottleneck restricting the development of the current society. The energy structure is effectively adjusted, the energy utilization link is optimized, the energy utilization efficiency is improved, and the pollution emission is reduced, so that the urgent need of regional development is met. The intelligent comprehensive energy system is a highly information-based energy coordination system comprising various energy input and delivery modes, can comprehensively coordinate the matching relationship among various requirements, realizes the comprehensive management and efficient utilization of energy, and has important significance for promoting energy conservation, emission reduction and industry upgrading.
The intelligent comprehensive energy system integrates energy consumption and supply, provides power, heat, cold and other services for users, and is connected with an urban power grid and an air grid. The intelligent comprehensive energy system technology integrates the technologies of electronic electricity, sensing and response, energy storage, new energy, cloud computing and the like, and is closely related to the aspects of low-carbon development, pollution emission and the like of urban areas. The intelligent integrated energy system service comprises equipment planning and operation allocation, and relates to various evaluation indexes such as energy facilities, energy supply quality, equipment energy consumption, pollution control, intelligent informatization and the like, and benefit requirements and attention points of a main body are different. Therefore, the comprehensive energy system evaluation has great complexity, and how to perform comprehensive, objective and scientific evaluation has important significance for improving the economy, environmental protection and intelligence of the comprehensive energy system. However, the existing comprehensive evaluation method is limited to a few aspects, the evaluation result is lack of comprehensiveness, meanwhile, the weight setting of different aspects is greatly influenced by subjective factors such as a scoring mode and expert speciality, and the evaluation result is lack of objectivity.
In order to make up for the problem that the comprehensive evaluation method is subjective, some comprehensive energy systems adopt an evaluation mode combining an entropy weight method and a hierarchical analysis method, however, the entropy weight method determines the weight and has higher requirements on data completeness and sample size, and when an intelligent comprehensive energy system evaluation system is constructed, a large amount of complete collected data or experts are required to score, so that the requirements on manpower and material resources are higher. Meanwhile, the entropy weight method determines the weight according to the entropy value of each index, which cannot reflect the understanding of a decision maker on the importance of each index, and may generate a situation contrary to the fact to a certain extent.
Disclosure of Invention
The invention aims to overcome the defects in the prior art and provides a comprehensive energy system evaluation method and device based on an analytic hierarchy process and a rough set.
The invention adopts the following technical scheme:
on one hand, the comprehensive energy system evaluation method based on the analytic hierarchy process and the rough set comprises the following steps:
s101, establishing a hierarchical structure model: analyzing the mutual relation of indexes in the designated comprehensive energy system, dividing the indexes into first-level, second-level and third-level indexes in a total-minute mode, and establishing a hierarchical structure model for evaluating the comprehensive energy system;
s102, constructing a group evaluation decision matrix: based on the hierarchical structure model, quantitatively carrying out pairwise comparison on all three-level indexes under the same two-level indexes, all two-level indexes under the same one-level indexes and all one-level indexes at the same level to obtain the relative importance of each index and construct a group evaluation decision matrix of each level; the value in the group evaluation decision matrix is a Santy scale;
s103, constructing a group evaluation decision rough number matrix: converting the group evaluation decision matrix into a group evaluation decision rough number matrix by adopting a rough set method;
s104, acquiring a rough evaluation weight range: based on the group evaluation decision rough number matrix, after accumulating and rooting the Santy scale of the indexes, acquiring the rough weight evaluation range of each index;
s105, obtaining an evaluation weight: based on the evaluation optimization coefficient, converting the weight evaluation range of each index into the evaluation weight value of each index at each level; the evaluation optimization coefficient is an expert experience value set based on a weight evaluation range among all indexes;
s106, obtaining an evaluation result: and accumulating and multiplying the evaluation weight values after the normalization processing by the first-level, second-level and third-level indexes according to the branches to obtain the global evaluation weight of each third-level index.
Preferably, each of the group evaluation decision matrices is represented as follows:
wherein D represents a group evaluation decision matrix;
n represents the number of experts involved in the evaluation,
representing the importance judgment Santy Scale for k experts comparing index i to j in the set of m indexes, k being [1, n ]](ii) a m represents the number of indexes included in the current group evaluation decision matrix, that is, the number of indexes included in the stage.
Preferably, the group evaluation decision rough number matrix corresponding to each group evaluation decision matrix is represented as follows:
wherein R represents a group evaluation decision rough number matrix;
is represented by r ij Is approximated under a coarse number, represented as set r ij All of them are less than or equal to
Average of elementsThe number of the first and second groups is,
is represented by r ij Is approximated on a coarse number, represented as set r ij All of them are greater than or equal to
The average number of elements, as follows:
preferably, the weight evaluation range of each index is expressed as follows:
wherein, W j The weight evaluation range of the index j is shown.
Preferably, the S105 specifically includes:
based on the evaluation optimization coefficient, converting the weight evaluation range of each index subjected to normalization processing into the evaluation weight value of each index at each level, and performing normalization processing on the evaluation weight values; the evaluation optimization coefficient is an expert experience value set based on the weight evaluation range among the indexes.
Preferably, the evaluation weight value of each index is expressed as follows:
wherein v is j Represents the weight evaluation range of the index j; alpha is belonged to 0,1]And represents an evaluation optimization coefficient.
In another aspect, an integrated energy system evaluation apparatus based on an analytic hierarchy process and a rough set includes:
the hierarchical structure model building module is used for analyzing the mutual relation of indexes in the designated comprehensive energy system, dividing the indexes into first-level, second-level and third-level indexes in a total-minute mode and building a hierarchical structure model for comprehensive energy system evaluation;
the group evaluation decision matrix construction module is used for quantitatively comparing all three-level indexes under the same two-level indexes, all two-level indexes under the same one-level indexes and all one-level indexes in a pairwise manner on the basis of the hierarchical structure model to obtain the relative importance of each index and construct a group evaluation decision matrix of each level; the value in the group evaluation decision matrix is a Santy scale;
the group evaluation decision rough number matrix construction module is used for converting the group evaluation decision matrix into a group evaluation decision rough number matrix by adopting a rough set method;
the rough evaluation weight range acquisition module is used for acquiring the rough weight evaluation range of each index after accumulating and rooting the Santy scale of the index based on the group evaluation decision rough number matrix;
the evaluation weight acquisition module is used for converting the weight evaluation range of each index into the evaluation weight value of each index of each level based on the evaluation optimization coefficient; the evaluation optimization coefficient is an expert experience value set based on the weight evaluation range among all indexes;
and the evaluation result acquisition module is used for multiplying the first-level, second-level and third-level indexes by the normalized evaluation weight values according to the branches to obtain the global evaluation weight of each third-level index.
As can be seen from the above description of the present invention, compared with the prior art, the present invention has the following advantages:
(1) according to the invention, on the basis of using an analytic hierarchy process, a rough set analysis method is assisted, a rough evaluation weight range is secondarily determined to obtain an evaluation weight, and the first-level, second-level and third-level indexes multiply the evaluation weight according to branches to obtain the global evaluation weight of each third-level index, so that the error and the subjectivity of an evaluation result are reduced, the understanding of a decision maker on each index weight can be effectively reflected, and the decision maker is further assisted to make a reasonable index weight; therefore, the invention is a more comprehensive, objective and widely applied evaluation method of the intelligent comprehensive energy system;
(2) the invention can process the missing sample data by adopting a rough set mode, has higher fault-tolerant capability, can solve the problem of empowerment under data missing and has higher application range.
Drawings
FIG. 1 is a flow chart of a method for evaluating an integrated energy system based on an analytic hierarchy process and a rough set according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a hierarchical structure model for evaluation of a comprehensive energy system for a building and global evaluation weights of three levels of indexes according to an embodiment of the present invention;
fig. 3 is a block diagram illustrating an architecture of an apparatus for evaluating an integrated energy system based on an analytic hierarchy process and a rough set according to an embodiment of the present invention.
Detailed Description
The invention will be further illustrated with reference to the following specific examples. It should be understood that these examples are for illustrative purposes only and are not intended to limit the scope of the present invention. Further, it should be understood that various changes or modifications of the present invention may be made by those skilled in the art after reading the teaching of the present invention, and such equivalents may fall within the scope of the present invention as defined in the appended claims.
Referring to fig. 1, a comprehensive energy system evaluation method based on an analytic hierarchy process and a rough set includes:
s101, establishing a hierarchical structure model: analyzing the mutual relation of indexes in the designated comprehensive energy system, dividing the indexes into first-level, second-level and third-level indexes in a total-minute mode, and establishing a hierarchical structure model for evaluating the comprehensive energy system;
s102, constructing a group evaluation decision matrix: based on the hierarchical structure model, quantitatively carrying out pairwise comparison on all three-level indexes under the same two-level indexes, all two-level indexes under the same one-level indexes and all one-level indexes at the same level to obtain the relative importance of each index and construct a group evaluation decision matrix of each level; the value in the group evaluation decision matrix is a Santy scale;
s103, constructing a group evaluation decision rough number matrix: converting the group evaluation decision matrix into a group evaluation decision rough number matrix by adopting a rough set method;
s104, acquiring a rough evaluation weight range: based on the group evaluation decision rough number matrix, after accumulating and rooting the Santy scale of the indexes, acquiring the rough weight evaluation range of each index;
s105, obtaining an evaluation weight: based on the evaluation optimization coefficient, converting the weight evaluation range of each index into the evaluation weight value of each index at each level; the evaluation optimization coefficient is an expert experience value set based on the weight evaluation range among all indexes;
s106, obtaining an evaluation result: and accumulating and multiplying the evaluation weight values after the normalization processing by the first-level, second-level and third-level indexes according to the branches to obtain the global evaluation weight of each third-level index.
In this embodiment, all the third-level indexes under the same second-level index, all the second-level indexes under the same first-level index, and all the first-level indexes are compared pairwise at the same level. In order to reduce the difficulty of comparison among indexes with different properties as much as possible and improve the accuracy, a 1-9-scale method of Santy is adopted to invite a plurality of experts to carry out relative scale comparison and to list all comparison matrixes obtained by evaluation of each expert. The scale rules for comparison of the two indices are shown in table 1 below.
TABLE 1
| Scale | Means of |
| 1 | Showing the same importance of the two factors compared |
| 3 | Indicating that one factor is slightly more important than the other factor when compared to the other factor |
| 5 | Indicating that one factor is significantly more important than the other factor when compared to the other factor |
| 7 | Indicating that one factor is more important than the other, compared to the other |
| 9 | Indicating that one factor is extremely important compared to the other factor |
| 2，4，6，8 | Median value of the above two adjacent judgments |
| Reciprocal of the | Judgment a of factor i compared with j ij A judgment a comparing the factor j with the factor i ji ＝1/a ij |
The first, second and third index evaluation decision matrixes of all experts are combined into a set, for example, k experts are used for a certain group of m third indexes l 3 Is evaluated by the weight matrix
Expressed as:
wherein the content of the first and second substances,
indicates that k experts group the m three-level indexes l 3 The importance of the medium index i compared to j determines the Santy scale. All n experts are aligned to the set of three-level indexes l 3 Integration of index evaluation weight matrix into group evaluation decision matrix
Expressed as:
wherein, each element in the matrix is an importance judgment Santy scale set of n experts in the expert group comparing the indexes i and j as
Further, the group decision matrix is used
Set r of (1) ij Conversion to the average coarse Interval form RN (r) ij ) To obtain a group evaluation decision rough number matrix
Such as
Approximation under rough number
Is a set r ij All of them are less than or equal to
The average number of elements is the average number of elements,
approximation on a rough number
Is a set r ij All of them are greater than or equal to
The average number of elements. Average coarse fraction RN (r) ij ) Is a set r ij The average of the approximation at the coarse number and the approximation at the coarse number of all elements in the equation:
further, the set of m tertiary indexes l 3 The rough evaluation weight range of each index can be determined by a group evaluation rough number matrix R l3 Determination of e.g./ 3 The rough evaluation weight range of the middle j index is as follows:
normalizing the obtained rough evaluation weight range, secondarily determining the tendency of an evaluation result, giving an evaluation optimization coefficient alpha (alpha is more than or equal to 0 and less than or equal to 1) by each expert according to the obtained rough evaluation weight range to express the tendency of the experts to the weight range, removing subjective factors in an analytic hierarchy process, improving the objectivity of the evaluation result, and converting the rough evaluation weight range into a clear evaluation weight value:
the evaluation weight value of each index is expressed as follows:
wherein v is j Represents the weight evaluation range of the index j; alpha is belonged to 0,1]And represents an evaluation optimization coefficient.
Furthermore, after the evaluation weight values of all levels of indexes are obtained, the first, second and third levels of indexes are finally multiplied by the evaluation weight values after normalization processing according to branches, the global weight of each third level of indexes is obtained, and then the evaluation weight of the intelligent comprehensive energy system can be obtained.
Referring to fig. 2, the evaluation of the integrated energy system for construction site in a certain area will be described as an example. Specifically, taking the three-level indexes of the energy supply (first level) -energy facility (second level) branch as an example, the three-level indexes are as follows: the construction condition of the renewable energy facility (the loading amount of the renewable energy in unit area), the construction condition of the electricity storage system (the loading amount of the electricity storage device in unit area), the coverage rate of the renewable energy power supply facility (the coverage rate of the renewable energy power supply), and the construction condition of the cold and heat storage system (the cold and heat storage system use) are four items, four experts are invited to respectively use an analytic hierarchy process, an evaluation weight matrix is constructed by utilizing a 1-9 scale method of Santy, and all evaluation weight matrices of the branch three-level indexes are integrated into a group evaluation decision matrix D which is respectively shown as the following table 2 and a column.
TABLE 2
Converting all elements in the group decision matrix D into an average coarse number interval form
With r 12 Set {3,1,3,5} is an example
Obtaining a group evaluation decision rough number matrix RN (r) 12 )＝[2.165,3.835]Similarly, a group evaluation decision rough matrix R can be obtained as follows:
according to the formula W i And then normalization processing is carried out to obtain a rough evaluation weight range W of the branch three-level index, and experts are asked to set a corresponding evaluation optimization coefficient alpha (alpha is more than or equal to 0 and less than or equal to 1) for the second time, the lower limit of the rough evaluation weight range tends to take a numerical value close to 0, and the upper limit of the rough evaluation weight range tends to take a numerical value close to 1. Finally according to the formula v j Converting the coarse evaluation weight range into clear evaluationThe valence weight values v are shown in table 3 below. And then carrying out normalization processing to obtain the actual evaluation weight of each index of the branch three-level index.
W＝[[0.7530,1.0000] [0.2831,0.4890] [0.6467,0.8138] [0.2132,0.2668]]
v＝[0.909433 0.3929133 0.71354 0.24536]
TABLE 3
Each index of the integrated energy system for construction sites is sequentially evaluated by the method to obtain the actual evaluation weight of each index of all the first-level, second-level and third-level indexes, and the evaluation weight values are multiplied by the first-level, second-level and third-level indexes according to branches (specifically, the actual weight of a certain third-level index, the actual weight of the second-level index corresponding to the third-level index and the actual weight of the first-level index corresponding to the second-level index are respectively obtained, and the three actual weights are multiplied to obtain the overall evaluation weight of each third-level index, which is shown in fig. 2 specifically.
Referring to fig. 3, an apparatus for evaluating an integrated energy system based on an analytic hierarchy process and a rough set includes:
the hierarchical structure model establishing module 301 is used for analyzing the mutual relation of indexes in the designated comprehensive energy system, dividing the indexes into first-level, second-level and third-level indexes in a total-minute mode, and establishing a hierarchical structure model for comprehensive energy system evaluation;
a group evaluation decision matrix construction module 302, configured to quantitatively compare all three-level indexes under the same second-level index, all second-level indexes under the same first-level index, and all first-level indexes in a pairwise manner based on the hierarchical structure model to obtain the relative importance of each index, and construct a group evaluation decision matrix of each level; the value in the group evaluation decision matrix is a Santy scale;
a group evaluation decision rough number matrix construction module 303, configured to convert the group evaluation decision matrix into a group evaluation decision rough number matrix by using a rough clustering method;
a rough evaluation weight range acquisition module 304, configured to perform cumulative root finding on the Santy scale of the indexes based on the group evaluation decision rough number matrix, and acquire a rough weight evaluation range of each index;
an evaluation weight obtaining module 305, configured to convert the weight evaluation range of each index into an evaluation weight value of each index at each level based on the evaluation optimization coefficient; the evaluation optimization coefficient is an expert experience value set based on the weight evaluation range among all indexes;
and the evaluation result obtaining module 306 is configured to multiply the normalized evaluation weight values of the first-level, second-level, and third-level indexes according to the branches to obtain a global evaluation weight of each third-level index.
The embodiment of the comprehensive energy system evaluation device based on the analytic hierarchy process and the rough set does not need repeated description.
The above description is only an embodiment of the present invention, but the design concept of the present invention is not limited thereto, and any insubstantial modifications made by using the design concept should fall within the scope of infringing the present invention.
Claims (7)
1. A comprehensive energy system evaluation method based on an analytic hierarchy process and a rough set is characterized by comprising the following steps:
s101, establishing a hierarchical structure model: analyzing the mutual relation of indexes in the designated comprehensive energy system, dividing the indexes into first-level, second-level and third-level indexes in a total-minute mode, and establishing a hierarchical structure model for evaluating the comprehensive energy system;
s102, constructing a group evaluation decision matrix: based on the hierarchical structure model, quantitatively carrying out pairwise comparison on all three-level indexes under the same two-level indexes, all two-level indexes under the same one-level indexes and all one-level indexes at the same level to obtain the relative importance of each index and construct a group evaluation decision matrix of each level; the value in the group evaluation decision matrix is a Santy scale;
s103, constructing a group evaluation decision rough number matrix: converting the group evaluation decision matrix into a group evaluation decision rough number matrix by adopting a rough set method;
s104, acquiring a rough evaluation weight range: based on the group evaluation decision rough number matrix, after accumulating and rooting the Santy scale of the indexes, acquiring the rough weight evaluation range of each index;
s105, obtaining an evaluation weight: based on the evaluation optimization coefficient, converting the weight evaluation range of each index into the evaluation weight value of each index at each level; the evaluation optimization coefficient is an expert experience value set based on the weight evaluation range among all indexes;
s106, obtaining an evaluation result: and accumulating and multiplying the normalized evaluation weight values of the first-level, second-level and third-level indexes according to the branches to obtain the global evaluation weight of each third-level index.
2. The analytic hierarchy process and rough set based integrated energy system evaluation method of claim 1, wherein each of the group evaluation decision matrices is represented as follows:
wherein D represents a group evaluation decision matrix;
n represents the number of experts involved in the evaluation,
representing the importance judgment Santy Scale for k experts comparing index i to j in the set of m indexes, k being [1, n ]](ii) a m represents the number of indexes included in the current group evaluation decision matrix, that is, the number of indexes included in the stage.
3. The analytic hierarchy process and coarse set-based comprehensive energy system evaluation method of claim 2, wherein the coarse number matrix of group evaluation decisions corresponding to each group evaluation decision matrix is represented as follows:
wherein R represents a group evaluation decision rough number matrix;
is represented by r ij Is approximated under a coarse number, represented as set r ij All of them are less than or equal to
The average number of elements is the average number of elements,
is represented by r ij Is approximated on a coarse number, represented as set r ij All of them are greater than or equal to
The average number of elements, as follows:
4. the method of claim 3, wherein the weight evaluation range of each index is expressed as follows:
wherein, W j The weight evaluation range of the index j is shown.
5. The method for evaluating an integrated energy system based on an analytic hierarchy process and a rough set according to claim 4, wherein the step S105 specifically comprises:
based on the evaluation optimization coefficient, converting the weight evaluation range of each index subjected to normalization processing into the evaluation weight value of each index at each level, and performing normalization processing on the evaluation weight values; the evaluation optimization coefficient is an expert experience value set based on the weight evaluation range among the indexes.
6. The analytic hierarchy process and rough set-based integrated energy system evaluation method of claim 5, wherein the evaluation weight values of each index are expressed as follows:
wherein v is j Represents the weight evaluation range of the index j; alpha is belonged to 0,1]And represents an evaluation optimization coefficient.
7. An integrated energy system evaluation device based on an analytic hierarchy process and a rough set, comprising:
the hierarchical structure model building module is used for analyzing the mutual relation of indexes in the designated comprehensive energy system, dividing the indexes into first-level, second-level and third-level indexes in a total-minute mode and building a hierarchical structure model for comprehensive energy system evaluation;
the group evaluation decision matrix construction module is used for quantitatively comparing all three-level indexes under the same two-level indexes, all two-level indexes under the same one-level indexes and all one-level indexes in a pairwise manner on the basis of the hierarchical structure model to obtain the relative importance of each index and construct a group evaluation decision matrix of each level; the value in the group evaluation decision matrix is a Santy scale;
the group evaluation decision rough number matrix construction module is used for converting the group evaluation decision matrix into a group evaluation decision rough number matrix by adopting a rough set method;
the rough evaluation weight range acquisition module is used for acquiring the rough weight evaluation range of each index after accumulating and rooting the Santy scale of the index based on the group evaluation decision rough number matrix;
the evaluation weight acquisition module is used for converting the weight evaluation range of each index into the evaluation weight value of each index of each level based on the evaluation optimization coefficient; the evaluation optimization coefficient is an expert experience value set based on the weight evaluation range among all indexes;
and the evaluation result acquisition module is used for multiplying the first-level, second-level and third-level indexes by the normalized evaluation weight values according to the branches to obtain the global evaluation weight of each third-level index.
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