CN108563863B - Energy consumption calculation and scheduling method for urban rail transit system - Google Patents
Energy consumption calculation and scheduling method for urban rail transit system Download PDFInfo
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Abstract
The invention provides an energy consumption calculating and scheduling method of an urban rail transit system. The method comprises the following steps: the method comprises the following steps: constructing a multi-layer network model of an urban rail transit energy consumption system based on the composition of the urban rail transit system; calculating the topological attribute and the data attribute of the node as input quantities, wherein the topological attribute comprises the degree, the betweenness and the clustering coefficient of the node, and the data attribute comprises the energy consumption and the node strength of the node; fusing the topological attribute and the data attribute of the node by using an OWA operator to obtain the weight of the node in the urban rail transit system; based on an emergence theory, the energy efficiency emergence state of the urban rail system, namely the energy efficiency characterization parameter, is calculated by using Choquet integral combined with the weight of the node; and regulating and controlling the energy efficiency of the urban rail operation system based on the urban rail system energy efficiency characterization parameters. For the urban rail transit system with a complex structure, the method can calculate the energy efficiency of the urban rail system, and can reasonably regulate and control the energy efficiency of the urban rail operation system by combining the calculated energy efficiency.
Description
Technical Field
The invention relates to the technical field of rail transit energy consumption calculation, in particular to an energy consumption calculation and scheduling method of an urban rail transit system.
Background
With the development of urban rail transit, the huge energy consumption problem of an urban rail transit system also gets extensive attention. In the existing research on the energy efficiency of the urban rail transit system, no learner utilizes a network model to research the urban rail transit energy efficiency from the perspective of the system, and a strategy and a basis are provided for improving the overall energy efficiency of the system.
Network science is a new cross-science that is specialized in studying qualitative and quantitative laws of complex systems in nature and society, and has been applied to various scientific and engineering fields. Through long-term development, network science has formed the subject architecture and theoretical system of the system. The focus of research on complex network models has gradually shifted in recent years from single networks to multi-layer networks. The multilayer network model is developed as a single-layer network model, and can be used for modeling and analyzing a network which cannot be described by the single-layer network and comprises heterogeneous nodes and heterogeneous edges, so that the multilayer network model is used as a relatively new research branch of network science, is also paid attention to and applied by students, and has very important significance for modeling the energy efficiency of the urban rail transit system by using the multilayer network model.
Disclosure of Invention
The embodiment of the invention provides an energy consumption calculating and scheduling method of an urban rail transit system, which aims to overcome the defects of the prior art.
An energy consumption calculation and scheduling method of an urban rail transit system comprises the following steps:
the method comprises the following steps that firstly, an energy consumption network model of the urban rail transit system is constructed according to the composition relation of the urban rail transit system and by combining energy consumption data;
step two, calculating topological attributes and data attributes of nodes in the urban rail transit system by analyzing operation energy consumption data of the urban rail transit system and combining the constructed energy efficiency network model of the urban rail transit system;
fusing the topological attribute and the data attribute of the node by using an OWA operator to obtain the importance of the node in the urban rail transit system;
calculating energy efficiency characterization parameters of the urban rail transit system by using the importance of the Choquet integral combined with the node based on the emerging theory;
and fifthly, regulating and controlling the energy efficiency of the urban rail transit system based on the energy efficiency characterization parameters of the urban rail transit system.
Further, the constructing an energy consumption network model of the urban rail transit system according to the composition relationship of the urban rail transit system and by combining energy consumption data includes:
1.1, constructing a layer in an energy consumption network model of the urban rail transit system, wherein the layer refers to a set of subsystems with the same structural level or functional level and interaction relations between the subsystems, all layers in the urban rail transit system are divided according to a division principle from an upper layer to a lower layer, and L is a set of layers in the energy consumption network model of the urban rail transit system;
1.2, abstracting a subsystem which is composed of a certain amount of parts in a specific relation and has a relatively independent function and energy consumption in the urban rail transit system as a node, wherein V is a set of nodes in an energy consumption network model of the urban rail transit system;
1.3, abstracting an interaction relation between subsystems and subsystem energy consumption in the urban rail transit system as a connecting edge, taking a Pearson coefficient between the subsystems and subsystem energy consumption data as the weight of the edge, wherein E is a set of edges in an energy consumption network model of the urban rail transit system, and a calculation formula of the weight w of the edge is as follows:
in the formula:
n is the sample size;
ai,bi-statistics of two variables respectively.
Further, the calculating of the topology attribute and the data attribute of the nodes in the urban rail transit system by analyzing the operation energy consumption data of the urban rail transit system and combining the constructed energy efficiency network model of the urban rail transit system includes:
2.1, the calculation formula of the centrality of the degree of the node i in the topological attribute is as follows:
in the formula:
l V l-the number of nodes in the node set V;
2.2, the calculation formula of the betweenness of the nodes i in the topological attribute is as follows:
in the formula:
JS-the sum of all shortest path numbers between node pairs;
2.3, the calculation formula of the clustering coefficient of the node i in the topological attribute is as follows:
in the formula:
2.4, acquiring the energy consumption of the node i in the data attribute from the energy consumption data;
2.5, the calculation formula of the node strength of the node i in the data attribute is as follows:
in the formula:
Further, the fusion of the topological attribute and the data attribute of the node by using the OWA operator to obtain the importance of the node in the urban rail transit system includes:
3.1, collecting attribute data of each node obtained by investigation and calculation, and constructing a node evaluation attribute matrix;
3.2, standardizing the attribute data in the node evaluation attribute matrix according to an attribute data standardization formula;
3.3, sorting the attribute data according to the size of the attribute data value of the normalized node;
and 3.4, clustering the attribute data values of the nodes by using an OWA operator to obtain the importance value of each node.
Further, the normalizing the attribute data in the node evaluation attribute matrix according to the attribute data normalization formula includes:
setting attribute types of attribute data in the node evaluation attribute matrix, wherein the attribute types comprise benefit type, cost type, fixed type, deviation type, interval type and deviation interval type, and the benefit type attribute means that the larger the value of the attribute data is, the better the attribute data is; the cost attribute means that the smaller the attribute data value, the better; fixed-type attributes are attributes whose data values approach a fixed value alphajThe better; the offset attribute means that the more the attribute data value is offset from a fixed value betajThe better; the interval type attribute means that the closer the attribute data value is to a fixed interval
The better; the off-interval type attribute means that the more the attribute data value is off the fixed interval
The better;
the attribute data is normalized as follows:
if the attribute value is benefit type, then order
In the formula:
maxaj-the largest attribute value in the jth column of attribute data;
if the attribute value is cost type, then order
In the formula:
minaj-the smallest attribute value in the jth column of attribute data;
if the attribute value is fixed, order
If the attribute value is of offset type, then order
If the attribute value is interval type, then order
If the attribute value is of the deviated interval type, then order
Further, the using the OWA operator to aggregate the attribute data values of the nodes to obtain the importance value of each node includes:
clustering the attribute data values of the nodes by using an OWA operator to obtain the importance value of each node
Importance value
The calculation formula of (a) is as follows:
wherein brijThe br is a sorting result for sorting the normalized attributes of the nodes according to the sequence of the attribute values from big to smallijWhere i refers to the number of nodes, j refers to the number of attributes, m refers to the total number of attribute data used for clustering, and γ is the addition of the OWA operatorAnd the weight vector is determined by using a minimum variance method to the weight vector of the OWA operator, and the calculation formula of gamma is as follows:
where Disp (γ) represents the degree to which the attribute is used equally, and the order (γ) represents the degree to which the fusion operation is similar to the or operation.
Further, based on the emerging theory, the energy efficiency characterization parameters of the urban rail transit system are calculated by using the Choquet integral in combination with the importance of the nodes, and the method comprises the following steps:
normalizing the calculated importance value of the node, and taking the normalized result of the importance value of the node as a Shapley value of the node, wherein the interval of the Shapley value is 0-1;
calculating fuzzy measure of each node set by establishing an optimization model taking Marchal entropy as a target function, wherein the optimization model taking Marchal entropy as the target function is as follows:
in the formula
using Choquet integrationCalculating the energy efficiency emergence state of the node, wherein the Choquet integral is defined as: let gλFor a lambda blur measure defined on P (X), f is a non-negative real valued measurable function defined on X, then f is with respect to gλIntegral (c) of (i) integral of (i) fdgλThe definition is shown as the following formula:
in the formula:
i——f(xi) Transformation of the vector so that 0 ≦ f (x)1)≤L≤f(xn)；
Xi＝(x1,x2,L,xn) And f (x)0)＝0。
Further, the energy efficiency characterization parameter based on the urban rail transit system regulates and controls the energy efficiency of the urban rail transit system, and includes:
by utilizing the property of Choquet integral, seeking a functional relation between the energy efficiency characterization parameters of the subsystems and the energy efficiency characterization parameters of the urban rail transit system, and determining the energy efficiency importance of each subsystem according to the functional relation;
according to the formula of Choquet integral, when gλ(Ai) Under the known condition, the energy efficiency characterization parameter ^ fd of the urban rail transit systemgλAnd the energy efficiency characterization parameter f (x) of the subsystemi) The functional relationship between the two is shown as the following formula:
determining an energy consumption threshold of a subsystem which can obtain the energy consumption data according to the maximum value and the minimum value of the investigated energy consumption data, and determining an energy efficiency characterization parameter threshold of a subsystem which can not obtain the energy consumption data according to the energy efficiency characterization parameter of the subsystem and the energy consumption threshold of the subsystem which can obtain the energy consumption data; and determining an energy efficiency regulation and control method and strategy of the urban rail transit system according to the energy efficiency importance of each subsystem and the energy efficiency characterization parameter threshold of the subsystem, wherein the energy consumption data is unavailable.
According to the technical scheme provided by the embodiment of the invention, the multi-layer network model is utilized to perform modeling analysis on the urban rail transit system, and the emergence theory and the fuzzy integral method are combined to provide the energy efficiency regulation and control method and strategy of the urban rail transit system based on the energy efficiency emergence state. The method can calculate the energy efficiency of the urban rail transit system and reasonably regulate and control the energy efficiency of the urban rail transit system.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without inventive labor.
Fig. 1 is a schematic diagram illustrating an implementation principle of an energy consumption calculation and scheduling method of an urban rail transit system according to an embodiment of the present invention;
fig. 2 is a processing flow chart of an energy consumption calculating and scheduling method of an urban rail transit system according to an embodiment of the present invention;
fig. 3 is a schematic diagram of an energy consumption multi-layer network model of an urban rail transit system according to an embodiment of the present invention;
fig. 4 is an abstract diagram of an energy efficiency network model of an urban rail transit system according to an embodiment of the present invention.
Detailed Description
For the convenience of understanding the embodiments of the present invention, the following description will be further explained by taking several specific embodiments as examples in conjunction with the drawings, and the embodiments are not to be construed as limiting the embodiments of the present invention.
Example one
The method considers the complex interaction relationship among urban rail transit energy consumption systems, utilizes a multilayer network model to perform modeling analysis on the urban rail transit system, calculates the energy efficiency emergence state of the urban rail transit system by combining an emergence theory and a fuzzy integral method on the basis, and finally provides the energy efficiency regulation and control method and strategy of the urban rail transit system based on the energy efficiency emergence state.
The schematic diagram of the implementation principle of the energy consumption calculation and scheduling method of the urban rail transit system provided by the embodiment of the invention is shown in fig. 1, and the specific processing flow is shown in fig. 2, and the method comprises the following processing steps:
1.1, constructing a layer in an energy consumption network model of the urban rail transit system, wherein the layer refers to subsystems with the same structural level or functional level and a set of interaction relations between the subsystems. All layers in the urban rail transit system are divided according to a division principle from an upper layer to a lower layer, and L is a set of layers in an energy consumption network model of the urban rail transit system. Fig. 3 is a schematic diagram of an energy consumption multi-layer network model of an urban rail transit system according to an embodiment of the present invention.
1.2, abstracting subsystems which are formed by a certain amount of parts in a specific relation and have relatively independent functions and energy consumption in the urban rail transit system into nodes. And V is a set of nodes in an energy consumption network model of the urban rail transit system.
1.3, abstracting an interaction relation between subsystems and subsystem energy consumption in the urban rail transit system as a connecting edge, taking a Pearson coefficient between the subsystems and subsystem energy consumption data as the weight of the edge, wherein E is a set of edges in an energy consumption network model of the urban rail transit system, and a calculation formula of the weight w of the edge is as follows:
in the formula:
n is the sample size;
ai,bi-statistics of two variables respectively.
And secondly, calculating the topological attribute and the data attribute of the node by analyzing the operation energy consumption data of the urban rail transit system and combining the constructed energy consumption network model of the urban rail transit system.
2.1, the calculation formula of the centrality of the degree of the node i in the topological attribute is as follows:
in the formula:
i V I is the number of nodes in the node set V.
2.2, the calculation formula of the betweenness of the nodes i in the topological attribute is as follows:
in the formula:
JS-the sum of all shortest path numbers between node pairs.
2.3, the calculation formula of the clustering coefficient of the node i in the topological attribute is as follows:
in the formula:
And 2.4, acquiring the energy consumption of the node i in the data attribute from the energy consumption data.
2.5, the calculation formula of the node strength of the node i in the data attribute is as follows:
in the formula:
And thirdly, fusing the topological attribute and the data attribute of the node by utilizing an OWA (Ordered Weighted arithmetic mean operator) operator to obtain the importance of the node in the urban rail transit system.
And 3.1, constructing an evaluation target attribute matrix. Is provided with
And | η | is the number of attribute sets, wherein | η | is the attribute set of the evaluation target. For any element O in the evaluation target OiI belongs to | O |, all available attribute ηjJ is determined by | η |, to obtain oiAbout ηjAttribute value of aij. Measuring all elements in the evaluation target to obtain attribute values of all elements with respect to eta, thereby forming an attribute matrix A of the evaluation target O with respect to an attribute set eta,
as shown in table 1.
TABLE 1 Attribute matrix
And 3.2, normalizing the attribute data. The attribute types are generally benefit type, cost type, fixed type, partial type, interval type, partial interval type and the like, wherein the benefit type attribute means that the larger the attribute data value is, the better the attribute data value is; the cost attribute means that the smaller the attribute data value, the better; fixed-type attributes are attributes whose data values approach a fixed value alphajThe better; the offset attribute means that the more the attribute data value is offset from a fixed value betajThe better; the interval type attribute means that the closer the attribute data value is to a fixed interval
The better; the off-partition type attribute means that the more the attribute data value is off the fixed partitionWorkshop
The better. In order to eliminate the influence of different dimensions on the evaluation result, the attribute data may be normalized as follows.
If the attribute value is benefit type, then order
In the formula:
maxaj-the largest attribute value in the jth column of attribute data.
If the attribute value is cost type, then order
In the formula:
minaj-the smallest attribute value in the jth column of attribute data.
If the attribute value is fixed, order
If the attribute value is of offset type, then order
If the attribute value is interval type, then order
If the attribute value is of the deviated interval type, then order
3.3, sorting the attribute data. Will evaluate the target viNormalized attribute arijSorting the attribute values according to the sequence of the attribute values from large to small, wherein the sorting result is brijListing the attributes of all evaluation targets to form a new attribute matrix NA, wherein the calculation formula of the NA is as follows:
and 3.4, calculating the comprehensive attribute value of the evaluation target. The integrated attribute value of the evaluation target (i.e., node) is the importance of the evaluation target. Clustering the attribute values of the evaluation targets by using an OWA operator to obtain the importance of each evaluation target
Evaluation target oiThe formula for calculating the importance value is as follows:
where γ is the weight vector of the OWA operator and m refers to the total number of attribute data used for clustering. For example, a node has 5 attributes, 3 a attributes, and 2B attributes. Therefore, | η | is 5, M is 3 when calculating the A attribute aggregation, and M is 2 when calculating the B attribute aggregation.
The invention chooses to determine the weight vector gamma of the OWA operator by using the Minimum Variance Method (MVM). The calculation formula for determining the weight vector γ of the OWA operator by the minimum variance method is as follows:
where Disp (γ) represents the degree to which the attribute is used equally, and the order (γ) represents the degree to which the fusion operation is similar to the or operation.
And fourthly, calculating the energy efficiency emergence state (namely the energy efficiency characterization parameters) of the urban rail transit system by using the Choquet integral combined with the weight of the node based on the emergence theory.
And 4.1, obtaining an abstract diagram of the energy efficiency network model of the urban rail transit system by combining the energy efficiency network model, wherein the abstract diagram is shown in figure 4.
4.2, calculating the node energy value available for the energy consumption data.
And calculating a node energy effective value obtained by the energy consumption data according to the energy consumption investigation data of the urban rail transit system by using an energy efficiency calculation formula, wherein the energy efficiency is equal to the rated energy consumption compared with the actual energy consumption.
And 4.3, calculating to obtain a Shapley value of the node according to the node weight.
The interval of the Shapley value is 0-1, and in order to meet the definition, the invention normalizes the calculated node weight, and takes the normalized result of the node weight as the Shapley value of the node.
4.4, calculating fuzzy measure of attribute set of urban rail transit system
And calculating the fuzzy measure of each attribute set, namely each node set, by establishing an optimization model taking Marchal entropy as a target function. An optimization model with Marchal entropy as an objective function is as follows.
In the formula
And 4.5, calculating the energy efficiency emergence state (namely the energy efficiency characterization parameters) of the urban rail transit system.
And calculating the energy efficiency emergence state of the urban rail transit system by using the Choquet integral. The definition of Choquet integral is: let gλFor a lambda blur measure defined on P (X), f is a non-negative real valued measurable function defined on X, then f is with respect to gλIntegral (c) of (i) integral of (i) fdgλIs defined as the formula:
in the formula:
i——f(xi) Transformation of the vector so that 0 ≦ f (x)1)≤L≤f(xn)；Xi＝(x1,x2,L,xn) And f (x)0)＝0。f(xi) The energy efficiency emergence state of the urban rail transit system is represented; gλ(Ai) Is the fuzzy measure for each node set; the Shapley values of the nodes are used to compute a fuzzy measure for each set of nodes using Marichal entropy.
And fifthly, regulating and controlling the energy efficiency of the urban rail transit system based on the energy efficiency characterization parameters of the urban rail transit system.
And 5.1, calculating the node energy efficiency importance. And (3) seeking a functional relation between the subsystem energy efficiency representation parameters and the system energy efficiency representation parameters by using the property of the Choquet integral and adopting reverse thinking, and determining the energy efficiency importance of each subsystem node according to the coefficient.
According to the formula of Choquet integral, when gλ(Ai) It is known that the original equation can be transformed into ^ fd regarding the system energy efficiency emergence stategλAnd subsystem energy efficiency emergence state f (x)i) The functional relation between the two is shown in the formula.
And 5.2, generating an energy efficiency control strategy of the urban rail transit system. And generating a control strategy based on the subsystem energy efficiency threshold according to the energy efficiency importance of each subsystem node.
Determining an energy consumption (energy efficiency) threshold of a subsystem which can obtain energy consumption data according to the maximum value and the minimum value of the investigated energy consumption data, and determining an energy efficiency characterization parameter threshold of a subsystem which can not obtain the energy consumption data according to an energy efficiency characterization parameter of the subsystem and the energy consumption (energy efficiency) threshold of the subsystem which can obtain the energy consumption data; and determining an energy efficiency regulation and control method and strategy of the urban rail transit system according to the energy efficiency importance of each subsystem and the energy efficiency characterization parameter threshold of the subsystem, wherein the energy consumption data is unavailable.
Example two
According to the above process, the following description is made of the embodiment using the actual data:
step 1: building a node attribute matrix
According to the node attribute classification, an attribute matrix of the node topology attribute is constructed
Wherein etaAAnd | is the number of the topological attributes of the node. According to the information of the nodes in the urban rail system energy efficiency network model, the | V | is 25 and the | eta is knownAAnd | is 3. And (3) inputting the obtained topological attribute of the multilayer network model into an attribute matrix, wherein the attribute matrix A of the node topological attribute is shown as a formula (a).
According to the node attribute classification, an attribute matrix of the node data attribute is constructed
Wherein etaBAnd | is the number of the node data attributes. According to the information of the nodes in the urban rail system energy efficiency network model, the | V | is 25 and the | eta is knownBAnd | is 2. According to the method for acquiring data attributes of the multi-layer network model provided in 3.3.2, each of the urban rail energy efficiency network models is acquiredAnd (4) inputting the rated energy consumption of the node and the node strength into the attribute matrix, wherein the attribute matrix B of the node data attribute is shown as a formula (B).
Step 2: attribute data normalization and attribute data ordering
It can be found that the topological attribute or the data attribute of the node is an attribute belonging to the benefit type, i.e. the larger the attribute value is, the more important the node is. Therefore, the attribute matrix of the node topology attribute and the attribute matrix of the data attribute of the node are normalized by the normalization formula of the benefit type attribute.
And (4) based on the normalized attribute matrix, sequencing the attributes belonging to the same node according to the attribute values to form a new matrix NA shown as a formula (c) and NB shown as a formula (d).
And step 3: calculating importance (i.e. weight) of a node
Due to the fact that the actual energy consumption of the urban rail system is unadditive, only L exists in an energy efficiency network model of the urban rail system4There is an interaction between the nodes of the layers, i.e. there is only L4The nodes in the layer possess the property of node strength. Therefore, the node weights between different layers are not comparable, and the weights of the nodes of different layers are evaluated separately herein.
Specific numerical values in the urban rail system energy efficiency network model are substituted into a weighted vector calculation formula of an OWA operator, and a weighted vector gamma of the node topological attribute can be obtained through MATLAB solutionA(0.584,0.333, 0.083). Weighting vector gamma of node data attributesB＝(0.75,0.25)。
Clustering attribute values by using an OWA operator to obtain the weight of each node
As shown in table 2.
Table 2: weights of network model nodes
And 4, step 4: urban rail system energy efficiency network model abstract diagram
The energy efficiency network model of the urban rail system can be abstracted as shown in FIG. 4:
and 5: node energy value calculation
By using an energy efficiency calculation formula and combining rated energy consumption data and actual energy consumption data of the subsystem nodes obtained through investigation, the energy efficiency of the subsystem nodes can be calculated and obtained as shown in table 3.
Table 3: energy efficiency of measurable subsystem of urban rail system energy consumption data
Step 6: node sharley value calculation
The sharley value of a node herein will be calculated by the weight of the node. The sharley values for the nodes are shown in table 4.
Table 4: shapley value of node in urban rail system energy efficiency model
And 7: node energy efficiency emergence state calculation
The above is all known information for computing system or subsystem energy efficiency. The importance of the attributes and attribute sets obtained by calculating the optimization model constructed by using the maximization of the Marichal entropy as the objective function is shown in Table 5.
Table 5: fuzzy measure of urban rail system attribute set
According to an example of a calculation process of energy efficiency inrush states in a small system, Choquet integration is used to integrate the energy efficiency inrush states of nodes from bottom to top, and energy efficiency inrush states of an urban rail system and subsystems are shown in table 6.
Table 6: energy efficiency emergence state of urban rail system
According to the method for identifying the key energy efficiency nodes of the urban rail system, the nodes in the energy efficiency network of the urban rail system are identified, and the energy efficiency importance of each layer of nodes is shown in table 7.
Table 7: node energy efficiency importance degree in urban rail system energy efficiency model
According to the energy efficiency calculation formula, the energy efficiency threshold results of the subsystems, which can be obtained by calculating the energy consumption data, are shown in table 8.
Table 8: energy consumption data measurable node energy efficiency threshold of urban rail system
According to the method for calculating the energy efficiency characterization parameters of the urban rail system, energy efficiency thresholds of systems and subsystems, which are unavailable in energy consumption data, in the urban rail system are calculated and shown in table 9.
Table 9: urban rail system energy efficiency threshold
According to the energy efficiency importance of each layer of node of the urban rail system, a calculation formula corresponding to the energy efficiency characterization parameters of the urban rail system and the subsystems is obtained and is shown in table 10.
Table 10: urban rail system energy efficiency representation parameter calculation formula
The energy efficiency control strategy of the urban rail system and the variation values of the control nodes on the energy efficiency characterization parameters of the urban rail system are shown in table 11. In the table
Representing the variation value of the energy efficiency representation parameter of the urban rail system; and delta theta represents the variation value of the subsystem energy efficiency characterization parameter.
TABLE 11 energy efficiency characterization parameter variation chart of urban rail system
In summary, the embodiment of the invention provides an energy efficiency regulation and control method and strategy of an urban rail transit system based on an energy efficiency emergence state by utilizing a multilayer network model to perform modeling analysis on the urban rail transit system and combining an emergence theory and a fuzzy integral method. The method can calculate the energy efficiency of the urban rail transit system and reasonably regulate and control the energy efficiency of the urban rail transit system. The method can be particularly applied to the following aspects:
1. the system researches the composition of an urban rail system, the composition of an urban rail energy consumption system and the energy consumption characteristics of the urban rail system.
2. An urban rail system energy efficiency network model is built based on the multilayer network model, characteristics of the model are analyzed, and an idea is provided for identification of key energy consumption nodes of the urban rail system.
3. Based on the emerging theory and an urban rail system energy efficiency network model, the energy efficiency emerging process from microcosmic to macroscopic in the urban rail system is researched, and the energy efficiency of the urban rail system is regulated and controlled from the system perspective.
Those of ordinary skill in the art will understand that: the figures are merely schematic representations of one embodiment, and the blocks or flow diagrams in the figures are not necessarily required to practice the present invention.
Those of ordinary skill in the art will understand that: modules in the devices in the embodiments may be distributed in the devices in the embodiments according to the description of the embodiments, or may be located in one or more devices different from the embodiments with corresponding changes. The modules of the above embodiments may be combined into one module, or further split into multiple sub-modules.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by a computer program, which can be stored in a computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. The storage medium may be a magnetic disk, an optical disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), or the like.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are included in the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (7)
1. An energy consumption calculation and scheduling method of an urban rail transit system is characterized by comprising the following steps:
the method comprises the following steps that firstly, an energy consumption network model of the urban rail transit system is constructed according to the composition relation of the urban rail transit system and by combining energy consumption data;
step two, calculating the topological attribute and the data attribute of the nodes in the urban rail transit system by analyzing the operation energy consumption data of the urban rail transit system and combining the constructed energy consumption network model of the urban rail transit system;
fusing the topological attribute and the data attribute of the node by using an OWA operator to obtain the importance of the node in the urban rail transit system;
calculating energy efficiency characterization parameters of the urban rail transit system by using the importance of the Choquet integral combined with the node based on the emerging theory;
fifthly, regulating and controlling the energy efficiency of the urban rail transit system based on the energy efficiency characterization parameters of the urban rail transit system;
the energy consumption network model of the urban rail transit system is constructed by combining energy consumption data according to the composition relationship of the urban rail transit system, and comprises the following steps:
1.1, constructing a layer in an energy consumption network model of the urban rail transit system, wherein the layer refers to a set of subsystems with the same structural level or functional level and interaction relations between the subsystems, all layers in the urban rail transit system are divided according to a division principle from an upper layer to a lower layer, and L is a set of layers in the energy consumption network model of the urban rail transit system;
1.2, abstracting a subsystem which is composed of a certain amount of parts in a specific relation and has a relatively independent function and energy consumption in the urban rail transit system as a node, wherein V is a set of nodes in an energy consumption network model of the urban rail transit system;
1.3, abstracting an interaction relation between subsystems and subsystem energy consumption in the urban rail transit system as a connecting edge, taking a Pearson coefficient between the subsystems and subsystem energy consumption data as the weight of the edge, wherein E is a set of edges in an energy consumption network model of the urban rail transit system, and a calculation formula of the weight w of the edge is as follows:
in the formula:
n is the sample size;
ai,bi-statistics of two variables respectively.
2. The method according to claim 1, wherein the calculating of the topological attribute and the data attribute of the node in the urban rail transit system by analyzing the operation energy consumption data of the urban rail transit system and combining the constructed energy consumption network model of the urban rail transit system comprises:
2.1, the calculation formula of the centrality of the degree of the node i in the topological attribute is as follows:
in the formula:
l V l-the number of nodes in the node set V;
2.2, the calculation formula of the betweenness of the nodes i in the topological attribute is as follows:
in the formula:
JS-the sum of all shortest path numbers between node pairs;
2.3, the calculation formula of the clustering coefficient of the node i in the topological attribute is as follows:
in the formula:
2.4, acquiring the energy consumption of the node i in the data attribute from the energy consumption data;
2.5, the calculation formula of the node strength of the node i in the data attribute is as follows:
in the formula:
3. The method according to claim 2, wherein the fusing the topological attribute and the data attribute of the node by using the OWA operator to obtain the importance of the node in the urban rail transit system comprises:
3.1, collecting attribute data of each node obtained by investigation and calculation, and constructing a node evaluation attribute matrix;
3.2, standardizing the attribute data in the node evaluation attribute matrix according to an attribute data standardization formula;
3.3, sorting the attribute data according to the size of the attribute data value of the normalized node;
and 3.4, clustering the attribute data values of the nodes by using an OWA operator to obtain the importance value of each node.
4. The method of claim 3, wherein the normalizing the attribute data in the node evaluation attribute matrix according to the attribute data normalization formula comprises:
setting attribute types of attribute data in the node evaluation attribute matrix, wherein the attribute types comprise benefit type, cost type, fixed type, deviation type, interval type and deviation interval type, and the benefit type attribute means that the larger the value of the attribute data is, the better the attribute data is; the cost attribute means that the smaller the attribute data value, the better; fixed form of genusThe attribute is that the closer the attribute data value is to a certain fixed value alpha, the better; the offset attribute means that the more the attribute data value is offset from a fixed value betajThe better; the interval type attribute means that the closer the attribute data value is to a fixed interval
The better; the off-interval type attribute means that the more the attribute data value is off the fixed interval
The better;
the attribute data is normalized as follows:
if the attribute value is benefit type, then order
In the formula:
max cj-the largest attribute value in the jth column of attribute data;
if the attribute value is cost type, then order
In the formula:
min cj-the smallest attribute value in the jth column of attribute data;
if the attribute value is fixed, order
If the attribute value is of offset type, then order
If the attribute value is interval type, then order
If the attribute value is of the deviated interval type, then order
5. The method of claim 4, wherein the clustering the attribute data values of the nodes using the OWA operator to obtain the importance value of each node comprises:
clustering the attribute data values of the nodes by using an OWA operator to obtain the importance value of each node
Importance value
The calculation formula of (a) is as follows:
wherein brijThe br is a sorting result for sorting the normalized attributes of the nodes according to the sequence of the attribute values from big to smallijWherein i refers to the number of nodes, j refers to the number of attributes, m refers to the total number of attribute data for aggregation, γ is the weighted vector of the OWA operator, and the weighted vector of the OWA operator is determined by the minimum variance method, and the calculation formula of γ is as follows:
where Disp (γ) represents the degree to which the attribute is used equally, and the order (γ) represents the degree to which the fusion operation is similar to the or operation.
6. The method according to claim 3, 4 or 5, wherein the calculating of the energy efficiency characterization parameters of the urban rail transit system by using Choquet integral in combination with the importance of the nodes based on the emerging theory comprises:
normalizing the calculated importance value of the node, and taking the normalized result of the importance value of the node as a Shapley value of the node, wherein the interval of the Shapley value is 0-1;
calculating fuzzy measure of each node set by establishing an optimization model taking Marchal entropy as a target function, wherein the optimization model taking Marchal entropy as the target function is as follows:
in the formula
energy efficiency flooding state for nodes using Choquet integrationLine calculations, the definition of Choquet integral is: let gλFor a lambda blur measure defined on P (X), f is a non-negative real valued measurable function defined on X, then f is with respect to gλIntegral (c) of (i) integral of (i) fdgλThe definition is shown as the following formula:
in the formula:
i——f(xi) Transformation of the vector so that 0 ≦ f (x)1)≤…≤f(xn)；Xi＝(x1,x2,…,xn) And f (x)0)＝0,f(xi) Typical is the energy efficiency emergence state of the urban rail transit system, gλ(Ai) Is the fuzzy measure for each node set, and the Shapley values of the nodes are used to calculate the fuzzy measure for each node set using Marichal entropy.
7. The method according to claim 6, wherein the controlling the energy efficiency of the urban rail transit system based on the energy efficiency characterization parameter of the urban rail transit system comprises:
by utilizing the property of Choquet integral, seeking a functional relation between the energy efficiency characterization parameters of the subsystems and the energy efficiency characterization parameters of the urban rail transit system, and determining the energy efficiency importance of each subsystem according to the functional relation;
according to the formula of Choquet integral, when gλ(Ai) Under the known condition, the energy efficiency characterization parameter ^ fd of the urban rail transit systemgλAnd the energy efficiency characterization parameter f (x) of the subsystemi) The functional relationship between the two is shown as the following formula:
determining an energy consumption threshold of a subsystem which can obtain the energy consumption data according to the maximum value and the minimum value of the investigated energy consumption data, and determining an energy efficiency characterization parameter threshold of a subsystem which can not obtain the energy consumption data according to the energy efficiency characterization parameter of the subsystem and the energy consumption threshold of the subsystem which can obtain the energy consumption data; and determining an energy efficiency regulation and control method and strategy of the urban rail transit system according to the energy efficiency importance of each subsystem and the energy efficiency characterization parameter threshold of the subsystem, wherein the energy consumption data is unavailable.
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