CN116050858A - Comprehensive energy service success evaluation method based on normal cloud model and application thereof - Google Patents
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Abstract
A comprehensive energy service success evaluation method based on a normal cloud model and application thereof comprise the following steps: and (3) selecting comprehensive energy service evaluation indexes: the regional comprehensive energy system is taken as an object, and comprises energy efficiency, economy and environmental evaluation indexes; a normal cloud model and a fuzzy entropy weighting method; and (3) constructing a success evaluation system: and establishing a cloud model aiming at a specific scene to obtain the values of Ex, en and super entropy, and further obtaining benefit evaluation weight, thereby establishing a comprehensive energy service achievement system. The comprehensive energy service effect can be evaluated from the aspects of energy efficiency, economy and environment; and establishing a success evaluation system model by adopting a method based on a normal cloud model and a fuzzy entropy weight method and a layering analysis method, obtaining different weights according to different typical scenes, and establishing different evaluation systems to obtain different evaluation results. The method has certain theoretical support significance for capacity planning and economic operation of the power exchange station and benefit coordination of the power exchange station and a power grid company.
Description
Technical Field
The invention relates to an evaluation method, in particular to a comprehensive energy service achievement evaluation method based on a normal cloud model, and application thereof.
Background
The comprehensive energy service aims at providing a solution meeting the requirements of users and conforming to the development direction of energy resources, and is important for ensuring the reliability of the comprehensive energy service effect evaluation result and constructing a reasonable evaluation system. Currently, the main evaluation methods include subjective weighting and objective weighting. The subjective weighting method mainly comprises a two-term coefficient method, a least squares method, an analytic hierarchy process, an expert investigation method and the like; the objective weighting method comprises a variation coefficient method, a complex correlation coefficient method, an entropy value method, a principal component analysis method and the like. In terms of evaluation index weighting, method 1 (Dong Fugui, shangmei. Distributed energy system multi-index comprehensive evaluation research [ J ]. Chinese motor engineering report, 2016, 36 (12): 3214-3223.) provides an AHP-entropy weighting method combining subjective weighting and objective weighting aiming at the problem of how to determine index weights; method 2 (Chen Baisen, liao Qingfen, liu Dichen, etc. comprehensive evaluation indexes of regional comprehensive energy systems and method [ J ]. Electric power system automation, 2018, 42 (4): 174-182.) provides a network analysis method and an inverse entropy weight method to determine the weights of the indexes; method 3 (once ringing, liu Yingxin, peripheral proc, etc. comprehensive energy system modeling and benefit evaluation system review and hope [ J ]. Electric network technology, 2018, 42 (6): 1697-1708.) provides a typical architecture of the comprehensive energy system, constructs comprehensive energy efficiency evaluation indexes and provides a comprehensive weighting method combining subjective and objective. However, for different service scenarios, an effective general evaluation strategy and an evaluation system are lacking at present. Therefore, the optimization of the comprehensive energy evaluation index system is very important.
Disclosure of Invention
In order to solve the defects in the prior art, the invention discloses a comprehensive energy service achievement evaluation method based on a normal cloud model, which has the following technical scheme:
the comprehensive energy service achievement evaluation method based on the normal cloud model is characterized by comprising the following steps of: the method comprises the following steps:
step 1: and (3) selecting comprehensive energy service evaluation indexes: the regional comprehensive energy system is taken as an object, and comprises energy efficiency, economy and environmental evaluation indexes;
step 2: normal cloud model and fuzzy entropy weighting method: the conversion of qualitative concepts and quantitative measurement and subjective and objective weights are combined;
step 3: and (3) constructing a success evaluation system: and establishing a cloud model aiming at a specific scene to obtain the values of Ex, en and super entropy, and further obtaining benefit evaluation weight, thereby establishing a comprehensive energy service achievement system.
Step 4: and (3) obtaining different weights according to different typical scenes, and establishing different evaluation systems to obtain different evaluation results.
The invention also discloses a nonvolatile storage medium, which is characterized in that the nonvolatile storage medium comprises a stored program, wherein the program controls equipment where the nonvolatile storage medium is located to execute the method when running.
The invention also discloses an electronic device which is characterized by comprising a processor and a memory; the memory has computer readable instructions stored therein, and the processor is configured to execute the computer readable instructions, where the computer readable instructions execute the method described above when executed.
Advantageous effects
1) The strategy can evaluate comprehensive energy service effects from energy efficiency, economy and environment aspects;
2) The strategy adopts a method based on a normal cloud model and a fuzzy entropy weight method, a yield evaluation system model is established through a layering analysis method, different weights are obtained according to different typical scenes, different evaluation systems are established, and different evaluation results are obtained. The method has certain theoretical support significance for capacity planning and economic operation of the power exchange station and benefit coordination of the power exchange station and a power grid company.
Drawings
FIG. 1 is a flow chart of a comprehensive energy service success evaluation method based on a normal cloud model;
fig. 2 is a state cloud graph under different cloud entropies in the comprehensive energy service success evaluation method based on a normal cloud model; wherein (a) is the state cloud relative paste progress before adjustment; (b) the state cloud relative paste progress before adjustment;
FIG. 3 is a diagram of a normal cloud model generated in the comprehensive energy service success evaluation method based on the normal cloud model;
fig. 4 is a schematic diagram of an analytic hierarchy process of the comprehensive energy service success rate evaluation method based on a normal cloud model.
Detailed Description
The invention constructs a corresponding evaluation system by taking factors such as benefits, environment, economy and the like as evaluation factors, and provides a comprehensive energy service effect evaluation method based on a normal cloud model.
Step 1: and (3) selecting comprehensive energy service evaluation indexes: the regional comprehensive energy system is taken as an object, and comprises energy efficiency, economy and environmental evaluation indexes:
the comprehensive energy service evaluation aims at a regional comprehensive energy system and comprisesAnd energy efficiency, economy, environment and other evaluation indexes. The energy efficiency evaluation index comprises omnibearing multidimensional evaluation of cold, heat, electricity and gas, such as reliable system function rate, network comprehensive loss, pipe network heat loss rate, natural gas system consumption rate and the like; the economic evaluation index comprises energy economy level, construction investment cost, operation maintenance cost, management service cost and the like; the environmental evaluation index comprises CO 2 Annual emissions, equivalent environmental benefits, environmental pollution emission levels, and carbon emission reductions, etc.
(1) Evaluation of energy utilization efficiency
The energy utilization level is an important index for measuring the performance of the comprehensive energy system, and the primary energy consumption and the primary energy efficiency can reflect the energy utilization benefit from different angles.
1) Primary energy consumption
The primary energy consumption is mainly divided into three parts, namely natural gas consumption, electric power consumption and renewable energy consumption, and the formulas are as follows:
E＝p e Q e +p g Q g +p re Q re (1)
wherein: e is the primary energy input value; p is p e 、p g And p re The coal-electricity, natural gas and renewable energy source coal index respectively; q (Q) e 、Q g And Q re Respectively input of electric energy, natural gas and renewable energy sources.
2) Primary energy efficiency
The primary energy efficiency characterizes the ratio of primary energy output energy to consumption energy, is an important index for reflecting the energy utilization rate of the system, and has the following formula
Wherein A is the utilization efficiency of primary energy; p is the effective output power of the system; f (F) h,out 、F c,out The system heat and cold output quantity is respectively the unit time; f (F) in Is the input quantity of primary energy.
(2) Economic benefit evaluation
The economic benefit is the influence of evaluation items on the economy and society, is an important attribute of attention of investors, and consists of investment construction cost and operation maintenance cost.
1) Investment construction cost
The investment cost comprises the purchase and placement cost of each device, reflects the economic benefit and the construction difficulty to a certain extent, and has the expression of
In the formula, C is investment cost; i i Investment for the unit capacity of the ith equipment; v (V) i Is the i-th device capacity.
2) Operating maintenance costs
The annual operation and maintenance cost of the comprehensive energy system can be divided into two parts, namely fixed cost and variable cost. The fixed cost refers to the labor and management costs required for routine maintenance; variable cost refers to the cost due to uncertain operation of the system. The expression is
Wherein C is 1 The annual operation maintenance cost of the comprehensive energy system is saved; alpha i 、α ri 、α si Coefficients of cogeneration, renewable power generation, and energy storage systems, respectively; c fi 、c fri 、c fsi Fixed costs for the three devices respectively; c vi 、c vri 、c vsi Variable costs for the three devices, respectively; t (T) i 、T ri 、T si The annual average number of hours of utilization for the three devices, respectively.
(3) Environmental benefit index
CO 2 Is a greenhouse gas which causes global warming, and responds to national 'carbon peak, carbon neutralization' action plan and new energy power system policy, CO 2 Emissions and NO 2 The emissions were included as an evaluation index.
1)CO 2 Discharge amount
CO 2 Emissions refer to the CO emitted when burning fossil fuels 2 Capacity, expressed as
M C ＝W g c g +W c c c (5)
Wherein M is C Is CO 2 Discharge amount; w (W) g 、W c The generated energy and the power supply amount of the power grid are respectively; c g 、c c CO for unit power generation of prime motor and coal power plant respectively 2 Discharge amount.
2)NO 2 Discharge amount
NO 2 Emission and CO 2 The carbon emission is similar and can be expressed as
M N ＝W g n g +W c n c (6)
Wherein M is N Is NO 2 Discharge amount; w (W) g 、W c The generated energy and the power supply amount of the power grid are respectively; n is n g 、n c NO per unit power generation of prime mover and coal power plant, respectively 2 Discharge amount.
The effective domains of the primary and secondary indexes can be divided into five levels, which can be expressed as x= (X) 1 ，X 2 ，X 3 ，X 4 ，X 5 ) Taking primary energy efficiency as an example, wherein (X 1 ，X 2 ，X 3 ，X 4 ，X 5 ) The value ranges of the (E) are respectively 95 and 100]，[80，95)[60，80)，[40，60)，[0，40)。
Step 2: normal cloud model and fuzzy entropy weighting method: the conversion of qualitative concepts and quantitative measurement and subjective and objective weights are combined;
the normal cloud model is used for describing a great amount of randomness and ambiguity and the association between the randomness and the ambiguity, and is described by qualitative and quantitative, and three data represent model characteristics: (1) expecting Ex, representing the distribution of data in space; (2) entropy En, representing the degree of uncertainty; (3) super entropy is used to represent the uncertainty of entropy, i.e., entropy of entropy. The normal cloud model is a model that takes normal distribution in space.
Let X be the set of values described by an exact number, called the discourse domain. C is a qualitative concept on the argument X, if X ε X is a random number where the qualitative concept C is randomly implemented at one time of X and X has a tendency to stabilize for the membership μ (X) ∈ (0, 1) of C. If X epsilon X, X- & gt mu (X), the mapping distribution of the qualitative concept C from the domain X to the interval [0,1] is called membership cloud, called cloud for short, and each X is called a cloud drop.
Cloud digital signature E, F, H reflects the overall nature of the qualitative concept, where E is the central value of the domain, representing the point of the qualitative concept; f is the measurable granularity and uncertainty of the qualitative concept, namely the qualitative concept is also measured, and generally, the larger F is, the more macroscopic the concept is, which indicates that the larger the value range of the qualitative concept is accepted in the domain space; h is uncertainty of measurement entropy, reflects randomness of appearance of qualitative concept sample values, and reveals association of ambiguity and randomness. The above is the basic concept of a normal cloud model.
With the intensive research of uncertainty, more and more students consider that only uncertainty itself is deterministic. While of the various uncertainties, randomness and ambiguity are most fundamental. The cloud model is characterized by describing a great deal of randomness and ambiguity existing and the association between the randomness and the ambiguity, and is characterized by qualitative and quantitative, and three data represent model characteristics: (1) expecting Ex, representing the distribution of data in space; (2) entropy En, representing the degree of uncertainty; (3) super entropy is used to represent the uncertainty of entropy, i.e., entropy of entropy. The normal cloud model is a model that takes normal distribution in space.
Center Ex of cloud model i Can be calculated by the following formula
Ex i ＝X min +θ i (X max -X min ) (7)
In θ i Is a parameter, typically 0.5; x is X max And X min Is the effective domain of X.
En i The value of (2) should satisfy the 3 sigma criterion
3ε 1 En i '＝max{X max -Ex i ,Ex i -X min } (8)
Wherein ε 1 ≥1。
En i Can be regarded as the corresponding cloud and the adjacent cloud En i Can be expressed as
In the method, in the process of the invention,
he should also satisfy the 3 sigma principle, i.e
3ε 2 He i ＝max{En' i -En i ,En i -En' i } (10)
Wherein ε 2 ≥1。
When two clouds are too close in practical application of value, the fuzzy concept of the characterization also can be too early overlapped as shown in fig. 2, so that En is readjusted i Taking epsilon as the size of (2) 1 ＝1、ε 2 =2 to solve the state cloud premature overlap problem.
Effective argument x= (X) of each index in the combination step one 1 ，X 2 ，X 3 ，X 4 ，X 5 ) From formulas (7) to (10), the aggregate cloud parameters Ex of the respective indices can be obtained i 、En i And He (He) i And generating 1000 cloud drops by adopting a forward cloud generator to form a normal cloud model, as shown in fig. 3, so as to obtain the membership distribution condition of each index.
The entropy weight method is to determine the weight of each index by knowing the value of the index of the evaluation object, entropy is a measure of the disorder degree of the system, and the smaller the entropy value is, the larger the discrete degree is, and the larger the index weight is. The fuzzy entropy weight method is a method for comprehensively deciding things according to a certain purpose by considering the influence of various factors under a fuzzy environment.
The weight determination method by the fuzzy entropy weight method is as follows:
1) Firstly, constructing a judgment matrix X= [ X ] ij ] m×n M is the number of indexes, n isNumber of objects.
2) Normalizing the matrix X to obtain b= [ B ] ij ] m×n The elements are
Wherein x is max 、x min Respectively the maximum and minimum values;
is the mean value.
3) Calculating entropy of evaluation index
Wherein i=1, 2, …, y; j=1, 2, …, n.
4) Calculating index weights
The normal cloud model can realize the transformation of qualitative concepts and quantitative measurements, and the concepts relate to qualitative concept ambiguity and comprise randomness of membership functions. The fuzzy entropy weighting method combines the fuzzy weighting method and the entropy weighting method to solve the problems that the subjectivity of the obtained weight is too strong, the objective game result between different objective functions cannot be represented, and the obtained weight is objective but the obtained result possibly runs contrary to the expected result due to different concerned degrees of each objective function.
Step 3: construction of success evaluation system
Based on a normal cloud model and a fuzzy entropy weight method, a cloud model is established for a specific scene to obtain the values of Ex, en and super entropy, further benefit evaluation weight is obtained, and a comprehensive energy service achievement system is established, wherein the method comprises the following specific steps of:
1) And (3) collecting data of different energy consumption scenes, solving the aggregate cloud of each index through formulas (7) - (10) based on a method of a normal cloud model, and constructing the normal cloud model to obtain the membership degree of each index.
2) Based on the aggregated cloud obtained in the first step, weights of the evaluation indexes are obtained by equations (11) - (13) by a fuzzy entropy weight method.
3) And performing analytic hierarchy process to divide the model into three layers of a target layer, a criterion layer and a basic layer, and determining the weight occupied by each index of each layer, as shown in fig. 4.
4) And establishing a comprehensive energy service achievement system to form comprehensive evaluation of the specific scene.
Step 4: and (3) obtaining different weights according to different typical scenes, and establishing different evaluation systems to obtain different evaluation results.
Firstly, carrying out normalization processing on data, obtaining an aggregation cloud through formulas (7) to (10), and obtaining each index entropy weight method weight according to formulas (11) to (13). Determining weights by subjective and objective combination weighting methods, i.e.
w＝αw 1 +βw 2 (14)
Wherein alpha and beta are subjective weight and objective weight coefficients respectively; w (w) 1 、w 2 Subjective and objective weights, respectively.
Aiming at the service problem of the comprehensive energy system, the method divides the service problem into 5 grades { excellent, good, general, poor and extremely poor }, and the overall performance evaluation of the comprehensive energy service project can be obtained by combining the normalized data with the obtained weight data.
Examples
Based on a certain comprehensive energy demonstration area in North China, the demonstration area is divided into three typical scenes by analyzing the power generation side forms, the energy consumption types, the energy architecture and the like of different comprehensive energy systems in the demonstration area: commercial complexes, smart communities and industrial parks. The three types of scenes are respectively numbered S 1 、S 2 And S is 3 。
The commercial complex is a complex whole formed by coupling together businesses, offices, living, catering, entertainment and the like of a city. It is the core of each city, so improving efficiency and reducing cost are critical issues. Aiming at the energy utilization characteristics of the commercial complex, the economic benefit is paid attention to, and the environmental benefit is paid attention to as much as possible, and a comprehensive energy service effect evaluation system is established, and the implementation steps are as follows:
center Ex of business complex cloud model i Can be calculated by the following formula
Ex i ＝X min +θ i (X max -X min ) (7)
In θ i Is a parameter, typically 0.5; x is X max And X min Is the effective domain of X.
En i The value of (2) should satisfy the 3 sigma criterion
3ε 1 En i '＝max{X max -Ex i ,Ex i -X min } (8)
Wherein ε 1 ＝1。
En i Can be regarded as the corresponding cloud and the adjacent cloud En i Can be expressed as
In the method, in the process of the invention,
he should also satisfy the 3 sigma principle, i.e
3ε 2 He i ＝max{En' i -En i ,En i -En' i } (10)
Wherein ε 2 ＝1。
The implementation flow of determining the weight by the fuzzy entropy weight method is as follows:
1) Firstly, constructing a judgment matrix X= [ X ] ij ] m×n M is the number of indexes, and n is the number of objects.
2) Normalizing the matrix X to obtain b= [ B ] ij ] m×n The elements are
Wherein x is max 、x min Respectively the maximum and minimum values;
is the mean value.
3) Calculating entropy of evaluation index
Wherein i=1, 2, …, y; j=1, 2, …, n.
4) Calculating index weights
5) Determination weight by combining subjective and objective weighting method
w＝αw 1 +βw 2 (14)
The weight ratios are shown in table 1.
Table 1 commercial complex evaluation system
With the economic development and the improvement of the living standard of people, the energy consumption of the building is rapidly increased, and the occupancy ratio is gradually increased. The increase of building energy consumption causes a plurality of economic environmental problems, so local resources and environments are fully considered, and intelligent cells are constructed, so that the energy economy of communities can be continuously developed. Aiming at the energy consumption characteristics of the intelligent community, considering the electricity reliability of residents, the energy benefit is important, the subjective duty ratio is evaluated by increasing the energy benefit, the weight of the entropy weight method is calculated by combining the formulas (11) to (14), and the weight of the comprehensive energy service effect evaluation index of the intelligent community is obtained, as shown in the table 2.
Table 2 smart cell evaluation system
The industrial park is characterized in that local government is divided into a region for gathering various production elements according to the development requirement of the local government, and scientific integration is carried out within a certain range. The industrial park has high energy density, high energy reliability requirement and low cost. And (3) establishing a comprehensive energy service achievement system according to formulas (11) to (14) by combining a subjective and objective weighting method aiming at the characteristics of the industrial park, wherein the weights occupy the weights shown in a table 3.
Table 3 industrial park evaluation system
Aiming at the service problem of the comprehensive energy system, the service problem is divided into five grades of { excellent, good, general, poor and extremely poor }, and the service evaluation results of the comprehensive energy system under three different typical scenes can be obtained through index weighting and data calculation, as shown in table 4.
Table 4 different scene evaluation results
As can be seen from table 4, the industrial park operation state evaluation results are good, while the intelligent community operation state evaluation results are poor, and the intelligent community operation state evaluation results are improved by adopting corresponding measures.
The foregoing has shown and described the basic principles, principal features and advantages of the invention. The method comprises the steps of firstly establishing a performance evaluation system model based on a normal cloud model and a fuzzy entropy weight method through a layering analysis method, providing comprehensive energy service evaluation indexes from three aspects of energy efficiency, economy and environment according to typical scenes, obtaining different weights according to different typical scenes, and establishing different evaluation systems to obtain different evaluation results.
It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, and that the above embodiments and descriptions are merely illustrative of the principles of the present invention, and various changes and modifications may be made therein without departing from the spirit and scope of the invention, which is defined by the appended claims. The scope of protection of the invention is defined by the appended claims.
Claims (9)
1. The comprehensive energy service achievement evaluation method based on the normal cloud model is characterized by comprising the following steps of: the method comprises the following steps:
step 1: and (3) selecting comprehensive energy service evaluation indexes: the regional comprehensive energy system is taken as an object, and comprises energy efficiency, economy and environmental evaluation indexes;
step 2: normal cloud model and fuzzy entropy weighting method: the conversion of qualitative concepts and quantitative measurement and subjective and objective weights are combined;
step 3: and (3) constructing a success evaluation system: establishing a cloud model aiming at a specific scene to obtain values of expected Ex, entropy En and super entropy so as to obtain benefit evaluation weight, thereby establishing a comprehensive energy service achievement system;
step 4: and (3) obtaining different weights according to different typical scenes, and establishing different evaluation systems to obtain different evaluation results.
2. The comprehensive energy service success rate evaluation method based on the normal cloud model as described in claim 1, wherein the method comprises the following steps: the energy efficiency evaluation index comprises omnibearing multidimensional evaluation of cold, heat, electricity and gas; the economic evaluation index comprises energy economy level, construction investment cost, operation maintenance cost and management service cost; the environmental evaluation index comprises CO 2 Annual emissions, equivalent environmental benefits, environmental pollution emission levels, and carbon emission reductions.
3. The comprehensive energy service success rate evaluation method based on the normal cloud model as described in claim 2, wherein the method comprises the following steps: the step 1 further comprises the following steps:
(1) Evaluation of energy utilization efficiency
The energy utilization level is an important index for measuring the performance of the comprehensive energy system, and the primary energy consumption and the primary energy efficiency reflect the energy utilization benefit from different angles;
1) Primary energy consumption
The primary energy consumption is mainly divided into three parts, namely natural gas consumption, electric power consumption and renewable energy consumption, and the formulas are as follows:
E＝p e Q e +p g Q g +p re Q re (1)
wherein: e is the primary energy input value; p is p e 、p g And p re The coal-electricity, natural gas and renewable energy source coal index respectively; q (Q) e 、Q g And Q re Respectively inputting electric energy, natural gas and renewable energy sources;
2) Primary energy efficiency
The primary energy efficiency characterizes the ratio of primary energy output energy to consumption energy, is an important index reflecting the energy utilization rate of the system, and has the following formula:
wherein A is the utilization efficiency of primary energy; p is the effective output power of the system; f (F) h,out 、F c,out The system heat and cold output quantity is respectively the unit time; f (F) in The input quantity of primary energy is used;
(2) Economic benefit evaluation
The system consists of investment construction cost and operation maintenance cost;
1) Investment construction cost
The investment cost comprises the purchase and placement cost of each device, reflects economic benefit and construction difficulty to a certain extent, and has the expression:
wherein, C is investment cost; i i Investment for the unit capacity of the ith equipment; v (V) i Is the i-th device capacity;
2) Operating maintenance costs
The annual operation maintenance cost of the comprehensive energy system can be divided into two parts, namely fixed cost and variable cost; the fixed cost refers to the labor and management costs required for routine maintenance; variable cost refers to the cost due to uncertain operation of the system; the expression is:
wherein C is 1 Maintenance costs for operation; alpha i 、α ri 、α si Coefficients of cogeneration, renewable power generation, and energy storage systems, respectively; c fi 、c fri 、c fsi Fixed costs for the three devices respectively; c vi 、c vri 、c vsi Variable costs for the three devices, respectively; t (T) i 、T ri 、T si The annual average utilization hours of the three devices respectively;
(3) Environmental benefit index
CO 2 Is a greenhouse gas that causes global weather to warm, CO 2 Emissions and NO 2 The emission is included in the evaluation index;
1)CO 2 discharge amount
CO 2 Emissions refer to the CO emitted when burning fossil fuels 2 Capacity, its expression is M C ＝W g c g +W c c c (5) Wherein M is C Is CO 2 Discharge amount; w (W) g 、W c The generated energy and the power supply amount of the power grid are respectively; c g 、c c CO2 emission amounts which are unit power generation amounts of the prime motor and the coal power plant respectively;
2)NO 2 discharge amount
NO 2 Emission and CO 2 The carbon emission is similar and can be expressed as M N ＝W g n g +W c n c (6) Wherein M is N Is NO 2 Discharge amount n g 、n c NO per unit power generation of prime mover and coal power plant, respectively 2 Discharge amount.
4. The comprehensive energy service success rate evaluation method based on the normal cloud model as described in claim 2, wherein the method comprises the following steps: the step 2 further comprises the following steps:
center Ex of cloud model i Can be calculated by the following formula:
Ex i ＝X min +θ i (X max -X min ) (7)
in θ i Is a parameter, typically 0.5; x is X max And X min Is the effective domain of X;
En i the value of (2) should satisfy the 3 sigma criterion
3ε 1 En i '＝max{X max -Ex i ,Ex i -X min } (8)
Wherein ε 1 ≥1；
En i Can be regarded as the corresponding cloud and the adjacent cloud En i Can be expressed as:
in the method, in the process of the invention,
he should also satisfy the 3 sigma principle, i.e
3ε 2 He i ＝max{En′ i -En i ,En i -En′ i } (10)
Wherein ε 2 ≥1。
5. The comprehensive energy service success rate evaluation method based on the normal cloud model as described in claim 4, wherein the method comprises the following steps: the step 2 further comprises the following steps:
the weight determination method by the fuzzy entropy weight method is as follows:
1) Firstly, constructing a judgment matrix X= [ X ] ij ] m×n M is the number of indexes, and n is the number of objects;
2) Normalizing the matrix X to obtain b= [ B ] ij ] m×n The elements are as follows:
wherein x is max 、x min Respectively the maximum and minimum values;
is the mean value;
3) Calculating entropy of evaluation index
Wherein i=1, 2, …, y; j=1, 2, …, n.
4) Calculating index weights
6. The comprehensive energy service success rate evaluation method based on the normal cloud model as described in claim 2, wherein the method comprises the following steps: the step 3 further comprises the following steps:
the method comprises the following specific steps of:
1) Collecting data of different energy consumption scenes, and solving an aggregation cloud by a normal cloud model method;
2) The weight of each evaluation index is obtained based on the aggregation cloud obtained in the first step through a fuzzy entropy weight method;
3) Performing analytic hierarchy process, dividing the model into a target layer, a criterion layer and a basic layer, and determining the weight of each index of each layer;
4) And establishing a comprehensive energy service achievement system to form comprehensive evaluation of the specific scene.
7. The comprehensive energy service success rate evaluation method based on the normal cloud model as described in claim 5, wherein the method comprises the following steps: the step 4 further comprises the following steps:
firstly, carrying out normalization processing on data, obtaining an aggregation cloud through formulas (7) to (10), and obtaining weights of each index entropy weight method according to formulas (11) to (13); determining weights by subjective and objective combination weighting methods, i.e.
w＝αw 1 +βw 2 (14) Wherein w is a weight; alpha and beta are subjective weight and objective weight coefficients respectively; w (w) 1 、w 2 Subjective and objective weights, respectively.
8. A non-volatile storage medium, characterized in that the non-volatile storage medium comprises a stored program, wherein the program, when run, controls a device in which the non-volatile storage medium is located to perform the method of any one of claims 1 to 7.
9. An electronic device comprising a processor and a memory; the memory has stored therein computer readable instructions for executing the processor, wherein the computer readable instructions when executed perform the method of any of claims 1 to 7.
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