CN114118786A - Comprehensive energy system energy efficiency evaluation method and device and terminal equipment - Google Patents
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Abstract
The application is suitable for the technical field of energy efficiency assessment, and discloses a method and a device for evaluating energy efficiency of a comprehensive energy system and terminal equipment. The energy efficiency evaluation method of the comprehensive energy system comprises the following steps: constructing an energy efficiency index system of the comprehensive energy system, and determining evaluation index values of various evaluation indexes in the energy efficiency index system of the comprehensive energy system; evaluating each evaluation index through multiple evaluation models to obtain the weight value of each evaluation index and the evaluation result corresponding to the multiple evaluation models; combining multiple evaluation models, calculating a weight coefficient corresponding to each evaluation model in different model combination modes according to the weight values, and calculating evaluation results of different model combination modes according to the evaluation index values and the weight coefficients; and calculating the correlation degree between the evaluation results of different model combination modes and the evaluation results corresponding to the various evaluation models, and determining the optimal model combination mode and the optimal evaluation result according to the correlation degree.
Description
Technical Field
The application belongs to the technical field of energy efficiency assessment, and particularly relates to a method and a device for evaluating energy efficiency of an integrated energy system and terminal equipment.
Background
The comprehensive energy system is mainly composed of a network, an energy exchange link, an energy storage and a plurality of end users. The integrated energy system integrates regional energy and optimizes the use, coordination, mutual response and complementarity of each energy subsystem. The energy efficiency evaluation is an effective index reflecting the level of application of energy efficiency. In a complex energy system, there are various types of energy input and output, and it is necessary to perform evaluation from an overall perspective, and the result of energy efficiency evaluation for the overall integrated energy system is also referred to as integrated energy efficiency.
The evaluation method of comprehensive energy efficiency is a decision problem related to a plurality of complex indexes. At present, when the energy efficiency of the park integrated energy system is evaluated, a unified standard is not formed, each evaluation model adopts multiple mathematical methods to evaluate the energy efficiency of the park integrated energy system, different parks often have different characteristics, a single method is adopted to evaluate, the obtained result is relatively one-sided and limited, and the evaluation accuracy is low.
Disclosure of Invention
In view of this, the embodiment of the invention provides an energy efficiency evaluation method for a comprehensive energy system, and the problem of low accuracy of a single evaluation method is solved.
The application is realized by the following technical scheme:
in a first aspect, an embodiment of the present application provides an energy efficiency evaluation method for an integrated energy system, where the method includes: constructing an energy efficiency index system of the comprehensive energy system, and determining evaluation index values of various evaluation indexes in the energy efficiency index system of the comprehensive energy system; evaluating each evaluation index through multiple evaluation models to obtain the weight value of each evaluation index and the evaluation result corresponding to the multiple evaluation models; combining multiple evaluation models, calculating a weight coefficient corresponding to each evaluation model in different model combination modes according to the weight values, and calculating evaluation results of different model combination modes according to the evaluation index values and the weight coefficients; and calculating the correlation degree between the evaluation results of different model combination modes and the evaluation results corresponding to the various evaluation models, and determining the optimal model combination mode and the optimal evaluation result according to the correlation degree.
Based on the first aspect, in some embodiments, constructing an energy efficiency index system of the integrated energy system, and determining an evaluation index value of each evaluation index in the energy efficiency index system of the integrated energy system includes: analyzing influence factors of the comprehensive energy system, and selecting evaluation indexes according to the influence factors to construct an energy efficiency index system of the comprehensive energy system; and collecting actual data of each evaluation index, and determining the evaluation index value according to the actual data.
Based on the first aspect, in some embodiments, the evaluating indexes are evaluated by multiple evaluation models to obtain the weight values of the evaluation indexes and the evaluation results corresponding to the multiple evaluation models, including: calculating the weight value of each evaluation index through an evaluation model; correspondingly multiplying the weight values of all the evaluation indexes by the evaluation index values and summing to obtain the evaluation result of the evaluation model; sequentially adopting a plurality of evaluation models to obtain an evaluation result corresponding to each evaluation model; the multiple evaluation models comprise an entropy weight method evaluation model, a principal component analysis method evaluation model and a pull-up grade method evaluation model.
Based on the first aspect, in some embodiments, the weight coefficient is calculated by:
wherein, Wi T、
Is a weight value vector, alpha, of an evaluation index determined based on an entropy weight method evaluation model, a pull-up rank method evaluation model, and a principal component analysis method evaluation model1，α2，α3Is a weight coefficient of each evaluation model.
Based on the first aspect, in some embodiments, the calculation formula of the evaluation result is:
wherein V is the evaluation result, Y*The method is a matrix obtained after the raw data of the index set is subjected to standardization processing, W is a weight coefficient, n is the number of indexes to be evaluated, and m is the number of types of evaluation models.
Based on the first aspect, in some embodiments, calculating a degree of correlation between evaluation results of different model combination manners and evaluation results corresponding to multiple evaluation models, and determining an optimal model combination manner and an optimal evaluation result according to the degree of correlation includes: ranking the evaluation results of different model combination modes and the evaluation result of the single evaluation model according to the evaluation values respectively, and calculating a spearman correlation coefficient according to the rank difference of the ranked same evaluation object; calculating the average correlation degree between the model combination mode and the single evaluation model according to the spearman correlation coefficient; and calculating test statistic according to the average correlation degree, and determining an optimal model combination mode and an optimal evaluation result according to the test statistic.
Based on the first aspect, in some embodiments, the formula for calculating the average degree of correlation is:
wherein x isiIs the spearman correlation coefficient, m is the number of classes of the evaluation model, dkThe average degree of correlation between the original m evaluation models and the kth combination evaluation method is obtained.
In a second aspect, an embodiment of the present application provides an apparatus for evaluating energy efficiency of an integrated energy system, where the apparatus includes: the index construction module is used for constructing an energy efficiency index system of the comprehensive energy system and determining an evaluation index value; the evaluation module is used for evaluating the evaluation indexes through the multiple evaluation models to obtain the weight values of the evaluation indexes and the evaluation results corresponding to the multiple evaluation models; the combined module is used for sending successful information of the ex-warehouse request to the user terminal when the ex-warehouse request is met; and the selection module is used for calculating the correlation degree between the evaluation results of different model combination modes and the evaluation results corresponding to the various evaluation models, and determining the optimal model combination mode and the optimal evaluation result according to the correlation degree.
In a third aspect, an embodiment of the present application provides a terminal device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor executes the computer program to implement the steps of the method for evaluating energy efficiency of an integrated energy system according to any one of the first aspect.
In a fourth aspect, the present application provides a computer-readable storage medium, where a computer program is stored, and when the computer program is executed by a processor, the steps of the method for evaluating the energy efficiency of an integrated energy system according to any one of the first aspect are implemented.
In the embodiment of the application, different evaluation methods are selected for dynamic combination on the basis of the common evaluation method of the comprehensive energy system, so that the optimal combination evaluation method suitable for the comprehensive energy system is determined, the defects of low accuracy and large limitation of a unified evaluation method are effectively overcome, and the evaluation result is more objective and accurate.
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In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the embodiments or the prior art descriptions will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without inventive exercise.
FIG. 1 is a schematic flow chart of an energy efficiency evaluation method of an integrated energy system provided by an embodiment of the application;
FIG. 2 is a schematic diagram of an energy efficiency evaluation index of the park integrated energy system provided by the embodiment of the application;
FIG. 3 is a schematic structural diagram of an energy efficiency evaluation device of an integrated energy system provided by an embodiment of the application;
fig. 4 is a schematic structural diagram of a terminal device according to an embodiment of the present application.
Detailed Description
In the following description, for purposes of explanation and not limitation, specific details are set forth, such as particular system structures, techniques, etc. in order to provide a thorough understanding of the embodiments of the present application. It will be apparent, however, to one skilled in the art that the present application may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present application with unnecessary detail.
It will be understood that the terms "comprises" and/or "comprising," when used in this specification and the appended claims, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
It should also be understood that the term "and/or" as used in this specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.
As used in this specification and the appended claims, the term "if" may be interpreted contextually as "when", "upon" or "in response to" determining "or" in response to detecting ". Similarly, the phrase "if it is determined" or "if a [ described condition or event ] is detected" may be interpreted contextually to mean "upon determining" or "in response to determining" or "upon detecting [ described condition or event ]" or "in response to detecting [ described condition or event ]".
Furthermore, in the description of the present application and the appended claims, the terms "first," "second," "third," and the like are used for distinguishing between descriptions and not necessarily for describing or implying relative importance.
Reference throughout this specification to "one embodiment" or "some embodiments," or the like, means that a particular feature, structure, or characteristic described in connection with the embodiment is included in one or more embodiments of the present application. Thus, appearances of the phrases "in one embodiment," "in some embodiments," "in other embodiments," or the like, in various places throughout this specification are not necessarily all referring to the same embodiment, but rather "one or more but not all embodiments" unless specifically stated otherwise. The terms "comprising," "including," "having," and variations thereof mean "including, but not limited to," unless expressly specified otherwise.
In order to make the objects, technical solutions and advantages of the present invention more apparent, the following description is made by way of specific embodiments with reference to the accompanying drawings.
The energy efficiency of an integrated energy system is a decision-making problem involving multiple complex indicators. The method is mainly used for calculating by combining methods of selecting evaluation indexes and giving weights to the indexes. Aiming at the limitation problem of using a single evaluation method for comprehensive evaluation, the invention provides a comprehensive energy system energy efficiency evaluation method, which can comprise a step 101 to a step 104 as shown in fig. 1.
Step 101: and constructing an energy efficiency index system of the comprehensive energy system, and determining evaluation index values of all evaluation indexes in the energy efficiency index system of the comprehensive energy system.
Taking the park integrated energy system as an example, firstly, an energy efficiency index system of the park integrated energy system is constructed, and an evaluation benchmark is provided for evaluating the development level of the park integrated energy system. Based on analysis of multidimensional influence factors of the park comprehensive energy system, an evaluation index system of the park comprehensive energy system is extracted from four aspects of safety, reliability, energy conservation, high efficiency, environmental protection and economic benefit. As shown in fig. 2, the park comprehensive energy evaluation system includes the following 12 evaluation indexes:
(1) average power failure time of users: average number of blackouts for user — average number of blackouts over a user's statistical period. The higher the index value is, the worse the safety reliability of the grid is.
(2) Voltage class balance: the voltage grade balance is an important index for judging whether the composition proportion level of the power transmission lines with various voltage grades is reasonable or not and whether the voltage grade matching is strong or not. If the voltage level balance is low, accidents such as loss of an access power supply, incapability of normal operation of a power grid, large-area power failure and the like can be caused, and the safety and reliability are seriously influenced.
(3) Energy self-sufficiency rate: energy autonomy is the ratio of total energy production to total energy consumption. Indicating the corresponding degree of national or regional energy production and consumption.
(4) Energy loss rate: the energy loss rate is the ratio of the amount of energy lost to the total consumption. The energy loss factor is used to represent the ratio of energy loss to total energy consumption. The smaller the amount, the better the energy utilization. The invention calculates the energy loss rate as the energy loss coefficient which is the energy loss/the total energy consumption x 100%.
(5) Abandon wind and abandon light rate: the reasons for wind, light and water distribution are complex, and depend on factors such as power supply, load, power supply and the like, and are mutually interwoven and influence mechanisms. The wind abandoning and light abandoning rate reflects the wind and light absorption capacity, and in general, the lower the numerical value, the stronger the energy-saving efficiency of the park is.
(6) The utilization rate of primary energy is as follows: the ratio of the total output energy of the system to the total input energy of the system.
(7) The utilization rate of the terminal equipment is as follows: the energy introduced at the beginning of the process reflects the energy demand condition of the user load and the energy consumption condition in the energy consumption process compared with the energy obtained by the user terminal.
(8) Clean energy installation proportion: the clean energy installed proportion reflects the proportion of the clean energy installed in the total installed electric power and the proportion of the clean energy calculated according to the capacity. The index is an important index for measuring the degree of clean energy construction and the structure of the power supply.
(9) Clean energy permeability: the permeability of the clean energy is the proportion of the generated energy of the clean energy to the total generated energy, and is the measure of the permeability of the clean energy in an energy system, and the level of the permeability can represent the environmental protection performance of the system, is closely related to the economy and is an important parameter for evaluating the energy contribution.
(10) Unit GDP carbon dioxide emission: also called energy consumption, is the ratio of carbon dioxide emission to the total domestic production value.
(11) The cost of the electric power consumption: firstly, the power generation cost and the power generation amount in the whole time of a project are standardized, and then the power generation cost of each degree of power is calculated to obtain the average power consumption cost.
(12) Unit GDP energy cost: the unit GDP energy cost calculates the cost spent on energy per unit GDP produced, numerically equal to the product of unit GDP energy consumption and standard coal price.
Analyzing influence factors of the comprehensive energy system, selecting evaluation indexes according to the influence factors to construct an energy efficiency index system of the comprehensive energy system, collecting actual data of each evaluation index, and determining the evaluation index values according to the actual data. The invention takes five parks as research objects, and each park respectively collects 12 evaluation index data for calculation and analysis.
Step 102: and evaluating each evaluation index through the multiple evaluation models to obtain the weight value of each evaluation index and the evaluation result corresponding to the multiple evaluation models.
Specifically, the weight values of the evaluation indexes are calculated through the evaluation model, and the weight values of the evaluation indexes are multiplied by the evaluation index values correspondingly and summed to obtain the evaluation result of the evaluation model. In the invention, an entropy weight method, a principal component analysis method and a pull-up grade method are adopted to respectively establish an evaluation model. And sequentially adopting a plurality of evaluation models to obtain the evaluation result corresponding to each evaluation model.
Step 1021: and calculating an evaluation result by adopting an entropy weight method evaluation model.
The entropy weight method fully considers the information quantity difference between different items to be evaluated, for an index, the dispersion degree of the index can be judged by using an entropy value, the smaller the information entropy value is, the larger the dispersion degree of the index is, the larger the influence of the index on the comprehensive evaluation (namely, the weight value of the evaluation index) is, and if the values of some index are all equal, the index does not play a role in the comprehensive evaluation. Therefore, the information entropy tool can be used for calculating the weight of each index.
Establishing a decision matrix, wherein the decision matrix is an original data matrix X of n evaluation indexes of m research targets { X }ij}m×n. The data is preprocessed to form a normalized matrix X '═ X'ij}m×n. Wherein x'ijThe larger the difference in value of (a), the smaller the entropy of the index j.
Calculating the characteristic specific gravity s of each research object under the j indexijThe calculation formula is as follows:
wherein 0. ltoreq.sij≤1。
Calculating an entropy value e of an indexjThe calculation formula is as follows:
when s isijWhen equal to 0 or 1, sijln(sij) The value of (d) is 0.
Determining the entropy weight w of each index according to the entropy value of each indexjThe calculation formula is as follows:
dj＝1-ej
wherein the coefficient of variation djThe larger the index weight is.
And correspondingly multiplying the entropy weight of the nth index and the standardized index value of the ith research object, and summing to obtain an evaluation result calculated by the ith evaluation target by adopting an entropy weight method evaluation model.
Step 1022: and calculating an evaluation result by adopting a pull-out grade method evaluation model.
The method is an objective evaluation method for reflecting the overall difference between evaluated objects, and the basic idea of the method is to select index weight coefficients so as to enlarge the difference between the evaluated objects as much as possible. From the geometrical point of view, n points (evaluation objects) in an m-dimensional evaluation space composed of m evaluation indexes are projected to a certain one-dimensional space, so that the projection points of the points on the one-dimensional space are dispersed most, namely the dispersion degree is maximum.
And preprocessing the original data to determine the index weight. Maximum evaluation index X1，X2，X3...XmAs a comprehensive evaluation function of the evaluation system:
yi＝xi1ω1+xi2ω2+…+ximωm＝xω (4)
wherein ω is (ω)1,ω2...ωm) Is a vector composed of all the item indexes. x ═ x1,x2...xm) Characterizing the mth standard observation at the ith system SiIn the middle of
The weight coefficients are:
can be written as y ═ a ω
In the formula, yiIs the comprehensive evaluation result of the ith system, xijIs the standard observed quantity of the j index of the ith system. Wherein, the sample variance composed of the variable y ═ x ω in the n systems is:
ns is obtained after conversion2＝ωTATAω＝ωTH ω, where H ═ ATA is a real symmetric matrix. And maximizing the sample variance to obtain a weight coefficient, namely a corresponding feature vector in the H matrix, and carrying out normalization processing on the weight coefficient.
The evaluation result can be calculated by using a linear weighting method, and the calculation formula is as follows:
wherein y isjIsjIs.
Step 1023: and calculating an evaluation result by adopting a principal component analysis evaluation model.
The principal component analysis method mainly discloses the internal structure among a plurality of variables through a few principal components, namely, the few principal components are derived from original variables, so that the information of the original variables is kept as much as possible and is not mutually correlated.
Let random vector X ═ X1,X2,…Xp)＝(xij)n×p(n.gtoreq.p), X thereofi＝(X1i,X2i,...,Xni) 1,2, p. If vector X is to be calculated1,X2,...,XPAfter transformation, new P vectors are generated, and each new vector FIIs X1,X2,...,XPLinear combination of (1), i.e. Fi＝ai1X1+ai2X2+…+aipXpWherein
The new vectors are not related to each other, i.e. Cov (F)i,Fj)＝0(i≠j,i,j＝1,2,..)。
And F2Is F1The largest variance in the uncorrelated linear combinations, and so on, FpIs a reaction of with F1,F2,...,Fp-1The largest variance in the linear combinations is irrelevant.
If S is a symmetric array, then an orthogonal array a can be found
Wherein λ1,λ2,…λp(λ1≥λ2≥…≥λp>0) Is the characteristic root of the covariance matrix S, if the unit vector corresponding to the characteristic root of S is a1,a2,…apLet us order
Then the eigenvector corresponding to the eigenroot of the real symmetric matrix S is orthogonal ai·aj0, aa '═ a' a ═ I. Var (F) can be derived according to the above definitions and theorem1)＝λ1Wherein Var (F)1) Is X1,X2,...,XPThe variance of the principal component (c).
If C is presentk/iRepresents the k-th component FkContribution ratio in i components, then
Cumulative contribution rate of
On the basis of the mathematical setting, after the data are normalized, the correlation coefficient matrix R is calculated according to the normalized matrix T, and the operation formula is as follows:
determining the number K (0) of the main components by adopting the principle that the accumulated contribution rate is more than 85 percent<K is less than or equal to P), and the unit characteristic quantity a of K main components is obtained1,a2,...,akThen, each principal component F is obtainediWhere i ═ 1, 2.., k.
The weight factor of the first estimate of the principal component is the ratio of the contribution rate to the total contribution rate. The principal component evaluation value has the weighting coefficients:
Ci＝Ci/k(i＝1,2,…k) (9)
comprehensive evaluation value F corresponding to jth evaluation objectjComprises the following steps:
and obtaining an evaluation result calculated by the evaluation model of the principal component analysis method.
Step 103: and combining the multiple evaluation models, calculating a weight coefficient corresponding to each evaluation model in different model combination modes according to the weight values, and calculating the evaluation results of the different model combination modes according to the evaluation index values and the weight coefficients.
The weight coefficients of the evaluation indexes determined by the entropy weight method, the rank-expanding method and the principal component analysis method have respective advantages and disadvantages, so that the weight coefficients of the methods have larger difference. The comprehensive weight method of the game theory aims to search for consistency or compromise of weight values on the basis of fully considering respective characteristics, so that weight dispersion is minimized. Determining comprehensive weight coefficient vector P of each evaluation index according to formulaT
Wherein, Wi T、
Is a weight value vector, alpha, of an evaluation index determined based on an entropy weight method evaluation model, a pull-up rank method evaluation model, and a principal component analysis method evaluation model1，α2，α3Is a weight coefficient of each evaluation model. In order to obtain a balanced composite weight result determined by the three methods, the optimization of three linear combination coefficients in the formula is concluded, so that the dispersion of the composite weight coefficients is minimized. Thus, one countermeasure model is introduced:
from the differential nature of the matrix, a system of linear equations can be derived:
wherein alpha is1，α2，α3It is the weighting factor of the single evaluation method that we need. After the weight coefficient is determined, the evaluation results under different model combination modes are calculated according to the following formula:
wherein V is the evaluation result, Y*The method is a matrix obtained after the raw data of the index set is subjected to standardization processing, W is a weight coefficient, n is the number of indexes to be evaluated, and m is the number of types of evaluation models. The above calculation method is used when the three methods are used together, and only the method is usedWhen two of them are used, only the formula is changed into
Then find alpha1，α2And (4) finishing.
Step 104: and calculating the correlation degree between the evaluation results of different model combination modes and the evaluation results corresponding to the various evaluation models, and determining the optimal model combination mode and the optimal evaluation result according to the correlation degree.
The post-test of the combination evaluation is to verify the closeness between the ranking result obtained by the combination method and the ranking result of the data obtained by the original method, and the similarity of the ranking results is verified by adopting a spearman grade correlation coefficient test method.
The formula of the spearman correlation coefficient is:
converting the combined evaluation result into a ranking value, wherein diThe rank difference of the ith evaluation object is sorted for the two evaluation schemes, n is the number of evaluated objects, and r is the spearman correlation coefficient.
If a certain combination includes m single evaluation methods, each single evaluation result can be used to calculate a spearman correlation coefficient with the combined evaluation result, and x is usediWherein i is 1, 2. M ═ x1,x2,...,xiDenotes all the calculated spearman correlation coefficients in this combination case.
dkThe average degree of correlation between the original m methods and the k combined evaluation method is obtained.
The results of the combined evaluation are not exactly the same as the results of the original multi-method, but they are very close, selected to be closest to the original multi-methodMethod of combining methods as the optimal combining method, in particular, solving test statistic tk，tkThe most significant one is the best combination method.
When n ≧ 10, the test statistic is:
wherein d iskAs before, when t has a degree of freedom of n-2, t iskThe values follow a t-spread. According to tkThe magnitude of the value determines an optimal model combination method and an optimal evaluation result obtained by the optimal model combination method.
In the case of the example 1, the following examples are given,
the index value of the research object is obtained from the ways of arranging data in different parks, investigating and examining testimony on the spot and the like according to the index system constructed by the invention.
And (4) evaluating each park independently by adopting the three methods to obtain different evaluation values, and sequencing the evaluation values of different parks.
| Park area | Entropy method of evaluation | Sorting |
| A | 0.0356 | 5 |
| II | 0.0614 | 3 |
| III | 0.1237 | 1 |
| Fourthly | 0.0592 | 2 |
| Five of them | 0.0435 | 4 |
| Park area | Evaluation value by pull-up grade method | Sorting |
| A | 0.0469 | 4 |
| II | 0.0397 | 5 |
| III | 0.0679 | 1 |
| Fourthly | 0.0548 | 2 |
| Five of them | 0.0503 | 3 |
| Park area | Evaluation value of principal component analysis | Sorting |
| A | 0.9564 | 1 |
| II | 0.7669 | 3 |
| III | 0.5508 | 4 |
| Fourthly | 0.8677 | 2 |
| Five of them | 0.4503 | 5 |
And determining the weight coefficient of each evaluation method, and calculating a combined evaluation result according to the weight coefficient. The weight coefficients are shown in the following table, wherein methods 1,2 and 3 are entropy weight method, pull-down rank method and principal component analysis method, respectively.
The comprehensive evaluation values of the 7 combinations obtained by screening are ranked in the following table.
Respectively calculating the spearman correlation coefficient between the combined evaluation value under each combination condition and each independent estimation method for the four different combinations, and then obtaining the test statistic t of each combination casek。
Wherein t iskThe higher the value ranking, the more accurate the combined evaluation result. From the above results, tkThe maximum value is the optimal combination, namely the combination of the method 1 and the method 3, and the simultaneous use of the principal component analysis method and the entropy weight method in the model calculation is the most consistent and reasonable combination evaluation.
The evaluation results obtained by combining the evaluation by the principal component analysis method and the entropy weight method are shown in the following table.
| Park area | Evaluation value | Sorting |
| Park I | 0.1227 | 1 |
| Fifth garden | 0.1173 | 2 |
| Park II | 0.0876 | 3 |
| Park four | 0.0556 | 4 |
| Park III | 0.0399 | 5 |
From the evaluation results of the parks, the comprehensive energy efficiency evaluation results of the park I and the park II are better, and the performances of the park IV and the park II are poorer.
And in combination with the specific index performance, compared with other parks, the park four is poorer in the aspects of self-supply rate of energy, wind abandonment and light abandonment rate, clean energy permeability, power consumption cost and unit GDP energy cost, and the park three is poorer in the three aspects of voltage level balance, primary energy utilization rate and clean energy installation ratio. Therefore, the park managers need to pay attention to the above indexes, find the problems of energy sources in the aspects of input, output, utilization, management and the like, and put forward corresponding improvement measures.
Referring to fig. 3, the energy efficiency evaluation apparatus of the integrated energy system in the embodiment of the present application may include: index construction module 310, evaluation module 320, combination module 330, and selection module 340.
And the index construction module 310 is used for constructing an energy efficiency index system of the comprehensive energy system and determining an evaluation index value.
The evaluation module 320 is configured to evaluate the evaluation indexes through multiple evaluation models to obtain weight values of each evaluation index and evaluation results corresponding to the multiple evaluation models.
The combination module 330 is configured to send the information of successful delivery request to the user terminal when the delivery request is satisfied.
The selecting module 340 is configured to calculate a degree of correlation between the evaluation results of different model combination manners and the evaluation results corresponding to the multiple evaluation models, and determine an optimal model combination manner and an optimal evaluation result according to the degree of correlation.
It should be noted that, for the information interaction, execution process, and other contents between the above-mentioned devices/units, the specific functions and technical effects thereof are based on the same concept as those of the embodiment of the method of the present application, and specific reference may be made to the part of the embodiment of the method, which is not described herein again.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-mentioned division of the functional units and modules is illustrated, and in practical applications, the above-mentioned function distribution may be performed by different functional units and modules according to needs, that is, the internal structure of the apparatus is divided into different functional units or modules, so as to perform all or part of the functions described above. Each functional unit and module in the embodiments may be integrated in one processing unit, or each unit may exist alone physically, or two or more units are integrated in one unit, and the integrated unit may be implemented in a form of hardware, or in a form of software functional unit. In addition, specific names of the functional units and modules are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present application. The specific working processes of the units and modules in the system may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
An embodiment of the present application further provides a terminal device, and referring to fig. 4, the terminal device 500 may include: at least one processor 510, a memory 520, and a computer program stored in the memory 520 and executable on the at least one processor 510, the processor 510 when executing the computer program implementing the steps of any of the various method embodiments described above, such as the steps 101 to 104 in the embodiment shown in fig. 1. Alternatively, the processor 510, when executing the computer program, implements the functions of the modules/units in the above-described device embodiments, such as the functions of the modules 310 to 340 shown in fig. 3.
Illustratively, the computer program may be divided into one or more modules/units, which are stored in the memory 520 and executed by the processor 510 to accomplish the present application. One or more modules/units may be a series of computer program segments capable of performing certain functions, which are used to describe the execution of the computer program in the terminal device 500.
Those skilled in the art will appreciate that fig. 4 is merely an example of a terminal device and is not limiting and may include more or fewer components than shown, or some components may be combined, or different components such as input output devices, network access devices, buses, etc.
The Processor 510 may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic, discrete hardware components, etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory 520 may be an internal storage unit of the terminal device, or may be an external storage device of the terminal device, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like. The memory 520 is used for storing computer programs and other programs and data required by the terminal device. The memory 520 may also be used to temporarily store data that has been output or is to be output.
The bus may be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, an Extended ISA (EISA) bus, or the like. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, the buses in the figures of the present application are not limited to only one bus or one type of bus.
The energy efficiency evaluation method of the comprehensive energy system provided by the embodiment of the application can be applied to terminal devices such as computers, wearable devices, vehicle-mounted devices, tablet computers, notebook computers, netbooks, Personal Digital Assistants (PDAs), Augmented Reality (AR)/Virtual Reality (VR) devices and mobile phones, and the embodiment of the application does not limit the specific types of the terminal devices at all.
The embodiment of the application also provides a computer-readable storage medium, wherein a computer program is stored in the computer-readable storage medium, and when the computer program is executed by a processor, the steps in the embodiments of the comprehensive energy system energy efficiency evaluation method can be realized.
The embodiment of the application provides a computer program product, and when the computer program product runs on a mobile terminal, the steps in each embodiment of the comprehensive energy system energy efficiency evaluation method can be realized when the mobile terminal is executed.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, all or part of the processes in 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 used by a processor to implement the steps of the embodiments of the methods described above. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer readable medium may include at least: any entity or device capable of carrying computer program code to a photographing apparatus/terminal apparatus, a recording medium, computer Memory, Read-Only Memory (ROM), Random Access Memory (RAM), an electrical carrier signal, a telecommunications signal, and a software distribution medium. Such as a usb-disk, a removable hard disk, a magnetic or optical disk, etc. In certain jurisdictions, computer-readable media may not be an electrical carrier signal or a telecommunications signal in accordance with legislative and patent practice.
In the above embodiments, the descriptions of the respective embodiments have respective emphasis, and reference may be made to the related descriptions of other embodiments for parts that are not described or illustrated in a certain embodiment.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus/network device and method may be implemented in other ways. For example, the above-described apparatus/network device embodiments are merely illustrative, and for example, a module or a unit may be divided into only one logical function, and may be implemented in other ways, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
Units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
The above embodiments are only used to illustrate the technical solutions of the present application, and not to limit the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not substantially depart from the spirit and scope of the embodiments of the present application and are intended to be included within the scope of the present application.
Claims (10)
1. The energy efficiency evaluation method of the comprehensive energy system is characterized by comprising the following steps:
constructing an energy efficiency index system of the comprehensive energy system, and determining evaluation index values of various evaluation indexes in the energy efficiency index system of the comprehensive energy system;
evaluating each evaluation index through multiple evaluation models to obtain the weight value of each evaluation index and the evaluation result corresponding to the multiple evaluation models;
combining multiple evaluation models, calculating a weight coefficient corresponding to each evaluation model in different model combination modes according to the weight values, and calculating evaluation results of different model combination modes according to the evaluation index values and the weight coefficients;
and calculating the correlation degree between the evaluation results of different model combination modes and the evaluation results corresponding to the various evaluation models, and determining the optimal model combination mode and the optimal evaluation result according to the correlation degree.
2. The method for evaluating the energy efficiency of the integrated energy system according to claim 1, wherein the step of constructing the energy efficiency index system of the integrated energy system and determining the evaluation index values of the evaluation indexes in the energy efficiency index system of the integrated energy system comprises the following steps:
analyzing influence factors of the comprehensive energy system, and selecting evaluation indexes according to the influence factors to construct an energy efficiency index system of the comprehensive energy system;
and collecting actual data of each evaluation index, and determining the evaluation index value according to the actual data.
3. The energy efficiency evaluation method of the integrated energy system according to claim 1, wherein the evaluation indexes are evaluated by a plurality of evaluation models to obtain the weight values of the evaluation indexes and the evaluation results corresponding to the plurality of evaluation models, and the evaluation method comprises the following steps:
calculating the weight value of each evaluation index through an evaluation model;
correspondingly multiplying the weight values of all the evaluation indexes by the evaluation index values and summing to obtain the evaluation result of the evaluation model;
sequentially adopting a plurality of evaluation models to obtain an evaluation result corresponding to each evaluation model; the multiple evaluation models comprise an entropy weight method evaluation model, a principal component analysis method evaluation model and a pull-up grade method evaluation model.
4. The energy efficiency evaluation method of the integrated energy system according to claim 1, wherein the weight coefficient is calculated by the formula:
wherein, Wi T、
Is a weight value vector, alpha, of an evaluation index determined based on an entropy weight method evaluation model, a pull-up rank method evaluation model, and a principal component analysis method evaluation model1，α2，α3Is a weight coefficient of each evaluation model.
5. The energy efficiency evaluation method of the integrated energy system according to claim 4, wherein the calculation formula of the evaluation result is:
wherein V is the evaluation result, Y*The method is a matrix obtained after the raw data of the index set is subjected to standardization processing, W is a weight coefficient, n is the number of indexes to be evaluated, and m is the number of types of evaluation models.
6. The energy efficiency evaluation method of the integrated energy system according to claim 1, wherein the step of calculating the degree of correlation between the evaluation results of different model combination modes and the evaluation results corresponding to the plurality of evaluation models and determining the optimal model combination mode and the optimal evaluation result according to the degree of correlation comprises the steps of:
ranking the evaluation results of different model combination modes and the evaluation result of the single evaluation model according to the evaluation values respectively, and calculating a spearman correlation coefficient according to the rank difference of the ranked same evaluation object;
calculating the average correlation degree between the model combination mode and the single evaluation model according to the spearman correlation coefficient;
and calculating test statistic according to the average correlation degree, and determining an optimal model combination mode and an optimal evaluation result according to the test statistic.
7. The energy efficiency evaluation method of the integrated energy system according to claim 6, wherein the formula for calculating the average degree of correlation is:
wherein x isiIs the spearman correlation coefficient, m is the number of classes of the evaluation model, dkThe average degree of correlation between the original m evaluation models and the kth combination evaluation method is obtained.
8. An energy efficiency evaluation device of an integrated energy system is characterized by comprising:
the index construction module is used for constructing an energy efficiency index system of the comprehensive energy system and determining an evaluation index value;
the evaluation module is used for evaluating the evaluation indexes through the multiple evaluation models to obtain the weight values of the evaluation indexes and the evaluation results corresponding to the multiple evaluation models;
the combined module is used for sending successful information of the ex-warehouse request to the user terminal when the ex-warehouse request is met;
and the selection module is used for calculating the correlation degree between the evaluation results of different model combination modes and the evaluation results corresponding to the various evaluation models, and determining the optimal model combination mode and the optimal evaluation result according to the correlation degree.
9. A terminal device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the method for evaluating the energy efficiency of an integrated energy system according to any one of claims 1 to 7 when executing the computer program.
10. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the method according to any one of claims 1 to 7.
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