CN115330555A - IES comprehensive benefit evaluation method based on digital portrait - Google Patents
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Abstract
The invention provides an IES comprehensive benefit evaluation method based on a digital portrait, which relates to the technical field of big data, and is used for collecting data, cleaning the data and screening important data characteristics; establishing a label model based on the important data characteristics, and constructing a label attribute system; the label model includes at least the following high-level labels: energy-saving and environment-friendly labels, performance labels, economic labels and social labels; and performing weight calculation on the label model by adopting an AHP-entropy weight method to form a technical portrait outline, and analyzing the influence of each label model on the operation, development and construction of the IES item by item. The invention provides an IES comprehensive benefit evaluation method, which is a subjective and objective combination weighting method based on an AHP-entropy method, and is more objective and fair in index weight calculation compared with a single weighting method, thereby not only considering the preference of a decision maker, but also avoiding subjective factor interference, and analyzing the mutual influence factors among indexes more systematically and more comprehensively.
Description
Technical Field
The invention relates to the technical field of big data, in particular to an IES comprehensive benefit evaluation method based on digital portrait.
Background
At present, the proportion of electric energy in terminal energy consumption is gradually increased, and the large-scale use of alternative clean energy and the improvement of the comprehensive utilization efficiency of the energy become important technical means for effectively exploring the potential of energy conservation and emission reduction of each energy department. New terminal electric energy consumption technologies such as electric hydrogen production, electric vehicle charging and discharging equipment, efficient ice storage and the like are continuously developed, not only are new requirements for safe and efficient operation of a power grid provided, but also new challenges are provided for exploring and innovatively designing a management mode and a popularization and application mechanism suitable for the new technologies. In order to promote the development and construction of IES and guarantee safe and reliable operation, a scientific and effective evaluation system is required to guarantee. Compared with the traditional power grid, the IES has larger difference and has the characteristic of multi-energy coupling complementation, the barrier between energy flows is broken, the energy flow is more complicated, the efficiency of an energy system can be greatly improved through the optimized operation, the large-scale renewable energy grid-connected consumption is promoted, and the IES becomes an important hand grip for realizing the purposes of optimizing an energy structure and assisting double-carbon. Because the new technology applied in the IES has wide coverage field and large related quantity, the unified management of various new technologies becomes the key for realizing the efficient utilization of the new technologies, thereby playing a guiding role in the promotion of the key technologies in the future.
With continuous development and construction of IES, the acquisition frequency of multi-link data of 'source-network-load-storage' is continuously improved, the dimensionality of data analysis is continuously expanded, and the operating characteristics and technical characteristics of IES are increasingly complex. The development of the digital portrait technology provides a more intuitive and concise display mode for IES technical characteristic analysis.
At present, a digital portrait technology develops preliminary research and application in the aspect of load characteristic analysis of power consumers, a literature 'interference source type identification based on maximum mutual information and power quality demand portrait technology' researches a user interference source portrait technology facing power quality management demands, and a complete portrait label system is constructed on the basis of massive power quality monitoring data to realize accurate quantification of power quality level and power consumption level of the users; the document 'electric power user portrait analysis based on big data technology' is based on massive power consumption data, takes an electric power user as a main body, gives a portrait label to the user by mining information of user characteristics and power consumption behavior data, analyzes different user behavior difference characteristics, and provides support for power supply enterprises to formulate differentiated power supply service; in the literature, "the transformer fine control strategy research based on the portrait", a panoramic label of the transformer equipment is formed based on the equipment portrait technology, and an equipment health degree evaluation optimization scheme of label correlation analysis is formed.
The research obtains good benefits in the aspects of user portrait and characteristic analysis of electric power equipment, provides basic support for developing fine operation management of electric power companies, but is deficient in portrait construction of related technologies such as IES. Therefore, a technical portrait model can be constructed according to an IES actual acquisition and operation data mining labeling model, a refined and comprehensive IES technical portrait can be sketched by comprehensively analyzing the technical level and characteristics by means of big data, the IES technical portrait can be deeply known, the scientific management level of IES technical popularization can be improved, the transformation of an industrial structure and a management mode can be realized, and an innovative service mode can be provided for the efficient utilization of IES resources in a big data environment. In addition, digital portrait is integrated into a traditional IES evaluation model, and the evaluation of overall, deep and accurate depiction of environment, performance, economy and social dimensions becomes a new trend, and the method has more engineering applicability and applicability.
The multidimensional label weight solving is the key for realizing the fine portrayal, the IES operating characteristic analysis and evaluation belong to a multi-attribute group decision problem, and the main task is to determine the corresponding evaluation label weight. The literature 'research on the optimization evaluation method of the building multi-energy system' evaluates the aspects of technology, economy, environment and the like of IES based on hierarchical analysis, and has strong subjective randomness and certain limitation in practical application due to the prior knowledge and self experience of decision experts. In the document, "benefit analysis and comprehensive evaluation of a distributed energy system" uses an entropy weight method to evaluate indexes such as primary energy utilization rate, annual cost, CO2 emission and the like, and the practicability of the evaluation method is verified from objective data.
Compared with a subjective evaluation method, the objective weighting method is determined according to objective data, but the obtained weight value is easy to violate the subjective will of a decision maker or cannot truly and accurately reflect the planning purpose. In order to improve the shortcomings of the subjective and objective weight determination methods, the advantages of the two methods can be combined to form a comprehensive weighting method for determining the evaluation weight. In addition, the existing IES evaluation system usually mainly selects the relevant indexes of electric power, the research on the incorporation of the technical performance and the social stability dimension into the evaluation system is relatively lacked, and the problems of wide technical scope, multiple influence factors and complex evaluation indexes are also difficult to develop the comprehensive evaluation on the operation characteristics and the technical characteristics of the digital paintings.
Therefore, it is necessary to summarize and consider the development and construction current situation and the operation technical characteristics of the IES, and establish a multi-dimensional evaluation label system for the integrated benefits and the technical popularization and application of the IES based on the massive data and the survey and detection data of the IES and based on a plurality of influence factors and evaluation indexes.
Disclosure of Invention
Based on the above problems, the invention provides an IES comprehensive benefit evaluation method based on a digital portrait.
In order to achieve the purpose, the invention provides the following technical scheme:
an IES comprehensive benefit evaluation method based on digital portrait specifically comprises the following steps:
step one, collecting data, cleaning the data and screening important data characteristics;
secondly, establishing a label model based on the important data characteristics, and constructing a label attribute system; the label model includes at least the following high-level labels: energy-saving and environment-friendly labels, performance labels, economic labels and social labels;
and thirdly, performing weight calculation on the label model by adopting an AHP-entropy weight method to form a technical portrait outline, and analyzing the influence of each label model on the operation, development and construction of the IES item by item.
Preferably, the construction process of the tag attribute system is as follows: and formulating a label rule, extracting labels based on the important data features, evaluating and analyzing the labels, and adjusting the label rule and the labels according to results.
Preferably, the high-level label further sets a corresponding middle-level label and a corresponding primary label, and establishes a label model.
Preferably, the energy-saving and environment-friendly labels at least comprise three energy-saving and environment-friendly medium-grade labels of total energy consumption, pollutant emission and energy saving; the performance labels at least comprise six performance intermediate grade labels of crossing, borrowability, novelty, gap, achievement transformation risk and reliability in the technical field; the economic tags comprise at least two economic mid-grade tags of cost and income; the social labels at least comprise three social intermediate labels of employment effect, social stability and life cycle.
Preferably, the AHP-entropy weight method comprises: and calculating the subjective weight of the label model by adopting an AHP method, calculating the objective weight of the label model by adopting an entropy weight method, and coupling the subjective weight and the objective weight to obtain the comprehensive weight of the label model.
Preferably, the method for calculating the subjective weight of the label model by using the AHP method comprises the following steps:
carrying out relative importance scale judgment on all labels in the label model, and establishing a judgment matrix; and carrying out consistency check on the judgment matrix.
Preferably, the method for performing consistency check on the judgment matrix comprises the following steps:
calculating the maximum eigenvalue lambda of the judgment matrix max And consistency ratio c.r:
BW is the sum of the numerical values of each column of the decision matrix; n is the order of the judgment matrix; CI is consistency label; RI is a random consistency label; r is the consistency ratio;
when n =1 or 2, CI is 0, and the judgment matrix is completely consistent; when n is more than or equal to 3, the C.R is continuously solved; if the C.R is less than or equal to 0.1, the judgment matrix meets the consistency requirement, otherwise, the judgment matrix needs to be further adjusted until the judgment matrix passes the consistency test.
Preferably, the method for calculating the objective weight of the label model by using the entropy weight method comprises the following steps:
and dividing all the labels in the label model into benefit type attribute labels and cost type attribute labels, carrying out non-dimensionalization processing to obtain a normalized decision matrix, and calculating information deviation degree and label objective weight for each label.
Preferably, the subjective weight and the objective weight are coupled to obtain a comprehensive weight of the label model, and the specific method is as follows:
wherein, ω is i Weight, μ, obtained by analytic hierarchy method for ith tag i Weight, α, obtained by entropy weighting for the ith label i 、β i Is an intermediate variable.
Preferably, the types of data collected include at least: IES state monitoring, environment monitoring, expert evaluation scoring, IES operating data and external data.
Compared with the prior art, the invention has the following advantages:
the invention provides an IES comprehensive benefit evaluation method based on digital portrait, which is characterized in that based on IES operation mass data and investigation and detection data, an evaluation label system is established from four dimensions of energy conservation, environmental protection, performance, economy and society, and a digital portrait evaluation label library oriented to IES technical characteristics and popularization and application analysis is constructed; integrating various IES technology popularization and application information, reasoning out knowledge hidden under surface layer information, and constructing a complete and three-dimensional IES technology portrait; the label is weighted by a weighting method based on subjective and objective combination of an Analytic Hierarchy Process (AHP) -entropy weight method, weights are calculated in a refined mode to form an outline for accurately depicting the IES technical portrait, compared with a single weighting method, the weighting method based on subjective and objective combination of the AHP-entropy weight method is more objective and fair in terms of obtaining of the index weights, favors of decision makers are considered, interference of subjective factors can be avoided, mutual influence factors among indexes are analyzed more systematically and comprehensively, the obtained index weights are more accurate, and therefore a scientific quantification method is provided for popularization and application of the IES technology.
Drawings
FIG. 1 is a digital portrait frame of IES technique in the IES comprehensive benefit evaluation method based on digital portrait of the present invention;
FIG. 2 is a flow for constructing an IES technical label attribute system in the IES comprehensive benefit evaluation method based on the digital portrait.
Detailed Description
Example one
The invention provides an IES comprehensive benefit evaluation method based on a digital portrait, which specifically comprises the following steps:
step one, collecting data, cleaning the data and screening important data characteristics; the types of the collected data mainly comprise IES state monitoring, environment monitoring, expert evaluation scoring, IES operation data and external data;
secondly, establishing a label model based on the important data characteristics, and constructing a label attribute system; the label model includes at least the following high-level labels: energy-concerving and environment-protective label, performance label, economic label and social label.
The most critical part of the IES technical sketch construction is to define tags according to multi-source characteristic power big data. By organically integrating environmental data, IES operating data, user energy data and the like and accurately describing complete technical information in a label form, a multi-dimensional and three-dimensional panoramic technical portrait is constructed, so that power enterprises can accurately acquire fine characteristics such as technical attributes, social satisfaction and the like. Strengthen the operation management, strengthen IES technical operation management, improve the comprehensive benefit of technical popularization.
The building process of the IES technical label attribute system is shown in FIG. 2, and from the technical aspect, the building of the IES technical portrait based on big data is divided into three steps: the first step is to mine the terminal data information base, such as internal collected information and operation data, external environment data and social benefits. And the second step is terminal image data processing, which cleans various collected data to make them structured and standardized, and performs important feature screening. And the third step is the construction of a technical portrait, which comprises the steps of accurately identifying technical variables, quantifying technical static data and evaluating dynamic behaviors, determining a technical label library and the like. Label extraction comes from each business system, after data extraction, data cleaning and extraction are carried out, label rules are formulated, and corresponding labels are extracted from the data according to the rules; after the label is printed, the label rule can be properly changed through evaluation and analysis; and finally, applying the label to service systems such as comprehensive benefit analysis, popularization and application evaluation, management mechanism formulation, operation characteristic analysis and the like, and collecting use feedback opinions in the application process so as to adjust label rules and update labels.
The image technology is applied to label the IES label, and a series of basic labels are classified, screened and compared to finally construct an IES evaluation label system based on the image technology. An IES multi-dimensional label system is established from 4 advanced labels of energy conservation, environmental protection, performance, economy and society. The label system is used as a high-grade label on the basis of environmental benefits, technical performance, economic benefits and social benefits, and a medium-grade label and a basic label are set on the basis. Wherein, the energy-saving and environment-friendly dimensionality comprises 3 middle-level labels; the performance dimension has 6 middle-level tags and 6 basic tags; the economic dimensionality comprises 2 middle-level tags and 4 basic tags; the social dimension has 3 middle-level tags, 3 basic tags. The complete system comprises 16 evaluation labels, and relates to aspects of electric power, performance, environmental protection, economic cost and the like.
Aiming at the energy-saving and environment-friendly labels, the energy-saving and environment-friendly label at least comprises three energy-saving and environment-friendly medium-grade labels of energy consumption total amount, pollutant discharge amount and energy saving amount; wherein the pollutant discharge amount takes the sulfur dioxide discharge amount into consideration.
The total energy consumption amount refers to the total amount of various energy consumed in various industries and enterprises in a certain region. Generally, the larger the coal consumption, the lower the environmental protection of system energy, and the more the natural gas consumption, the more the cleanness of system energy can be reflected. The specific calculation formula is as follows:
B 21 ＝∑a (1)
in the formula, a is the sum of terminal energy consumption (tce) and standard coal; b21 is the total energy consumption.
The specific calculation formula of the sulfur dioxide emission is as follows:
B 22 ＝M×S×0.8×2×(1-n) (2)
in the formula, B22 is total annual sulfur dioxide emission (t); m is annual raw coal consumption (t); s is the sulfur content; 0.8 is the conversion rate of sulfur dioxide in the combustion process; 2 is the weight gain proportion of sulfur converted into sulfur dioxide; and n is the desulfurization degree. The boiler adopting the desulfurization measure is calculated according to the value of the detection data, and the desulfurization rate of the boiler without the desulfurization measure is 0; data on the sulfur content of the coal were obtained without the conditions, and were calculated temporarily as the 1.2% sulfur content.
The specific calculation formula of the energy saving is as follows:
B 23 ＝(g-h)*i (3)
wherein g is the comprehensive energy consumption of business income of a report period unit (tce/ten thousand yuan);
h is the comprehensive energy consumption of business income of the unit of the base term (tce/ten thousand yuan); i is the report period revenue (ten thousand yuan); b23 is revenue (comparable) energy savings (tce).
Aiming at a performance label, the performance label mainly evaluates the leadership in the aspects of IES technology integration capability, technical routes, technical application and the like; technical evaluation means that technical performance, level and economic benefit, and possible influence on environment, ecology, and even the whole society, economy, politics, culture, psychology and the like are fully evaluated and estimated. According to the technical advancement evaluation definition, the IES development status and technical characteristics of China are combined, and an evaluation label system combining the qualitative and quantitative evaluation is established according to the principles of systematicness, feasibility, innovativeness and the like.
Performance labels were analyzed primarily from complexity, timeliness, maturity. Based on the intermediate label, the portrait advanced label which characterizes the technical performance is constructed, the technology is classified and ordered from the macroscopic level, and a basis is provided for IES development, construction, planning, operation and the like,
the method specifically comprises six middle-level labels and six basic labels. Among the complexities are: the technical field is wide in span and can be used for reference; the timeliness comprises the following steps: novelty, disparity; the maturity includes: the risk and reliability of outcome transformation are shown in table 1.
TABLE 1 evaluation tags in Performance dimension
For economic tags, economic tags include cost tags and income tags. The cost label is divided into two items of cost profit margin and unit product cost, and the profit label is divided into two sub-labels of selling price ratio and income profit margin, as shown in table 2.
Cost profit margin: the higher the label, the lower the cost of the IES for obtaining more revenue, the better the cost control, and the stronger the profitability obtained by using the technology, the more obvious the advancement.
Unit product cost: the unit product cost means the cost consumed on average to produce a unit product. The unit product cost not only reflects the IES operation management level, but also more importantly reflects the IES production level and the technical equipment capability, and the lower the label is, the more advanced the adopted technology is.
The selling price ratio is the ratio of the selling price of the product after the new technology is adopted to the selling price of the product under the original technology. The larger the ratio, the greater the contribution of new technology adoption to increasing energy service product revenue, and the more advanced compared to similar technologies.
Revenue profit margin, which is the ratio of total profit realized by the IES to contemporaneous sales revenue, is an important label reflecting the profitability of the IES operation. The label directly reflects the relationship between sales revenue and profit, and indirectly reflects the influence of technical advancement on the profitability of the IES operation. The higher the label, the greater the ability of the IES sales revenue to generate profits, and the more advanced the technology employed.
TABLE 2 evaluation tags in economic Categories
Aiming at the social label, the influence of the technology on the social employment effect is pointed out, firstly, employment opportunities are newly added after the new technology is adopted, and the employment problem of the region with excess labor force is favorably solved; however, in areas with labor shortage, production is influenced by mobilizing other working labor force, and the negative effect is obvious. The elimination of local backward labor is beneficial to saving labor and is beneficial to regions with labor shortage; but the method causes employment difficulty in areas with excess labor force and has negative effects.
Social stability is used to illustrate the impact of advanced technologies on the security of the IES site, both positive and negative. As shown in table 3.
TABLE 3 evaluation tags in social Categories
For quantitative indexes, the index characteristic value can be directly calculated through statistical data obtained by an energy information acquisition system, and for qualitative indexes, experts are required to give the index score according to system operation data, project planning schemes, similar conditions, national policies, the prior art and other data, by combining own experience and referring to the scoring basis. In the label analysis process, a quantitative and qualitative combined method is used for grading each label and giving corresponding scores, so that an operation basis is provided for the application of an evaluation method.
And thirdly, performing weight calculation on the label model by adopting an AHP-entropy weight method to form a technical portrait outline, and analyzing the influence of each label model on the operation, development and construction of the IES item by item.
First, subjective weights are calculated using the AHP method.
After n evaluation indexes are compared pairwise, the n evaluation indexes are quantized by using a scale aij, and the specific value of aij refers to a table 1 to obtain a judgment matrix A:
the positive reciprocal array meeting the condition has a maximum characteristic root which is larger than zero, and the corresponding characteristic vector is used as a weight vector after being normalized, namely the weight value of each element in a single level; the relative importance scale is shown in table 4.
TABLE 4 relative importance Scale
Because the judgment and evaluation of each expert are subjective, the influence on the judgment matrix is not negligible, and therefore, the consistency check of the judgment matrix is required. The maximum eigenvalue λ max and the consistency ratio c.r of the judgment matrix need to be obtained, and the calculation formula is as follows:
wherein BW is the sum of the number of each column of the decision matrix; n is the order of the judgment matrix; CI is consistency label; RI is a random consistency label; c.r is the consistency ratio. When n =1 or 2, CI is 0, and the judgment matrix is completely consistent; and when n is more than or equal to 3, continuously solving the C.R. If C.R is less than or equal to 0.1, the judgment matrix meets the consistency requirement, which indicates that the estimation of the omega i is in an acceptable range, otherwise, the judgment matrix needs to be further adjusted until the judgment matrix passes the consistency check. The values of the random identity label RI are shown in table 5.
TABLE 5 average random consistency Label RI
Secondly, an entropy weight method is adopted to calculate objective weight.
Assuming that a new terminal energy consumption technology a = { a1, a2, a3, · an }, and a comprehensive evaluation label system of each technology is u = { u1, u2, ·, un }. New technique ai (i =1,2,.., m)On the label u j The attribute value under (j =1,2.., n) is a ij Then the decision matrix is a = (aij) M × N, M = (1,2,..., M), N = (1,2,..., N). Generally, the tag type generally has benefit type and cost type, and since the dimensions of different attributes may be different, in order to eliminate the influence of different dimensions on the decision result, the attribute tag needs to be subjected to non-dimensionalization processing.
For benefit type attributes, generally one can have:
for the cost-type attribute, let:
the matrix R = (rij) m × n obtained by the above non-dimensionalization process is referred to as a normalized decision matrix.
Calculating the entropy Ej of the attribute label j:
in the formula: j =1,2,.. M,1/ln (n) is a constant related to the number of samples, with the aim of making the number of samples constant
rij satisfies 0<rij<1 and rij =1, and when rij =0, rijln (rij) =0.
Then, calculating the information deviation degree:
d j ＝1-E j (10)
and finally, calculating the label weight:
aiming at an IES technical evaluation system, the intermediate label and the basic label weight are coupled by combining the advantages of the two to obtain the comprehensive coupling weight of the intermediate label and the basic label. The comprehensive weight calculation not only considers the subjective and objective weights, but also reflects the characteristic of difference of the subjective and objective weights, well combines the advantages of an analytic hierarchy process and an entropy weight method, and can more comprehensively and scientifically perform comprehensive weight calculation on the IES technical evaluation label.
Obtaining the comprehensive coupling weight pi of the ith intermediate label by the following formula:
wherein, ω i is the weight of the ith intermediate label obtained by an analytic hierarchy process, μ i is the weight of the ith intermediate label obtained by an entropy weight process, and α i and β i are intermediate variables.
Example two
And selecting an actual national IES demonstration park as an embodiment II to evaluate and verify, and verifying the applicability of the model and the method provided by the invention in the aspect of engineering application.
A comprehensive evaluation system with 16 labels in total of 4 dimensions is constructed for an IES system in a clinical harbor school district, and comprehensive analysis and comment are carried out on 4 aspects of energy conservation, environmental protection, performance, economy and society as shown in a table 6. As shown in table 6.
TABLE 6 comprehensive energy system technology evaluation tag system for college parks
And (4) judging the credibility of the matrix for accurately acquiring the weight calculation result influencing the analytic hierarchy process by combining the original data and expert scoring. Combining the scale values of the analytic hierarchy process judgment matrix, calculating the high-level label weight as shown in table 7 by describing the judgment matrix of the relative importance degree among the labels, the middle-level label weight as shown in table 8, and the basic label weight as shown in table 9:
table 7 advanced label weights for AHP
TABLE 8 Total score and weight of the intermediate level tags for AHP
TABLE 9 base tag and weight for AHP
And obtaining the weight of each level of label by an analytic hierarchy process, wherein the consistency coefficient CR of each matrix is less than 0.1, which indicates that the generated judgment matrix meets the consistency test.
The objective weight of the label system was obtained by the entropy weight method, and the specific results are shown in tables 10 and 11.
Table 10 mid-level label entropy weight
TABLE 11 base tag entropy weight
In order to scientifically and reasonably evaluate the operation state and the benefit of the clinical IES, the comprehensive coupling weight of the secondary label is obtained by combining a layer analysis method and an entropy weight method. The final integrated coupling weight results are shown in tables 12 and 13:
TABLE 12 intermediate label final coupling weights based on AHP and entropy weight
TABLE 13 basic tag Final coupling weights based on AHP and entropy weight method
By analyzing the comprehensive weight distribution of the labels in the tables 12 and 13, the occupation ratio of the performance labels is the largest in the high-grade labels, and the difference of the rest labels is smaller but less different in the social labels. The weight of reliability under the performance label is greatest and the outcome transformation risk is second. This shows that in the IES technology evaluation system, whether reliability is important, and in addition, in the energy-saving and environment-friendly label, total energy consumption, energy saving, and sulfur dioxide emission are particularly critical links, which has important reference value for the subsequent green energy IES construction. For other general evaluation tags, such as economic, as a common tag for evaluating the IES evaluation, the economic tag is often one of the tags that the decision maker focuses on first. For social links, enough attention is often lacked, and later green energy IES designers and builders should pay more attention to employment effect and social stability, so that the green energy IES is built into a comprehensive IES which is human-oriented, intelligent, informationized and embodies green and energy power characteristics.
The above are merely embodiments of the present invention, which are described in detail and with particularity, and therefore should not be construed as limiting the scope of the invention. It should be noted that, for those skilled in the art, various changes and modifications can be made without departing from the spirit of the present invention, and these changes and modifications are within the scope of the present invention.
Claims (10)
1. An IES comprehensive benefit evaluation method based on digital portrait is characterized in that:
the method specifically comprises the following steps:
step one, collecting data, cleaning the data and screening important data characteristics;
secondly, establishing a label model based on the important data characteristics, and constructing a label attribute system; the label model includes at least the following high-level labels: energy-saving and environment-friendly labels, performance labels, economic labels and social labels;
and thirdly, performing weight calculation on the label model by adopting an AHP-entropy weight method to form a technical portrait outline, and analyzing the influence of each label model on the operation, development and construction of the IES item by item.
2. The method of claim 1 for IES integrated benefit evaluation based on digital representation, wherein the method comprises: the construction process of the label attribute system comprises the following steps: and formulating a label rule, extracting labels based on the important data features, evaluating and analyzing the labels, and adjusting the label rule and the labels according to results.
3. The method of claim 1 for IES integrated benefit evaluation based on digital representation, wherein the method comprises: and the high-level label is further provided with a corresponding middle-level label and a corresponding primary label, and a label model is established.
4. The method of claim 3 for IES integrated benefit evaluation based on digital representation, wherein the method comprises: the energy-saving and environment-friendly labels at least comprise three energy-saving and environment-friendly medium-grade labels of total energy consumption, pollutant emission and energy saving; the performance labels at least comprise six performance intermediate grade labels of crossing, borrowability, novelty, gap, achievement transformation risk and reliability in the technical field; the economic tags comprise at least two economic mid-grade tags of cost and income; the social labels at least comprise three social intermediate labels of employment effect, social stability and life cycle.
5. The method of claim 3 for IES integrated benefit evaluation based on digital representation, wherein the method comprises: the AHP-entropy weight method comprises the following steps: and calculating the subjective weight of the label model by adopting an AHP method, calculating the objective weight of the label model by adopting an entropy weight method, and coupling the subjective weight and the objective weight to obtain the comprehensive weight of the label model.
6. The method of claim 5 for IES integrated benefit evaluation based on digital representation, wherein the method comprises:
the method for calculating the subjective weight of the label model by adopting the AHP method comprises the following steps:
carrying out relative importance scale judgment on all labels in the label model, and establishing a judgment matrix;
and carrying out consistency check on the judgment matrix.
7. The method of claim 6 for IES integrated benefit evaluation based on digital representation, wherein the method comprises:
the method for carrying out consistency check on the judgment matrix comprises the following steps:
calculating the maximum eigenvalue lambda of the judgment matrix max And consistency ratio c.r:
BW is the sum of the numerical values of each column of the decision matrix; n is the order of the judgment matrix; CI is consistency label; RI is a random consistency label; r is the consistency ratio;
when n =1 or 2, CI is 0, and the judgment matrix is completely consistent; when n is more than or equal to 3, the C.R is continuously solved; if the C.R is less than or equal to 0.1, the judgment matrix meets the consistency requirement, otherwise, the judgment matrix needs to be further adjusted until the judgment matrix passes the consistency test.
8. The method of claim 5 for IES integrated benefit evaluation based on digital representation, wherein the method comprises:
the method for calculating the objective weight of the label model by adopting the entropy weight method comprises the following steps:
and dividing all the labels in the label model into benefit type attribute labels and cost type attribute labels, carrying out non-dimensionalization processing to obtain a normalized decision matrix, and calculating information deviation degree and label objective weight for each label.
9. The method of claim 5 for IES integrated benefit evaluation based on digital representation, wherein the method comprises:
coupling the subjective weight and the objective weight to obtain a comprehensive weight of the label model, wherein the specific method comprises the following steps:
wherein, ω is i Weight, μ, obtained by analytic hierarchy method for ith tag i Weight, α, obtained by entropy weighting for the ith label i 、β i Is an intermediate variable.
10. The method of claim 1 for IES integrated benefit evaluation based on digital representation, wherein the method comprises: the types of data collected include at least: IES state monitoring, environment monitoring, expert evaluation scoring, IES operation data and external data.
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| CN116611744A (en) * | 2023-07-17 | 2023-08-18 | 中国石油大学(华东) | Comprehensive weighting method for comprehensive evaluation of SOFC combined heat and power system |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN115907308A (en) * | 2023-01-09 | 2023-04-04 | 佰聆数据股份有限公司 | User portrait-based electric power material supplier evaluation method and device |
| CN116611744A (en) * | 2023-07-17 | 2023-08-18 | 中国石油大学(华东) | Comprehensive weighting method for comprehensive evaluation of SOFC combined heat and power system |
| CN116611744B (en) * | 2023-07-17 | 2023-10-27 | 中国石油大学(华东) | Comprehensive weighting method for comprehensive evaluation of SOFC combined heat and power system |
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