Building power grid-friendly multi-dimensional evaluation index evaluation method, equipment and medium

Abstract

The invention discloses a building power grid friendly multi-dimensional evaluation index evaluation method and device and a medium, and the method comprises the steps: constructing a multi-dimensional evaluation index system based on a green building evaluation standard, carrying out the expert scoring, constructing a judgment matrix, carrying out the dual validity detection through employing the individual consistency detection and group consistency detection, and obtaining an effective judgment matrix, based on statistical characteristics of a weight sample set of the effective judgment matrix, performing inversion calculation on the basic weight and the divergence degree of the indexes in the effective judgment matrix, correcting the weight model, and based on the corrected weight, performing comprehensive scoring on the multi-dimensional evaluation index system by adopting coupling coordination. By constructing a three-in-one multi-dimensional evaluation index system and adopting dual validity inspection of individual consistency inspection and group consistency inspection, the problem of evaluation system distortion caused by extreme deviation of individual experts is effectively avoided, the weight is corrected through the basic weight and the divergence degree, fine tuning is performed, and the evaluation accuracy is improved. And the evaluation precision of index scoring is improved.

Description

Technical Field

[0001] This invention relates to the field of building management and evaluation technology, specifically to a method, equipment, and medium for evaluating multi-dimensional evaluation indicators that are grid-friendly for buildings. Background Technology

[0002] With the development of new power systems, buildings are transforming from simple energy consumers into integrated producers and consumers, encompassing energy generation, load, and storage. However, existing building assessment technologies suffer from the following technical challenges:

[0003] The lagging nature of the evaluation index system: Existing green building or energy-saving building evaluation standards (such as LEED and GB / T50378) mainly focus on the thermal performance and static energy-saving indicators of the building itself, and lack quantitative evaluation indicators for the two-way interaction between the building and the power grid (such as peak shaving and valley filling capabilities, flexible load adjustment potential, and demand response execution accuracy).

[0004] Limitations of weighting methods: Traditional analytic hierarchy process (AHP) or Delphi method often uses arithmetic or geometric mean when processing expert opinion fusion. This method masks the dispersion of expert opinions. Even if the consistency test is passed, experts' confidence levels for different indicators still vary. Traditional methods cannot identify and handle this "cognitive risk," resulting in insufficient robustness of the evaluation model.

[0005] The masking effect of comprehensive evaluation models: Traditional linear weighted summation scoring methods are prone to masking shortcomings. For example, a building may have extremely high low-carbon attributes, but if its cost is extremely high or its grid interaction capability is extremely poor, its total score may still be very high under traditional linear summation. Such evaluation results cannot truly reflect the sustainability and coordination in actual engineering projects. Summary of the Invention

[0006] The technical problem this invention aims to solve is the lack of a single evaluation index system, the dispersion of expert evaluation opinions, and insufficient evaluation accuracy. The goal is to provide a building power grid-friendly multi-dimensional evaluation index method, equipment, and medium. By constructing a three-in-one multi-dimensional evaluation index system and employing dual validity checks of individual consistency and group consistency, the invention effectively avoids the problem of evaluation system distortion caused by extreme biases of individual experts. Through inversion calculation, the invention effectively judges the cloud model digital characteristics of the indicators in the matrix, performs automatic algorithm correction (cloud entropy penalty) for fine-tuning, handles micro-level cognitive differences, and improves the evaluation accuracy of indicator scores.

[0007] This invention is achieved through the following technical solution:

[0008] The first aspect of this invention provides a multi-dimensional evaluation method for building grid-friendly performance indicators, comprising the following specific steps:

[0009] Construct a multi-dimensional evaluation index system based on green building evaluation standards;

[0010] Experts score the multidimensional evaluation indicators and construct a judgment matrix;

[0011] The judgment matrix is ​​subjected to a dual validity test using individual consistency test and group consistency test to obtain a valid judgment matrix;

[0012] Construct a weight model for an effective judgment matrix to obtain a weight sample set;

[0013] Based on the statistical characteristics of the weighted sample set, the cloud model digital features of the j-th indicator in the effective judgment matrix are inverted and calculated. The cloud model digital features include basic weights and divergence degree.

[0014] The weight model is modified based on the basic weights and the degree of divergence.

[0015] Based on the revised weights, a coupled coordination method is used to comprehensively score the multidimensional evaluation index system.

[0016] Furthermore, the multidimensional evaluation index system includes:

[0017] From a low-carbon perspective, the carbon intensity during operation, the carbon intensity throughout the entire life cycle, the proportion of renewable energy, the self-consumption rate of renewable energy, and the total carbon reduction benefits of demand response.

[0018] From a cost perspective, this includes total life cycle cost, annual comprehensive energy intensity, dynamic cost payback period, static cost payback period, static energy saving rate, and total demand response revenue.

[0019] Grid-friendly dimensions include electrification rate, annual average peak load reduction ratio, response speed, response availability, equipment controllability coverage, effective demand response capacity ratio, overall efficiency of energy storage systems, and total energy-saving benefits of demand response.

[0020] Furthermore, the individual consistency test specifically includes:

[0021] Obtain the n×n judgment matrix given by a single expert;

[0022] Calculate the largest eigenvalue of the judgment matrix;

[0023] Calculate the consistency index (CI);

[0024] Based on the matrix order n, retrieve the pre-stored random consistency index, and combine it with the consistency index CI to calculate the consistency ratio;

[0025] When CR is less than a set threshold, the expert judgment matrix is ​​deemed to have passed the consistency check.

[0026] Furthermore, the group consistency test specifically includes:

[0027] Each expert ranks the indicators at the same level, forming a ranking matrix;

[0028] Calculate the overall ranking and average ranking for each indicator;

[0029] Based on the overall and average rankings of each indicator, calculate the Kendall synergy coefficient of the expert group for ranking indicators at the same level.

[0030] A significance test was performed on the Kendall synergy coefficient to obtain the corresponding significance probability;

[0031] If both Kendall's coefficient of synergy and the significance probability are within the set thresholds, then the expert panel's opinion is considered to have statistical consistency.

[0032] Furthermore, a weight model for an effective judgment matrix is ​​constructed, specifically including:

[0033] The eigenvector method is used to calculate m sets of individual weight vectors for m effective judgment matrices;

[0034] For the j-th indicator, the weight sample set is extracted as the weight model.

[0035] Furthermore, the inversion calculation of the cloud model digital features of the j-th index specifically includes:

[0036] The expected value is calculated as the basic weight of the j-th indicator, which is used to characterize the average perception of the importance of the indicator by the expert group.

[0037] The entropy is calculated as the degree of divergence of the j-th indicator, which is used to characterize the degree of dispersion and uncertainty of the expert group's cognition.

[0038] Furthermore, the modification of the weight model specifically includes:

[0039] Obtain the average value of cloud entropy for all indicators at the current level, and calculate the normalized dispersion based on the degree of divergence;

[0040] The confidence coefficient is defined based on the normalized dispersion.

[0041] Based on the basic weight and corresponding confidence coefficient of the j-th indicator, the basic weight is multiplied by the quality coefficient to obtain the credibility importance score of the j-th indicator.

[0042] The total credibility importance score is obtained by summing the credibility importance scores of all j-th indicators.

[0043] Divide the credibility importance score of each indicator by the total credibility importance score to complete the normalization, and output the corrected weight of the j-th indicator.

[0044] Furthermore, the comprehensive scoring of the multi-dimensional evaluation index system based on the modified weights and using coupling coordination specifically includes:

[0045] Based on the corrected weights, dimensional scores are calculated for the criteria layer corresponding to the multidimensional evaluation index system.

[0046] The strength of interaction and comprehensive development index among multiple criterion layers are quantified based on dimensional scores.

[0047] The coupling coordination degree is obtained as a comprehensive score based on the interaction strength and comprehensive development index between multiple criterion layers.

[0048] A second aspect of the present invention provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement a building grid-friendly multidimensional evaluation index assessment method.

[0049] A third aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements a building grid-friendly multidimensional evaluation index assessment method.

[0050] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0051] This invention extracts the entropy value of expert scoring weights by using a reverse cloud generator and combines it with a Gaussian penalty function to establish a weight adaptive correction mechanism based on cognitive divergence. Unlike the simple averaging of traditional AHP, this mechanism can automatically identify and reduce the weights of indicators with high expert controversy and uncertain technical maturity (noise reduction), while retaining the weights of indicators with high consensus (fidelity preservation). This effectively avoids the problem of the evaluation system being distorted due to the extreme bias of individual experts.

[0052] This invention introduces a coupling coordination degree model from physics, which differs from the traditional linear weighted summation. This model not only focuses on the absolute scores of each indicator, but also on the balance of development among the three subsystems of low carbon, cost, and grid friendliness. If a building has a serious imbalance (e.g., although it is extremely low carbon, its cost is extremely high), its coupling degree will decrease significantly, thereby lowering the final score. This effectively guides building technology solutions towards the synergistic optimization of low carbon, cost, and grid friendliness.

[0053] This invention constructs a complete quality control closed loop from the data source to the algorithm terminal: the front end uses Delphi's multi-round iterative feedback (CR / Kendall W test) to clean the data and remove obvious logical errors, and the back end uses the automatic correction of the cloud model algorithm (cloud entropy penalty) to fine-tune and handle micro-cognitive differences. This combination of subjective and objective methods significantly improves the scientific nature of the evaluation results.

[0054] The index system proposed in this invention systematically quantifies the grid-friendly attributes of buildings for the first time. In particular, it introduces key indicators such as the proportion of annual average peak load reduction and the proportion of effective demand response capacity, providing clear calculation standards and methodological support for the assessment of building flexibility resources under the new power system. Attached Figure Description

[0055] To more clearly illustrate the technical solutions of the exemplary embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly described below. It should be understood that the following drawings only show some embodiments of the present invention and should not be considered as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort. In the drawings:

[0056] Figure 1 This is a flowchart of the evaluation method in an embodiment of the present invention. Detailed Implementation

[0057] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the embodiments and accompanying drawings. The illustrative embodiments and descriptions of this invention are only for explaining this invention and are not intended to limit this invention.

[0058] As one possible implementation method, such as Figure 1As shown, this embodiment provides a multi-dimensional evaluation method for building grid-friendly assessment indicators. Based on current green building evaluation standards, it decomposes and constructs a multi-level evaluation indicator system from multiple dimensions, inviting experts in the field of green building. The relative importance of indicators at the same level is compared pairwise and scored to form an initial judgment matrix. First, the individual expert scores are checked for consistency, eliminating logically contradictory scores. Then, all valid individual matrices are integrated to conduct a group consistency check, ensuring overall coordination of expert opinions. If the check fails, feedback is provided to the experts for adjustment. Based on the valid judgment matrix, a basic weight model is built using methods such as the analytic hierarchy process (AHP). The weights are then adjusted based on actual project scenarios and data availability to improve their rationality and applicability. A coupling and coordination model is introduced, combining the weights of each indicator with actual monitoring data to calculate the coupling and coordination degree between indicators, ultimately yielding a comprehensive green building evaluation score and achieving collaborative evaluation of multi-dimensional indicators. By constructing a three-in-one multidimensional evaluation index system and adopting dual validity tests of individual consistency test and group consistency test, the problem of evaluation system distortion caused by extreme bias of individual experts is effectively avoided. By inverting calculation to effectively judge the cloud model digital characteristics of the indicators in the matrix, the algorithm is automatically corrected (cloud entropy penalty) for fine-tuning, micro-cognitive differences are handled, and the evaluation accuracy of indicator scores is improved.

[0059] The specific implementation process of this embodiment includes:

[0060] Step S1: Construct a multi-level, three-in-one, multi-dimensional evaluation index system based on green building evaluation standards;

[0061] This embodiment aims to construct a scientific and standardized evaluation system to comprehensively assess a building's performance across three core dimensions: low carbon, cost, and grid friendliness. In the context of sustainable building development, low carbon, cost, and grid friendliness are three closely related yet significantly different core dimensions. Low carbon: This primarily refers to how buildings minimize their total carbon emissions during construction and operation through efficient building envelopes, energy-saving equipment, renewable energy utilization, and low-carbon building materials. Cost: This refers not only to the building's life-cycle cost-effectiveness but, more importantly, to the incremental cost value achievable through optimized energy consumption behavior, including reducing owners' electricity bills and minimizing grid investment and operating costs. Grid friendliness: This emphasizes the interaction between the building and the power system. It focuses on the building's electricity consumption behavior at specific times (such as peak electricity demand or periods of high renewable energy generation), responding to grid signals and alleviating grid pressure through strategies such as reducing peak demand, load shifting, and smart energy storage.

[0062] S11. Establish a hierarchical model comprising a target layer, a criterion layer, and an indicator layer. The criterion layer is divided into three core dimensions: low-carbon dimension (A), cost dimension (B), and grid-friendly dimension (C). The indicator layer contains 19 specific quantitative indicators.

[0063] Low-carbon dimensions include:

[0064] Operational carbon intensity, the annual carbon emissions per unit area during the building's operational phase;

[0065] Life cycle carbon intensity, which covers carbon emissions per unit area throughout the entire process of building materials, construction, operation, and demolition;

[0066] The proportion of renewable energy in end-use energy (including self-produced and purchased green electricity);

[0067] Renewable self-consumption rate: The proportion of local renewable energy directly consumed by buildings (excluding grid connection).

[0068] Total carbon reduction benefit of demand response: the total reduction in carbon emissions compared to baseline load during the period of participation in a demand response event;

[0069] Cost dimensions include:

[0070] Life cycle cost, including the net present value of CAPEX, OPEX, and maintenance and dismantling costs;

[0071] Annual comprehensive energy intensity, total annual energy expenditure per unit area;

[0072] Dynamic cost recovery period, which takes into account the time value of money, is the period for incremental investment to recover.

[0073] Static cost payback period, which is the payback period for incremental investment without considering the time value of money;

[0074] Static energy saving rate is the percentage reduction in annual energy consumption relative to a benchmark building.

[0075] Total demand response revenue, including subsidies, electricity savings, and other benefits obtained from participating in demand response events;

[0076] Grid friendliness dimensions include:

[0077] Electrification rate, the proportion of electricity consumption in end-use energy;

[0078] The percentage of peak load reduction per year, a core indicator, is the proportion of the total load reduction during peak hours on all response days within the assessment year to the total electricity consumption on that day.

[0079] Response speed, the time required from receiving an instruction to adjusting to 90% of the target load;

[0080] Response availability is the percentage of requests that were successfully executed out of the total number of requests.

[0081] Equipment controllability coverage, the proportion of flexible load capacity that can be automatically adjusted by the BEMS system;

[0082] Effective demand response capacity ratio, a core indicator, is the ratio of the sum of effective capacity whose actual response power falls within the range of 90%-110% of the target power to the declared capacity.

[0083] Overall efficiency of energy storage system: the ratio of total discharge to total charge of energy storage system (including converter losses).

[0084] Total energy saving benefit of demand response is the reduction in average daily energy consumption on the response date compared to the baseline date (positive values ​​represent savings).

[0085] This step is used to remove invalid data that is logically flawed or has extremely poor group consistency;

[0086] S12. The evaluation index system constructed in step S11 covers a total of 19 specific indicators across three dimensions: low carbon, cost, and grid friendliness. These indicators differ in nature and measurement methods, leading to challenges related to data heterogeneity.

[0087] First, the physical dimensions of the indicators are different. For example, the unit of carbon intensity over the entire life cycle is 1600 kJ / m². However, the static payback period is measured in years, making a direct comparison between the two impossible. Secondly, the scales (dimensions) of the indicators differ significantly. For example, the value of total life-cycle cost (yuan / m²) may be much larger than the value of electrification rate (%).

[0088] More importantly, the indicators differ in nature. The indicator system includes both benefit-oriented indicators (or positive indicators) and cost-oriented indicators (or negative indicators). The higher the value of a benefit-oriented indicator, the better its performance, such as the proportion of renewable energy and response availability; the lower the value of a cost-oriented indicator, the better, such as total life cycle cost and response speed.

[0089] This heterogeneity in dimensions, scale, and properties makes it impossible to directly compare the raw data of different indicators, and also makes it impossible to perform subsequent weighted summaries to calculate the comprehensive score.

[0090] Therefore, before conducting a comprehensive evaluation, the raw data of all specific indicators must be standardized. The core purpose of standardization is to: eliminate the influence of physical dimensions between different indicators to make them comparable; unify the values ​​of all indicators to the same dimensionless scale (usually the [0,1] interval) to achieve scale homogenization; and unify the properties of all indicators, that is, to transform all indicators (including cost-type indicators) into benefit-type indicators to achieve property homogenization and ensure that larger values ​​represent better performance.

[0091] Through standardization, all indicator data are homogeneous and additive, which is a scientific prerequisite for subsequent weight determination and comprehensive evaluation.

[0092] Step S2: Data acquisition and double validity test based on the Delphi method;

[0093] Determining the weight coefficients of the indicators is a core step in constructing a comprehensive evaluation system, and its results directly affect the scientific validity, rationality, and application orientation of the evaluation system. Due to the limited research on "grid-friendly low-carbon buildings" and the lack of substantial measured data from completed projects to support objective weighting, this study adopts a subjective weighting method. Specifically, this study uses the Analytic Hierarchy Process (AHP) as its basic framework and combines iterative ideas from the Delphi Method to determine the weights of the criterion and indicator layers.

[0094] S21. Expert scoring: Invite m experts (m-20 in this example) to construct a judgment matrix based on the Saaty1-9 scaling method.

[0095] S22. Consistency Ratio (CR): For each expert's judgment matrix, calculate the largest eigenvalue. For an n-order positively reciprocal matrix, the condition is met if and only if they are completely identical. The greater the deviation of the matrix, Also bigger. It can be solved using the power method or the characteristic equation.

[0096] Calculate the Consistency Index (CI). CI measures the deviation of the judgment matrix from perfect consistency (at which point...). The degree of judgment. n is the order of the judgment matrix:

[0097] ;

[0098] Find the average random consistency index . The values ​​were obtained by Saaty through a large number of random experiments. They are fixed values ​​that depend on the order n of the matrix, as shown in Table 1.

[0099]

[0100] Calculate the consistency ratio (CR):

[0101] ;

[0102] A judgment is made. When CR < 0.1, the expert's judgment matrix is ​​considered to have acceptable consistency and passes the test; otherwise, the matrix is ​​considered to have too many logical contradictions and the expert needs to re-examine and revise its scores.

[0103] This study examined all 80 judgment matrices submitted by 20 experts (4 matrices from 20 experts). As described in (1), matrices that failed the initial test were iteratively corrected. Ultimately, all matrices passed the standard, and the test results are summarized in Tables 2 to 5.

[0104]

[0105]

[0106]

[0107]

[0108] The test results of the four matrices in Tables 2 to 5 clearly show that all 80 judgment matrices exhibit good consistency. Specifically, in the criterion level (n=3, RI=0.58) and the cost dimension (n=6, RI=1.24), the CR values ​​of all experts are much less than 0.10, showing a high degree of consistency. In the higher-order low-carbon dimension (n=5, RI=1.12) and grid-friendly dimension (n=8, RI=1.41), all CR values ​​are also controlled within the acceptable range of 0.10, with the maximum values ​​being 0.0921 (Expert 3) and 0.08 (Expert 11), respectively.

[0109] In summary, all 80 judgment matrices submitted by the 20 experts passed the consistency test criterion of CR < 0.10. This indicates that all experts' scores are logically consistent, their judgments are reasonable, and there are no obvious logical contradictions. This provides a high-quality and reliable data foundation for subsequent expert opinion fusion and weight calculation.

[0110] S23. The Kendall's W test, after ensuring the logical consistency of each expert's internal judgment, further tests are needed to examine the consensus of the expert group's opinions. If the test assesses whether "individual experts contradict themselves," then the inter-expert consensus test assesses whether "the opinions of all experts converge." This study uses Kendall's W for this test. This method determines the degree of consensus among the expert group's opinions by analyzing the correlation of group ranking data generated by 20 experts ranking various indicators.

[0111] Calculate the synergy coefficient W for the expert group's ranking of indicators at the same level:

[0112] The calculation steps for Kendall's W synergy coefficient are as follows: First, extract the corresponding weight vector from the judgment matrix of each expert who passed the CR test, and then sort the n indicators according to the weight, obtaining an m×n sorting matrix, where the elements... Indicates the first The expert commented on the first The ranking is given by each indicator. Next, each indicator is calculated. The obtained sorted sum :

[0113] ;

[0114] Next, calculate the total sum of the sorted orders. mean :

[0115] ;

[0116] Then, calculate the sum of squared deviations S of the sorted sums:

[0117] ;

[0118] Finally, the W synergy coefficient is calculated, with a value ranging from [0,1]. The closer the value is to 1, the higher the consensus among experts.

[0119] ;

[0120] Final judgment: If W>0.7 and the significance P<0.05, the expert group's opinion is considered to have statistical consistency; otherwise, the next round of Delphi iteration is initiated.

[0121] In this embodiment, the weighted ranking results of 20 experts were subjected to Kendall's W test, and the results are shown in Table 6.

[0122]

[0123] The test results in Table 6 clearly demonstrate the high degree of synergy among the expert groups. Firstly, regarding the significance level, all four matrices show... The significance (p-value) of all tests was less than 0.001. This value is far below the significance threshold of 0.05, indicating that the expert group's review results have high statistical significance, and the consensus they reached is not a random occurrence but a statistically significant convergence. Secondly, in terms of the degree of consistency, the Kendall W coefficients for all matrices performed excellently: 0.782 for the criterion layer, 0.772 for the low-carbon dimension, 0.767 for the cost dimension, and even higher at 0.787 for the grid-friendly dimension. These coefficients are all significantly greater than 0.7, which is generally considered to represent a level of "strong consistency" in statistics. This indicates that the 20 experts have a high degree of consensus on the relative importance of each indicator. In summary, the p-value and W coefficient results together demonstrate the excellent reliability of the expert group's opinions in this study, showing that despite some differences in the experts' backgrounds, they shared a high degree of consistency in their core understanding of the evaluation system, and the collected data is of extremely high quality, laying a solid foundation for subsequent data fusion to determine the final weights.

[0124] Step S3: Construct and refine the weight model of the effective judgment matrix.

[0125] Unlike traditional AHP which directly performs geometric averaging on the matrix, this step proposes a post-aggregation algorithm that first computes the weight vector in parallel and then applies confidence penalty based on cloud entropy.

[0126] S31. Parallel computation of individual weight vectors

[0127] For the m valid judgment matrices obtained in step S2 The eigenvector method is used to solve for each set of individual weight vectors, resulting in m sets of individual weight vectors.

[0128] For the j-th indicator, extract its weight sample set:

[0129] ;

[0130] S32, Cloud Model Parameter Extraction (Reverse Cloud Generator)

[0131] Using sample sets The statistical characteristics of the indicator are used to inversely calculate the cloud model digital characteristics of the indicator. .

[0132] Calculate expectation (Basic Weight): Represents the average perception of the importance of this indicator by the expert group.

[0133] ;

[0134] Calculate entropy (Disagreement): Characterizes the degree of dispersion and uncertainty in the expert group's perception.

[0135] ;

[0136] S33. Construct an adaptive weight correction model based on Gaussian kernels.

[0137] To modify the weighting model by adjusting the degree of disagreement, and to prevent excessive reduction of indicator weights due to large disagreements among individual experts, we construct a Gaussian penalty function with a sensitivity adjustment factor to reasonably adjust the importance of indicators.

[0138] S331. Calculate the normalized dispersion. :

[0139] ;

[0140] in This is the average cloud entropy of all indicators at this level.

[0141] S332. Calculate the confidence coefficient (penalty factor). :

[0142] ;

[0143] Parameter description: This is the sensitivity adjustment constant. The larger the entity, the more lenient the punishment; The smaller the value, the more severe the punishment. This embodiment is preferred. The aim is to reduce the weight of indicators whose divergence is significantly higher than the average level.

[0144] S333, Final Weight Composition and Normalization:

[0145] .

[0146] Table 7 shows the final weights of each indicator.

[0147]

[0148] Step S4: Perform a comprehensive scoring of the multi-dimensional evaluation index system based on the coupling coordination degree model.

[0149] S41. Dimensional Score Calculation: Combining with the final weights Calculate the scores for each of the three criterion layers;

[0150] ;

[0151] ;

[0152] ;

[0153] S42. Calculate the coupling degree C: quantify the interaction strength between the three systems.

[0154] ;

[0155] S43, Calculate the Comprehensive Development Index :

[0156] ;

[0157] S44. Calculate the coupling coordination degree :

[0158] ;

[0159] S45. Output Results: Output the overall score. As a basis for evaluation.

[0160] As one possible implementation, this embodiment provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements a building grid-friendly multi-dimensional evaluation index assessment method.

[0161] As one possible implementation, this embodiment provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements a building grid-friendly multidimensional evaluation index assessment method.

[0162] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A multi-dimensional evaluation method for building grid-friendly assessment, characterized in that, The specific steps include the following: Construct a multi-dimensional evaluation index system based on green building evaluation standards; Experts score the multidimensional evaluation indicators and construct a judgment matrix; The judgment matrix is ​​subjected to a dual validity test using individual consistency test and group consistency test to obtain a valid judgment matrix; Construct a weight model for an effective judgment matrix to obtain a weight sample set; Based on the statistical characteristics of the weighted sample set, the cloud model digital features of the j-th indicator in the effective judgment matrix are inverted and calculated. The cloud model digital features include basic weights and divergence degree. The weight model is modified based on the basic weights and the degree of divergence. Based on the revised weights, a coupled coordination method is used to comprehensively score the multidimensional evaluation index system.

2. The multi-dimensional evaluation index assessment method for building grid-friendly systems according to claim 1, characterized in that, The multidimensional evaluation index system includes: From a low-carbon perspective, the carbon intensity during operation, the carbon intensity throughout the entire life cycle, the proportion of renewable energy, the self-consumption rate of renewable energy, and the total carbon reduction benefits of demand response. From a cost perspective, this includes total life cycle cost, annual comprehensive energy intensity, dynamic cost payback period, static cost payback period, static energy saving rate, and total demand response revenue. Grid-friendly dimensions include electrification rate, annual average peak load reduction ratio, response speed, response availability, equipment controllability coverage, effective demand response capacity ratio, overall efficiency of energy storage systems, and total energy-saving benefits of demand response.

3. The multi-dimensional evaluation index assessment method for building grid-friendly architecture according to claim 1, characterized in that, The individual consistency test specifically includes: Obtain the n×n judgment matrix given by a single expert; Calculate the largest eigenvalue of the judgment matrix; Calculate the consistency index (CI); Based on the matrix order n, retrieve the pre-stored random consistency index, and combine it with the consistency index CI to calculate the consistency ratio; When CR is less than the set threshold, the expert judgment matrix is ​​deemed to have passed the consistency test.

4. The multi-dimensional evaluation index assessment method for building grid-friendly architecture according to claim 1, characterized in that, The group consistency test specifically includes: Each expert ranks the indicators at the same level, forming a ranking matrix; Calculate the overall ranking and average ranking for each indicator; Based on the overall and average rankings of each indicator, calculate the Kendall synergy coefficient of the expert group for ranking indicators at the same level. A significance test was performed on the Kendall synergy coefficient to obtain the corresponding significance probability; If both Kendall's coefficient of synergy and the significance probability are within the set thresholds, then the expert panel's opinion is considered to have statistical consistency.

5. The multi-dimensional evaluation index assessment method for building grid-friendly architecture according to claim 1, characterized in that, Constructing a weight model for an effective judgment matrix specifically includes: The eigenvector method is used to calculate m sets of individual weight vectors for m effective judgment matrices; For the j-th indicator, the weight sample set is extracted as the weight model.

6. The multi-dimensional evaluation index assessment method for building grid-friendly architecture according to claim 5, characterized in that, The inversion calculation of the cloud model digital features of the j-th index specifically includes: The expected value is calculated as the basic weight of the j-th indicator, which is used to characterize the average perception of the importance of the indicator by the expert group. The entropy is calculated as the degree of divergence of the j-th indicator, which is used to characterize the degree of dispersion and uncertainty of the expert group's cognition.

7. The multi-dimensional evaluation index assessment method for building grid-friendly architecture according to claim 6, characterized in that, The modification of the weight model specifically includes: Obtain the average value of cloud entropy for all indicators at the current level, and calculate the normalized dispersion based on the degree of divergence; The confidence coefficient is defined based on the normalized dispersion. Based on the basic weight and corresponding confidence coefficient of the j-th indicator, the basic weight is multiplied by the quality coefficient to obtain the credibility importance score of the j-th indicator. The total credibility importance score is obtained by summing the credibility importance scores of all j-th indicators. Divide the credibility importance score of each indicator by the total credibility importance score to complete the normalization, and output the corrected weight of the j-th indicator.

8. The multi-dimensional evaluation index assessment method for building grid-friendly systems according to claim 1, characterized in that, The method of comprehensively scoring the multidimensional evaluation index system based on the modified weights and using coupling coordination specifically includes: Based on the corrected weights, dimensional scores are calculated for the criteria layer corresponding to the multidimensional evaluation index system. The strength of interaction and comprehensive development index among multiple criterion layers are quantified based on dimensional scores. The coupling coordination degree is obtained as a comprehensive score based on the interaction strength and comprehensive development index between multiple criterion layers.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the multi-dimensional evaluation index assessment method for building grid-friendly systems as described in any one of claims 1 to 8.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by the processor, the program implements the building grid-friendly multidimensional evaluation index assessment method as described in any one of claims 1 to 8.

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