Target-driven decision-making layer evaluation and decision-making method for optimizing building reconstruction

Abstract

The invention relates to the technical field of building reconstruction scheme evaluation, in particular to a target-driven decision-making level evaluation method for optimizing building reconstruction, and provides an optimized building reconstruction decision-making method based on investor types. The objective-driven decision-making level evaluation method for optimizing building reconstruction comprises the steps of model framework establishment, prototype building group data summarization and prototype design, evaluation of each energy-saving reconstruction measure, evaluation of an influence strategy of combined reconstruction of each reconstruction measure on energy, environment and economy, and the like. The optimized building reconstruction decision-making method based on the investor type is realized through a target-driven decision-making layer evaluation method for optimizing building reconstruction. The method provided by the invention performs comprehensive energy, environment and economic evaluation on building reconstruction on a macroscopic scale. The importance of a model framework of a simple evaluation method is emphasized, and the method integrates data mining analysis and hierarchical clustering (AHC) into a multi-target decision process, so that a building stakeholder can select the most appropriate technical transformation scheme on the macroscale.

Description

Technical Field

[0001] The present invention relates to the technical field of building renovation scheme evaluation, and in particular to a target-driven decision-making layer evaluation method for optimizing building renovation and an investor-type-based decision-making method for optimizing building renovation. Background Art

[0002] As an important means to achieve the core concept of a sustainable society, building renovation faces a variety of implementation challenges, including constraints from climate conditions, building types, regulations and policies. To address these issues, single-objective or multi-objective optimization methods are usually used, with the latter being more suitable for formulating ideal renovation strategies. In multi-objective optimization, it has become a general trend to comprehensively consider the variables of 3E (energy, environment and economy), which helps to improve the interpretability, applicability and comparability of the results.

[0003] Common multi-objective optimization algorithms seek the best combination of decision variables by optimizing a series of objective functions simultaneously. These variables include but are not limited to electricity consumption, CO2 emissions, cost indicators (such as investment, energy, life cycle cost or payback period). Objective functions usually aim to minimize life cycle cost (LCC), maximize energy saving, renewable energy adaptability and energy saving compatibility, and minimize CO2 and other greenhouse gas (GHG) emissions. Although there are many algorithms to provide guidance for building renovation, it is still a challenge for specific building projects to achieve the best combination of renovation solutions at the lowest cost.

[0004] Specific building types are often complex and varied, and are limited by the available budget to achieve provincial energy performance targets. Therefore, the ideal retrofit solution may require a combination of low-cost and high-cost measures to maximize long-term energy benefits while controlling the budget. Life cycle costing provides stakeholders with a more realistic long-term evaluation tool to determine the best retrofit solution for a given budget.

[0005] Generally speaking, the best renovation plan should pursue the minimum energy consumption and environmental impact under the premise of economic feasibility. However, most studies focus on the optimization of specific buildings (micro level) rather than the entire building complex (meso or macro level). Considering that energy efficiency policies mainly focus on macro interventions, recent research focus has shifted to establishing an assessment model framework to evaluate the renovation potential of existing buildings at the macro scale. For example, Lotteau et al. pointed out that the adaptive method of environmental assessment is a common means of neighborhood-scale assessment of the built environment. Mastrucci et al. also emphasized the importance of energy and environmental assessment models in macro-assessment. In order to enhance the interpretability and comparability of the results, in addition to energy and environmental indicators, economic indicators should also be included in the macro-assessment.

[0006] Nevertheless, due to the complexity and uncertainty of applying multi-objective optimization methods on a large scale, there are relatively few studies on optimal energy transformation using three or more objective functions at the macro level. To fill this research gap, the method of the present invention is proposed. Summary of the invention

[0007] The purpose of the present invention is to overcome the shortcomings of the prior art, adapt to actual needs, and provide a target-driven decision-making layer evaluation method for optimizing building renovation and an optimized building renovation decision-making method based on investor type to solve the technical problems raised in the above background technology.

[0008] In order to achieve the purpose of the present invention, the technical solution adopted by the present invention is:

[0009] A goal-driven decision-level evaluation method for optimizing building renovation is disclosed, the method comprising:

[0010] S1. Establishment of a model framework. The model framework is established under the assumption that there is a lack of sufficient urban building model data in developing societies to support the aggregation of building stock at the building level. The model framework includes three mutually coordinated model layers: building stock aggregation model, individualized 3E model, and life cycle cost-environmental assessment model.

[0011] S2, prototype building complex data aggregation and prototype design;

[0012] S3. Evaluate individualized transformation measures;

[0013] S4. Evaluate the impact strategy of each transformation measure on energy, environment and economy. This step adopts a digital method and uses the 3E (energy, environment and economy) results of separate pre-simulation to conduct LCC (minimize life cycle cost) evaluation. It includes nine steps.

[0014] The building stock summary model described is used to describe the existing buildings under investigation, including a feature-based building energy efficiency assessment to develop building prototypes;

[0015] The individualized 3E model performs individualized 3E (energy, environment and economy) simulations on the selected renovation measures on the prototype building, performs sensitivity analysis by selecting decision variables, and the selected decision variables include power consumption, carbon dioxide emissions, and cost indicators (investment and operating costs); and uses the results of the individualized 3E (energy, environment and economy) model as input data for the LCC (minimization of life cycle costs) environmental assessment model;

[0016] The life cycle cost-environmental assessment model includes an LCC (minimize life cycle cost) environmental assessment model, and the LCC (minimize life cycle cost) environmental assessment model is obtained by combining the LCC (minimize life cycle cost) method with the environmental assessment model.

[0017] In step S2, the prototype building complex data aggregation and prototype design include the following steps:

[0018] S21. Combining the empirical database generated by a large-scale building sample survey with top-down macroeconomic and statistical tools, define a performance indicator system for building energy efficiency through Pearson and Bissell correlation analysis;

[0019] S22, use cluster analysis to refine the index;

[0020] S23, using the Euclidean distance square method and Z value normalization method for similarity measurement and data normalization;

[0021] S24. Classify and design prototype buildings, each prototype design represents a specific type of building.

[0022] In step S3, the individualized transformation measures are evaluated, including the following steps:

[0023] S31. Use building performance modeling software to evaluate the 3E impact of each retrofit measure on the prototype building;

[0024] S32. Ensure the evaluation of the interactions between renovation measures through BIM platform support software;

[0025] S33. Compare the simulation results of each prototype building with its original model to evaluate the respective 3E (energy, environment and economy) impact of each retrofit measure;

[0026] Step S4 includes nine steps:

[0027] Step 1: Calculate the integrated circuit of each combined transformation strategy, that is, the sum of the integrated circuits of all transformation measures;

[0028] Step 2: Calculate the annual energy cost - EC for each combination of retrofit strategies using equation (1) to assess the impact on energy:

[0029] EC = ET × EP (1);

[0030] In formula (1), ET is the annual energy consumption and EP is the unit electricity price;

[0031] Step 3: Calculate the total LCC (minimum life cycle cost) for each combination of transformation strategies;

[0032] Step 4: Classify the transformation development level DL using IC. In this step, IC values ​​are sorted in ascending order and used as the basis for DL ​​rating, ranging from 0% to 100%, with 0% for DL ​​without transformation activities and 100% for DL ​​including all transformation activity strategies;

[0033] Step 5: Reduce the number of data points by using the 8-point partition average method to reduce computational complexity; plot the DL, IC, EC, and total LCC values ​​as new data points on the graph;

[0034] Step 6: Generate R2 correlation coefficients for IC, EC, and total LCC curves based on DL;

[0035] Step 7: Use environmental assessment models to determine the best renovation options that minimize life cycle costs;

[0036] Step 8: Use the generated profiles to conduct building performance tests and determine the thresholds required to achieve the established performance targets; estimate the required changes by calculating the reduction in the selected variables required to achieve the established targets, and all renovation options with IC values ​​exceeding this threshold are considered suitable to achieve the established targets;

[0037] Step 9: Adopt AHC (Adaptive Hierarchical Clustering) data mining technology to determine the center point strategy representing all possible transformation solutions outside the threshold.

[0038] Step 9 specifically includes the following steps:

[0039] S91: Data preprocessing, use z value to normalize the summary or simulated data, set the mean (μi) of each specific i-th group of data (xi) to zero, and the standard deviation (σi) to 1. The calculation formula of Z-score (Zxi) is:

[0040]

[0041] S92: Cluster analysis, including:

[0042] S921: Using the AHC (Anatomically Hierarchical Clustering) algorithm, each data is initialized as a cluster. As the algorithm proceeds, the clusters are further merged in pairs. The goal of this algorithm is to minimize the square of the Euclidean distance d between two data sets x and y. The Ward's criterion is used to minimize the square of the Euclidean distance d:

[0043]

[0044] S922: Define the k value of potential clusters and identify possible outliers;

[0045] S923: Determine the number of clusters (C = {C1, C2, ..., Ck|k≤N}) and the relevant k values ​​(x, x12, ..., xn) of N groups of data;

[0046] S923: Use the k-means algorithm to divide around the center point. The goal of the k-means algorithm is to minimize the variance within the cluster:

[0047]

[0048] In formula (4), μi is the average value / center point of the data points in Ci.

[0049] In this algorithm, random centroids (μ(1)1,μ(1)2,...,μ(1)k) are started and each data point is assigned to the cluster with the closest centroid by minimizing the squared Euclidean distance:

[0050]

[0051] In formula (5), xa is the data point assigned to cluster C(t);

[0052] S924: Calculate a new center point based on the average value of all data points in the cluster:

[0053]

[0054] Until the IC critical value of the variable is achieved;

[0055] S925: Repeat steps S921 to S924 until all data points are assigned to a cluster and the cluster assignments tend to be consistent, and all data points belonging to a center point form a cluster.

[0056] Furthermore, the present invention also discloses a decision-making method for optimizing building renovation based on investor type, and the decision-making method for optimizing building renovation based on investor type is implemented based on the target-driven decision-layer evaluation method for optimizing building renovation described above;

[0057] The investor-type-based optimized building renovation decision method determines the most reasonable renovation plan by comparing the 3E (energy, environment and economy) benefits of step 7 and step 9. Specifically, the optimized building renovation plan for each building prototype is determined through technical plan 1 or technical plan 2:

[0058] Technical solution 1:

[0059] The decision criteria are determined by comparing the investment cost (IC) of the renovation options for each renovation measure with the set target and minimum LCC, as well as the energy reduction results that these options can achieve.

[0060] Technical solution 2:

[0061] The decision is made based on the DL value of the transformation plan when achieving the set performance goals and minimizing the life cycle cost (LCC).

[0062] The technical solution 1 is specifically implemented by using equation (7). If equation (7) holds true, it means that it is more advantageous to invest in a transformation solution with a set target; otherwise, a transformation solution based on minimizing life cycle cost (LCC) is adopted;

[0063] %IC ST:LCC <%ER ST:LCC (7);

[0064] In formula (7):

[0065]

[0066]

[0067] Where ICj and ERj are the investment cost and energy saving impact of the jth target setting performance target or minimum LCC.

[0068] The second technical solution specifically compares two DL values: %DLST:LCC and %ERST:LCC; wherein %DLST:LCC refers to the DL value required by the transformation scheme to achieve the set performance target, and %ERST:LCC refers to the DL value required by the transformation scheme to achieve the minimum LCC;

[0069] If formula (10) is applicable, that is, when the difference between %DLST:LCC and %ERST:LCC reaches a certain condition, it is decided to invest in the transformation plan with set performance targets;

[0070] If formula (10) is not applicable, select the transformation solution that minimizes LCC (minimizes life cycle cost);

[0071] %DL ST:LCC <%ER ST:LCC (10);

[0072] In formula (10):

[0073]

[0074] DLj is the level of transformation development required to achieve the performance target or minimum LCC set by the jth objective.

[0075] The beneficial effects of the present invention are:

[0076] The proposed method combines a coordinated life cycle cost-environmental valuation approach to provide a simple and comprehensive assessment of 3E (energy, environment and economy). The model not only helps support sustainable urban planning and decision-making, especially in developing societies, but also makes decision recommendations based on investor priorities and sets performance targets for the city / region.

[0077] The method of the present invention verifies the effectiveness of the model framework through case studies to achieve the set energy goals of the building. The case studies verify and identify the retrofit options to achieve the set energy efficiency goals and optimal building performance. Therefore, the method of the present invention provides an innovative framework for building investors to easily and comprehensively evaluate a wider range of retrofit options based on the set performance goals.

[0078] The method of the present invention provides a comprehensive 3E (energy, environment and economy) assessment of building renovation at a macro scale. Although the existing literature mentions the complexity and application limitations of various decision-making models in a large range, the present method emphasizes the importance of a model framework for a simple evaluation method that incorporates data mining analysis AHC (Anatomically Hierarchical Clustering) into the multi-objective decision-making process, which helps building stakeholders choose the most appropriate renovation plan at a macro scale. Specifically, the method of the present invention has the following advantages:

[0079] 1. Prototype verification of existing building complexes through Poly-AHC (Analytic Hierarchical Clustering) improves the pertinence and efficiency of the renovation plan.

[0080] 2. Energy, environmental and economic assessments are not only targeted at specific renovation strategies, but are also compared with performance targets set by standards and regulations to ensure the practical feasibility and effectiveness of the solutions.

[0081] 3. Evaluating a wider range of transformation measures under the energy demand side, energy supply side and energy conservation groups provides stakeholders with a more comprehensive choice.

[0082] 4. The AHC (Anatomical Hierarchical Clustering) paired comparison method was used to determine the most appropriate transformation plan, improving the scientificity and accuracy of decision-making. BRIEF DESCRIPTION OF THE DRAWINGS

[0083] Figure 1 Schematic diagram of the model framework of the method of the present invention. DETAILED DESCRIPTION

[0084] The present invention is further described below in conjunction with the accompanying drawings and embodiments:

[0085] Glossary:

[0086] 3E: Energy, Environment and Economy;

[0087] LCC: Minimize life cycle costs;

[0088] AHC: Aggregate Hierarchical Clustering;

[0089] IC: investment cost;

[0090] DL: Transformation development level.

[0091] Example 1: A goal-driven decision-making evaluation method for optimizing building renovation, see Figure 1 .

[0092] The method comprises the following steps:

[0093] S1. Model framework is established. The model framework is established under the assumption that there is a lack of sufficient urban building model data in developing societies to support the aggregation of building stocks at the building level. The model framework includes three coordinated model layers: building stock aggregation model, individualized 3E (energy, environment and economy) model, and life cycle cost-environmental assessment model; see Figure 1 .

[0094] The building stock summary model is used to describe the existing buildings under investigation, including a feature-based building energy performance assessment to develop building prototypes.

[0095] The individualized 3E model performs individualized 3E (energy, environment and economy) simulations on the selected renovation measures on the prototype building, performs sensitivity analysis by selecting decision variables, and the selected decision variables include power consumption, carbon dioxide emissions, and cost indicators (investment and operating costs); and uses the results of the individualized 3E (energy, environment and economy) model as input data for the LCC (minimization of life cycle costs) environmental assessment model;

[0096] The life cycle cost-environmental assessment model includes an LCC (minimize life cycle cost) environmental assessment model, and the LCC (minimize life cycle cost) environmental assessment model is obtained by combining the LCC (minimize life cycle cost) method with the environmental assessment model.

[0097] S2. Conduct individualized 3E (energy, environment and economy) simulations on the selected renovation measures on the prototype building, i.e., data aggregation and prototype design of the prototype building group; the input variables of the simulation can come from literature, alternative resources, questionnaires and field measurements. The model conducts sensitivity analysis based on the selected decision variables required for subsequent analysis. The selected decision variables include power consumption, carbon dioxide emissions, and cost indicators (investment and operating costs). It includes the following steps:

[0098] S21. Combining the empirical database generated by a large-scale building sample survey with top-down macroeconomic and statistical tools, define a performance indicator system (PIS) for building energy efficiency through Pearson and Bissell correlation analysis;

[0099] S22, use cluster analysis to refine the index;

[0100] S23, using the Euclidean distance square method and Z value standardization method to measure similarity and normalize data to simplify and control variables;

[0101] S24. Classify and design prototype buildings, each prototype design represents a specific type of building.

[0102] S3. The results of the individualized 3E (energy, environment and economy) model are used as input data for the LCC (minimization of life cycle cost) environmental assessment model. Through numerical simulation, the benefits of the combined transformation measures are evaluated, that is, the individualized transformation measures are evaluated. In this step, the LCC (minimization of life cycle cost) method is an economic and energy assessment model that describes the trend between transformation costs and energy-saving benefits. Here, the unified concept of minimizing the total LCC (minimization of life cycle cost) defines the optimal combination of a series of transformation strategies. By combining the LCC (minimization of life cycle cost) method with the environmental assessment model, a unique optimal strategy can be obtained.

[0103] This step S3 includes the following steps:

[0104] S31. Evaluate the 3E (energy, environment, and economy) impact of each retrofit measure on the prototype building using building performance modeling software such as the Integrated Environmental Solutions Virtual Environment (IES-VE);

[0105] S32. The BIM platform supports the software to ensure the evaluation of the interaction between the renovation measures; for example, after replacing the lighting equipment with LED lighting equipment, the impact on indoor heat gain can be considered during the simulation by specifying the simulation conditions in the building template manager. In addition, local measured meteorological data are used to improve the accuracy of the simulation;

[0106] S33. Compare the simulation results of each prototype building with its original model to evaluate the respective 3E (energy, environment and economy) impact of each retrofit measure;

[0107] S4. In order to accurately estimate the impact of all possible combined transformation strategies, it is necessary to consider the mutual influence between measures in a specific transformation strategy. Therefore, it is necessary to conduct a comprehensive simulation of all possible combined transformation measures, that is, to evaluate the impact of the combined transformation of each transformation measure on the 3E strategy. This step adopts a digital method, using the results of the separate pre-simulation of 3E (energy, environment and economy) to conduct LCC (minimize life cycle cost) evaluation, and perform key simplifications to reduce computational complexity. It includes nine steps,

[0108] Step 1: Calculate the integrated circuit of each combined transformation strategy, that is, the sum of the integrated circuits of all transformation measures;

[0109] Step 2: Calculate the annual energy cost - EC for each combination of retrofit strategies using equation (1) to assess the impact on energy:

[0110] EC = ET × EP (1);

[0111] In formula (1), ET is the annual energy consumption and EP is the unit electricity price;

[0112] Step 3: Calculate the total LCC (minimum life cycle cost) for each combination of transformation strategies;

[0113] Step 4: Classify the transformation development level DL using IC. In this step, IC values ​​are sorted in ascending order and used as the basis for DL ​​rating, ranging from 0% to 100%, with 0% for DL ​​without transformation activities and 100% for DL ​​including all transformation activity strategies;

[0114] Step 5: Reduce the number of data points by using the 8-point partition average method to reduce computational complexity; plot the DL, IC, EC, and total LCC values ​​as new data points on the graph;

[0115] Step 6: Generate R2 correlation coefficients for IC, EC, and total LCC curves based on DL;

[0116] Step 7: Use environmental assessment models to determine the best renovation options that minimize life cycle costs;

[0117] Step 8: Use the generated profiles to conduct building performance tests and determine the thresholds required to achieve the established performance targets; estimate the required changes by calculating the reduction in the selected variables required to achieve the established targets, and all renovation options with IC values ​​exceeding this threshold are considered suitable to achieve the established targets;

[0118] Step 9: Adopt AHC (Adaptive Hierarchical Clustering) data mining technology to determine the center point strategy representing all possible transformation solutions outside the threshold.

[0119] Step 9 specifically includes the following steps:

[0120] S91: Data preprocessing, use z value to normalize the summary or simulated data, set the mean (μi) of each specific i-th group of data (xi) to zero, and the standard deviation (σi) to 1. The calculation formula of Z-score (Zxi) is:

[0121]

[0122] S92: Cluster analysis, including:

[0123] S921: Using the AHC (Anatomically Hierarchical Clustering) algorithm, each data is initialized as a cluster. As the algorithm proceeds, the clusters are further merged in pairs. The goal of this algorithm is to minimize the square of the Euclidean distance d between two data sets x and y. The Ward's criterion is used to minimize the square of the Euclidean distance d:

[0124]

[0125] S922: Define the k value of potential clusters and identify possible outliers;

[0126] S923: Determine the number of clusters (C = {C1, C2, ..., Ck|k≤N}) and the relevant k values ​​(x, x12, ..., xn) of N groups of data;

[0127] S923: Use the k-means algorithm to divide around the center point. The goal of the k-means algorithm is to minimize the variance within the cluster:

[0128]

[0129] In formula (4), μi is the average value / center point of the data points in Ci.

[0130] In this algorithm, random centroids (μ(1)1,μ(1)2,...,μ(1)k) are started and each data point is assigned to the cluster with the closest centroid by minimizing the squared Euclidean distance:

[0131]

[0132] In formula (5), xa is the data point assigned to cluster C(t);

[0133] S924: Calculate a new center point based on the average value of all data points in the cluster:

[0134]

[0135] Until the IC critical value of the variable is achieved;

[0136] S925: Repeat steps S921 to S924 until all data points are assigned to a cluster and the cluster assignments tend to be consistent, and all data points belonging to a center point form a cluster.

[0137] Example 2, an investor type-based decision-making method for optimizing building renovation, this investor type-based decision-making method for optimizing building renovation is implemented based on the target-driven decision-level evaluation method for optimizing building renovation as described in Example 1.

[0138] The investor-type-based optimized building renovation decision method determines the most reasonable renovation plan by comparing the 3E (energy, environment and economy) benefits of step 7 and step 9. Specifically, the optimized building renovation plan for each building prototype is determined through technical plan 1 or technical plan 2:

[0139] Technical solution 1:

[0140] The decision criteria are determined by comparing the investment cost (IC) of the renovation options for each renovation measure with the set target and minimum LCC, as well as the energy reduction results that these options can achieve.

[0141] The technical solution 1 is specifically implemented by using equation (7). If equation (7) holds true, it means that it is more advantageous to invest in a transformation solution with a set target; otherwise, a transformation solution based on minimizing life cycle cost (LCC) is adopted;

[0142] %IC ST:LCC <%ER ST:LCC (7);

[0143] In formula (7):

[0144]

[0145]

[0146] Where ICj and ERj are the investment cost and energy saving impact of the jth target setting performance target or minimum LCC.

[0147] Technical solution 2:

[0148] The decision is made based on the DL value of the transformation plan when achieving the set performance goals and minimizing the life cycle cost (LCC).

[0149] The second technical solution specifically compares two DL values: %,ST:LCC and %ERST:LCC; wherein %DLST:LCC refers to the DL value required by the transformation scheme to achieve the set performance target, and %ERST:LCC refers to the DL value required by the transformation scheme to achieve the minimum LCC;

[0150] If formula (10) is applicable, that is, when the difference between %DLST:LCC and %ERST:LCC reaches a certain condition, it is decided to invest in the transformation plan with set performance targets;

[0151] If formula (10) is not applicable, select the transformation solution that minimizes LCC (minimizes life cycle cost);

[0152] %DL ST：LCC <%ER ST:LCC (10);

[0153] In formula (10):

[0154]

[0155] DLj is the level of transformation development required to achieve the performance target or minimum LCC set by the jth objective.

[0156] The embodiments of the present invention disclose preferred embodiments, but are not limited thereto. A person skilled in the art can easily understand the spirit of the present invention based on the above embodiments and make different extensions and changes. However, as long as they do not deviate from the spirit of the present invention, they are all within the protection scope of the present invention.

Claims

1. A goal-driven decision-level evaluation method for optimizing building renovation, characterized by: S1. Framework establishment. The model framework is established under the assumption that there is a lack of sufficient urban building model data in developing societies to support the aggregation of building stocks at the building level. The model framework includes three mutually coordinated model layers: building stock aggregation model, energy, environment and economic model, and life cycle cost assessment model. S2, prototype building complex data aggregation and prototype design; S3. Evaluate each transformation measure; S4. Evaluate the impact strategy of the combination of each transformation measure on energy, environment and economy. This step adopts a numerical method and uses the energy, environment and economic results of separate pre-simulations to conduct an assessment under life cycle costs.

2. A goal-driven decision-making layer evaluation method for optimizing building renovation according to claim 1, characterized in that: The building stock summary model described is used to describe the existing buildings under investigation, including a feature-based building energy performance assessment to develop different building prototypes; The energy, environment and economic model performs individual simulations of selected renovation measures on the prototype building, performs sensitivity analysis by selecting decision variables, including power consumption, carbon dioxide emissions, and cost indicators; and uses the results of the energy, environment and economic model as input data for the environmental assessment model under life cycle cost; The life cycle cost assessment model includes an environmental assessment model, and the life cycle cost environmental assessment model is obtained by combining the life cycle cost method with the environmental assessment model.

3. A goal-driven decision-making layer evaluation method for optimizing building renovation according to claim 2, characterized in that: In step S2, the prototype building complex data aggregation and prototype design include the following steps: S21. Combining the empirical database generated by a large-scale building sample survey with top-down macroeconomic and statistical tools, define a performance indicator system for building energy efficiency through Pearson and Bissell correlation analysis; S22, use cluster analysis to refine the index; S23, using the Euclidean distance square method and Z value normalization method for similarity measurement and data normalization; S24. Classify and design prototype buildings, each prototype design represents a specific type of building.

4. A goal-driven decision-making layer evaluation method for optimizing building renovation according to claim 3, characterized in that: In step S3, the individualized transformation measures are evaluated, including the following steps: S31. Use building performance modeling software to assess the energy, environmental, and economic impacts of each retrofit measure on the prototype building; S32. Ensure the evaluation of the interactions between renovation measures through BIM platform support software; S33. The simulation results of each prototype building are compared with its original model to evaluate the 3E impact of each retrofit measure.

5. A goal-driven decision-making layer evaluation method for optimizing building renovation according to claim 4, characterized in that: Step S4 includes nine steps: Step 1: Calculate the integrated circuit of each combined transformation strategy, that is, the sum of the integrated circuits of all transformation measures; Step 2: Calculate the annual energy cost - EC for each combination of retrofit strategies using equation (1) to assess the impact on energy: EC = ET × EP (1); In formula (1), ET is the annual energy consumption and EP is the unit electricity price; Step 3: Calculate the total life cycle cost of each combination of renovation strategies; Step 4: Classify the transformation development level DL using IC. In this step, IC values ​​are sorted in ascending order and used as the basis for DL ​​rating, ranging from 0% to 100%, with 0% for DL ​​without transformation activities and 100% for DL ​​including all transformation activity strategies; Step 5: Reduce the number of data points by using the 8-point partition average method to reduce computational complexity; plot the DL, IC, EC, and total LCC values ​​as new data points on the graph; Step 6: Generate R2 correlation coefficients for IC, EC, and total LCC curves based on DL; Step 7: Use environmental assessment models to determine the best renovation options that minimize life cycle costs; Step 8: Use the generated profiles to conduct building performance tests and determine the thresholds required to achieve the established performance targets; estimate the required changes by calculating the reduction in the selected variables required to achieve the established targets, and all renovation options with IC values ​​exceeding this threshold are considered suitable to achieve the established targets; Step 9: Use AHC data mining technology to determine the center point strategy that represents all possible transformation solutions outside the threshold.

6. A goal-driven decision-making layer evaluation method for optimizing building renovation according to claim 5, characterized in that: Step 9 specifically includes the following steps: S91: Data preprocessing, use z value to normalize the summary or simulated data, set the mean (μi) of each specific i-th group of data (xi) to zero, and the standard deviation (σi) to 1. The calculation formula of Z-score (Zxi) is: S92: Cluster analysis, including: S921: Using the AHC algorithm, each data is initialized as a cluster. As the algorithm proceeds, clusters are further merged in pairs. The goal of this algorithm is to minimize the square of the Euclidean distance d between two data sets x and y. The Ward standard is used to minimize the square of the Euclidean distance d: S922: Define the k value of potential clusters and identify possible outliers; S923: Determine the number of clusters (C = {C1, C2, ..., Ck|k≤N}) and the relevant k values ​​(x, x12, ..., xn) of N groups of data; S923: Use the k-means algorithm to divide around the center point. The goal of the k-means algorithm is to minimize the variance within the cluster: In formula (4), μi is the average value / center point of the data points in Ci. In this algorithm, random centroids (μ(1)1,μ(1)2,...,μ(1)k) are started and each data point is assigned to the cluster with the closest centroid by minimizing the squared Euclidean distance: In formula (5), xa is the data point assigned to cluster C(t); S924: Calculate a new center point based on the average value of all data points in the cluster: Until the IC critical value of the variable is achieved; S925: Repeat steps S921 to S924 until all data points are assigned to a cluster and the cluster assignments tend to be consistent, and all data points belonging to a center point form a cluster.

7. A method for optimizing building renovation decision-making based on investor type, characterized by: This investor type-based decision-making method for optimizing building renovation is implemented based on the goal-driven decision-making layer evaluation method for optimizing building renovation as described in claim 5; The optimized building renovation decision method based on investor type determines the most reasonable renovation plan by comparing the 3E benefits of step 7 and step 9. Specifically, the optimized building renovation plan for each building prototype is determined through technical plan 1 or technical plan 2: Technical solution 1: The decision criteria are determined by comparing the investment cost of the renovation options for each renovation measure with the set targets and minimum LCC, as well as the energy reduction results that these options can achieve. Technical solution 2: The decision is made based on the DL value corresponding to the transformation plan when achieving the set performance goals and minimizing life cycle costs.

8. The method for optimizing building renovation decision-making based on investor type as claimed in claim 7, characterized in that: The technical solution 1 is specifically implemented by using equation (7). If equation (7) holds true, it means that it is more advantageous to invest in a transformation solution with a set target; otherwise, a transformation solution based on minimizing life cycle costs is adopted; %IC ST:LCC <%IS ST:LCC (7); In formula (7): Where ICj and ERj are the investment cost and energy saving impact of the jth target setting performance target or minimum LCC.

9. The method for optimizing building renovation decision-making based on investor type as claimed in claim 8, characterized in that: The second technical solution specifically compares two DL values: %DLST:LCC and %ERST:LCC; wherein %DLST:LCC refers to the DL value required by the transformation scheme to achieve the set performance target, and %ERST:LCC refers to the DL value required by the transformation scheme to achieve the minimum LCC; If formula (10) is applicable, that is, when the difference between %DLST:LCC and %ERST:LCC reaches a certain condition, it is decided to invest in the transformation plan with set performance targets; If formula (10) is not applicable, choose the transformation scheme that minimizes LCC; %DLsr:LCC<%ERsr:LCC(10); In formula (10): DLj is the level of transformation development required to achieve the performance target or minimum LCC set by the jth objective.

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