A building energy-saving transformation analysis and evaluation system and method

Abstract

The present invention discloses a building energy-saving transformation analysis and evaluation system and method, which relates to the field of building energy-saving technology, and aims to solve the problem that the energy-saving transformation evaluation of a single building ignores the synergy effect and energy sharing of a building group. It includes data collection, functional area division, energy efficiency evaluation, optimization calculation, energy sharing, simulation prediction and report generation modules; it also includes the following steps: collecting data of buildings in a target area or building group, constructing a multi-dimensional data set; dividing the building group into functional areas; constructing a group-level energy efficiency evaluation model based on a data envelopment analysis method; based on a multi-objective optimization model; evaluating the linkage effect between building groups based on a regional energy sharing model; simulating and predicting the energy-saving transformation effect of a building group; based on prediction results and evaluation analysis. It can provide a decision-making basis for building energy-saving transformation, optimize energy-saving transformation plans, predict energy-saving effects, improve the energy efficiency of building groups, and reduce energy consumption.

Description

Technical Field

[0001] The present invention relates to the technical field of building energy conservation, and in particular to a building energy conservation transformation analysis and evaluation system and method. Background Art

[0002] With the increasingly serious global energy shortage and environmental pollution problems, energy-saving transformation in the building sector has become an important measure to achieve green development and low-carbon goals. Building energy conservation can not only significantly reduce the energy consumption of buildings, but also play a key role in reducing greenhouse gas emissions and improving energy utilization efficiency.

[0003] Existing energy-saving renovation assessment methods usually rely on the energy consumption data of individual buildings for analysis, ignoring the possible energy efficiency differences and interdependencies between multiple buildings. In some areas or building groups, there is energy surplus or uneven energy demand. Evaluating the energy efficiency renovation potential of each building alone often cannot effectively reflect the overall energy efficiency level of the region, resulting in unsatisfactory results of the renovation plan.

[0004] In the energy-saving renovation assessment at the building group level, there may be opportunities for energy sharing or mutual support between buildings. Existing methods often do not consider how to optimize energy efficiency within a building group, resulting in the inability to improve the energy efficiency of the entire region through regional coordination mechanisms. For example, some buildings may have excess energy consumption during a specific period of time, while other buildings require additional energy support. How to reasonably allocate and dispatch these energies within the region has become an important issue in energy-saving renovation assessments.

[0005] In order to solve the above problems, a building energy-saving renovation analysis and evaluation system and method are proposed. Summary of the invention

[0006] The purpose of the present invention is to solve the problem that the existing building energy-saving renovation assessment methods usually only analyze single buildings, ignore the synergy effect at the building group or regional level, and fail to effectively consider the energy sharing and mutual assistance mechanism between buildings in the building group, and propose a building energy-saving renovation analysis and assessment system and method.

[0007] In order to achieve the above object, the present invention adopts the following technical solutions:

[0008] A building energy-saving transformation analysis and evaluation system, comprising:

[0009] Data collection module, used to collect basic information, energy consumption data and environmental data of buildings in the target area or building group, and construct a multi-dimensional data set;

[0010] The functional area division module is used to divide the building group into multiple functional areas according to the use nature, building structure and energy consumption characteristics of the building. At the same time, by collecting the unit energy consumption data of each building, the relative energy efficiency level of each building in the building group is analyzed, an energy efficiency benchmark model is constructed, high-energy consumption buildings are identified, and the energy efficiency performance ranking of the building in the group is obtained;

[0011] The energy efficiency evaluation module calculates the unit energy consumption of each building based on the data envelopment analysis method, constructs input variables and output variables, and builds an energy efficiency evaluation model for the building group. By analyzing the input and output variables, the overall energy efficiency of the building group is evaluated, and an energy efficiency evaluation function is constructed to obtain the overall energy efficiency evaluation results of the building group.

[0012] The optimization calculation module calculates the optimal energy-saving transformation plan based on a multi-objective optimization model by considering the transformation cost, energy-saving potential and other constraints;

[0013] The shared energy module is used to calculate the energy surplus of each building, identify buildings with energy surplus, and establish an energy sharing optimization model. By modeling the sharing of energy surplus between buildings, the energy configuration in the region is optimized and the energy utilization efficiency is improved. The energy sharing and scheduling efficiency between building groups is analyzed through optimization algorithms to ensure the overall maximum utilization of energy in the region.

[0014] The simulation prediction module is used to simulate and predict the energy-saving renovation effects of building groups using simulation software, and to formulate energy-saving renovation priority rankings based on the simulation results;

[0015] The report generation module generates an analysis report on the energy-saving renovation effect of the building group based on the prediction results and evaluation analysis. The analysis report includes energy-saving potential, energy efficiency improvement and optimization suggestions, providing a decision-making basis for subsequent energy-saving renovation.

[0016] A method for analyzing and evaluating a building energy-saving transformation system, using the above-mentioned building energy-saving transformation analysis and evaluation system, comprises the following steps:

[0017] Step 1: Collect basic information, energy consumption data and environmental data of buildings in the target area or building group to construct a multi-dimensional data set;

[0018] Step 2: Divide the building groups into functional areas and construct a building group energy efficiency benchmark model. Use the building group energy efficiency benchmark model to identify building groups with high energy consumption. The specific analysis process is as follows: Divide the building groups into multiple functional areas according to the use nature, building structure and energy consumption characteristics of the buildings;

[0019] Based on the energy efficiency benchmark model of the building group, by collecting the unit energy consumption data of each building, analyzing the relative energy efficiency level of each building in the building group, and constructing the energy efficiency benchmark model;

[0020] By comparing the unit energy consumption values ​​of each building, we can identify high-energy-consuming buildings and rank the building in terms of energy efficiency performance among the group.

[0021] Step 3: Based on the data envelopment analysis method, a group-level energy efficiency evaluation model is constructed. The specific analysis process is as follows: Calculate the unit energy consumption of each building, that is, the energy consumption per square meter or cubic meter of building area, as the basic indicator of energy efficiency evaluation;

[0022] Based on the data envelopment analysis method, input variables are constructed, including: building unit energy consumption, renovation cost, equipment depreciation rate, and output variables, including: energy saving potential, user comfort improvement value;

[0023] The energy efficiency evaluation model of building groups is constructed by using data envelopment analysis method. The overall energy efficiency evaluation of building groups is evaluated by analyzing input and output variables, and the energy efficiency evaluation function is constructed to obtain the overall energy efficiency evaluation results of building groups.

[0024] According to the evaluation results of the data envelopment analysis method, the energy efficiency value of each building is ranked to determine high-efficiency buildings and low-efficiency buildings, providing decision support for subsequent renovation or optimization;

[0025] Step 4: Based on the multi-objective optimization model, the optimal energy-saving renovation plan is calculated in combination with the renovation cost, energy-saving potential and other constraints;

[0026] Step 5: Construct a regional energy sharing model to evaluate the linkage effect between building groups. The specific analysis process is as follows: calculate the energy surplus of each building, that is, the difference between the power generation and energy consumption of the building in a certain period of time, and identify the buildings with energy surplus;

[0027] Based on the energy consumption of the building group, an energy sharing optimization model is established. By modeling the sharing of energy surplus between buildings, the energy configuration in the region is optimized and the energy utilization efficiency is improved.

[0028] Through linear programming or mixed integer programming optimization algorithms, analyze the energy sharing and scheduling efficiency among building groups to maximize the overall utilization of energy in the region;

[0029] Output the optimized energy sharing plan between building groups, including the energy sharing amount between each pair of buildings and the energy surplus or gap of each building, to ensure the optimal energy utilization among the building groups;

[0030] Step 6: Simulate and predict the energy-saving renovation effects of the building group, and formulate energy-saving renovation priorities based on the simulation results;

[0031] Step 7: Based on the prediction results and evaluation analysis, generate an analysis report on the energy-saving renovation effect of the building group. The analysis report includes energy-saving potential, energy efficiency improvement and optimization suggestions, providing a decision-making basis for subsequent energy-saving renovation.

[0032] Preferably, the step one further includes: formatting the building area data to ensure that all data are unified into square meters; converting the building's service life data into a standard format; cleaning and marking the building energy consumption data to handle outliers, missing values ​​and duplicate data.

[0033] Preferably, the calculation of the unit energy consumption index includes the following steps: by evaluating the energy consumption combination of the building group, including the data of electricity, natural gas and water energy consumption, the building group is divided into more detailed functional areas; using the energy consumption data E 建筑 and building area data A 建筑 , calculate the unit energy consumption index of the building Compare the building's unit energy consumption index with the energy efficiency benchmark model of the functional area to identify high-energy-consuming buildings.

[0034] Preferably, the data envelopment analysis method performs group overall energy efficiency evaluation based on the following variables:

[0035] Define input variables: unit energy consumption of the building, renovation cost and equipment depreciation rate and output variables: energy saving potential of the building and user comfort improvement value;

[0036] Construct a data envelopment analysis method model;

[0037] By calculating:

[0038]

[0039] Where: y ij is the j-th output of the i-th building, x ik is the kth input of the i-th building, m and n are the number of output and input variables respectively; output the relative energy efficiency value of each building; determine the high-efficiency and low-efficiency buildings.

[0040] Preferably, the objective function calculation steps of the multi-objective optimization model are:

[0041] Set the goal to minimize the cost of transformation C 改造 ; also consider energy saving potential Iterative optimization through genetic algorithm; calculate the optimal transformation plan;

[0042] Including energy saving, renovation sequence and budget allocation:

[0043]

[0044] in: is the renovation cost of the i-th building.

[0045] Preferably, the step five further comprises:

[0046] Calculate the energy surplus S for each building excess (b i ), that is, the building's power generation capacity P generation (b i ) and total energy consumption E building (b i )

[0047] Energy consumption dataset based on building groups building and energy surplus S excess , build an energy sharing optimization model, and analyze the energy sharing and scheduling efficiency among building groups through linear programming or mixed integer programming optimization algorithms;

[0048] Output the optimized energy sharing plan between the building groups, including the sharing amount S between each pair of buildings shared (b i ,b j ) and the energy surplus or deficit S for each building final .

[0049] Preferably, the energy-saving renovation priority ranking of the building group is performed based on the following steps:

[0050] Define energy saving potential, renovation cost and urgency evaluation indicators; calculate weights using the analytic hierarchy process;

[0051] According to the priority scoring model:

[0052] S 优先级 =w 1 P 节能 +w 2 C 改造 +w 3 C 紧迫性

[0053] Where: w 1 ,w 2 ,w 3 are the corresponding weights, reflecting the importance of energy saving, renovation cost and urgency indicators;

[0054] Sort the buildings; renovate high-scoring buildings first.

[0055] Preferably, the energy-saving transformation effect evaluation step includes:

[0056] Obtain energy consumption data before and after the renovation;

[0057] Calculate and evaluate the energy efficiency improvement of buildings:

[0058]

[0059] Among them, E 改造 Energy efficiency after energy saving transformation, E 原始 for energy efficiency before renovation;

[0060] Combine energy consumption reductions with cost savings;

[0061] Generate energy-saving transformation effect analysis report.

[0062] Preferably, the step 6 further comprises: using the energy consumption data E of the building building and environmental data E 环境 Conduct comprehensive simulations to ensure that the prediction results take into account the impact of environmental factors and improve the accuracy of the prediction.

[0063] The present invention has the following beneficial effects:

[0064] 1. In the present invention, by constructing a comprehensive analysis and evaluation model, it is possible to break through the limitations of traditional single-building energy-saving evaluation and realize the overall analysis of energy-saving transformation of regions or building groups. By collaboratively evaluating the energy efficiency of building groups, not only the energy efficiency level of a single building is considered, but also factors such as energy flow and resource sharing between buildings are fully utilized, so as to accurately identify the priority and key transformation goals of energy-saving transformation. Through a multi-dimensional evaluation system, decision makers are helped to weigh multiple factors such as energy-saving potential, cost-effectiveness, urgency, etc. in the transformation strategy, providing more scientific decision-making support and ensuring the maximization of the overall energy-saving transformation effect of the building group.

[0065] 2. In the present invention, multi-objective optimization, data envelopment analysis (DEA) and other technologies are used to achieve a comprehensive evaluation of the energy-saving transformation effect of building groups. It can accurately evaluate the energy efficiency improvement, economic benefits and environmental benefits before and after the transformation. By conducting an overall energy efficiency evaluation of the building group, it can accurately guide the formulation of energy-saving transformation strategies, avoid resource waste, and provide data support for subsequent transformation effect monitoring and optimization, which has significant economic and environmental benefits. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] Figure 1 This is a system architecture and method flow chart of a building energy-saving renovation analysis and evaluation system and method proposed by the present invention. DETAILED DESCRIPTION

[0067] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0068] like Figure 1 As shown, the present invention proposes a building energy-saving transformation analysis and evaluation system, comprising:

[0069] Data collection module, used to collect basic information, energy consumption data and environmental data of buildings in the target area or building group, and construct a multi-dimensional data set;

[0070] The functional area division module is used to divide the building group into multiple functional areas according to the use nature, building structure and energy consumption characteristics of the building. At the same time, by collecting the unit energy consumption data of each building, the relative energy efficiency level of each building in the building group is analyzed, an energy efficiency benchmark model is constructed, high-energy consumption buildings are identified, and the energy efficiency performance ranking of the building in the group is obtained;

[0071] The energy efficiency evaluation module calculates the unit energy consumption of each building based on the data envelopment analysis (DEA) method, constructs input variables and output variables, and builds an energy efficiency evaluation model for the building group. By analyzing the input and output variables, the overall energy efficiency of the building group is evaluated, and an energy efficiency evaluation function is constructed to obtain the overall energy efficiency evaluation results of the building group.

[0072] The optimization calculation module calculates the optimal energy-saving transformation plan based on a multi-objective optimization model by considering the transformation cost, energy-saving potential and other constraints;

[0073] The shared energy module is used to calculate the energy surplus of each building, identify buildings with energy surplus, and establish an energy sharing optimization model. By modeling the sharing of energy surplus between buildings, the energy configuration in the region is optimized and the energy utilization efficiency is improved. The energy sharing and scheduling efficiency between building groups is analyzed through optimization algorithms to ensure the overall maximum utilization of energy in the region.

[0074] The simulation prediction module is used to simulate and predict the energy-saving renovation effects of building groups using simulation software, and to formulate energy-saving renovation priority rankings based on the simulation results;

[0075] The report generation module generates an analysis report on the energy-saving renovation effect of the building group based on the prediction results and evaluation analysis. The analysis report includes energy-saving potential, energy efficiency improvement and optimization suggestions, providing a decision-making basis for subsequent energy-saving renovation.

[0076] A method for analyzing and evaluating a building energy-saving transformation system comprises the following steps:

[0077] Step 1: Collect basic information, energy consumption data and environmental data of buildings in the target area or building group to construct a multi-dimensional data set;

[0078] Step 2: Divide the building groups into functional areas and construct a building group energy efficiency benchmark model. Use the building group energy efficiency benchmark model to identify building groups with high energy consumption. The specific analysis process is as follows: Divide the building groups into multiple functional areas according to the use nature, building structure and energy consumption characteristics of the buildings;

[0079] Based on the energy efficiency benchmark model of the building group, by collecting the unit energy consumption data of each building, analyzing the relative energy efficiency level of each building in the building group, and constructing the energy efficiency benchmark model;

[0080] By comparing the unit energy consumption values ​​of each building, we can identify high-energy-consuming buildings and rank the building in terms of energy efficiency performance among the group.

[0081] Step 3: Based on the data envelopment analysis (DEA) method, a group-level energy efficiency evaluation model is constructed. The specific analysis process is as follows: Calculate the unit energy consumption of each building, that is, the energy consumption per square meter or cubic meter of building area, as the basic indicator of energy efficiency evaluation;

[0082] Based on the data envelopment analysis method (DEA), input variables are constructed, including: building unit energy consumption, renovation cost, equipment depreciation rate, and output variables, including: energy saving potential and user comfort improvement value;

[0083] The data envelopment analysis method (DEA) is used to build an energy efficiency evaluation model for building groups. The overall energy efficiency evaluation of the building group is conducted by analyzing the input and output variables, and the energy efficiency evaluation function is constructed to obtain the overall energy efficiency evaluation results of the building group.

[0084] According to the evaluation results of the DEA method, the energy efficiency value of each building is ranked to identify high-efficiency buildings and low-efficiency buildings, providing decision support for subsequent renovation or optimization;

[0085] Step 4: Based on the multi-objective optimization model, the optimal energy-saving renovation plan is calculated in combination with the renovation cost, energy-saving potential and other constraints;

[0086] Step 5: Construct a regional energy sharing model to evaluate the linkage effect between building groups. The specific analysis process is as follows: calculate the energy surplus of each building, that is, the difference between the power generation and energy consumption of the building in a certain period of time, and identify the buildings with energy surplus;

[0087] Based on the energy consumption of the building group, an energy sharing optimization model is established. By modeling the sharing of energy surplus between buildings, the energy configuration in the region is optimized and the energy utilization efficiency is improved.

[0088] Through linear programming or mixed integer programming optimization algorithms, analyze the energy sharing and scheduling efficiency among building groups to maximize the overall utilization of energy in the region;

[0089] Output the optimized energy sharing plan between building groups, including the energy sharing amount between each pair of buildings and the energy surplus or gap of each building, to ensure the optimal energy utilization among the building groups;

[0090] Step 6: Simulate and predict the energy-saving renovation effects of the building group, and formulate energy-saving renovation priorities based on the simulation results;

[0091] Step 7: Based on the prediction results and evaluation analysis, generate an analysis report on the energy-saving renovation effect of the building group. The analysis report includes energy-saving potential, energy efficiency improvement and optimization suggestions, providing a decision-making basis for subsequent energy-saving renovation.

[0092] Step 1: Collect basic information, energy consumption data and environmental data of buildings in the target area or building group to construct a multi-dimensional data set.

[0093] Building-level data: Collect basic information of all buildings in the target area or building group (such as building type, construction age, building area, building use, structural type, etc.), and ensure multi-dimensional coverage of the data. These data can be used for subsequent energy efficiency analysis and diagnosis.

[0094] Energy consumption data collection: Collect energy consumption data of each building through smart metering equipment (such as smart electricity meters, heat metering equipment, etc.) or existing energy management systems (such as BMS). The data should cover the average annual energy consumption, monthly fluctuations, daily energy consumption, etc. of each building to fully understand its energy efficiency performance.

[0095] Environmental data: Obtain environmental data of the target area, including local climate data, temperature, humidity, sunshine duration, etc., in order to evaluate the external environment of the building. These data can help analyze the relationship between the building's energy consumption and environmental factors.

[0096] Dataset construction:

[0097] Data Integration:

[0098] Integrate the collected basic building information, energy consumption data and environmental data to construct a multi-dimensional data set, where the data format can be CSV, JSON or database table, etc.

[0099] Data formatting:

[0100] Format the data to ensure data uniformity and ensure that the units of energy consumption data are consistent.

[0101] Data cleaning:

[0102] Clean the raw data and handle outliers, missing values, duplicate data, and other issues to ensure data integrity and accuracy.

[0103] Data Tags:

[0104] Mark each building in the building complex and record the unique identifier ID of each building for subsequent analysis and tracking.

[0105] Data Storage:

[0106] The processed multidimensional data sets are stored in the database to facilitate subsequent query, analysis and use.

[0107] Step 2: Divide the building groups into functional areas and construct a benchmark model for building group energy efficiency:

[0108] 1. Functional area division of building groups:

[0109] Based on the use of the building 性质 、Building Structure 结构 and energy consumption characteristics C 能耗 , divide the building complex into functional areas. The specific division steps are as follows:

[0110] Collect basic information about the building 建筑 , including building type, area, age, etc.;

[0111] According to the nature of the building 性质 , such as office, retail, accommodation, catering, etc., dividing the building group into multiple functional areas U 功能区域 ;

[0112] Analyze building structures 结构 , such as the number of building floors, building materials, thermal insulation performance, etc.;

[0113] Evaluating building energy consumption characteristics C 能耗 , such as electricity consumption, fuel consumption, energy efficiency, etc.

[0114] 2. Energy efficiency benchmark model construction:

[0115] By collecting the unit energy consumption data of each building in the building complex Analyze the relative energy efficiency levels of buildings within a building complex;

[0116] Based on the functional area division, the energy efficiency benchmark model F is constructed基准 The model associates the energy efficiency level within a building group with the functional area and calculates the average energy efficiency level U of each functional area. 平均能耗 :

[0117]

[0118] By comparing the unit energy consumption value U of each building 能耗 and the average energy consumption level U of the functional area 平均能耗 , identify high-energy-consuming buildings and rank the building’s energy efficiency performance in the group.

[0119] Step 3: Based on the data envelopment analysis (DEA) method, a group-level energy efficiency evaluation model is constructed:

[0120] 1. Calculation of unit energy consumption:

[0121] Calculate the specific energy consumption of each building, that is, the energy consumption per square meter or cubic meter of building area As a basic indicator for energy efficiency evaluation.

[0122] 2.DEA method construction:

[0123] Based on the data envelopment analysis method (DEA), the input variable x is defined as ik and the output variable y ij . Specifically include:

[0124] Input variables:

[0125] U 能耗 : Unit energy consumption of the building;

[0126] C 改造 : Renovation cost, covering the capital investment required for building energy-saving renovation;

[0127] D 折旧 : Equipment depreciation rate, taking into account the impact of equipment service life on energy efficiency.

[0128] Output variables:

[0129] P 节能潜力 : Energy saving potential of buildings, quantifying the energy saved after renovation;

[0130] S 舒适度 : User comfort improvement value, reflecting the comfort improvement brought about by building energy efficiency transformation.

[0131] 3. Construction of energy efficiency evaluation model:

[0132] The data envelopment analysis method (DEA) is used to construct an energy efficiency evaluation model for building groups. The overall energy efficiency of the building group is evaluated by analyzing the input and output variables, and the energy efficiency evaluation function is constructed:

[0133]

[0134] in:

[0135] y ij is the jth output of the i-th building;

[0136] x ik is the kth input of the i-th building;

[0137] m, n are the number of output and input variables respectively.

[0138] According to the DEA method evaluation results, the energy efficiency value of each building is i Sort and identify high-efficiency and low-efficiency buildings, and provide decision support for subsequent renovation or optimization.

[0139] Step 4: Calculate the optimal energy-saving transformation plan based on the multi-objective optimization model:

[0140] 1. Multi-objective optimization model setting:

[0141] Set up a multi-objective optimization model, the objective function includes the transformation cost C 改造 Minimization and energy saving potential 节能 consider:

[0142]

[0143] in: is the renovation cost of the i-th building; is the energy saving potential of the i-th building; i is the weight of energy-saving potential, reflecting the balance between energy-saving priority and budget constraints.

[0144] 2. Constraints:

[0145] Total budget limit:

[0146]

[0147] Among them, B 总预算 is the total available budget.

[0148] Renovation cycle limit:

[0149]

[0150] in, is the renovation time of the i-th building.

[0151] 3. Calculation of the optimal transformation plan:

[0152] Through optimization algorithms such as genetic algorithms and particle swarm optimization, the optimal energy-saving transformation plan is calculated to ensure that a plan that balances the transformation cost and energy-saving potential is found while meeting the constraints.

[0153] Step 5: Evaluate the linkage effect between building groups based on the regional energy sharing model:

[0154] By building a regional energy sharing model, the linkage effect between building groups is evaluated to improve energy efficiency. By calculating the energy surplus of each building, sharing optimization modeling, scheduling and optimization, efficient use of regional energy is ultimately achieved.

[0155] 1. Energy surplus calculation:

[0156] Calculate the excess energy of each building and set the energy transfer efficiency. i Energy Surplus S excess (b i ) can be calculated by the following formula:

[0157] Input: Building b i Total energy consumption E building (b i ), and building b i The power generation capacity P generation (b i ).

[0158] Among them, P generation (b i ) is building b i The power generation capacity (such as photovoltaic power generation, unit: kWh), E building (b i ) is building b i Total energy consumption (in kilowatt-hours).

[0159] Calculation formula:

[0160] S excess (b i )=P generation (b i )-E building (b i )

[0161] If S excess (b i )>0, it means building b i There is an energy surplus; if S excess (b i )≤0, it means building b i Insufficient energy.

[0162] 2. Shared optimization model:

[0163] Energy sharing modeling and optimization are carried out based on the energy consumption of the building group. Each building can share energy with other buildings to optimize the energy configuration of the group.

[0164] Input: Energy consumption dataset E of building groups building ={E building (b 1 ),E building (b 2 ),…,E building (b n )}.

[0165] Setting: Energy transfer efficiency η transfer , indicating that energy from a building b i Transfer to another building b i Transmission efficiency η transfer The value range is 0≤η transfer ≤1.

[0166] For each pair of buildings b i and b j If building b i There is an energy surplus, and the building b j If energy is needed, energy sharing can be carried out. j The energy demand is D demand (b j ), then building b i To building b j Shared energy S shared (b i ,b j ) is calculated as:

[0167] S shared (b i ,b j )=min(S excess (b i ),D demand (b j ))×η transfer

[0168] Among them, S shared (b i ,b j ) is building b i To building b j Amount of energy shared.

[0169] 3. Scheduling and optimization:

[0170] Through optimization algorithms such as linear programming and mixed integer programming, the energy sharing and scheduling efficiency among building groups are analyzed to ensure maximum energy utilization in the region.

[0171] Input: Energy sharing dataset S among building groups shared ={S shared (b i ,b j )}, the energy demand dataset D of the building demand ={D demand (b 1 ),D demand (b 2 ),…,D demand (b n )}, and the building energy surplus dataset S excess ={S excess (b 1 ),S excess (b 2 ),…,S excess (b n )}.

[0172] Optimization goal: Maximize the energy sharing benefits of the building groups in the region by optimizing the scheduling process. This optimization problem can be modeled by the following linear programming model:

[0173]

[0174] The constraints include:

[0175] For each building b j , its energy demand D demand (b j ) must meet:

[0176]

[0177] For each building b i , its energy surplus S excess (b i ) must meet:

[0178]

[0179] Solve the optimization problem and obtain the energy sharing amount S between each pair of buildings shared (b i ,b j ), and perform scheduling and energy allocation based on the results.

[0180] 4. Output results:

[0181] Output the optimized energy sharing scheme between building groups, that is, the energy sharing amount S between each pair of buildings shared (b i ,b j ), and the energy surplus or energy deficit for each building.

[0182] For each building b i , output its energy surplus S excess (b i ) and the amount of energy it receives from other buildings S received (b i ).

[0183]

[0184] Calculate the final energy status of each building:

[0185] S final (b i )=S excess (b i )-S received (b i )

[0186] If S final (b i )>0, it means building b i There is still energy left; if S final (b i )≤0, it means building b i Insufficient energy.

[0187] Step 6: Simulate and predict the energy-saving transformation effect of the building group:

[0188] 1. Simulation transformation effect:

[0189] Use simulation software (such as EnergyPlus, TRNSYS, etc.) to simulate and predict the energy consumption reduction of buildings after renovation, and predict the energy consumption E of each building after renovation. 改造 :

[0190] E 改造 =E 原始 -ΔE 节能

[0191] Where E 原始 represents the energy consumption before transformation, ΔE 节能 Indicates energy saving.

[0192] 2. Establish transformation priorities:

[0193] Priority model: Construct a priority scoring model based on multiple indicators:

[0194] S 优先级 =w 1 P 节能 +w 2 C 改造 +w 3 C 紧迫性

[0195] in:

[0196] w 1 ,w 2 ,w 3 The corresponding weights reflect the importance of indicators such as energy saving, transformation cost and urgency.

[0197] Weight calculation: The analytic hierarchy process (AHP) is used to calculate weights to ensure the consistency of the evaluation model.

[0198] Sorting and decision-making: Sort buildings according to priority scores, and give priority to the renovation of buildings with high energy-saving potential, high renovation returns and low investment costs.

[0199] Step 7: Generate an analysis report on the energy-saving transformation effect of the building group based on the prediction results and evaluation analysis:

[0200] 1. Analysis report generation:

[0201] Based on the prediction results and evaluation analysis, an analysis report on the energy-saving transformation effect of the building group is generated, and the analysis report includes:

[0202] Energy saving potential represents the energy savings after the renovation of each building;

[0203] Energy efficiency improvement:

[0204]

[0205] Optimization suggestions: Based on simulation results and evaluation analysis, provide energy-saving renovation suggestions for building groups, including technical solutions, return on investment, renovation priorities, etc.

[0206] In one embodiment, there is a building complex consisting of commercial, office and residential buildings, totaling 10 buildings (numbered b1 to b10). The building complex is located in a high-tech development zone, and the building owner hopes to reduce energy consumption, improve energy efficiency and achieve energy sharing within the building complex through energy-saving renovation.

[0207] Step 1: Data collection and multi-dimensional data set construction:

[0208] 1. Data collection:

[0209] Basic information: collect information such as the building area, service life, and building structure performance of each building. For example, the basic information of building b1 is:

[0210] Building area (square meters): 9000

[0211] Use life: 15 years

[0212] Building structure performance: reinforced concrete structure, good thermal insulation performance

[0213] Energy consumption data: Collect annual electricity, natural gas and water consumption data for each building. For example, the energy consumption data for building b1 is:

[0214] Electricity consumption (kWh): 2,500,000

[0215] Natural gas consumption (cubic meters): 100,000

[0216] Water consumption (cubic meters): 15,000

[0217] Environmental data: Collect environmental data around the building. For example, the environmental data of building b1 includes:

[0218] Average annual temperature: 20℃

[0219] Average annual humidity: 60%

[0220] Annual average wind speed: 3m / s

[0221] Average annual sunshine time: 6 hours

[0222] 2. Dataset construction:

[0223] The above data are integrated to construct a multidimensional dataset containing basic building information, energy consumption data, and environmental data, which is stored in the PostgreSQL database.

[0224] Step 2: Division of functional areas of building groups and construction of energy efficiency benchmark model:

[0225] 1. Functional area division of building groups:

[0226] According to the nature of the building's use, the building complex is divided into three functional areas:

[0227] Commercial area (b1, b2, b3): mainly used for retail, catering and entertainment.

[0228] Office area (b4, b5, b6): mainly used for office.

[0229] Residential area (b7, b8, b9, b10): mainly used for living.

[0230] 2. Energy efficiency benchmark model construction:

[0231] By collecting the unit energy consumption data of each building, the relative energy efficiency level of each building in the building group is analyzed. For example, the unit energy consumption of building b1 is:

[0232]

[0233] Based on the functional area division, the average energy efficiency level of each functional area is calculated:

[0234] Average energy consumption in commercial areas: 280 kWh / m2

[0235] Average unit energy consumption of office area: 180 kWh / m2

[0236] Average specific energy consumption in residential areas: 150 kWh / m2

[0237] By comparing the unit energy consumption of each building with the average unit energy consumption of the functional area to which it belongs, high energy consumption buildings can be identified. For example, the energy efficiency performance of building b1 is poor, ranking first in the commercial area.

[0238] Step 3: Energy efficiency evaluation based on DEA method:

[0239] 1. Calculation of unit energy consumption:

[0240] The specific energy consumption of each building is calculated as the basic indicator for energy efficiency evaluation.

[0241] 2.DEA method construction:

[0242] Define input variables, including building unit energy consumption, renovation costs, and equipment depreciation rate.

[0243] Define output variables, including energy saving potential and user comfort improvement value.

[0244] 3. Construction of energy efficiency evaluation model:

[0245] Using the DEA method, the overall energy efficiency of the building group is evaluated. For example, the energy efficiency value of building b1 is 0.82, ranking fifth in the building group.

[0246] Step 4: Energy-saving transformation plan based on multi-objective optimization model:

[0247] 1. Multi-objective optimization model setting:

[0248] Set the objective function, taking into account the renovation cost and energy saving potential. For example, the renovation cost of building b1 is 1,200,000 yuan, and the estimated energy saving potential is 10%.

[0249] 2. Constraints:

[0250] The time period is set to one year and the total budget is RMB 5,000,000.

[0251] 3. Calculation of the optimal transformation plan:

[0252] Through the optimization algorithm, the optimal energy-saving renovation plan is calculated. For example, buildings b1, b3 and b6 are selected for renovation, with a total investment of 3,500,000 yuan and an estimated total energy saving of 250,000 kWh.

[0253] Step 5: Regional energy sharing model:

[0254] 1. Energy surplus calculation:

[0255] Calculate the energy surplus of each building. For example, if building b1 generates 2,600,000 kWh of electricity and consumes 2,500,000 kWh of energy during a certain period of time, its energy surplus is:

[0256] S excess (b1) = 2600000-2500000 = 100000 kWh

[0257] 2. Shared optimization model:

[0258] Assuming the energy transfer efficiency is 0.95, calculate the energy sharing between building groups. For example, building b1 can provide 50,000 kWh of energy to building b2.

[0259] 3. Scheduling and optimization:

[0260] Through linear programming, energy sharing and scheduling among building groups can be optimized. For example, b1, b4, and b7 in the building group can participate in energy sharing to maximize energy utilization.

[0261] 4. Output results:

[0262] Output the energy sharing plan, including the sharing amount between each pair of buildings and the energy surplus or deficit of each building. For example, the final state of building b1 is:

[0263] S final (b1) = 100,000-50,000 = 50,000 kWh

[0264] Step 6: Simulation and prediction:

[0265] 1. Simulation transformation effect:

[0266] TRNSYS software was used to simulate the energy consumption of the buildings after renovation. For example, the energy consumption of building b1 was expected to be reduced by 2,250,000 kWh after renovation.

[0267] 2. Establish transformation priorities:

[0268] According to the simulation results, energy-saving renovation priorities are formulated in combination with the importance of the building, renovation costs, and energy-saving potential.

[0269] Step 7: Generate analysis report:

[0270] 1. Analysis report generation:

[0271] Based on the prediction results and evaluation analysis, an analysis report on the energy-saving transformation effect of the building group is generated. For example, the report content includes:

[0272] Energy saving potential: The energy saving potential of building b1 after renovation is 10%, and it is expected to save 250,000 kWh of energy consumption.

[0273] Energy efficiency improvement:

[0274]

[0275] Optimization suggestions: It is recommended to carry out LED lighting renovation on building B1 and improve the air tightness of the building to achieve energy saving goals.

[0276] Through the above specific scenario embodiments, the present invention can efficiently evaluate and optimize the energy-saving transformation effect of building groups in practical applications, realize energy sharing among building groups, and improve the regional energy utilization level.

[0277] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A building energy-saving transformation analysis and evaluation system, characterized in that: include: Data collection module, used to collect basic information, energy consumption data and environmental data of buildings in the target area or building group, and construct a multi-dimensional data set; The functional area division module is used to divide the building group into multiple functional areas according to the use nature, building structure and energy consumption characteristics of the building. At the same time, by collecting the unit energy consumption data of each building, the relative energy efficiency level of each building in the building group is analyzed, an energy efficiency benchmark model is constructed, high-energy consumption buildings are identified, and the energy efficiency performance ranking of the building in the group is obtained; The energy efficiency evaluation module calculates the unit energy consumption of each building based on the data envelopment analysis method, constructs input variables and output variables, and builds an energy efficiency evaluation model for the building group. By analyzing the input and output variables, the overall energy efficiency of the building group is evaluated, and an energy efficiency evaluation function is constructed to obtain the overall energy efficiency evaluation results of the building group. The optimization calculation module calculates the optimal energy-saving transformation plan based on a multi-objective optimization model by considering the transformation cost, energy-saving potential and other constraints; The shared energy module is used to calculate the energy surplus of each building, identify buildings with energy surplus, and establish an energy sharing optimization model. By modeling the sharing of energy surplus between buildings, the energy configuration in the region is optimized and the energy utilization efficiency is improved. The energy sharing and scheduling efficiency between building groups is analyzed through optimization algorithms to ensure the overall maximum utilization of energy in the region. The simulation prediction module is used to simulate and predict the energy-saving renovation effects of building groups using simulation software, and to formulate energy-saving renovation priority rankings based on the simulation results; The report generation module generates an analysis report on the energy-saving renovation effect of the building group based on the prediction results and evaluation analysis. The analysis report includes energy-saving potential, energy efficiency improvement and optimization suggestions, providing a decision-making basis for subsequent energy-saving renovation.

2. A method for analyzing and evaluating a building energy-saving transformation system, using the building energy-saving transformation analysis and evaluation system as claimed in claim 1, comprising the following steps: Step 1: Collect basic information, energy consumption data and environmental data of buildings in the target area or building group to construct a multi-dimensional data set; Step 2: Divide the building groups into functional areas and construct a building group energy efficiency benchmark model. Use the building group energy efficiency benchmark model to identify building groups with high energy consumption. The specific analysis process is as follows: Divide the building groups into multiple functional areas according to the use nature, building structure and energy consumption characteristics of the buildings; Based on the energy efficiency benchmark model of the building group, by collecting the unit energy consumption data of each building, analyzing the relative energy efficiency level of each building in the building group, and constructing the energy efficiency benchmark model; By comparing the unit energy consumption values ​​of each building, we can identify high-energy-consuming buildings and rank the building in terms of energy efficiency performance among the group. Step 3: Based on the data envelopment analysis method, a group-level energy efficiency evaluation model is constructed. The specific analysis process is as follows: Calculate the unit energy consumption of each building, that is, the energy consumption per square meter or cubic meter of building area, as the basic indicator of energy efficiency evaluation; Based on the data envelopment analysis method, input variables are constructed, including: building unit energy consumption, renovation cost, equipment depreciation rate, and output variables, including: energy saving potential, user comfort improvement value; The energy efficiency evaluation model of building groups is constructed by using data envelopment analysis method. The overall energy efficiency evaluation of building groups is evaluated by analyzing input and output variables, and the energy efficiency evaluation function is constructed to obtain the overall energy efficiency evaluation results of building groups. According to the evaluation results of the data envelopment analysis method, the energy efficiency value of each building is ranked to determine high-efficiency buildings and low-efficiency buildings, providing decision support for subsequent renovation or optimization; Step 4: Based on the multi-objective optimization model, the optimal energy-saving renovation plan is calculated in combination with the renovation cost, energy-saving potential and other constraints; Step 5: Construct a regional energy sharing model to evaluate the linkage effect between building groups. The specific analysis process is as follows: calculate the energy surplus of each building, that is, the difference between the power generation and energy consumption of the building in a certain period of time, and identify the buildings with energy surplus; Based on the energy consumption of the building group, an energy sharing optimization model is established. By modeling the sharing of energy surplus between buildings, the energy configuration in the region is optimized and the energy utilization efficiency is improved. Through linear programming or mixed integer programming optimization algorithms, analyze the energy sharing and scheduling efficiency among building groups to maximize the overall utilization of energy in the region; Output the optimized energy sharing plan between building groups, including the energy sharing amount between each pair of buildings and the energy surplus or gap of each building, to ensure the optimal energy utilization among the building groups; Step 6: Simulate and predict the energy-saving renovation effects of the building group, and formulate energy-saving renovation priorities based on the simulation results; Step 7: Based on the prediction results and evaluation analysis, generate an analysis report on the energy-saving renovation effect of the building group. The analysis report includes energy-saving potential, energy efficiency improvement and optimization suggestions, providing a decision-making basis for subsequent energy-saving renovation.

3. A method for analyzing and evaluating a building energy-saving transformation system according to claim 2, characterized in that: The step one further includes: formatting the building area data to ensure that all data are unified into square meters; converting the building's service life data into a standard format; cleaning and marking the building energy consumption data to handle outliers, missing values ​​and duplicate data.

4. The method of a building energy-saving transformation analysis and evaluation system according to claim 2, characterized in that: The calculation of the unit energy consumption index includes the following steps: By evaluating the energy consumption combination of building groups, including data on electricity, natural gas, and water energy consumption, the building groups can be divided into more detailed functional areas; using the energy consumption data E 建筑 and building area data A 建筑 , calculate the unit energy consumption index of the building Compare the building's unit energy consumption index with the energy efficiency benchmark model of the functional area to identify high-energy-consuming buildings.

5. The method of a building energy-saving transformation analysis and evaluation system according to claim 2, characterized in that: The data envelopment analysis method is based on the following variables to evaluate the overall energy efficiency of the group: Define input variables: unit energy consumption of the building, renovation cost and equipment depreciation rate and output variables: energy saving potential of the building and user comfort improvement value; Construct a data envelopment analysis method model; By calculating: Where: y ij is the j-th output of the i-th building, x ik is the kth input of the i-th building, m and n are the number of output and input variables respectively; output the relative energy efficiency value of each building; determine the high-efficiency and low-efficiency buildings.

6. The method of a building energy-saving transformation analysis and evaluation system according to claim 2, characterized in that: The objective function calculation steps of the multi-objective optimization model are: Set the goal to minimize the cost of transformation C 改造 , while considering the energy saving potential P i 节能 , iterative optimization is performed through genetic algorithms to calculate the optimal transformation plan; Including energy saving, renovation sequence and budget allocation: Where: C i 改造 is the renovation cost of the i-th building.

7. The method of a building energy-saving transformation analysis and evaluation system according to claim 2, characterized in that: The step five further comprises: Calculate the energy surplus S for each building excess (b i ), that is, the building's power generation capacity P generation (b i ) and total energy consumption E building (b i ) Energy consumption dataset based on building groups building and energy surplus S excess , build an energy sharing optimization model, and analyze the energy sharing and scheduling efficiency among building groups through linear programming or mixed integer programming optimization algorithms; Output the optimized energy sharing plan between the building groups, including the sharing amount S between each pair of buildings shared (b i ,b j ) and the energy surplus or deficit S for each building final .

8. The method of a building energy-saving transformation analysis and evaluation system according to claim 2, characterized in that: The energy-saving renovation priority ranking of the building group is based on the following steps: Define energy saving potential, renovation cost and urgency evaluation indicators; calculate weights using the analytic hierarchy process; According to the priority scoring model: S 优先级 =w1P 节能 +w2C 改造 +w3C 紧迫性 Among them: w1, w2, w3 are corresponding weights, reflecting the importance of energy saving, transformation cost and urgency indicators; Sort the buildings; renovate high-scoring buildings first.

9. The method of a building energy-saving transformation analysis and evaluation system according to claim 2, characterized in that: The evaluation steps of the energy-saving transformation effect include: Obtain energy consumption data before and after the renovation; Calculate and evaluate the energy efficiency improvement of buildings: Among them, E 改造 Energy efficiency after energy saving transformation, E 原始 for energy efficiency before renovation; Generate an energy-saving transformation effect analysis report based on energy consumption reduction and cost savings.

10. The method of a building energy-saving transformation analysis and evaluation system according to claim 2, characterized in that: The step six further comprises: using the energy consumption data E of the building building and environmental data E 环境 Conduct comprehensive simulations to ensure that the prediction results take into account the impact of environmental factors and improve the accuracy of the prediction.

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