Building envelope energy saving scheme optimization method based on energy consumption simulation data analysis

Abstract

The present application relates to the technical field of building energy-saving design, in particular to a building envelope energy-saving scheme optimization method based on energy consumption simulation data analysis, comprising: obtaining the building envelope design scheme to be evaluated and annual hourly heat load, cold load and comprehensive energy consumption simulation data; calling an improved particle swarm optimization algorithm which is adaptive to the particle learning mode according to the dynamic coupling relationship between energy consumption simulation and envelope parameters, completing the iterative optimization of design scheme parameters and generating an optimized parameter set; performing multi-objective normalization processing on the optimized parameters to obtain the optimized values of heat transfer coefficient, shading coefficient, air tightness grade, etc.; inputting the comprehensive evaluation results into an economic evaluation model, combining energy prices and material costs, screening the economic feasibility scheme list that meets the energy-saving rate constraint, and finally determining the building envelope energy-saving optimization scheme.

Description

Technical Field

[0001] This invention relates to the field of building energy-saving design technology, and in particular to a method for optimizing energy-saving schemes for building envelopes based on energy consumption simulation data analysis. Background Technology

[0002] The structural parameters of a building envelope directly determine the building's annual heating and cooling load output and overall energy consumption level. Building energy-saving design generally relies on energy consumption simulation results for scheme demonstration. Traditional building envelope energy-saving design often uses static standard parameter selection methods, failing to comprehensively utilize hourly heating load, cooling load, and comprehensive energy consumption data throughout the year as design basis, thus failing to fully leverage energy consumption simulation data. Conventional particle swarm optimization algorithms use fixed learning modes and cannot adjust rules based on the dynamic coupling relationship between energy consumption simulation data and building envelope parameters. The parameter optimization process tends to converge prematurely, making it difficult to obtain globally optimal building envelope parameters.

[0003] The dimensions and index systems of various performance parameters of the building envelope differ, and the optimization parameter set lacks a standardized multi-objective normalization process, making it impossible to simultaneously integrate the optimization results of key indicators such as heat transfer coefficient, shading coefficient, and airtightness level. Energy-saving scheme evaluations only focus on energy-saving effects, failing to incorporate local energy prices and building envelope material costs for quantitative accounting, and thus cannot generate multiple alternative economic schemes under a given energy-saving rate constraint.

[0004] Traditional design processes lack an adaptive parameter optimization mechanism driven by energy consumption data, and performance parameter evaluation and economic assessment are separated, failing to form a systematic framework. The construction industry urgently needs to establish an intelligent parameter optimization model based on hourly energy consumption simulation data, build a multi-objective parameter normalization evaluation framework, integrate energy consumption indicators and economic costs to complete scheme selection, and meet the industry application requirements of accurate optimization of energy-saving design parameters for building envelopes, unified evaluation of indicators, and economic feasibility implementation. Summary of the Invention

[0005] The purpose of this invention is to address the shortcomings of existing technologies by proposing an optimization method for energy-saving building envelopes based on energy consumption simulation data analysis.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: a method for optimizing energy-saving schemes for building envelopes based on energy consumption simulation data analysis, comprising: Obtain the target building envelope design scheme and its corresponding energy consumption simulation data set of the building to be evaluated. The energy consumption simulation data set includes the building's heat load data, cooling load data and comprehensive energy consumption data under hourly operating conditions throughout the year. Based on the heat load data, cooling load data, and comprehensive energy consumption data, an improved particle swarm optimization algorithm is invoked to iteratively optimize the parameters of the target building envelope design scheme, generating a set of optimized building envelope parameters. The improved particle swarm optimization algorithm adaptively adjusts the learning mode of the particles according to the dynamic coupling relationship between simulated energy consumption and building envelope parameters. The set of optimization parameters for the building envelope is subjected to multi-objective normalization to generate a comprehensive evaluation result that includes optimized values ​​for heat transfer coefficient, shading coefficient, and airtightness level. The comprehensive evaluation results are input into the preset economic evaluation model, and combined with local energy price and material cost data, a list of economically feasible solutions that meet the preset energy saving rate constraints is calculated. Based on the aforementioned list of economically feasible options, a final energy-saving optimization scheme for the building envelope is generated.

[0007] As a further aspect of the present invention, the step of obtaining the target building envelope design scheme and its corresponding energy consumption simulation data set of the building to be evaluated includes: Collect the design drawings and technical specifications of the building to be evaluated, and extract the building geometric model, spatial functional zoning, personnel and equipment work and rest schedules and detailed drawings of the building envelope from them; Based on the building's geometric model, spatial functional zoning, and personnel and equipment work and rest schedules, a physical model of the building is established in the energy consumption simulation software, and corresponding indoor thermal disturbance parameters are set. Based on the detailed construction drawings of the building envelope, initial thermal parameters of the building envelope are set in the physical model. The thermal parameters include the heat transfer coefficient, solar heat gain coefficient, and visible light transmittance of the walls, roof, windows, and shading components in each orientation. Set typical annual meteorological data for the building's location to the physical model, and select the corresponding energy consumption simulation engine; implementation steps; The energy consumption simulation engine is run to perform dynamic simulation calculations throughout the year, and outputs hourly heat load data, cooling load data and comprehensive energy consumption data to form the energy consumption simulation data set.

[0008] As a further aspect of the present invention, based on the detailed structural drawings of the enclosure structure, initial thermal parameters of the enclosure structure are set in the physical model, including: Identify the type, thickness, and arrangement order of each layer of material in the detailed structural drawing of the enclosure structure; Query the thermal parameters database of building materials to obtain the thermal conductivity, heat storage coefficient and correction factor of each material under standard operating conditions; Based on the material arrangement, thickness, and thermal conductivity, calculate the overall heat transfer coefficient of two types of opaque components: walls and roofs. Based on the type of window profile, number of glass layers, glass type, and spacer gas, determine the window's heat transfer coefficient, solar heat gain coefficient, and visible light transmittance. Calculate the external shading coefficient based on the type, size, installation location, and angle of the shading component; The calculated comprehensive heat transfer coefficient, heat transfer coefficient of the external window, solar heat gain coefficient, visible light transmittance, and external shading coefficient are used as initial values ​​for the corresponding building envelope components in the physical model.

[0009] As a further aspect of the present invention, the improved particle swarm optimization algorithm adaptively adjusts the learning mode of particles based on the dynamic coupling relationship between simulated energy consumption and building envelope parameters, including: Each particle in the particle swarm is defined as representing a combination of building envelope parameters, which includes the heat transfer coefficient of the exterior wall, the heat transfer coefficient of the exterior window, the window-to-wall ratio, and the shading structure parameters. Initialize the particle swarm and randomly generate the initial positions and initial velocities of the particles. The initial positions represent the initial values ​​of the enclosure structure parameters, and the initial velocities represent the direction and magnitude of parameter adjustments. An objective function is constructed, which uses the simulated total annual energy consumption of the building as an evaluation index. The total annual energy consumption is calculated based on the sum of the heat load data and the cooling load data. In each iteration, the fitness of the enclosure structure parameter combination represented by the current particle is calculated based on the objective function value corresponding to each particle. Based on the fitness, the weight distribution of particles between individual learning factors and social learning factors is dynamically adjusted, wherein particles with fitness lower than the current population average fitness will receive higher individual learning weights to enhance their exploration of their own historical optimal solutions. Calculate the sensitivity matrix of simulated energy consumption changes relative to changes in building envelope parameters based on the dynamic coupling relationship; The particle velocity update process is corrected based on the sensitivity matrix, so that the particle's motion direction preferentially moves toward the parameter adjustment direction with the highest energy consumption reduction during the iteration process; After the particles complete the velocity and position update, the combination of the enclosure structure parameters is used as input to re-execute the energy consumption simulation to obtain the updated total annual energy consumption value. Compare the updated total annual energy consumption value with the total annual energy consumption value corresponding to the historical best solution, and update the individual historical best solution and the global historical best solution. Repeat the steps of fitness calculation, learning factor weight adjustment, sensitivity matrix correction, particle velocity and position update, energy consumption simulation and optimal solution update until the preset convergence condition is met or the maximum number of iterations is reached. Output the combination of retaining structure parameters corresponding to the global historical optimal solution as the set of retaining structure optimization parameters.

[0010] As a further aspect of the present invention, the set of optimization parameters for the building envelope is subjected to multi-objective normalization processing to generate a comprehensive evaluation result including optimized values ​​for heat transfer coefficient, shading coefficient, and airtightness level, including: The optimized values ​​of the heat transfer coefficient of the exterior wall, the heat transfer coefficient of the roof, the heat transfer coefficient of the exterior window, and the shading coefficient of the exterior window are extracted from the set of optimized parameters of the building envelope. The optimized values ​​of the heat transfer coefficient of the exterior wall, the roof, and the window are weighted and averaged to calculate the optimized value of the overall heat transfer coefficient of the building. The weighting is determined based on the area ratio of each building envelope. The optimized value of the shading coefficient of the exterior window is seasonally corrected. The seasonal correction is based on the annual variation of the solar altitude angle of the building's location to adjust the effective shading coefficient for different seasons. Combining the data on the length of door and window gaps in the set of optimized parameters for the building envelope, and the airtightness test data of door and window nodes, the optimized value of the overall airtightness level of the building is calculated. The optimized values ​​of the overall building heat transfer coefficient, the seasonally corrected effective shading coefficient, and the optimized values ​​of the overall building air tightness level are standardized to fall within the range of zero to one, thus forming the comprehensive evaluation result.

[0011] As a further aspect of the present invention, the optimized value of the external window shading coefficient is seasonally corrected, including: Obtain the annual solar trajectory data for the building's location, including the solar altitude angle and azimuth angle for typical days each month; Calculate the effective solar radiation illuminance received by the exterior windows of each major orientation of the building on a typical day of each month; Based on the effective solar radiation illuminance, calculate the monthly solar heat gain coefficient of the exterior window under unshaded conditions; The optimized value of the external window shading coefficient is regarded as the shading efficiency of the fixed shading device, and its effective shading coefficient is calculated month by month in actual operation. The calculation of the effective shading coefficient needs to take into account the influence of changes in the solar incidence angle on the actual shading effect of the shading components. The calculated monthly effective shading coefficients are categorized according to the heating season, transition season, and cooling season to obtain the seasonally corrected effective shading coefficients.

[0012] As a further aspect of the present invention, the comprehensive evaluation results are input into a preset economic evaluation model, and combined with local energy price and material cost data, a list of economically feasible solutions that meet the preset energy-saving rate constraints is calculated, including: The economic evaluation model includes an incremental cost calculation module, an operating cost saving calculation module, and an investment payback period calculation module. The incremental cost calculation module receives the comprehensive evaluation results and queries the corresponding material and structure unit price database based on the optimized values ​​of the heat transfer coefficient and the shading coefficient to calculate the incremental material cost and construction cost relative to the benchmark scheme. The operating cost saving calculation module, based on the comprehensive evaluation results, calls a simplified energy consumption estimation model to calculate the annual energy savings for heating, cooling and lighting after adopting the optimized scheme, and combines the local energy price data input by the economic evaluation model to convert it into annual operating cost savings. The investment payback period calculation module calculates the static investment payback period based on the incremental material cost, incremental construction cost, and annual operating cost savings. An energy-saving rate threshold is set as the preset energy-saving rate constraint. Combinations of building envelope parameters that meet the energy-saving rate requirements and have a static investment payback period shorter than the preset number of years are selected to form the economically feasible scheme list.

[0013] As a further aspect of the present invention, the operating cost saving calculation module, based on the comprehensive evaluation results, calls a simplified energy consumption estimation model to calculate the annual energy savings for heating, cooling, and lighting after adopting the optimized scheme, including: The simplified energy consumption estimation model includes a heating quarter energy consumption estimation sub-model, a cooling quarter energy consumption estimation sub-model, and a lighting energy consumption estimation sub-model. The heating season energy consumption estimation sub-model calculates the theoretical heat load for the heating season based on the optimized values ​​of the overall building heat transfer coefficient and the overall building air tightness level, as well as the number of heating days in the building's location, and then multiplies it by the average energy efficiency coefficient of the heating system to obtain the estimated heating energy consumption. The cooling quarterly energy consumption estimation sub-model calculates the theoretical cooling load for the cooling season based on the seasonally corrected effective shading coefficient, the optimized value of the overall building heat transfer coefficient, and the air conditioning hours at the building location. Then, it multiplies the theoretical cooling load by the average energy efficiency coefficient of the cooling system to obtain the estimated cooling energy consumption. The lighting energy consumption estimation sub-model estimates the potential reduction in artificial lighting energy consumption based on the visible light transmittance parameters of the exterior windows and the natural lighting standards of each functional space in the building. Subtract the corresponding estimated values ​​under the optimized scheme from the estimated values ​​of heating energy consumption, cooling energy consumption, and lighting energy consumption under the baseline design scheme to obtain the energy savings in heating, cooling energy consumption, and lighting energy consumption.

[0014] As a further aspect of the present invention, based on the aforementioned list of economically feasible solutions, a final energy-saving optimization scheme for the building envelope is generated, including: For each scheme in the economic feasibility list, its comprehensive technical performance score is calculated. The comprehensive technical performance score is calculated by weighting the optimized value of the overall building heat transfer coefficient, the seasonally corrected effective shading coefficient, and the optimized value of the overall building air tightness level of the corresponding scheme. For each option in the list of economically feasible options, its comprehensive economic score is calculated. The comprehensive economic score is calculated based on a weighted average of static investment payback period, incremental cost, and annual operating cost savings. Set weighting coefficients for technical performance and economic efficiency, and sum the weighted scores of the comprehensive technical performance and economic efficiency of each scheme to obtain the total score of the scheme. The list of economically feasible solutions is sorted from highest to lowest based on the total score. The scheme with the highest overall score is selected, and its corresponding set of building envelope optimization parameters, economic indicators, and robustness evaluation conclusions are extracted and integrated to generate a final building envelope energy-saving optimization scheme document containing technical parameters, economic indicators, and implementation points.

[0015] As a further aspect of the present invention, the method further includes performing sensitivity analysis and robustness verification on the optimized scheme, including: Based on the set of optimized parameters for the building envelope, positive and negative fluctuations are applied to key parameters to form multiple parameter perturbation schemes; For each set of parameter perturbation schemes, re-execute the complete energy consumption simulation to obtain the corresponding energy consumption simulation results; The energy consumption simulation results corresponding to each set of parameter disturbance schemes are compared with the energy consumption simulation results of the baseline optimization scheme, and the energy consumption change rate is calculated. Analyze the relationship between the energy consumption change rate and the parameter disturbance amplitude to determine the sensitivity ranking of each building envelope parameter to the total energy consumption. By changing the input meteorological data and using at least another set of typical meteorological year data, the energy consumption performance of the building envelope energy-saving optimization scheme under different climatic conditions is re-evaluated. Based on the comprehensive sensitivity analysis results and energy consumption performance under different climatic conditions, the robustness of the proposed energy-saving optimization scheme for the building envelope is evaluated, and a robustness assessment report is generated.

[0016] Compared with the prior art, the advantages and positive effects of the present invention are as follows: The particle swarm optimization algorithm adaptively adjusts the particle learning mode based on the dynamic coupling relationship between simulated energy consumption and building envelope parameters. It relies on hourly heat load, cooling load, and comprehensive energy consumption data throughout the year for iterative parameter optimization, dynamically changing the iteration rules based on the inherent correlation characteristics of the data. This overcomes the problem of fixed search paths caused by fixed learning modes, expands the traversal range of the parameter space, and reduces the likelihood of convergence getting trapped in local maxima. It fully preserves the correlation characteristics between energy consumption conditions and building envelope parameters, outputting a set of optimized building envelope parameters that matches the building's energy consumption operation patterns, providing a reliable parameter foundation for subsequent evaluation stages.

[0017] Multi-objective normalization of the parameter set for building envelope optimization can eliminate dimensional differences among various performance parameters and unify the quantitative standards for indicators such as heat transfer coefficient, shading coefficient, and airtightness level. This achieves the integration and standardization of multi-dimensional performance parameters, forming a standardized comprehensive evaluation result and eliminating the problem of inconsistent evaluation logic among different indicators.

[0018] The normalized comprehensive evaluation results are imported into the economic assessment model, and local energy price and material cost data are incorporated for quantitative calculation. This enables the screening and ranking of multiple schemes under a given energy-saving rate constraint. A list of economically feasible schemes with reference value is generated, establishing a correlation between energy-saving performance and construction costs. Based on the scheme list, the final energy-saving optimization scheme for the building envelope is formed, constructing a complete design process from energy consumption simulation analysis, intelligent parameter optimization, multi-objective normalized evaluation to economic screening. The calculation logic and judgment criteria for building envelope energy-saving optimization are standardized, the execution process of each stage of the design is standardized, and the application mode of systematic parameter optimization and scheme decision-making in building energy-saving engineering is adapted. Attached Figure Description

[0019] Figure 1 This is a state diagram of the energy-saving scheme optimization method for building envelope based on energy consumption simulation data analysis as described in this invention. Figure 2 A flowchart for obtaining energy consumption simulation data sets; Figure 3 A flowchart for generating multi-objective normalized comprehensive evaluation results. Detailed Implementation

[0020] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0021] In the description of this invention, it should be understood that the terms "length," "width," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," and "outer," etc., indicating orientation or positional relationships, are based on the orientation or positional relationships shown in the accompanying drawings and are only for the convenience of describing the invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the invention. Furthermore, in the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0022] See Figure 1 The present invention provides a method for optimizing energy-saving schemes for building envelopes based on energy consumption simulation data analysis. The overall implementation scheme is as follows: First, the target building envelope design scheme and its corresponding energy consumption simulation data set are obtained. This energy consumption simulation data set includes the building's heat load data, cooling load data, and comprehensive energy consumption data under hourly operating conditions throughout the year. Second, based on the heat load data, cooling load data, and comprehensive energy consumption data, an improved particle swarm optimization algorithm is used to iteratively optimize the parameters of the target building envelope design scheme, generating a set of optimized parameters for the building envelope. This improved particle swarm optimization algorithm adaptively adjusts the learning mode of the particles based on the dynamic coupling relationship between simulated energy consumption and building envelope parameters. Third, the set of optimized parameters for the building envelope is subjected to multi-objective normalization processing to generate a comprehensive evaluation result including optimized values ​​for heat transfer coefficient, shading coefficient, and airtightness level. Fourth, the comprehensive evaluation result is input into a preset economic evaluation model. Combined with local energy prices and material cost data, a list of economically feasible schemes that meet the preset energy-saving rate constraints is calculated. Fifth, based on the list of economically feasible schemes, the final energy-saving optimization scheme for the building envelope is generated.

[0023] In one embodiment of the present invention, the specific process of acquiring energy consumption simulation data and setting initial parameters is described. See also... Figure 2 The process involves collecting design drawings and technical specifications of the building to be evaluated, extracting its geometric model, spatial functional zoning, personnel and equipment work schedules, and detailed structural drawings of the building envelope. Based on these elements, a physical model of the building is created in energy simulation software, and corresponding indoor thermal disturbance parameters are set. Initial thermal parameters for the building envelope are set in the physical model based on the detailed structural drawings. These parameters include the heat transfer coefficient, solar heat gain coefficient, and visible light transmittance of walls, roof, windows, and shading components in each orientation. Typical meteorological data for the building's location is used to set the physical model, and a corresponding energy simulation engine is selected. The energy simulation engine is then run to perform dynamic simulation calculations throughout the year, outputting hourly heat load data, cooling load data, and comprehensive energy consumption data, forming an energy simulation dataset.

[0024] The specific steps for setting initial thermal parameters based on the detailed structural drawings of the building envelope, as described above, include: identifying the type, thickness, and arrangement order of materials in each layer of the detailed structural drawings; querying a database of building material thermal parameters to obtain the thermal conductivity, heat storage coefficient, and correction coefficient for each material under standard operating conditions; calculating the comprehensive heat transfer coefficient of the two types of opaque components—walls and roofs—based on the material arrangement order, thickness, and thermal conductivity; determining the heat transfer coefficient, solar heat gain coefficient, and visible light transmittance of the windows based on their profile type, number of glass layers, glass type, and spacer gas; calculating the external shading coefficient of the shading components based on their type, size, installation location, and angle; and assigning the calculated comprehensive heat transfer coefficient, window heat transfer coefficient, solar heat gain coefficient, visible light transmittance, and external shading coefficient as initial values ​​to the corresponding building envelope components in the physical model.

[0025] In practice, design drawings and technical specifications of the building to be evaluated are collected, from which the building's geometric model, spatial functional zoning, personnel and equipment work schedules, and detailed structural drawings of the building envelope are extracted. Based on the building's geometric model, spatial functional zoning, and personnel and equipment work schedules, a physical model of the building is established in energy consumption simulation software, and corresponding indoor thermal disturbance parameters are set. Based on the detailed structural drawings of the building envelope, initial thermal parameters of the building envelope are set in the physical model, including the heat transfer coefficient, solar heat gain coefficient, and visible light transmittance of walls, roof, windows, and shading components in each orientation. Typical meteorological year data for the building's location are set for the physical model, and a corresponding energy consumption simulation engine is selected and run to perform dynamic simulation calculations throughout the year, outputting hourly heat load data, cooling load data, and comprehensive energy consumption data, forming an energy consumption simulation dataset.

[0026] In some embodiments, the process of setting initial thermal parameters of the building envelope based on detailed structural drawings includes identifying the type, thickness, and arrangement order of materials in each layer of the detailed structural drawings. A database of building material thermal parameters is consulted to obtain the thermal conductivity, heat storage coefficient, and correction factor for each material under standard operating conditions. Based on the material arrangement order, thickness, and thermal conductivity, the combined heat transfer coefficient of the two types of opaque components—walls and roofs—is calculated. The formula for calculating the combined heat transfer coefficient is expressed as: ; in: The overall heat transfer coefficient is expressed in watts per square kelvin. This represents the total thermal resistance of the building envelope, measured in Kelvin per watt per square meter. Heat transfer resistance of the inner surface The sum of the thermal resistances of each material layer and the heat transfer resistance of the outer surface The composition is such that the thermal resistance of each material layer is calculated by dividing the material thickness by its thermal conductivity. Based on the window profile type, number of glass layers, glass type, and spacer gas, the heat transfer coefficient, solar heat gain coefficient, and visible light transmittance of the window are determined. The external shading coefficient is calculated based on the type, size, installation location, and angle of the shading components. The calculated overall heat transfer coefficient, window heat transfer coefficient, solar heat gain coefficient, visible light transmittance, and external shading coefficient are then used as initial values ​​for the corresponding building envelope components in the physical model.

[0027] Understandably, energy simulation software can include EnergyPlus, DesignBuilder, or IESVE. A database of building material thermal parameters can be stored locally or on a cloud server, containing standardized thermal performance parameters for common building materials. Indoor thermal disturbance parameters include occupancy density, equipment power density, and lighting power density, which are dynamically set based on spatial functional zoning and occupancy / equipment work schedules. Typical meteorological year data includes hourly dry-bulb temperature, wet-bulb temperature, solar radiation intensity, and wind speed and direction throughout the year; this data drives the energy simulation engine to perform dynamic calculations.

[0028] Optionally, detailed structural drawings of the building envelope are provided in the form of CAD drawings or BIM models. Material data is obtained by extracting layer information or component attributes using analysis software. The process of building the physical model includes importing the building geometry model into the energy consumption simulation software interface, defining thermal zone boundaries, and assigning spatial functional attributes. The choice of energy consumption simulation engine depends on the simulation accuracy and computational efficiency requirements; either DOE-2.2 or TRNSYS kernels can be used.

[0029] In some embodiments, after running the energy consumption simulation engine, the output hourly heat load data, cooling load data, and comprehensive energy consumption data are stored in CSV or SQLite format, forming a structured energy consumption simulation data set. The comprehensive energy consumption data is obtained by summing the heat load data and cooling load data after conversion using the system energy efficiency coefficient, which is determined based on the building's pre-set heating and cooling system types.

[0030] It is understandable that setting the initial thermal parameters of the building envelope is the foundation for subsequent optimization iterations, ensuring that the starting point of energy consumption simulation reflects actual design conditions. The calculation of the overall heat transfer coefficient is based on the one-dimensional steady-state heat transfer principle, the external window parameters are obtained from product certification databases or standard test reports, and the calculation of the external shading coefficient considers the relationship between geometric projection and the sun's position. In practice, all parameter assignment processes are completed through scripts or graphical interfaces, achieving automated data transfer and model updates.

[0031] In one embodiment of the present invention, the specific implementation process of the improved particle swarm optimization algorithm is described. Each particle in the particle swarm represents a combination of building envelope parameters, including the heat transfer coefficient of the exterior walls, the heat transfer coefficient of the exterior windows, the window-to-wall ratio, and shading parameters. The particle swarm is initialized by randomly generating the initial positions and velocities of the particles. The initial positions represent the initial values ​​of the building envelope parameters, and the initial velocities represent the direction and magnitude of parameter adjustments. An objective function is constructed, using the simulated total annual energy consumption of the building as the evaluation index. The total annual energy consumption is calculated by summing heat load data and cooling load data. In each iteration, the fitness of the building envelope parameter combination represented by the current particle is calculated based on the objective function value corresponding to each particle. Based on the fitness, the weight allocation between the individual learning factor and the social learning factor is dynamically adjusted. Particles with fitness lower than the current population average fitness will receive higher individual learning weights to enhance their exploration of their own historical optimal solutions. The sensitivity matrix of simulated energy consumption changes relative to changes in building envelope parameters is calculated based on the dynamic coupling relationship between simulated energy consumption and building envelope parameters. The particle velocity update process is corrected based on the sensitivity matrix, ensuring that the particle's motion direction prioritizes the parameter adjustment direction with the highest energy consumption reduction during iteration. After the particle completes velocity and position updates, the updated retaining structure parameter combination is used as input to re-execute the energy consumption simulation, obtaining the updated annual total energy consumption value. The updated annual total energy consumption value is compared with the annual total energy consumption value corresponding to the historical optimal solution, updating the individual historical optimal solution and the global historical optimal solution. The steps of fitness calculation, learning factor weight adjustment, sensitivity matrix correction, particle velocity and position update, energy consumption simulation, and optimal solution update are repeated until the preset convergence condition is met or the maximum number of iterations is reached. The retaining structure parameter combination corresponding to the global historical optimal solution is output as the retaining structure optimization parameter set.

[0032] In practical implementation, each particle in the particle swarm represents a combination of building envelope parameters, including the heat transfer coefficient of the exterior walls, the heat transfer coefficient of the exterior windows, the window-to-wall ratio, and shading parameters. The particle swarm is initialized by randomly generating initial positions and velocities for each particle. The initial position represents the initial value of the building envelope parameters, and the initial velocity represents the direction and magnitude of parameter adjustment. An objective function is constructed, using the simulated total annual building energy consumption as the evaluation index. This total annual building energy consumption is calculated by summing heat load and cooling load data. During each iteration, the fitness of the building envelope parameter combination represented by the current particle is calculated based on the objective function value. Based on the fitness, the weight allocation between the individual learning factor and the social learning factor is dynamically adjusted. Particles with fitness lower than the current population average fitness receive higher individual learning weights to enhance their exploration of their historical optimal solutions. The sensitivity matrix of simulated energy consumption changes relative to changes in building envelope parameters is calculated based on the dynamic coupling relationship between simulated energy consumption and building envelope parameters. The particle velocity update process is corrected based on the sensitivity matrix, so that the particle's motion direction prioritizes the parameter adjustment direction with the highest energy consumption reduction during the iteration process.

[0033] In some embodiments, the particle velocity update process is corrected based on the sensitivity matrix, and the corrected velocity update formula is expressed as: ; in: Indicates the first In the nth iteration The particle in the first Speed ​​in dimensions; Indicates inertia weight; Indicates the first In the nth iteration The particle in the first Speed ​​in dimensions; Indicates an individual's learning factor; Represents a random number between 0 and 1; Indicates as of the date The second iteration The particle in the first The individual's historical best position on the dimension; Indicates the first In the nth iteration The particle in the first Current position on the dimension; Represents social learning factors; Represents a random number between 0 and 1; Indicates as of the date In the second iteration, the entire particle swarm is at the... The global historical best position on the dimension; Indicates the sensitivity impact coefficient; This indicates that the sensitivity matrix corresponds to the first... The sensitivity components of each building envelope parameter are determined. After the particle completes the velocity and position update, the updated combination of building envelope parameters is used as input to re-execute the energy consumption simulation, obtaining the updated total annual energy consumption value of the building. The updated total annual energy consumption value of the building is compared with the total annual energy consumption value of the building corresponding to the historical best solution, and the individual historical best solution and the global historical best solution are updated accordingly.

[0034] It can be understood that the fitness calculation function is the reciprocal of the objective function value; the lower the building's total annual energy consumption, the higher the fitness value. The dynamic coupling relationship between simulated energy consumption and building envelope parameters is obtained through multiple simulation sampling and multiple linear regression analysis. Each component in the sensitivity matrix represents the change in simulated energy consumption caused by a unit change in the corresponding building envelope parameter. The strategy for dynamically adjusting the learning factor weights is to increase the individual learning factor when the particle fitness is lower than the population average fitness. The value is then reduced accordingly for the social learning factor. The value.

[0035] Optionally, particle position updates follow the formula ,in Indicates the first In the nth iteration The particle in the first The position on the dimension. Each parameter in the combination of building envelope parameters is constrained within a preset physically feasible range during the iteration process. For example, the range of values ​​for the external wall heat transfer coefficient is determined according to national energy-saving design standards. The convergence condition can be that the change in the building's total annual energy consumption value corresponding to the global historical optimal solution is less than a set threshold in several consecutive iterations, or that the preset maximum number of iterations is reached.

[0036] In some embodiments, when initializing the particle swarm, the initial positions of the particles are uniformly and randomly distributed within the feasible region of each enclosure structure parameter, and the initial velocity is set to zero or a small random value. The weight allocation of individual learning factors and social learning factors is based on the formula. and Perform calculations, where This represents the numerical value of the basic learning factor. Indicates the adjustment range of the learning factor. Indicates the first The fitness value of each particle. This represents the average fitness value of the current population. The steps of fitness calculation, learning factor weight adjustment, sensitivity matrix correction, particle velocity and position update, energy consumption simulation, and optimal solution update are repeated until the preset convergence condition is met or the maximum number of iterations is reached. The combination of retaining structure parameters corresponding to the global historical optimal solution is output as the retaining structure optimization parameter set.

[0037] It is understandable that the energy consumption simulation is automatically re-executed in each iteration by calling the application programming interface of the external simulation engine. The simulation engine receives the updated combination of building envelope parameters and returns the corresponding total annual energy consumption value of the building. The global historical optimal solution update mechanism compares the total annual energy consumption values ​​of the building corresponding to the individual historical optimal solutions of all particles, and selects the solution with the lowest total annual energy consumption value as the new global historical optimal solution. The set of optimized building envelope parameters is stored in the form of a data structure, including the optimized external wall heat transfer coefficient, external window heat transfer coefficient, window-to-wall ratio, and shading structure parameters.

[0038] In one embodiment of the present invention, the specific process of performing multi-objective normalization processing on the set of optimization parameters for the building envelope to generate a comprehensive evaluation result is described. See also... Figure 3 The optimized values ​​for the heat transfer coefficients of the exterior walls, roof, windows, and shading coefficients were extracted from the set of optimization parameters for the building envelope. A weighted average was then calculated to obtain the overall optimized heat transfer coefficient of the building, with the weights determined based on the area proportion of each building envelope component. A seasonal correction was applied to the window shading coefficient, adjusting the effective shading coefficient for different seasons according to the annual variation of the solar altitude angle at the building's location. Combined with data on door and window gap lengths from the set of optimization parameters for the building envelope, and airtightness test data for door and window joints, the overall optimized airtightness level of the building was calculated. Finally, the optimized values ​​for the overall heat transfer coefficient, the seasonally corrected effective shading coefficient, and the overall optimized airtightness level were standardized to fall within the range of zero to one, forming a comprehensive evaluation result.

[0039] The steps for seasonally correcting the optimized value of the window shading coefficient include: obtaining annual solar trajectory data for the building's location, including the solar altitude and azimuth angles on typical days each month; calculating the effective solar radiation illuminance received by the windows of each major orientation of the building on typical days each month; calculating the monthly solar heat gain coefficient of the windows under unshaded conditions based on the effective solar radiation illuminance; treating the optimized window shading coefficient as the shading efficiency of a fixed shading device and calculating its effective shading coefficient for each month in actual operation; considering the impact of changes in the solar incidence angle on the actual shading effect of the shading components in the calculation of the effective shading coefficient; and categorizing the calculated monthly effective shading coefficients according to the heating season, transition season, and cooling season to obtain the seasonally corrected effective shading coefficient.

[0040] In practice, the set of optimization parameters for the building envelope is subjected to multi-objective normalization to generate a comprehensive evaluation result that includes optimized values ​​for heat transfer coefficient, shading coefficient, and airtightness level. Optimized values ​​for heat transfer coefficient of exterior walls, roof, windows, and shading coefficient of exterior windows are extracted from the set of optimization parameters. These optimized values ​​are then weighted and averaged to calculate the overall optimized heat transfer coefficient of the building. The weighting is determined based on the area proportion of each building envelope component. Seasonal corrections are applied to the optimized shading coefficient of exterior windows, adjusting the effective shading coefficient for different seasons based on the annual variation of the solar altitude angle at the building's location. Finally, data on door and window gap lengths from the set of optimization parameters, along with airtightness test data for door and window joints, are used to calculate the overall optimized airtightness level of the building. The optimized values ​​of the overall building heat transfer coefficient, the seasonally corrected effective shading coefficient, and the optimized values ​​of the overall building air tightness level are standardized to fall within the range of zero to one, thus forming a comprehensive evaluation result.

[0041] In some embodiments, the weighted average formula used to calculate the optimized value of the overall building heat transfer coefficient is: ; in: This represents the optimized value of the overall building heat transfer coefficient, expressed in watts per square kelvin. Indicates the first The area of ​​the building envelope, such as the area of ​​exterior walls, roof, or exterior windows; Indicates the corresponding first Optimized values ​​for the heat transfer coefficient of the building envelope; This represents the sum of the areas of all the aforementioned building envelope structures. The process of seasonally correcting the optimized value of the window shading coefficient includes obtaining the annual solar trajectory data for the building's location, including the solar altitude angle and azimuth angle on typical days each month. The effective solar radiation illuminance received by the windows of each major orientation of the building on typical days each month is calculated. Based on the effective solar radiation illuminance, the monthly solar heat gain coefficient of the windows under unshaded conditions is calculated. The optimized value of the window shading coefficient is considered as the shading efficiency of a fixed shading device, and its effective shading coefficient is calculated monthly in actual operation. The calculation of the effective shading coefficient needs to consider the impact of changes in the solar incidence angle on the actual shading effect of the shading components. The calculated monthly effective shading coefficients are categorized according to the heating season, transition season, and cooling season to obtain the seasonally corrected effective shading coefficient.

[0042] It is understandable that solar trajectory data is derived from typical meteorological year documents or astronomical calculation modules. The calculation of effective solar irradiance needs to consider the orientation of the windows, the solar azimuth angle, the solar altitude angle, and atmospheric transparency. The monthly solar heat gain coefficient of the windows under unshaded conditions can be obtained through architectural optics calculation software or by consulting relevant product optical performance databases. When calculating the monthly effective shading coefficient, the geometry of the shading components and their relative position to the windows must be considered, and the actual shadow area ratio must be calculated through projective geometry. Seasonal classification is based on the climate zones and conventions of the building's location; for example, December, January, and February are classified as the heating season, June, July, and August as the cooling season, and the remaining months as the transitional season.

[0043] Optionally, the calculation of the optimized value of the overall building airtightness level follows the method specified in the national standard. The optimized door and window gap length data is compared with the corresponding airtightness level indicators. The airtightness test data for door and window nodes comes from laboratory test reports. Data standardization uses the min-max normalization method, mapping the actual value of each indicator to the [0,1] interval. The mapping formula is as follows: ,in This represents the normalized index value. This represents the original index value. and These represent the preset minimum and maximum values ​​of the indicator, respectively.

[0044] In some embodiments, the data involved in the calculation of the seasonal correction of the shading coefficient can be organized into a table for display and storage. Referring to Table 1, the intermediate values ​​of the solar heat gain coefficient of a southeast-facing window under shading conditions are recorded.

[0045] Table 1: Calculation Table of Solar Heat Gain Coefficient and Effective Shading Coefficient for Southeast-Facing Windows by Month

[0046] It is understandable that the unshaded solar heat gain coefficient (SHGC) in the table is an inherent property of the exterior window glass system, while the calculated effective shading coefficient (SC_eff) is the result of correcting the optimized shading coefficient of the exterior window for the solar incidence angle. The calculation of the effective shading coefficient (SC_eff) takes into account the differences in shading efficiency of fixed shading components due to variations in solar altitude and azimuth angles across different months. By categorizing the calculated effective shading coefficients (SC_eff) for each month by season and calculating the seasonal average, the seasonally corrected effective shading coefficient can be obtained, which is used for subsequent data standardization processing and comprehensive evaluation.

[0047] In one embodiment of the present invention, the specific workflow of the economic evaluation model is described. The economic evaluation model includes an incremental cost calculation module, an operating cost saving calculation module, and an investment payback period calculation module. The incremental cost calculation module receives the comprehensive evaluation results and queries the corresponding material and construction unit price database based on the optimized values ​​of the heat transfer coefficient and the shading coefficient to calculate the incremental material cost and construction cost relative to the benchmark scheme. The operating cost saving calculation module, based on the comprehensive evaluation results, calls a simplified energy consumption estimation model to calculate the annual energy savings for heating, cooling, and lighting after adopting the optimized scheme, and converts it into annual operating cost savings by combining the local energy price data input into the economic evaluation model. The investment payback period calculation module calculates the static investment payback period based on the incremental material cost, incremental construction cost, and annual operating cost savings. An energy saving rate threshold is set as a preset energy saving rate constraint, and combinations of building envelope parameters that meet the energy saving rate requirements and have a static investment payback period shorter than the preset number of years are selected to form a list of economically feasible schemes.

[0048] The specific process of the operating cost saving calculation module calling the simplified energy consumption estimation model is as follows: The simplified energy consumption estimation model includes a heating season energy consumption estimation sub-model, a cooling season energy consumption estimation sub-model, and a lighting energy consumption estimation sub-model. The heating season energy consumption estimation sub-model calculates the theoretical heat load for the heating season based on the optimized values ​​of the overall building heat transfer coefficient and the overall building airtightness level, as well as the number of heating days in the building's location, and then multiplies it by the average energy efficiency coefficient of the heating system to obtain the estimated heating energy consumption value. The cooling season energy consumption estimation sub-model calculates the theoretical cooling load for the cooling season based on the seasonally corrected effective shading coefficient, the optimized value of the overall building heat transfer coefficient, and the number of air conditioning hours in the building's location, and then multiplies it by the average energy efficiency coefficient of the cooling system to obtain the estimated cooling energy consumption value. The lighting energy consumption estimation sub-model estimates the potential reduction in artificial lighting energy consumption based on the visible light transmittance parameters of the exterior windows and the natural lighting standards of each functional space in the building. Subtract the corresponding estimated values ​​under the optimized scheme from the estimated values ​​of heating energy consumption, cooling energy consumption, and lighting energy consumption under the baseline design scheme to obtain the energy savings in heating, cooling energy consumption, and lighting energy consumption.

[0049] In practical implementation, the comprehensive evaluation results are input into a pre-set economic assessment model. Combined with local energy prices and material cost data, a list of economically feasible solutions that meet the pre-set energy-saving rate constraints is calculated. The economic assessment model includes an incremental cost calculation module, an operating cost saving calculation module, and an investment payback period calculation module. The incremental cost calculation module receives the comprehensive evaluation results and, based on the optimized values ​​of the heat transfer coefficient and shading coefficient, queries the corresponding material and structural unit price database to calculate the incremental material and construction costs relative to the benchmark solution. The operating cost saving calculation module, based on the comprehensive evaluation results, calls a simplified energy consumption estimation model to calculate the annual energy savings for heating, cooling, and lighting after adopting the optimized solution, and, combined with the local energy price data input into the economic assessment model, converts this into annual operating cost savings. The investment payback period calculation module calculates the static investment payback period based on the incremental material costs, incremental construction costs, and annual operating cost savings. An energy-saving rate threshold is set as a pre-set energy-saving rate constraint. Combinations of building envelope parameters that meet the energy-saving rate requirements and have a static investment payback period shorter than the pre-set period are selected to form a list of economically feasible solutions.

[0050] In some embodiments, the process by which the operating cost saving calculation module invokes a simplified energy consumption estimation model based on the comprehensive evaluation results includes activating the simplified energy consumption estimation model, which includes a heating season energy consumption estimation sub-model, a cooling season energy consumption estimation sub-model, and a lighting energy consumption estimation sub-model. The heating season energy consumption estimation sub-model calculates the theoretical heat load for the heating season based on the optimized values ​​of the overall building heat transfer coefficient and the overall building airtightness level, as well as the number of heating days in the building's location, and then multiplies it by the average energy efficiency coefficient of the heating system to obtain the estimated heating energy consumption. The cooling season energy consumption estimation sub-model calculates the theoretical cooling load for the cooling season based on the seasonally corrected effective shading coefficient, the optimized value of the overall building heat transfer coefficient, and the number of air conditioning hours in the building's location, and then multiplies it by the average energy efficiency coefficient of the cooling system to obtain the estimated cooling energy consumption. The lighting energy consumption estimation sub-model estimates the potential reduction in artificial lighting energy consumption based on the visible light transmittance parameters of the exterior windows and the natural lighting standards of each functional space in the building. Subtract the corresponding estimated values ​​under the optimized scheme from the estimated values ​​of heating energy consumption, cooling energy consumption, and lighting energy consumption under the baseline design scheme to obtain the energy savings in heating, cooling energy consumption, and lighting energy consumption.

[0051] It is understandable that local energy price data is typically stored in a database in tabular form. The operating cost savings calculation module retrieves the required energy unit price by querying this database, as shown in the table below. The material and construction unit price database links market cost information for building materials and construction methods of different performance levels. The baseline design scheme refers to the initial design scheme that has not been optimized by the optimization algorithm or the scheme with the prescribed indicators in the local energy-saving design standards. The energy saving rate threshold is set by the user or determined according to project requirements, such as requiring the building envelope to have an energy saving rate of not less than 15%. The preset lifespan is the upper limit of the investment payback period acceptable to the project investor, such as 8 years, see Table 2.

[0052] Table 2: Local Energy Price List

[0053] Optionally, the formula for calculating the static payback period is expressed as follows: ; in: This indicates the static payback period, in years. This represents the total incremental cost, which is the sum of the incremental material cost and the incremental construction cost, expressed in yuan. This represents the annual operating cost savings, expressed in yuan per year. When the incremental cost calculation module queries the material and construction unit price database, it needs to match the unit prices of wall insulation materials, roof insulation materials, and exterior window profiles that meet the optimized value of the building's overall heat transfer coefficient. It also needs to match the corresponding unit prices of shading components based on the seasonally adjusted effective shading coefficient. The average energy efficiency coefficient of the heating system and the average energy efficiency coefficient of the cooling system are determined based on the energy efficiency rating of the main equipment selected in the building design.

[0054] In some embodiments, after calculating the energy savings for heating and cooling, the operating cost savings calculation module performs cost conversion based on local energy price data. Heating energy savings are multiplied by the corresponding energy unit price based on the heat source type (e.g., natural gas boiler, municipal heating). Cooling and lighting energy savings are typically electricity consumption multiplied by the electricity unit price. The annual operating cost savings for heating, cooling, and lighting are added together to obtain the total annual operating cost savings. The investment payback period calculation module receives the incremental total cost and the total annual operating cost savings and calculates the payback period according to the static investment payback period formula. The economic evaluation model iterates through all combinations of building envelope parameters that meet the technical performance requirements, calculates their static investment payback periods sequentially, compares them with preset years, and verifies whether their energy saving rate reaches the preset energy saving rate threshold. All schemes that meet both conditions are recorded and output to form an economically feasible scheme list.

[0055] Understandably, energy efficiency verification is accomplished by comparing the estimated total annual energy consumption of the building under the optimized scheme with that under the baseline scheme. The estimated total annual energy consumption of the building is obtained by adding the estimated heating energy consumption, cooling energy consumption, and lighting energy consumption output from the simplified energy consumption estimation model. The list of economically feasible schemes is a data structure in which each record contains a specific combination of building envelope parameters, as well as the corresponding incremental total cost, annual operating cost savings, static investment payback period, and calculated energy efficiency rate.

[0056] In one embodiment of the present invention, the process of generating the final scheme and conducting robustness verification is described. Based on a list of economically feasible schemes, a final energy-saving optimization scheme for the building envelope is generated. For each scheme in the list of economically feasible schemes, its comprehensive technical performance score is calculated. This comprehensive technical performance score is weighted based on the optimized value of the overall building heat transfer coefficient, the seasonally corrected effective shading coefficient, and the optimized value of the overall building airtightness level. For each scheme in the list of economically feasible schemes, its comprehensive economic score is calculated. This comprehensive economic score is weighted based on the static investment payback period, incremental costs, and annual operating cost savings. Technical performance weight coefficients and economic weight coefficients are set, and the comprehensive technical performance score and comprehensive economic score for each scheme are weighted and summed to obtain the total scheme score. The list of economically feasible schemes is sorted from highest to lowest according to the total scheme score. The scheme with the highest total scheme score is selected, and its corresponding set of building envelope optimization parameters, economic indicators, and robustness assessment conclusions are extracted and integrated to generate a final energy-saving optimization scheme document for the building envelope containing technical parameters, economic indicators, and implementation points.

[0057] After generating the final scheme document, the process includes sensitivity analysis and robustness verification of the optimized scheme. Based on the set of optimized parameters for the building envelope, positive and negative fluctuations are applied to key parameters to form multiple parameter perturbation schemes. For each set of parameter perturbation schemes, a complete energy consumption simulation is re-executed to obtain the corresponding energy consumption simulation results. The energy consumption simulation results corresponding to each set of parameter perturbation schemes are compared with the energy consumption simulation results of the baseline optimized scheme, and the energy consumption change rate is calculated. The relationship between the energy consumption change rate and the parameter perturbation amplitude is analyzed to determine the sensitivity ranking of each building envelope parameter's impact on total energy consumption. By changing the input meteorological data and using at least another set of different typical meteorological year data, the energy consumption performance of the building envelope energy-saving optimization scheme under different climatic conditions is re-evaluated. By combining the sensitivity analysis results and the energy consumption performance under different climatic conditions, the robustness of the building envelope energy-saving optimization scheme is evaluated, and a robustness assessment report is generated.

[0058] In practical implementation, a final energy-saving optimization scheme for the building envelope is generated based on a list of economically feasible schemes. A comprehensive technical performance score is calculated for each scheme in the list, weighted by the optimized value of the overall building heat transfer coefficient, the seasonally corrected effective shading coefficient, and the optimized value of the overall building airtightness level. A comprehensive economic score is also calculated for each scheme in the list, weighted by the static investment payback period, incremental costs, and annual operating cost savings. Technical performance and economic weighting coefficients are set, and the comprehensive technical performance and economic scores for each scheme are weighted and summed to obtain the total scheme score. The economically feasible schemes are sorted from highest to lowest total score. The scheme with the highest total score is selected, and its corresponding set of building envelope optimization parameters, economic indicators, and robustness assessment conclusions are extracted and integrated to generate a final energy-saving optimization scheme document for the building envelope, containing technical parameters, economic indicators, and implementation key points.

[0059] In some embodiments, the weighted summation formula for calculating the total score of the scheme is expressed as: ; in: Indicates the total score of the plan; Indicates the weighting coefficient of technical performance; This indicates the overall score for technical performance; This represents the economic weighting coefficient; This represents the overall economic performance score. Technical performance weighting coefficients. Economic weighting coefficient The sum is 1. Overall technical performance score. The calculation is derived by weighted summing of three normalized indicators: the optimized value of the overall building heat transfer coefficient, the seasonally corrected effective shading coefficient, and the optimized value of the overall building airtightness level. The weights of each sub-indicator are determined by expert scoring or the analytic hierarchy process. Economic comprehensive score. The calculation is obtained by normalizing three indicators: static investment payback period, incremental cost, and annual operating cost savings, and then weighting and summing them. Among them, the static investment payback period and incremental cost are cost-type indicators, and the smaller the value, the better. The annual operating cost savings are benefit-type indicators, and the larger the value, the better.

[0060] Understandable, overall technical performance rating Overall economic score The calculation process itself involves the normalization and weighting of various sub-indicators, and its calculation principle is consistent with the aforementioned method for normalizing indicators such as the optimized value of the overall building heat transfer coefficient. After the schemes are ranked from high to low based on their total scores, the scheme with the highest total score is considered to be the scheme with the best overall performance under the preset technical and economic weight preferences. The set of optimized parameters for the building envelope corresponding to this optimal scheme is extracted, including the specific optimized values ​​of the external wall heat transfer coefficient, the external window heat transfer coefficient, the window-to-wall ratio, and the shading structure parameters. The extracted economic indicators include the incremental total cost of the scheme, the annual operating cost savings, and the static investment payback period. The extracted robustness assessment conclusions come from the subsequent sensitivity analysis and robustness verification steps.

[0061] Optionally, after generating the final energy-saving optimization scheme document for the building envelope, sensitivity analysis and robustness verification of the optimized scheme are performed. Based on the set of optimized parameters for the building envelope, positive and negative fluctuations are applied to key parameters to form multiple sets of parameter perturbation schemes. Key parameters include the heat transfer coefficient of the exterior walls, the heat transfer coefficient of the exterior windows, and the window-to-wall ratio. The fluctuation range can be set to ±5% or ±10% relative to the optimized value. For each set of parameter perturbation schemes, a complete energy consumption simulation is re-executed to obtain the corresponding energy consumption simulation results. The energy consumption simulation results corresponding to each set of parameter perturbation schemes are compared with the energy consumption simulation results of the baseline optimized scheme to calculate the energy consumption change rate.

[0062] In some embodiments, the relationship between the rate of change in energy consumption and the magnitude of parameter disturbances is analyzed to determine the sensitivity ranking of each building envelope parameter to the total energy consumption. Sensitivity ranking can be quantified by calculating the sensitivity index of each parameter, which is the ratio of the rate of change in energy consumption to the relative rate of change of the parameter. The input meteorological data is changed, using at least another set of typical meteorological year data, to re-evaluate the energy consumption performance of the building envelope energy-saving optimization scheme under different climatic conditions. Different climatic conditions can be meteorological data from the same region but different years, or meteorological data from different regions with significantly different climatic characteristics. The robustness of the building envelope energy-saving optimization scheme is evaluated by combining the sensitivity analysis results and the energy consumption performance under different climatic conditions, and a robustness assessment report is generated.

[0063] Understandably, re-executing a complete energy consumption simulation means that for each set of parameter perturbation schemes, the energy consumption simulation engine must be invoked, the adjusted combination of building envelope parameters and meteorological data input, and a dynamic simulation calculation for the entire year must be performed to output the building's total annual energy consumption value. The formula for calculating the energy consumption change rate is the difference between the building's total annual energy consumption value of the optimized scheme's baseline and the building's total annual energy consumption value of the parameter perturbation scheme, divided by the building's total annual energy consumption value of the optimized scheme's baseline. The robustness assessment report includes a ranking list of sensitivity indices for each parameter, a comparison of simulated energy consumption under different typical meteorological year data, and an overall evaluation of the scheme's stability and climate adaptability. The final building envelope energy-saving optimization scheme document will integrate the optimized technical parameters, economic indicators, implementation points, and the robustness assessment report as attachments.

[0064] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention in any other way. Any person skilled in the art may make changes or modifications to the above-disclosed technical content to create equivalent embodiments that can be applied to other fields. However, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the protection scope of the present invention.

Claims

1. A method for optimizing energy-saving schemes for building envelopes based on energy consumption simulation data analysis, characterized in that, The method includes: Obtain the target building envelope design scheme and its corresponding energy consumption simulation data set of the building to be evaluated. The energy consumption simulation data set includes the building's heat load data, cooling load data and comprehensive energy consumption data under hourly operating conditions throughout the year. Based on the aforementioned heat load data, cooling load data, and comprehensive energy consumption data, an improved particle swarm optimization algorithm is invoked to iteratively optimize the parameters of the target building envelope design scheme, generating a set of optimized parameters for the building envelope. The improved particle swarm optimization algorithm adaptively adjusts the particle learning mode based on the dynamic coupling relationship between simulated energy consumption and building envelope parameters, including: Each particle in the particle swarm is defined as representing a combination of building envelope parameters, which includes the heat transfer coefficient of the exterior wall, the heat transfer coefficient of the exterior window, the window-to-wall ratio, and the shading structure parameters. Initialize the particle swarm and randomly generate the initial positions and initial velocities of the particles. The initial positions represent the initial values ​​of the enclosure structure parameters, and the initial velocities represent the direction and magnitude of parameter adjustments. An objective function is constructed, which uses the simulated total annual energy consumption of the building as an evaluation index. The total annual energy consumption is calculated based on the sum of the heat load data and the cooling load data. In each iteration, the fitness of the enclosure structure parameter combination represented by the current particle is calculated based on the objective function value corresponding to each particle. Based on the fitness, the weight distribution of particles between individual learning factors and social learning factors is dynamically adjusted, wherein particles with fitness lower than the current population average fitness will receive higher individual learning weights to enhance their exploration of their own historical optimal solutions. Calculate the sensitivity matrix of simulated energy consumption changes relative to changes in building envelope parameters based on the dynamic coupling relationship; The particle velocity update process is corrected based on the sensitivity matrix, so that the particle's motion direction preferentially moves toward the parameter adjustment direction with the highest energy consumption reduction during the iteration process; After the particles complete the velocity and position update, the combination of the enclosure structure parameters is used as input to re-execute the energy consumption simulation to obtain the updated total annual energy consumption value. Compare the updated total annual energy consumption value with the total annual energy consumption value corresponding to the historical best solution, and update the individual historical best solution and the global historical best solution. Repeat the steps of fitness calculation, learning factor weight adjustment, sensitivity matrix correction, particle velocity and position update, energy consumption simulation and optimal solution update until the preset convergence condition is met or the maximum number of iterations is reached, and output the combination of retaining structure parameters corresponding to the global historical optimal solution as the set of retaining structure optimization parameters. The set of optimization parameters for the building envelope is subjected to multi-objective normalization to generate a comprehensive evaluation result that includes optimized values ​​for heat transfer coefficient, shading coefficient, and airtightness level. The comprehensive evaluation results are input into the preset economic evaluation model, and combined with local energy price and material cost data, a list of economically feasible solutions that meet the preset energy saving rate constraints is calculated. Based on the aforementioned list of economically feasible options, a final energy-saving optimization scheme for the building envelope is generated.

2. The method for optimizing energy-saving schemes for building envelopes based on energy consumption simulation data analysis according to claim 1, characterized in that, The acquisition of the target building envelope design scheme and its corresponding energy consumption simulation data set for the building to be evaluated includes: Collect the design drawings and technical specifications of the building to be evaluated, and extract the building geometric model, spatial functional zoning, personnel and equipment work and rest schedules and detailed drawings of the building envelope from them; Based on the building's geometric model, spatial functional zoning, and personnel and equipment work and rest schedules, a physical model of the building is established in the energy consumption simulation software, and corresponding indoor thermal disturbance parameters are set. Based on the detailed construction drawings of the building envelope, initial thermal parameters of the building envelope are set in the physical model. The thermal parameters include the heat transfer coefficient, solar heat gain coefficient, and visible light transmittance of the walls, roof, windows, and shading components in each orientation. Set typical annual meteorological data for the building's location to the physical model, and select the corresponding energy consumption simulation engine; implementation steps; The energy consumption simulation engine is run to perform dynamic simulation calculations throughout the year, and outputs hourly heat load data, cooling load data and comprehensive energy consumption data to form the energy consumption simulation data set.

3. The method for optimizing energy-saving schemes for building envelopes based on energy consumption simulation data analysis according to claim 2, characterized in that, Based on the detailed structural drawings of the enclosure structure, initial thermal parameters of the enclosure structure are set in the physical model, including: Identify the type, thickness, and arrangement order of each layer of material in the detailed structural drawing of the enclosure structure; Query the thermal parameters database of building materials to obtain the thermal conductivity, heat storage coefficient and correction factor of each material under standard operating conditions; Based on the material arrangement, thickness, and thermal conductivity, calculate the overall heat transfer coefficient of two types of opaque components: walls and roofs. Based on the type of window profile, number of glass layers, glass type, and spacer gas, determine the window's heat transfer coefficient, solar heat gain coefficient, and visible light transmittance. Calculate the external shading coefficient based on the type, size, installation location, and angle of the shading component; The calculated comprehensive heat transfer coefficient, heat transfer coefficient of the external window, solar heat gain coefficient, visible light transmittance, and external shading coefficient are used as initial values ​​for the corresponding building envelope components in the physical model.

4. The method for optimizing energy-saving schemes for building envelopes based on energy consumption simulation data analysis according to claim 1, characterized in that, The set of optimization parameters for the building envelope is subjected to multi-objective normalization to generate a comprehensive evaluation result that includes optimized values ​​for heat transfer coefficient, shading coefficient, and airtightness level, including: The optimized values ​​of the heat transfer coefficient of the exterior wall, the heat transfer coefficient of the roof, the heat transfer coefficient of the exterior window, and the shading coefficient of the exterior window are extracted from the set of optimized parameters of the building envelope. The optimized values ​​of the heat transfer coefficient of the exterior wall, the roof, and the window are weighted and averaged to calculate the optimized value of the overall heat transfer coefficient of the building. The weighting is determined based on the area ratio of each building envelope. The optimized value of the shading coefficient of the exterior window is seasonally corrected. The seasonal correction is based on the annual variation of the solar altitude angle of the building's location to adjust the effective shading coefficient for different seasons. Combining the data on the length of door and window gaps in the set of optimized parameters for the building envelope, and the airtightness test data of door and window nodes, the optimized value of the overall airtightness level of the building is calculated. The optimized values ​​of the overall building heat transfer coefficient, the seasonally corrected effective shading coefficient, and the optimized values ​​of the overall building air tightness level are standardized to fall within the range of zero to one, thus forming the comprehensive evaluation result.

5. The method for optimizing energy-saving schemes for building envelopes based on energy consumption simulation data analysis according to claim 4, characterized in that, Seasonal corrections are made to the optimized value of the external window shading coefficient, including: Obtain the annual solar trajectory data for the building's location, including the solar altitude angle and azimuth angle for typical days each month; Calculate the effective solar radiation illuminance received by the exterior windows of each major orientation of the building on a typical day of each month; Based on the effective solar radiation illuminance, calculate the monthly solar heat gain coefficient of the exterior window under unshaded conditions; The optimized value of the external window shading coefficient is regarded as the shading efficiency of the fixed shading device, and its effective shading coefficient is calculated month by month in actual operation. The calculation of the effective shading coefficient needs to take into account the influence of changes in the solar incidence angle on the actual shading effect of the shading components. The calculated monthly effective shading coefficients are categorized according to the heating season, transition season, and cooling season to obtain the seasonally corrected effective shading coefficients.

6. The method for optimizing energy-saving schemes for building envelopes based on energy consumption simulation data analysis according to claim 1, characterized in that, The comprehensive evaluation results are input into a preset economic assessment model. Combined with local energy prices and material cost data, a list of economically feasible solutions that meet the preset energy-saving rate constraints is calculated, including: The economic evaluation model includes an incremental cost calculation module, an operating cost saving calculation module, and an investment payback period calculation module. The incremental cost calculation module receives the comprehensive evaluation results and queries the corresponding material and structure unit price database based on the optimized values ​​of the heat transfer coefficient and the shading coefficient to calculate the incremental material cost and construction cost relative to the benchmark scheme. The operating cost saving calculation module, based on the comprehensive evaluation results, calls a simplified energy consumption estimation model to calculate the annual energy savings for heating, cooling and lighting after adopting the optimized scheme, and combines the local energy price data input by the economic evaluation model to convert it into annual operating cost savings. The investment payback period calculation module calculates the static investment payback period based on the incremental material cost, incremental construction cost, and annual operating cost savings. An energy-saving rate threshold is set as the preset energy-saving rate constraint. Combinations of building envelope parameters that meet the energy-saving rate requirements and have a static investment payback period shorter than the preset number of years are selected to form the economically feasible scheme list.

7. The method for optimizing energy-saving schemes for building envelopes based on energy consumption simulation data analysis according to claim 6, characterized in that, The operating cost saving calculation module, based on the comprehensive evaluation results, calls a simplified energy consumption estimation model to calculate the annual energy savings for heating, cooling, and lighting after adopting the optimized scheme, including: The simplified energy consumption estimation model includes a heating quarter energy consumption estimation sub-model, a cooling quarter energy consumption estimation sub-model, and a lighting energy consumption estimation sub-model. The heating season energy consumption estimation sub-model calculates the theoretical heat load for the heating season based on the optimized values ​​of the overall building heat transfer coefficient and the overall building air tightness level, as well as the number of heating days in the building's location, and then multiplies it by the average energy efficiency coefficient of the heating system to obtain the estimated heating energy consumption. The cooling quarterly energy consumption estimation sub-model calculates the theoretical cooling load for the cooling season based on the seasonally corrected effective shading coefficient, the optimized value of the overall building heat transfer coefficient, and the air conditioning hours at the building location. Then, it multiplies the theoretical cooling load by the average energy efficiency coefficient of the cooling system to obtain the estimated cooling energy consumption. The lighting energy consumption estimation sub-model estimates the potential reduction in artificial lighting energy consumption based on the visible light transmittance parameters of the exterior windows and the natural lighting standards of each functional space in the building. Subtract the corresponding estimated values ​​under the optimized scheme from the estimated values ​​of heating energy consumption, cooling energy consumption, and lighting energy consumption under the baseline design scheme to obtain the energy savings in heating, cooling energy consumption, and lighting energy consumption.

8. The method for optimizing energy-saving schemes for building envelopes based on energy consumption simulation data analysis according to claim 1, characterized in that, Based on the aforementioned list of economically feasible solutions, a final energy-saving optimization scheme for the building envelope is generated, including: For each scheme in the economic feasibility list, its comprehensive technical performance score is calculated. The comprehensive technical performance score is calculated by weighting the optimized value of the overall building heat transfer coefficient, the seasonally corrected effective shading coefficient, and the optimized value of the overall building air tightness level of the corresponding scheme. For each option in the list of economically feasible options, its comprehensive economic score is calculated. The comprehensive economic score is calculated based on a weighted average of static investment payback period, incremental cost, and annual operating cost savings. Set weighting coefficients for technical performance and economic efficiency, and sum the weighted scores of the comprehensive technical performance and economic efficiency of each scheme to obtain the total score of the scheme. The list of economically feasible solutions is sorted from highest to lowest based on the total score. The scheme with the highest overall score is selected, and its corresponding set of building envelope optimization parameters, economic indicators, and robustness evaluation conclusions are extracted and integrated to generate a final building envelope energy-saving optimization scheme document containing technical parameters, economic indicators, and implementation points.

9. The method for optimizing energy-saving schemes for building envelopes based on energy consumption simulation data analysis according to claim 1, characterized in that, The method further includes sensitivity analysis and robustness verification of the optimized scheme, including: Based on the set of optimized parameters for the building envelope, positive and negative fluctuations are applied to key parameters to form multiple parameter perturbation schemes; For each set of parameter perturbation schemes, re-execute the complete energy consumption simulation to obtain the corresponding energy consumption simulation results; The energy consumption simulation results corresponding to each set of parameter disturbance schemes are compared with the energy consumption simulation results of the baseline optimization scheme, and the energy consumption change rate is calculated. Analyze the relationship between the energy consumption change rate and the parameter disturbance amplitude to determine the sensitivity ranking of each building envelope parameter to the total energy consumption. By changing the input meteorological data and using at least another set of typical meteorological year data, the energy consumption performance of the building envelope energy-saving optimization scheme under different climatic conditions is re-evaluated. Based on the comprehensive sensitivity analysis results and energy consumption performance under different climatic conditions, the robustness of the proposed energy-saving optimization scheme for the building envelope is evaluated, and a robustness assessment report is generated.

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