A building and microclimate collaborative optimization design method, device, equipment and medium

Abstract

This invention relates to the field of architectural design technology, specifically to a method, apparatus, equipment, and medium for the collaborative optimization design of buildings and microclimates. This invention constructs pedestrian thermal discomfort and total building energy consumption through architectural design variables, and continuously optimizes the values ​​of these variables to collaboratively optimize both pedestrian thermal discomfort and total building energy consumption until both optimized values ​​meet the optimization objectives. The final value of the architectural design variables at this point is the optimization target value. As can be seen from the above analysis, this invention considers both microclimate-related pedestrian thermal discomfort and building energy consumption when optimizing architectural design variables, thus ensuring that the final optimized pedestrian thermal discomfort and total building energy consumption, corresponding to the optimization target values, meet the actual needs of users.

Description

Technical Field

[0001] This invention relates to the field of architectural design technology, specifically to a method, apparatus, equipment, and medium for the collaborative optimization design of buildings and microclimates. Background Technology

[0002] Architectural design not only affects the local microclimate surrounding a building but also its energy consumption, and vice versa. Current technologies optimize building design solely from the perspective of microclimate optimization, aiming to provide a comfortable living environment for users in the surrounding area. However, they neglect the impact of architectural design on energy consumption, resulting in optimized designs not necessarily reducing overall energy consumption.

[0003] In summary, existing technologies neglect building energy consumption when optimizing building design.

[0004] Therefore, existing technologies still need to be improved and enhanced. Summary of the Invention

[0005] To address the aforementioned technical problems, this invention provides a method, apparatus, equipment, and medium for the collaborative optimization design of buildings and microclimates, which solves the problem that existing technologies neglect building energy consumption when optimizing building design.

[0006] To achieve the above objectives, the present invention adopts the following technical solution:

[0007] In a first aspect, the present invention provides a method for the collaborative optimization design of buildings and microclimates, comprising:

[0008] Obtain architectural design variables and determine the initial values ​​of the architectural design variables. Based on the initial values ​​of the architectural design variables, predict the microclimate of the environment surrounding the building.

[0009] Based on the microclimate, the pedestrian thermal discomfort in the environment surrounding the building is obtained;

[0010] Obtain building design values ​​related to energy consumption, and based on the microclimate, the building design values, and the initial values ​​of the design variables of the building design variables, obtain the total building energy consumption;

[0011] By optimizing the initial values ​​of the building design variables, the pedestrian thermal discomfort and the total building energy consumption are synergistically optimized until both the optimized pedestrian thermal discomfort and the optimized total building energy consumption meet the optimization objectives, thus obtaining the optimized target values ​​of the building design variables.

[0012] In one implementation, predicting the microclimate of the building's surrounding environment based on the initial values ​​of the building design variables includes:

[0013] Select the preferred design variables that are only related to climate from the aforementioned architectural design variables;

[0014] A temperature change prediction model is applied to the initial values ​​of the preferred design variables to obtain temperature data of the building's surrounding environment;

[0015] A wind speed change prediction model is applied to the initial values ​​of the preferred design variables to obtain wind speed data of the surrounding environment of the building; and the temperature data and the wind speed data are used as microclimate.

[0016] In one implementation, obtaining pedestrian thermal discomfort in the environment surrounding the building based on the microclimate includes:

[0017] Based on the temperature data and the wind speed data, the physiological equivalent temperature for men and women is obtained.

[0018] The neutral physiological equivalent temperature is obtained, and the pedestrian thermal discomfort in the environment surrounding the building is obtained based on the male physiological equivalent temperature, the female physiological equivalent temperature, and the neutral physiological equivalent temperature.

[0019] In one implementation, the total building energy consumption is obtained based on the microclimate, the building design values, and the initial values ​​of the building design variables, including:

[0020] Based on the microclimate, the building design values, and the initial values ​​of the design variables, the power consumption for cooling, lighting, and electrical equipment is obtained.

[0021] The total building energy consumption is obtained by weighting the power consumption of cooling, lighting, and electrical equipment.

[0022] In one implementation, the initial values ​​of the building design variables are optimized to synergistically optimize pedestrian thermal discomfort and total building energy consumption until both the optimized pedestrian thermal discomfort and the optimized total building energy consumption meet the optimization objectives, thereby obtaining the optimization target values ​​of the building design variables, including:

[0023] Constraint design variables are selected from the architectural design variables, and constraint conditions are constructed from the constraint design variables;

[0024] Under the constraints, the initial values ​​of the building design variables, which include the constrained design variables, are optimized to synergistically optimize pedestrian thermal discomfort and total building energy consumption until both the optimized pedestrian thermal discomfort and the optimized total building energy consumption meet the optimization objectives, thereby obtaining the optimized target values ​​of the building design variables.

[0025] In one implementation, constraint design variables are selected from the architectural design variables, and constraint conditions are constructed from the constraint design variables, including:

[0026] The building height and building aspect ratio are selected from the architectural design variables, and the building height and building aspect ratio are used as constraint design variables;

[0027] The building volume is determined by the building height, the building aspect ratio, and the building width. The building volume is equal to a set volume as a constraint condition, wherein the value of the building width is within a set range.

[0028] In one implementation, the optimization objective is to minimize pedestrian thermal discomfort and minimize total building energy consumption.

[0029] Secondly, embodiments of the present invention also provide a building and microclimate collaborative optimization design device, wherein the device comprises the following components:

[0030] The microclimate prediction module is used to acquire building design variables, determine the initial values ​​of the design variables of the building design variables, and predict the microclimate of the environment surrounding the building based on the initial values ​​of the design variables of the building design variables.

[0031] The pedestrian thermal discomfort calculation module is used to obtain the pedestrian thermal discomfort of the surrounding environment of the building based on the microclimate.

[0032] The total building energy consumption calculation module is used to obtain building design values ​​related to energy consumption, and to obtain the total building energy consumption based on the microclimate, the building design values, and the initial values ​​of the design variables of the building design variables.

[0033] The optimization module is used to optimize the initial values ​​of the building design variables to collaboratively optimize pedestrian thermal discomfort and total building energy consumption, until the optimized pedestrian thermal discomfort and total building energy consumption both meet the optimization objectives, thereby obtaining the optimized target values ​​of the building design variables.

[0034] Thirdly, embodiments of the present invention also provide a terminal device, wherein the terminal device includes a memory, a processor, and a building and microclimate co-optimization design program stored in the memory and executable on the processor, wherein when the processor executes the building and microclimate co-optimization design program, it implements the steps of the building and microclimate co-optimization design method described above.

[0035] Fourthly, embodiments of the present invention also provide a computer-readable storage medium storing a building and microclimate co-optimization design program, wherein when the building and microclimate co-optimization design program is executed by a processor, the steps of the building and microclimate co-optimization design method described above are implemented.

[0036] Beneficial Effects: This invention constructs pedestrian thermal discomfort and total building energy consumption through building design variables, and continuously optimizes the values ​​of these variables to synergistically optimize both pedestrian thermal discomfort and total building energy consumption until both optimized values ​​meet the optimization objectives. The final value of the building design variables at this point represents the optimization target value. As the above analysis shows, this invention considers both microclimate-related pedestrian thermal discomfort and building energy consumption when optimizing building design variables, thus ensuring that the final optimized pedestrian thermal discomfort and total building energy consumption, corresponding to the optimization target values, meet the actual needs of users. Attached Figure Description

[0037] Figure 1 This is an overall flowchart of the present invention;

[0038] Figure 2 This is a flowchart illustrating the optimization design and evaluation process in an embodiment of the present invention.

[0039] Figure 3 The structural diagram of the building and microclimate collaborative optimization design device provided by this invention;

[0040] Figure 4 This is a block diagram illustrating the internal structure of a terminal device provided in an embodiment of the present invention. Detailed Implementation

[0041] The technical solutions of the present invention will be clearly and completely described below with reference to the embodiments and accompanying drawings. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0042] Research has found that architectural design not only affects the local microclimate surrounding a building but also its energy consumption, and vice versa. Current technologies optimize architectural design solely from the perspective of microclimate, aiming to provide a comfortable living environment for users in the surrounding area. However, they neglect the impact of architectural design on energy consumption, meaning that optimized architectural design does not necessarily reduce energy consumption.

[0043] To address the aforementioned technical problems, this invention provides a method, apparatus, equipment, and medium for the collaborative optimization design of buildings and microclimates, which solves the problem that existing technologies neglect building energy consumption when optimizing building design.

[0044] The building and microclimate co-optimization design method of this embodiment can be applied to terminal devices, which can be data processing terminal products, such as televisions and computers. In this embodiment, as... Figure 1 As shown, the building and microclimate co-optimization design method specifically includes the following steps:

[0045] S100, acquire architectural design variables, determine the initial values ​​of the architectural design variables, and predict the microclimate of the building's surrounding environment based on the initial values ​​of the architectural design variables;

[0046] S200, based on the microclimate, obtain the pedestrian thermal discomfort of the environment surrounding the building;

[0047] S300, obtain building design values ​​related to energy consumption, and obtain the total building energy consumption based on the microclimate, the building design values, and the initial values ​​of the design variables of the building design variables;

[0048] S400, by optimizing the initial values ​​of the building design variables, the pedestrian thermal discomfort and the total building energy consumption are optimized in a coordinated manner until the optimized pedestrian thermal discomfort and the optimized total building energy consumption both meet the optimization objectives, thereby obtaining the optimized target values ​​of the building design variables.

[0049] The application scenarios for optimizing architectural design variables in steps S100 to S400 are as follows:

[0050] Before adding or renovating a building within a building cluster, initial values ​​must be assigned to the building's design variables (such as the initial values ​​for building height and aspect ratio). Then, based on these initial values, the potential microclimate around the building is predicted. Next, based on this microclimate, the potential thermal discomfort experienced by pedestrians due to the building's initial design variable values ​​is predicted. Finally, based on the thermal discomfort, microclimate, building design values, and initial design variable values, the total building energy consumption is predicted. Finally, the initial design variable values ​​are adjusted to optimize pedestrian thermal discomfort and total building energy consumption until both are minimized. The resulting value of the building design variables represents the optimization target value.

[0051] Example 1: In this example, as shown in Table 1, the building design variables include cantilever tilt angle, window solar thermal gain coefficient, window-to-wall ratio, wall solar absorptivity, skylight solar thermal gain coefficient, skylight-to-roof ratio, building height, building length-to-width ratio, overall heat transfer coefficient of the building envelope, building orientation, and wall emissivity. Among these, the cantilever tilt angle is the tilt angle of the components extending to the building's perimeter. The cantilever tilt angle affects the building's daylighting, and consequently, its energy consumption.

[0052] Table 1

[0053] Architectural design variables Default optimization range unit Cantilever Inclination Angle 0～180 ° Window solar thermal gain coefficient 0～0.48 <![CDATA[W / (m 2 ·K)]]> Window-to-wall ratio 0.1～0.4 - Wall solar energy absorption rate 0.1～0.9 - Sunlight solar thermal gain coefficient 0.1～0.3 <![CDATA[W / (m 2 ·K)]]> Skylight to roof ratio 0～0.9 - Building height 6～200 m Building length-to-width ratio 1～9 - Overall heat transfer coefficient of building envelope 1.1～14.0 <![CDATA[W / (m 2 ·K)]]> Building orientation 0～360 ° Wall emissivity 0～1 -

[0054] In this embodiment, step S100 includes the following specific steps S101, S102, and S103:

[0055] S101, Select preferred design variables that are only related to climate from the architectural design variables.

[0056] The preferred design variables are the building height, building aspect ratio, overall heat transfer coefficient of the building envelope, building orientation, and wall emissivity listed in Table 1. The magnitude of these five design variables directly affects the climate around the building.

[0057] S102, apply the temperature change prediction model to the initial values ​​of the preferred design variables to obtain temperature data of the building's surrounding environment.

[0058] like Figure 2As shown, optimization ranges are first assigned to all architectural design variables, including preferred design variables. In a custom Python program (where the default value for the population size p in the NSGA-II optimization model is 100) and the default value for the iteration count n is 80, the designer can change the optimization ranges of the architectural design variables according to the actual design requirements of the scenario; otherwise, the default optimization ranges in Table 1 can be used. The initial values ​​of the design variables are the values ​​of the aforementioned optimization ranges.

[0059] A temperature change prediction model was applied to the initial values ​​of the five preferred design variables to obtain temperature data of the building's surrounding environment. This temperature data characterizes the temperature change in the surrounding area before and after the building adopts these initial values. The temperature change prediction model is based on the SVR algorithm. The surrounding environment refers to the pedestrian area around the building, defined as a location 3.0 meters away from the building and 1.5 meters above it. The temperature data of the building's surrounding environment refers to the temperature change at this location.

[0060] S103, apply the wind speed change prediction model to the initial values ​​of the preferred design variables to obtain wind speed data of the surrounding environment of the building; and use the temperature data and the wind speed data as microclimate.

[0061] Applying a wind speed change prediction model to the initial values ​​of the five preferred design variables mentioned above, wind speed data of the surrounding environment of the building is obtained. This wind speed data is used to characterize the wind speed change in the surrounding environment before and after the building adopts these initial values. The wind speed change prediction model is based on the LightGBM algorithm. By encapsulating the wind speed change prediction model and the temperature change prediction model, we obtain... Figure 2 The microclimate proxy model is used in the system. Whenever wind speed and temperature data are needed, the encapsulated microclimate proxy model is automatically invoked to predict wind speed and temperature.

[0062] In this embodiment, step S200 includes the following specific steps: obtaining the male physiological equivalent temperature PET based on the temperature data and the wind speed data. male PET with female physiological equivalent temperature female ; Obtaining the neutral physiological equivalent temperature PET n Based on the male physiological equivalent temperature, the female physiological equivalent temperature, and the neutral physiological equivalent temperature, the pedestrian thermal dysregulation D of the building's surrounding environment is obtained. dis .

[0063] Pedestrian Heat Disorder D dis Thermal comfort for pedestrians under sunny summer conditions.

[0064] By applying the pythermalcomfort.models toolkit in Python to temperature and wind speed data, the male physiological equivalent temperature (PET) was obtained. male PET with female physiological equivalent temperature female .

[0065] D dis =PET ave -PET n

[0066]

[0067] PET n The value is 28℃.

[0068] Example 2, based on Example 1, in this example, step S300 includes the following specific steps: based on the microclimate, the building design value, and the initial value of the building design variable, obtain the cooling power consumption, lighting power consumption, and electrical equipment power consumption; perform a weighted calculation on the cooling power consumption, lighting power consumption, and electrical equipment power consumption to obtain the total building energy consumption.

[0069] The architectural design values ​​include the occupancy rate, lighting usage rate, electrical equipment usage rate, and air conditioning equipment usage rate in Table 2, and the values ​​of various parameters in Table 3.

[0070] Table 2

[0071]

[0072]

[0073] Table 3

[0074]

[0075]

[0076] In a custom Python program, designers can either provide the architectural design values ​​mentioned above, or directly use the architectural design values ​​from Tables 2 and 3.

[0077] Microclimate data includes temperature and wind speed data. The automated building energy consumption simulation model from the Python eppy toolkit was applied to the temperature and wind speed data, all building design values, and the initial values ​​of 11 building design variables to obtain the cooling electricity consumption E. CE (unit: kWh / m2) and lighting power consumption E LE (unit: kWh / m2) and electrical equipment power consumption E EE(Unit: kWh / m2). Cooling power consumption E CE Lighting power consumption E LE Electrical equipment power consumption E EE Adding them together gives the total building energy consumption E. tot (Unit: kWh / m2)

[0078] E tot =E CE +E LE +E EE

[0079] This embodiment integrates EnergyPlus (an automated building energy simulation model) with optimization techniques using the Eppy toolkit, fully automating the building energy simulation process. When new design scenarios need optimization, only the parameters of the design variables need to be adjusted, thus reducing labor costs and improving energy simulation efficiency.

[0080] The automated energy consumption simulation model and microclimate surrogate model in this embodiment are versatile and can realize microclimate simulation and building energy consumption simulation under different scenarios and various building design schemes. The automated energy consumption simulation model in this embodiment can be automatically called by Python to perform building energy consumption simulation using Energyplus, and has versatility and high fidelity. Using alternative models based on SVR and LightGBM can provide high-precision and efficient local microclimate prediction. The temperature change surrogate model based on the SVR algorithm can achieve a prediction accuracy of 0.194℃ (within the range of ±0.5℃ of the thermometer measurement error).

[0081] The wind speed change surrogate model based on the LightGBM algorithm achieves a prediction accuracy of 0.352 m / s (within the measurement error range of 0–1 m / s for anemometers). The wind speed change surrogate model and temperature change surrogate model were developed through extensive high-resolution CFD simulations and GIS spatial analysis techniques, providing accurate calculations of pedestrian thermal comfort.

[0082] Example 3, based on Example 1 or Example 2, in this example, step S300 includes the following specific steps:

[0083] S301, select the building height h and the building length-to-width ratio x from the building design variables, and use the building height and the building length-to-width ratio as constraint design variables;

[0084] S302, determine the building volume V constructed by the building height h, the building aspect ratio x, and the building width d, and use the building volume equal to a set volume as a constraint condition, wherein the value of the building width is within a set range.

[0085] V = h * x * d 2

[0086] Where, d min ≤d≤d max d min The value is 15m, d max The value of is 125m. The value of V is 127500m. 3 That is, through the value of V and d min ≤d≤d max These two constraints restrict the values ​​of the building height h and the building aspect ratio x.

[0087] S303, under the constraints, optimize the initial values ​​of the building design variables that include the constrained design variables, so as to synergistically optimize the pedestrian thermal discomfort and the total building energy consumption, until the optimized pedestrian thermal discomfort and the optimized total building energy consumption both meet the optimization objectives, and obtain the optimized target values ​​of the building design variables.

[0088] Pedestrian Heat Disorder D dis Right now Figure 2 Optimization objective 1, total building energy consumption E tot Right now Figure 2 The optimization objective is 2. Under the above two constraints, the values ​​of 11 architectural design variables are iteratively adjusted. Each iteration will cause optimization objective 1 and optimization objective 2 to change. The value of the 11 architectural design variables that minimizes both optimization objective 1 and optimization objective 2 is the optimization objective value.

[0089] In other words, based on the settings of the collaborative optimization model, a self-written Python program will automatically call the non-dominated evolutionary algorithm NSGA-II for multi-objective optimization iteration. The goal of iterative optimization is to obtain a set of building design solutions that minimizes both optimization objectives 1 and 2 under constraints, i.e., reducing pedestrian thermal discomfort while simultaneously reducing total building energy consumption. During the iteration process, a microclimate proxy model will be automatically invoked to return the local microclimate changes (i.e., pedestrian temperature and wind speed changes) generated by the current building design scheme. This data will then be used to calculate pedestrian thermal discomfort for optimization objective 1 and the local microclimate changes (i.e., pedestrian temperature and wind speed changes) generated by the current building design scheme, and automatically fed into the automated energy consumption simulation model as its weather input. Thus, the automated energy consumption simulation model will simulate optimization objective 2 under the current building design scheme, which is the total building energy consumption.

[0090] By iteratively applying the above architectural design schemes (the architectural design schemes are the values ​​of the above 11 architectural design variables), an architectural design scheme that minimizes both optimization objective 1 and optimization objective 2 is obtained.

[0091] This embodiment improves building energy efficiency while optimizing local microclimate, achieving a balance between the two to avoid the negative impact of a single optimization objective.

[0092] Example 4, based on Example 3, uses the entropy-TOPSIS method to automatically evaluate the optimization target values ​​of building design variables and recommends the optimal values ​​for these variables. The principle of the entropy-TOPSIS method is to use a larger building height and a smaller overall heat transfer coefficient of the building envelope within the specified building requirements to reduce building energy consumption and pedestrian thermal discomfort, thus better aligning with building design principles.

[0093] The optimized design solution provided by the model of this invention can achieve a trade-off between energy efficiency and pedestrian thermal comfort, helping to reduce total building energy consumption by approximately 59.5% to 63.6% (0.097 to 0.104 kWh / m²) on a typical summer design day. 2 This design optimizes pedestrian thermal discomfort by approximately 11.2% to 19.4% (1.08–1.88 K). Compared to existing simulation methods, the computation time for this design optimization is reduced by 99.98% (from 42,684.44 hours to 8.89 hours), and the total computational cost is reduced by 97.48% (from 42,684.44 hours to 1,075.69 hours).

[0094] In summary, the proposed urban 3D building and microclimate collaborative optimization design method and evaluation system based on a surrogate model employs a multi-objective optimization approach to optimize 11 building optimization design variables within a given optimization range. The optimization model automatically invokes a microclimate surrogate model and an automated building performance simulation model to achieve comprehensive optimization of building design and local microclimate, while simultaneously reducing building energy consumption and alleviating pedestrian thermal discomfort. Furthermore, it automatically uses the entropy-TOPSIS method to evaluate the optimization objective values ​​and provide the optimal solution. The program is written in Python, and the entire optimization evaluation process is automatically implemented within Python.

[0095] This embodiment also provides a building and microclimate collaborative optimization design device, such as Figure 3 As shown, the device comprises the following components:

[0096] The microclimate prediction module 01 is used to acquire building design variables, determine the initial values ​​of the design variables of the building design variables, and predict the microclimate of the environment surrounding the building based on the initial values ​​of the design variables of the building design variables.

[0097] Pedestrian thermal discomfort calculation module 02 is used to obtain pedestrian thermal discomfort in the environment surrounding the building based on the microclimate.

[0098] The total building energy consumption calculation module 03 is used to obtain building design values ​​related to energy consumption, and to obtain the total building energy consumption based on the microclimate, the building design values, and the initial values ​​of the design variables of the building design variables.

[0099] The optimization module 04 is used to optimize the initial values ​​of the design variables of the building design variables in order to coordinate the optimization of pedestrian thermal discomfort and the total building energy consumption, until the optimized pedestrian thermal discomfort and the optimized total building energy consumption both meet the optimization objectives, and obtain the optimization target value of the building design variables.

[0100] Based on the above embodiments, the present invention also provides a terminal device, the principle block diagram of which can be as follows: Figure 4 As shown, the terminal device includes a processor, memory, network interface, and display screen connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides the environment for the operation of the operating system and computer programs in the non-volatile storage media. The network interface is used to communicate with external terminals via a network connection. When the computer program is executed by the processor, it implements a building and microclimate co-optimization design method. The display screen can be a liquid crystal display (LCD) or an e-ink display.

[0101] Those skilled in the art will understand that Figure 4 The schematic diagram shown is only a partial structural diagram related to the present invention and does not constitute a limitation on the terminal device to which the present invention is applied. The specific terminal device may include more or fewer components than shown in the figure, or combine certain components, or have different component arrangements.

[0102] In one embodiment, a terminal device is provided, comprising a memory, a processor, and a building and microclimate co-optimization design program stored in the memory and executable on the processor. When the processor executes the building and microclimate co-optimization design program, it implements the following operation instructions:

[0103] Obtain architectural design variables and determine the initial values ​​of the architectural design variables. Based on the initial values ​​of the architectural design variables, predict the microclimate of the environment surrounding the building.

[0104] Based on the microclimate, the pedestrian thermal discomfort in the environment surrounding the building is obtained;

[0105] Obtain building design values ​​related to energy consumption, and based on the microclimate, the building design values, and the initial values ​​of the design variables of the building design variables, obtain the total building energy consumption;

[0106] By optimizing the initial values ​​of the building design variables, the pedestrian thermal discomfort and the total building energy consumption are synergistically optimized until both the optimized pedestrian thermal discomfort and the optimized total building energy consumption meet the optimization objectives, thus obtaining the optimized target values ​​of the building design variables.

[0107] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided by this invention can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and RAMbus dynamic RAM (RDRAM), etc.

[0108] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for collaborative optimization design of buildings and microclimates, characterized in that, include: Obtain architectural design variables and determine the initial values ​​of the architectural design variables. Based on the initial values ​​of the architectural design variables, predict the microclimate of the environment surrounding the building. Based on the microclimate, the pedestrian thermal discomfort in the environment surrounding the building is obtained; Obtain building design values ​​related to energy consumption, and based on the microclimate, the building design values, and the initial values ​​of the design variables of the building design variables, obtain the total building energy consumption; By optimizing the initial values ​​of the building design variables, the pedestrian thermal discomfort and the total building energy consumption are synergistically optimized until both the optimized pedestrian thermal discomfort and the optimized total building energy consumption meet the optimization objectives, thus obtaining the optimized target values ​​of the building design variables.

2. The building and microclimate collaborative optimization design method as described in claim 1, characterized in that, Based on the initial values ​​of the building design variables, predict the microclimate of the environment surrounding the building, including: Select the preferred design variables that are only related to climate from the aforementioned architectural design variables; A temperature change prediction model is applied to the initial values ​​of the preferred design variables to obtain temperature data of the building's surrounding environment; A wind speed change prediction model is applied to the initial values ​​of the preferred design variables to obtain wind speed data of the surrounding environment of the building; and the temperature data and the wind speed data are used as microclimate.

3. The building and microclimate collaborative optimization design method as described in claim 2, characterized in that, Based on the microclimate, pedestrian thermal dysregulation in the environment surrounding the building is obtained, including: Based on the temperature data and the wind speed data, the physiological equivalent temperature for men and women is obtained. The neutral physiological equivalent temperature is obtained, and the pedestrian thermal discomfort in the environment surrounding the building is obtained based on the male physiological equivalent temperature, the female physiological equivalent temperature, and the neutral physiological equivalent temperature.

4. The building and microclimate collaborative optimization design method as described in claim 1, characterized in that, Based on the microclimate, the building design values, and the initial values ​​of the building design variables, the total building energy consumption is obtained, including: Based on the microclimate, the building design values, and the initial values ​​of the design variables, the power consumption for cooling, lighting, and electrical equipment is obtained. The total building energy consumption is obtained by weighting the power consumption of cooling, lighting, and electrical equipment.

5. The building and microclimate collaborative optimization design method as described in claim 1, characterized in that, By optimizing the initial values ​​of the building design variables, the pedestrian thermal discomfort and the total building energy consumption are synergistically optimized until both the optimized pedestrian thermal discomfort and the optimized total building energy consumption meet the optimization objectives. The optimized target values ​​of the building design variables are then obtained, including: Constraint design variables are selected from the architectural design variables, and constraint conditions are constructed from the constraint design variables; Under the constraints, the initial values ​​of the building design variables, which include the constrained design variables, are optimized to synergistically optimize pedestrian thermal discomfort and total building energy consumption until both the optimized pedestrian thermal discomfort and the optimized total building energy consumption meet the optimization objectives, thereby obtaining the optimized target values ​​of the building design variables.

6. The building and microclimate collaborative optimization design method as described in claim 5, characterized in that, Selecting constraint design variables from the architectural design variables, and constructing constraint conditions from the constraint design variables, includes: The building height and building aspect ratio are selected from the architectural design variables, and the building height and building aspect ratio are used as constraint design variables; The building volume is determined by the building height, the building aspect ratio, and the building width. The building volume is equal to a set volume as a constraint condition, wherein the value of the building width is within a set range.

7. The building and microclimate collaborative optimization design method as described in claim 5, characterized in that, The optimization objectives are to minimize pedestrian thermal discomfort and minimize total building energy consumption.

8. A device for collaborative optimization design of buildings and microclimates, characterized in that, The device comprises the following components: The microclimate prediction module is used to acquire building design variables, determine the initial values ​​of the design variables of the building design variables, and predict the microclimate of the environment surrounding the building based on the initial values ​​of the design variables of the building design variables. The pedestrian thermal discomfort calculation module is used to obtain the pedestrian thermal discomfort of the surrounding environment of the building based on the microclimate. The total building energy consumption calculation module is used to obtain building design values ​​related to energy consumption, and to obtain the total building energy consumption based on the microclimate, the building design values, and the initial values ​​of the design variables of the building design variables. The optimization module is used to optimize the initial values ​​of the building design variables to collaboratively optimize pedestrian thermal discomfort and total building energy consumption, until the optimized pedestrian thermal discomfort and total building energy consumption both meet the optimization objectives, thereby obtaining the optimized target values ​​of the building design variables.

9. A terminal device, characterized in that, The terminal device includes a memory, a processor, and a building and microclimate co-optimization design program stored in the memory and executable on the processor. When the processor executes the building and microclimate co-optimization design program, it implements the steps of the building and microclimate co-optimization design method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a building and microclimate co-optimization design program, which, when executed by a processor, implements the steps of the building and microclimate co-optimization design method as described in any one of claims 1-7.

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