A method for determining optimal fresh air volume of a residence based on multi-objective optimization

Abstract

The application discloses a method for determining optimal fresh air volume of a residence based on multi-target optimization, which comprises the following steps: establishing a residence building model by using an EnergyPlus simulation software, and establishing a ventilation model based on the residence building model; setting the envelope structure of the residence building model, and setting the internal and external disturbances of the residence building model; setting a target function; setting a GenOpt optimization tool; starting the GenOpt optimization program, calling the EnergyPlus simulation software for iterative simulation, and obtaining a minimum target value; and determining the optimal fresh air volume of the residence under different importance weights of each target. The application proposes a method for determining the optimal fresh air volume of a typical residence building based on simulation and multi-target optimization method, and solves the problem of double-target optimization of indoor air quality and energy consumption.

Description

Technical Field

[0001] This invention relates to the field of residential building ventilation systems, and in particular to a method for determining the optimal fresh air volume for a residential building based on multi-objective optimization. Background Technology

[0002] One of the key points of using mechanical ventilation is that the designed fresh air volume must meet the requirements. There is no uniform standard for minimum fresh air volume between domestic and international regulations. In Belgium, the recommended air exchange rate for residential buildings is 1.3 hours. -1 The Netherlands is 1.2 hours -1 The recommended value in Japan is 1.1h. -1 South Korea is 0.7h -1 The United States is 0.5h -1 In the UK, the minimum ventilation time for most residential homes is set at 0.5 hours. -1 It is recommended that the optimal average annual ventilation rate for detached houses in the UK is 0.4 h. -1 The apartment is 0.7h -1 Currently, my country designs the ventilation volume of mechanical ventilation in residential buildings according to the "Code for Design of Heating, Ventilation and Air Conditioning of Civil Buildings" (GB50736-2012). The standard classifies the minimum fresh air volume for residential buildings into four levels based on the per capita living area, with the highest being 0.7 h⁻¹. -1 With the further deterioration of indoor and outdoor environments, whether the fresh air volume designed according to my country's current standards can meet the indoor air quality requirements of residential buildings, and the effectiveness of fresh air system applications, require further research.

[0003] Currently, most newly built high-end residential buildings in my country are equipped with mechanical ventilation systems. Scholars have conducted research on indoor air quality in residences with mechanical ventilation systems in Northeast China, evaluating the indoor air quality under designed fresh air volumes. Increasing the fresh air volume can achieve better indoor air quality. However, one cannot simply increase the fresh air volume indiscriminately, as this means increased energy consumption for delivery. Therefore, a balance needs to be found between indoor air quality and energy consumption.

[0004] Current research in my country on residential fresh air volume indicates that the air exchange rate can be determined based on the per capita living area, and should not exceed 0.9 hours. -1 The minimum is 0.5h -1 This is different from the 1h standard in my country's local standards. -1 There are some discrepancies, and whether they can meet the indoor pollutant limits still needs further discussion. Furthermore, ASHRAE 62 is widely authoritative in residential fresh air volume design, and the calculation method for residential fresh air volume design in my country is also based on ASHRAE 62. However, whether the calculation method for residential fresh air volume design in ASHRAE 62 is suitable for residential buildings in my country requires further investigation.

[0005] Increasing the air supply volume can effectively improve indoor air quality, but it also increases the energy consumption of the fresh air system. The determination of the fresh air volume has undergone an iterative process of reduction, increase, and reduction both domestically and internationally. Determining the fresh air volume is a dual-objective optimization problem that considers both indoor air quality and energy consumption. In today's social context of "dual carbon goals" and "healthy buildings," how to balance these two objectives and determine the optimal fresh air volume requires accurate optimization methods and in-depth research, taking into account my country's climate, environmental conditions, and building characteristics. Summary of the Invention

[0006] To overcome the shortcomings of the prior art, the present invention provides a method for determining the optimal fresh air volume in a residential building based on multi-objective optimization, in order to solve at least one of the above-mentioned technical problems.

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

[0008] A method for determining the optimal fresh air volume in a residential building based on multi-objective optimization includes:

[0009] A residential building model was created using EnergyPlus simulation software, and a ventilation model was then built based on the residential building model.

[0010] Set the enclosure structure of the residential building model and set the internal and external disturbances of the residential building model;

[0011] Set the target function;

[0012] Configure the Genopt optimization tool;

[0013] Start the Genopt optimization program, call the EnergyPlus simulation software for iterative simulation, and obtain the minimum target value;

[0014] Determine the optimal fresh air volume for a residential building under different importance weights for each objective.

[0015] The above technical solution is based on simulation and multi-objective optimization methods. It proposes a method for determining the optimal fresh air volume for typical residential buildings, which solves the problem of dual-objective optimization of indoor air quality and energy consumption.

[0016] As a further technical solution, the method employs a centralized fresh air system to construct a ventilation model, and when establishing the ventilation model, infiltration air is considered and the infiltration air volume is set to 0.2h. -1 .

[0017] As a further technical solution, the objective function of the method includes two indicators: indoor CO2 concentration and ventilation energy consumption. The indoor CO2 concentration is the average concentration level of each room during the year-round operation of the fresh air system; the ventilation energy consumption is the energy consumption of the fans supplying fresh air to each room throughout the year; and the inequality constraint is the range of fresh air supply volume.

[0018] As a further technical solution, the method employs the Hooke-Jeeves algorithm, the PSO algorithm, or a hybrid algorithm (an algorithm that combines the Hooke-Jeeves algorithm and the PSO algorithm) as the optimization algorithm.

[0019] As a further technical solution, after starting the optimization program, the method first performs simulation under the initial fresh air supply volume condition, judges the target value of the data obtained by the EnergyPlus simulation software through the objective function, and then uses the optimization algorithm to simulate the initial variable value to the left and right and compares it with the previous value until the minimum value is found.

[0020] As a further technical solution, when EnergyPlus simulation software and GenOpt are coupled, the optimization variables and their ranges are customized according to the requirements of the simulation system, and the optimization simulation file is configured. GenOpt identifies the results output by EnergyPlus simulation software and determines whether the output results meet the target value according to the objective function. If the target value is not met, a new set of design variable values ​​is determined for the next optimization and the simulation begins. If the target value is met, the optimization ends and the results are output.

[0021] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0022] This invention uses EnergyPlus simulation software to establish a simulation model for residential buildings, and on this basis, a ventilation model is built. Then, the EnergyPlus simulation software is coupled with the optimization tool GenOpt to determine the optimal fresh air volume for the residential building through a multi-objective optimization method, thus solving the problem of dual-objective optimization of indoor air quality and energy consumption. Attached Figure Description

[0023] Figure 1 This is a flowchart of a method for determining the optimal fresh air volume in a residential building based on multi-objective optimization, according to an embodiment of the present invention.

[0024] Figure 2 This is a schematic diagram of a residential building according to an embodiment of the present invention.

[0025] Figure 3 This is a schematic diagram of a residential building model according to an embodiment of the present invention.

[0026] Figure 4 This is a schematic diagram illustrating the fresh air supply volume processing method in Genopt and Energyplus according to an embodiment of the present invention.

[0027] Figure 5 This is a schematic diagram illustrating the configuration file settings in Genopt according to an embodiment of the present invention.

[0028] Figure 6 This is a schematic diagram illustrating the coupling between GenOpt and EnergyPlus according to an embodiment of the present invention. Detailed Implementation

[0029] The technical solutions of various embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0030] This invention provides a method for determining the optimal fresh air volume for residential buildings based on multi-objective optimization. This method is designed for typical residential buildings and determines the optimal fresh air volume based on simulation and multi-objective optimization, thus solving the problem of dual-objective optimization of indoor air quality and energy consumption.

[0031] The method described in this invention uses EnergyPlus simulation software to establish a simulation model for a typical residential building, and on this model, a ventilation model is built. Then, the EnergyPlus simulation software is coupled with the optimization tool GenOpt to determine the optimal fresh air volume of the residential building through a multi-objective optimization method.

[0032] like Figure 1 As shown, the method specifically includes the following steps:

[0033] Step 1: Establish a simulation model of a typical residential building. The specific process is as follows:

[0034] Step 101: Create a residential building model. This includes setting the building dimensions and layout.

[0035] In this embodiment, the building model in step 101 is a newly built, fully furnished residential building in Xi'an, located on the 7th floor, with a residential area of ​​106.5m². 2 All rooms have a ceiling height of 2.6m. They are equipped with a fresh air + filtration system, which is a direct-flow system with airflow adjustment. Each room—master bedroom, secondary bedroom, study, and living room—has one supply air vent; the house only exhausts air through door gaps, and the system itself does not have exhaust vents. Figure 2 To test the general layout of a residential bedroom, a simulation model was built using EnergyPlus, taking a typical residence as the research object. Figure 3 As shown.

[0036] Step 102: Establish a ventilation model.

[0037] In this simulation optimization, window opening by residents is not considered, i.e., no window opening is performed. However, infiltration is taken into account, with the infiltration ventilation rate set to 0.2 h. -1During the modeling process, each functional room is considered a hot zone, and the residential mechanical fresh air system is a constant air volume system that supplies air to each hot zone.

[0038] The net floor area of ​​the residential rooms is 97.27 square meters. 2 The living room, master bedroom, second bedroom, study, kitchen, bathroom, and toilet each have an area of ​​38.86 square meters. 2 17m 2 14.4m 2 9.6m 2 5.89m 2 5.76m 2 5.76m 2 The measured maximum air exchange rate under mechanical ventilation conditions was 2.78 h. -1 The air exchange rate under closed conditions is 0.17 h. -1 Therefore, in this optimization study, the optimization variable is the fresh air supply volume, and its optimization range is selected as 0.2-2.8h. -1 It was found that some functional rooms had relatively small net areas. This study was designed for a family of three; therefore, for rooms with smaller functional areas, the maximum air supply volume was set at 120 m³ / h. 3 / h. The fresh air system only ventilates the living room, master bedroom, secondary bedroom, and study. Table 1 shows the parameter settings for the optimization variables for each room.

[0039] Table 1 Parameter settings for optimization variables

[0040]

[0041] The fresh air system uses a single fan with the following performance parameters: air volume 650m³ / h. 3 / h, wind pressure is 150Pa, power is 0.1kW, and speed is 1450r / min. The power calculation formula for the fan under partial load is calculated according to the fan similarity law, as shown in equations (1) and (2).

[0042]

[0043]

[0044] Among them, Q p N p P p ρ p The values ​​are the flow rate, rotational speed, shaft work, and air density under rated operating conditions; Q m N m P m ρ m For flow rate, rotational speed, shaft work, and air density under similar operating conditions; λ l It represents the geometric similarity ratio.

[0045] The residential fresh air system in this invention is a centralized system. When the system is turned on, fresh air is supplied to all rooms, and individual room ventilation is not possible. Therefore, the system control mode is designed so that ventilation occurs only when people are present in the residence, and not when no one is present. Table 2 shows the schedule of occupant activity in the residence.

[0046] Table 2 Personnel Activity Schedule

[0047]

[0048] Step 103: Setting up internal and external disturbances within the building. This involves setting up disturbance settings for residents in the residential rooms, including setting CO2 emission levels for indoor occupants and their daily routines.

[0049] In this multi-objective optimization, indoor CO2 concentration in residential buildings is considered a typical indoor pollutant, and indoor air quality is targeted based on indoor CO2 concentration. Only CO2 emitted by human beings is considered as an indoor pollution source, with the human activity level being a sitting position and a respiratory flow rate of 0.604 m³ / person. 3 The CO2 generation rate is 2.5% per hour. The indoor occupancy patterns in residential buildings are shown in Table 2. In step 103, the external disturbance is simulated based on meteorological data from typical meteorological years in different climate zones, and the outdoor CO2 concentration of the building is set accordingly.

[0050] Step 104: Setting output variables. In this invention, the target values ​​are the indoor CO2 concentration and the annual ventilation energy consumption of the residence. Therefore, the average CO2 concentration and annual ventilation energy consumption of each room during the ventilation period are output respectively.

[0051] Step 2: Setting the objective function.

[0052] Multi-objective problems consist of several objective functions and inequality constraints. In this study, the objective functions include indoor CO2 concentration (f... i (x)) and ventilation energy consumption (g) i (x)). The indoor CO2 concentration is taken as the average concentration level of each room during the year-round operation of the fresh air system; the ventilation energy consumption is the fan energy consumption for supplying fresh air to each room throughout the year. The inequality constraint is the range of fresh air supply volume. For both indoor CO2 concentration and ventilation energy consumption, the minimum value is sought. A standard multi-objective optimization model for a room can be obtained, as shown in equation (3).

[0053] minF i (X)=[f i (x),g i (x)] T (3)

[0054] Among them, f i(x) represents the indoor CO2 concentration during the entire year-round operation of the fresh air system; g i (x) represents the room's annual ventilation energy consumption.

[0055] Multiple objectives are unified and transformed into a function of the unified objective, which serves as the evaluation function for this multi-objective optimization problem. In practical problems, the influence of the dimensions of the objective function value needs to be considered. Therefore, the objective function needs to be dimensionless, as shown in equations (4) and (5).

[0056]

[0057]

[0058] in, and These are the dimensionless values ​​of indoor CO2 concentration and ventilation energy consumption, respectively; f imin (x) and f imax (x) are f i (x) represents the minimum and maximum values ​​under constraints; g imin (x) and g imax (x) are g i (x) represents the minimum and maximum values ​​under constraints.

[0059] After making the objective function dimensionless, the two objective functions are integrated. When the various sub-objective functions are unified into the overall objective function, a weighting factor is introduced to balance the relative importance of each index, resulting in the objective function of a multi-objective optimization problem for a room, as shown in equation (6).

[0060]

[0061] Where, ω if for The weights; ω ig for The weight.

[0062] In this study, the objective functions of the four rooms—living room, master bedroom, secondary bedroom, and study—are integrated to form the objective function of the multi-objective optimization problem, as shown in equation (7).

[0063] minF(x)=minF1(x)+minF2(x)+minF3(x)+minF4(x) (7)

[0064] Wherein, minF1(x) is the minimum value of the objective function for the living room; minF2(x) is the minimum value of the objective function for the master bedroom; minF3(x) is the minimum value of the objective function for the secondary bedroom; and minF4(x) is the minimum value of the objective function for the study.

[0065] Step 3: Setting up the GenOpt optimization tool.

[0066] GenOpt offers open interfaces for both the simulation program and the minimization algorithm. GenOpt has built-in optimization algorithms, and by simply modifying the configuration file, it can be easily coupled with any external program (such as EnergyPlus, SPARK, DOE-2, TRNSYS, or any user-written program). The configured program can find the values ​​of the user-selected design parameters and output the minimum value of the objective function, such as annual electricity consumption, peak power demand, or PPD value, thereby achieving optimal system operation.

[0067] Step 301: Define the optimization variables and their range.

[0068] This invention utilizes EnergyPlus and GenOpt software for simulation to optimize the balance between indoor air quality and energy consumption. Indoor air quality is indicated by the average indoor CO2 concentration, while energy consumption considers annual ventilation energy consumption. A multi-objective optimization method is employed to explore the optimal fresh air volume for residential buildings, providing guidance for the design of residential building fresh air systems. Figure 4 The left side shows the command file's independent variable definition for the fresh air supply volume (GenOpt); the right side shows the definition method for the fresh air supply volume of each room in EnergyPlus corresponding to GenOpt. The optimization variable is the fresh air supply volume. First, the fresh air supply volume of each functional room is defined as a variable, such as... Figure 4 As shown on the left, the optimization range, initial value, and optimization step size for the fresh air supply volume of each functional room are given. The processing methods in GenOpt and Energyplus are as follows: Figure 4 As shown on the right.

[0069] The GenOpt software can recognize the defined fresh air supply volume variable symbol and use the Function function to add or subtract from the variable in EnergyPlus. This allows it to obtain the target value under the corresponding fresh air supply volume condition by calculating the objective function when the fresh air supply volume changes.

[0070] Step 302: Configure the file.

[0071] GenOpt connects with EnergyPlus through configuration files in .ini format. Code 1-26 means that GenOpt starts the simulation of the residential building model shice1-te.idf file created by EnergyPlus by launching the EnergyPlus simulation program in the cfg folder. This file contains optimization variables, which can be changed through the GenOpt command file. The GenOpt software can use the variable identifier to add or subtract variables in EnergyPlus using the Function function, thereby obtaining the target value under the objective function when the fresh air supply volume changes. Code 27-101 means the setting of the objective function. The final form of the objective function used in this example is shown in equation (7). Figure 5 As shown. The specific implementation steps are as follows: First, obtain the code of the output variable from the eso file in code 17. The code of each output variable is shown as Delimiter17-21. Then, code 29-66 means to implement the target function in a programming way. Finally, code 102-108 means to record the target value obtained from each optimization operation and write it to the command.txt text file.

[0072] Step 303: Determining the Optimization Algorithm. In optimization problems, it is necessary to select an optimization algorithm. In GenOpt, when the independent variable is continuous, both the Hooke-Jeeves algorithm (global pattern search algorithm) and the Particle Swarm Optimization (PSO) algorithm can be used; when the independent variable is discrete, the PSO algorithm can be used; when the independent variable has both continuous and discrete aspects, both the PSO algorithm and a hybrid algorithm can be used. The hybrid algorithm combines the Hooke-Jeeves algorithm and the PSO algorithm. First, the PSO algorithm is executed at randomly selected initial grid points to obtain the minimum value of the objective function. Then, the Hooke-Jeeves algorithm is executed at the particle where the objective function value has been obtained. In this invention, since the independent variable is continuous, the Hooke-Jeeves algorithm, the PSO algorithm, and the hybrid algorithm are all used.

[0073] Step 4: Start the GenOpt optimization program.

[0074] After starting the optimization program, it first performs a simulation under the initial fresh air supply conditions. The objective function is used to determine the target value from the data obtained from the EnergyPlus software simulation. Then, the optimization algorithm simulates the initial variable values ​​to the left and right, comparing them with previous values ​​until it finds the minimum value. For example... Figure 6The coupling process between EnergyPlus and GenOpt software is presented. Users define optimization variables and their ranges according to the simulation system requirements and configure the optimization simulation file. GenOpt identifies the results output by EnergyPlus and determines whether the output results meet the target value based on the objective function. If the target value is not met, a new set of design variable values ​​is determined for the next optimization and simulation begins; if the target value is met, the optimization ends and the results are output.

[0075] To obtain the optimal fresh air volume under different weights for the dual objectives of indoor CO2 concentration and ventilation energy consumption in residential buildings, simulation optimization was performed on the objective functions of the two objectives under different weights. Nine cases were considered for different weights: ω if =0.1, ω ig =0.9; ω if =0.2, ω ig =0.8; ω if =0.3, ω ig =0.7; ω if =0.4, ω ig =0.6; ω if =ω ig =0.5; ω if =0.6, ω ig =0.4; ω if =0.7, ω ig =0.3; ω if =0.8, ω ig =0.2; ω if =0.9, ω ig =0.1.

[0076] The optimization results for two objectives with different weights were obtained by coupling GenOpt and EnergyPlus. Table 3 shows the average indoor CO2 concentration, air exchange rate, and annual ventilation energy consumption of each room's fresh air system during the entire year's operation period after optimization by each algorithm under different weights.

[0077] Table 3. Results of each algorithm after optimization under different weights.

[0078]

[0079]

[0080]

[0081]

[0082]

[0083] 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 or all of the technical features therein; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the technical solutions of the embodiments of the present invention.

Claims

1. A method for determining the optimal fresh air volume in a residential building based on multi-objective optimization, characterized in that, include: A residential building model was established using EnergyPlus simulation software, and a ventilation model was then built based on this model. Specifically, a centralized fresh air system was used to construct the ventilation model, and infiltration air was considered when building the model, with the infiltration air volume set to 0.2 h⁻¹. -1 ; Set the building envelope structure of the residential building model and set the internal and external disturbances of the residential building model; the internal and external disturbances include setting the CO2 emission of indoor personnel and the work and rest patterns of personnel; The objective function is defined, comprising two indicators: indoor CO2 concentration in each room and ventilation energy consumption of the centralized fresh air system. The indoor CO2 concentration in each room represents the average concentration level of each room during the year-round operation of the fresh air system. The ventilation energy consumption is the fan energy consumption of the centralized fresh air system supplying fresh air to each room throughout the year. Inequality constraints define the range of fresh air supply volume. Both indoor CO2 concentration and ventilation energy consumption are dimensionless. The dimensionless indoor CO2 concentration and ventilation energy consumption are integrated to obtain the objective function for a single room's multi-objective optimization problem. The objective functions for the living room, master bedroom, secondary bedroom, and study are integrated to obtain the overall objective function for a multi-region, multi-variable, multi-objective optimization problem. The GenOpt optimization tool is set up; the method uses the Hooke-Jeeves algorithm, PSO algorithm, or a hybrid algorithm as the optimization algorithm; the GenOpt optimization program is started, and the EnergyPlus simulation software is called to iteratively simulate and obtain the minimum target value; after starting the optimization program, the simulation is first performed under the initial fresh air supply volume condition, and the target value is judged by the data obtained by the EnergyPlus simulation software through the objective function. Then, the optimization algorithm is used to simulate the initial variable value to the left and right and compare it with the previous value until the minimum value is found; Determine the optimal fresh air volume for a residential building under different importance weights for each objective.

2. The method for determining the optimal fresh air volume in a residential building based on multi-objective optimization as described in claim 1, characterized in that, When EnergyPlus simulation software and GenOpt are coupled, the optimization variables and their ranges are customized according to the requirements of the simulation system, and the optimization simulation file is configured; GenOpt identifies the results output by EnergyPlus simulation software and determines whether the output results meet the target value according to the objective function. If the target value is not met, a new set of design variable values ​​will be determined for the next optimization and simulation will begin. If the target value is met, the optimization ends and the result is output.

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