Building self-optimization thermal engineering method and system

Abstract

The invention provides a building self-optimization thermal engineering method and system, and the method comprises the steps: constructing a heat dissipation function of a building envelope of a target building and a heating and ventilation heat consumption function of the target building based on the building structure information and outdoor environment information of the target building; based on the heat dissipation function of the building envelope and the heating and ventilation heat consumption function, a target function considering ventilation energy consumption and air conditioner energy consumption is constructed, and constraint conditions are determined; and solving the objective function based on the constraint conditions by adopting a self-optimization algorithm to obtain the thermal parameters meeting the thermal load requirements of the building. According to the method, the building energy consumption is analyzed through the heating and ventilation energy consumption analysis part and the enclosure structure energy consumption analysis part, and the self-optimization energy-saving objective function is constructed by taking the operation mode of the air conditioner in the building as an energy-saving optimization research object, so that energy conservation and environmental protection are realized on the premise that the environment in the green building reaches a comfortable environment for a human body.

Description

Technical Field

[0001] The present invention relates to the technical field of building thermal energy conservation, and particularly to a building self-optimizing thermal engineering method and system. Background Art

[0002] Building thermology studies the influence of indoor and outdoor thermal and moisture actions on building envelopes and indoor thermal environments, and researches how to create a suitable indoor thermal environment to meet the needs of various office, production activities, people's work and life.

[0003] In traditional building thermal energy conservation optimization schemes, generally the amplitude-frequency response characteristics and phase-frequency response characteristics of the temperature wave of the building envelope are used as the thermal performance indicators of the building envelope. The energy consumption caused by the heat transfer of the building envelope accounts for the vast majority of the total energy consumption. Accurately grasping the thermal performance of the building envelope can achieve thermal energy conservation optimization.

[0004] However, most of the existing methods detect the thermal performance of a certain component or a certain wall of the building, and the detection range is extremely limited. The uncertainty brought by using the evaluation results of the thermal performance of some building envelopes to characterize the thermal performance of the entire building envelope causes defects in the existing building thermal engineering optimization methods. Summary of the Invention

[0005] The present invention provides a building self-optimizing thermal engineering method and system to solve the defect that in the prior art, the thermal performance of a building only considers a certain part of the building and it is difficult to conduct an overall evaluation of the thermal performance of the building, and to realize a liberalized energy conservation thermal engineering method considering the whole building.

[0006] The present invention provides a building self-optimizing thermal engineering method, including:

[0007] Based on the building structure information and outdoor environment information of the target building, construct the heat dissipation function of the building envelope of the target building and the heat consumption function of heating, ventilation and air conditioning of the target building;

[0008] Based on the heat dissipation function of the building envelope and the heat consumption function of heating, ventilation and air conditioning, construct an objective function considering ventilation energy consumption and air conditioning energy consumption, and determine the constraint conditions;

[0009] Adopt a self-optimization algorithm to solve the objective function based on the constraint conditions to obtain thermal engineering parameters that meet the building heat load requirements.

[0010] According to the building self-optimizing thermal engineering method provided by the present invention, the step of constructing the heat dissipation function of the building envelope of the target building and the heat consumption function of heating, ventilation and air conditioning of the target building specifically includes:

[0011] Divide the building surface of the target building into multiple grid units, and construct the heat dissipation function of the building envelope based on the divided grid units:

[0012]

[0013] In the formula, Q S represents the heat dissipation of the building envelope, K represents the overall heat transfer coefficient of the target building, and Q u represents the heat transfer of a single grid unit formed by dividing the u-th material of the target building; n u represents the number of grid units occupied by the u-th material, S represents the area of each grid unit, and ΔT represents the temperature difference between the inner and outer surfaces of the building.

[0014] According to an automatic building thermal optimization method provided by the present invention, the steps of constructing the heat dissipation function of the building envelope and the heat consumption function of heating and ventilation of the target building specifically include:

[0015] Divide the building surface of the target building into multiple grid units, and construct the heat consumption function of heating and ventilation based on the divided grid units:

[0016] Q W =α×[WWR×K win +(1 - WWR)×K m ×HDD 18 ;

[0017] In the formula, Q W represents the annual heating heat consumption per unit area of the building envelope of the target building, WWR represents the window-wall ratio, α is an adjustment parameter, and K win represents the heat transfer coefficient of the window, K win represents the average heat transfer coefficient of the exterior wall, and HDD 18 represents the heating degree days when the indoor heating temperature is 18 °C.

[0018] According to an automatic building thermal optimization method provided by the present invention, the overall heat transfer coefficient of the target building is calculated in the following manner:

[0019]

[0020] In the formula, k u represents the heat transfer coefficient of the u-th material, n represents the number of material categories on the outer surface of the target building, k in and k out respectively represent the heat transfer coefficients on the inner and outer sides of the target building, and L u is the thickness of the u-th material.

[0021] According to a building self-optimizing thermal engineering method provided by the present invention, the constraint conditions include: heat load demand constraint, building envelope thermal performance constraint, ventilation energy consumption constraint, air conditioning energy consumption constraint, and energy efficiency regulation and stability constraint.

[0022] According to a building self-optimizing thermal engineering method provided by the present invention, the self-optimizing algorithm is constructed based on the particle swarm algorithm. The steps of using the self-optimizing algorithm to solve the objective function based on the constraint conditions to obtain the thermal engineering parameters that meet the building heat load demand specifically include:

[0023] Map the objective function to the solution space, and form a particle population with all solutions of the objective function, with each solution being a particle;

[0024] On the basis of avoiding particle collisions, move the particles to the optimal position among adjacent particles and update the particle positions;

[0025] Calculate the fitness value of the particles, set a fitness threshold, form a new particle swarm with the particles that meet the fitness threshold and continuously iterate to the maximum number of iterations, and select the particle with the largest fitness value from the particle swarm after the last iteration is completed as the result of solving the objective function, and use the thermal engineering parameters corresponding to the particle with the largest fitness value as the thermal engineering parameters that meet the building heat load demand

[0026] Among them, the learning factor used for particle position update is dynamically adjusted.

[0027] The present invention also provides a building self-optimizing thermal engineering system, including:

[0028] A construction module for constructing a heat dissipation function of the building envelope of the target building and a heat consumption function of heating and ventilation of the target building based on the building structure information and outdoor environment information of the target building;

[0029] A determination module for constructing an objective function considering ventilation energy consumption and air conditioning energy consumption based on the heat dissipation function of the building envelope and the heat consumption function of heating and ventilation, and determining the constraint conditions;

[0030] An output module for using a self-optimizing algorithm to solve the objective function based on the constraint conditions to obtain the thermal engineering parameters that meet the building heat load demand.

[0031] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, it implements the building self-optimizing thermal engineering method as described in any one of the above.

[0032] The present invention also provides a non-transitory computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the building self-optimizing thermal engineering method as described in any one of the above is implemented.

[0033] The present invention also provides a computer program product, including a computer program, and when the computer program is executed by a processor, the building self-optimizing thermal engineering method as described in any one of the above is implemented.

[0034] The building self-optimizing thermal engineering method and system provided by the present invention analyze building energy consumption from the heating and ventilation energy consumption analysis part and the envelope structure energy consumption analysis part, ensuring the comprehensiveness of the analysis and providing an accurate data basis for subsequent energy-saving optimization measures; taking the air-conditioning operation mode in the building as the research object of energy-saving optimization, constructing a self-optimizing energy-saving objective function, enabling the indoor environment of a green building to achieve energy conservation and environmental protection on the premise of reaching a human-comfortable environment, and taking into account high efficiency, low cost and green sustainable development. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following will briefly introduce the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0036] Figure 1 is a schematic flowchart of the building self-optimizing thermal engineering method provided by the present invention;

[0037] Figure 2 is a schematic structural diagram of the building self-optimizing thermal engineering system provided by the present invention;

[0038] Figure 3 is a schematic structural diagram of the electronic device provided by the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0039] To make the objectives, technical solutions, and advantages of the present invention clearer, the following will clearly and completely describe the technical solutions in the present invention with reference to the drawings in the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the protection scope of the present invention.

[0040] The following will be combined with Figure 1 to introduce the building self-optimizing thermal engineering method of the present invention, including:

[0041] Step 101: Based on the building structure information and outdoor environment information of the target building, construct the heat dissipation function of the building envelope of the target building and the heat consumption function of heating and ventilation of the target building.

[0042] Building energy efficiency design covers a wide range, mainly including two aspects: one is the energy consumption problem during the normal function of the building, that is, using energy reasonably and effectively on the premise of meeting people's comfortable thermal environment, which is the energy efficiency of the heating and ventilation system; the other is the insulation and heat insulation problem of the building envelope, that is, the reasonable use and correct layout of high-efficiency insulation and heat insulation materials to reduce the impact of the external environment on the indoor thermal environment, which is the energy efficiency of the envelope system.

[0043] Optionally, take parameters related to building energy efficiency such as the shape factor of the building, the heat transfer coefficient and thermal inertia index of the roof and exterior walls, the window-wall area ratio, airtightness, the heat transfer coefficient of the partition wall and floor, annual heating power consumption, annual air-conditioning power consumption, shading coefficient, etc. as thermal parameters, and construct the heat dissipation function of the building envelope with parameters of the target building and the heat consumption function of heating and ventilation of the target building, so as to construct a comprehensive thermal model of the target building from two aspects of the heating, ventilation and building envelope systems of the building.

[0044] It can be understood that to construct the heat dissipation function of the building envelope of the target building, it is also necessary to collect the building structure information of the target building, and the building structure information includes the dimensions and materials of each outer surface of the building, etc.; to construct the heat consumption function of heating and ventilation of the target building, it is also necessary to collect the outdoor environment information of the target building.

[0045] Optionally, a construction information acquisition module is used for the acquisition of the above information. For example, analyze the building thermal environment and collect outdoor environment information as a meteorological data source. The outdoor environment information includes information such as temperature and solar radiation.

[0046] Step 102: Based on the heat dissipation function of the building envelope and the heat consumption function of heating and ventilation, construct an objective function considering ventilation energy consumption and air-conditioning energy consumption, and determine the constraint conditions.

[0047] To achieve the energy conservation and emission reduction goals of green buildings, it is necessary to reduce the heating energy consumption of the target building, and at the same time ensure that the internal thermal environment of the building is within the range of human comfort. Therefore, it is necessary to construct a self-optimizing energy-saving objective function for green buildings and determine the constraint conditions based on the factors considered when constructing the objective function to achieve the dynamic balance of the building thermal environment.

[0048] Specifically, among the factors affecting thermal parameters, each factor has an impact on building energy consumption, and there are situations where various factors interact with each other. In a specific embodiment, the shape coefficient refers to the ratio of the surface area of ​​the building in contact with the outdoor atmosphere to the volume enclosed by the outer surface. The shape coefficient has a significant impact on building energy consumption; the smaller the shape coefficient, the smaller the surface area corresponding to the unit building area, and the smaller the heat transfer loss of the external envelope structure. Therefore, from the perspective of building energy conservation, the shape coefficient should be controlled at a lower level, but the shape coefficient is also closely related to building shape, plane layout, lighting and ventilation and other conditions. If the shape coefficient limit is set too small, it will restrict the creativity of architects, resulting in rigid building shape, difficult plane layout, and even damage to building functions.

[0049] Therefore, based on the above considerations, the heat dissipation function Q can be determined according to the target building. S and heat dissipation function Q W The specific thermal parameters used and the objectives of the thermal system of the target building are considered, the target building is constructed, and the corresponding constraints are determined.

[0050] On this basis, in this implementation, ventilation energy consumption and air conditioning energy consumption are also taken into consideration when constructing the objective function, and a method for constructing the objective function is proposed as follows:

[0051] Q total =Q S +Q W +Q vent +Q cool ;

[0052] In the formula, the objective function Q total Represents the comprehensive energy consumption of the target building, including heat dissipation of the building envelope, heating and ventilation energy consumption, air conditioning energy consumption, etc.; Q vent is the ventilation energy consumption, which indicates the energy loss caused by building ventilation; Q cool Air conditioning energy consumption refers to the energy consumed by the air conditioning system to maintain a comfortable temperature inside the building.

[0053] On this basis, solve the objective function, that is, make Q total Smallest Q S and Q W .

[0054] Step 103, using a self-optimization algorithm, solving the objective function based on the constraint conditions to obtain thermal parameters that meet the building heat load demand.

[0055] Optionally, the self-optimization algorithm may be a particle swarm algorithm, an ant colony algorithm, a genetic algorithm, an adaptive learning algorithm, or the like.

[0056] An auto-optimization algorithm is adopted to solve the objective function under given constraints, and the optimal solution is paired with a thermal parameter, which is used as the thermal parameter that meets the building heat load requirements.

[0057] It can be understood that different auto-optimization algorithms are selected, and the definition of the optimal solution is also different, which is specifically determined according to the selected auto-optimization algorithm. By solving the objective function, the heat dissipation function of the building envelope structure and the heating and ventilation heat consumption function of the target building in dynamic balance can be obtained, and then the values of the thermal parameters as variables can be determined.

[0058] In the present invention, the building energy consumption is analyzed from the heating and ventilation energy consumption analysis part and the building envelope energy consumption analysis part to ensure the comprehensiveness of the analysis, providing an accurate data basis for subsequent energy-saving optimization measures; taking the air-conditioning operation mode in the building as the research object of energy-saving optimization, an auto-optimization energy-saving objective function is constructed, enabling the indoor environment of a green building to achieve energy conservation and environmental protection on the premise of meeting the human comfort environment, taking into account high efficiency, low cost and green sustainable development.

[0059] In the auto-optimization thermal engineering method of the building of the present invention, the steps of constructing the heat dissipation function of the building envelope structure of the target building and the heating and ventilation heat consumption function of the target building specifically include:

[0060] Divide the building surface of the target building into multiple grid units, and construct the heat dissipation function of the building envelope structure based on the divided grid units:

[0061]

[0062] In the formula, Q S represents the heat dissipation of the building envelope structure, K represents the overall heat transfer coefficient of the target building, and Q u represents the heat transfer of a single grid unit formed by dividing the u-th material of the target building; n u represents the number of grid units occupied by the u-th material, S represents the area of each grid unit, and ΔT represents the temperature difference between the inner and outer surfaces of the building.

[0063] For the convenience of thermal performance analysis of the whole building, improving the accuracy of the analysis results, dividing the building plane into grids also simplifies complex problems and reduces the computational complexity. Optionally, in this embodiment, a grid division module is constructed to set the analysis step, select the analysis step size, and divide the building plane into grids according to the analysis step, and divide the building surface into multiple grid units.

[0064] Optionally, the analysis step size can be an empirical value.

[0065] It is understandable that when the target building is a single-family building, the building surface of the target building includes side walls and a roof; when the target building is a multi-story building that is not the top floor, the building surface of the target building is the side wall of the target building.

[0066] Divide the building surface of the target building into grid cells, and count the number of grid cells corresponding to each material. Based on this, construct the heat dissipation function of the building envelope.

[0067] Optionally, in this embodiment, the thermal parameters mainly considered when constructing the heat dissipation function of the building envelope are the window-wall ratio. Therefore, the number of grid cells corresponding to each material counted is the number of grid cells occupied by the wall and the window. If different thermal parameters are considered, the statistics can be adjusted accordingly.

[0068] Optionally, in this embodiment, construct an energy consumption analysis module for constructing the heat dissipation function Q of the building envelope from the perspective of the energy consumption analysis of the building envelope. S 。

[0069] Optionally, the overall heat transfer coefficient K of the target building is obtained by fitting the heat transfer coefficients of each material.

[0070] In a feasible embodiment, the overall heat transfer coefficient of the target building is calculated in the following manner:

[0071]

[0072] In the formula, k u represents the heat transfer coefficient of the u-th material, n represents the number of material categories on the outer surface of the target building, k in and k out respectively represent the inner and outer heat transfer coefficients of the target building, and L u is the thickness of the u-th material.

[0073] Among them, the calculation method of K is derived based on the general heat transfer formula of ordinary walls.

[0074] In the building self-optimizing thermal method of the present invention, the steps of constructing the heat dissipation function of the building envelope of the target building and the heat consumption function of heating and ventilation of the target building specifically include:

[0075] Divide the building surface of the target building into multiple grid cells, and construct the heating and ventilation heat consumption function based on the divided grid cells:

[0076] Q W =α×[WWR×K win +(1 - WWR)×K m ×HDD 18 ;

[0077] In the formula, QW represents the annual heating heat consumption per unit area of the envelope structure of the target building, WWR represents the window-wall ratio, α is an adjustment parameter, K win represents the heat transfer coefficient of the window, K win represents the average heat transfer coefficient of the exterior wall, HDD 18 represents the heating degree days when the indoor heating temperature is 18 degrees Celsius.

[0078] Optionally, in this embodiment, an energy consumption analysis module is constructed to construct a heating and ventilation heat consumption function Q from the perspective of heating and ventilation W .

[0079] Among them, the meshing method is the same as that when determining the heat dissipation function of the building envelope structure, so it will not be elaborated here.

[0080] Generally speaking, that is, considering the heat consumption of the window and the wall parts, a heating and ventilation heat consumption function is constructed.

[0081] In the self-optimizing thermal engineering method of the building of the present invention, the constraint conditions include: heat load demand constraint, envelope structure thermal performance constraint, ventilation energy consumption constraint, air conditioning energy consumption constraint, and energy efficiency regulation and stability constraint.

[0082] Based on the objective function constructed in this embodiment, optionally, a heat load demand constraint is defined. The heat load demand can be constrained by the building envelope structure and the heating energy consumption Q S and Q W Therefore, the comprehensive energy consumption and thermal environment comfort of the building will be considered in the constraint conditions:

[0083] X 1 ≤Q S +Q W +Q vent +Q cool ≤X 2 ;

[0084] In the formula, X 1 and X 2 are the minimum and maximum energy consumption ranges required to maintain a comfortable thermal environment in the building respectively, ensuring that the comprehensive energy consumption of the building is within a reasonable range.

[0085] Optionally, a maintenance structure thermal performance constraint is defined, specifically including a heat transfer coefficient limit:

[0086] K≤K max ;

[0087] In the formula, K max is the maximum allowable heat transfer coefficient, ensuring that the thermal insulation performance of the building envelope structure meets the energy-saving standards.

[0088] It also includes a window-wall ratio limit:

[0089] WWR min WWR ≤ ≤ WWR max ;

[0090] In the formula, WWR min and WWR max are the minimum and maximum values of the window-wall ratio respectively. A reasonable window-wall ratio can effectively reduce energy consumption.

[0091] Optionally, a ventilation energy consumption constraint is defined. Excessive ventilation will increase heat loss, while insufficient ventilation will affect indoor air quality. Therefore, the ventilation rate needs to be reasonably restricted:

[0092] Q cool ≤ Q cool，？max ;

[0093] In the formula, Q cool，？max is the maximum air-conditioning energy consumption limit. This constraint ensures that the air-conditioning energy consumption will not cause unnecessary energy waste due to excessive adjustment of the optimization algorithm.

[0094] Specifically, since the comprehensive energy consumption of the target building takes into account the influence of the envelope structure, ventilation indoor and outdoor environmental factors, and air-conditioning load, and at the same time, the envelope structure, ventilation indoor and outdoor environmental factors will also affect the air-conditioning load, during the optimization process, the air-conditioning load needs to be used as a constraint condition. By adjusting these parameters, Q cool is reduced to a reasonable range to ensure that the air-conditioning energy consumption will not increase excessively due to the adjustment of the optimization algorithm, resulting in energy waste or unstable operation.

[0095] In a specific iteration method, if Q cool exceeds Q cool,？max after iteration, the solution is determined to be inappropriate, and the optimization algorithm needs to adjust parameters (such as improving the insulation performance of the envelope structure, optimizing the window-wall ratio, etc.) to reduce the air-conditioning load.

[0096] Optionally, an energy efficiency regulation and stability constraint is defined:

[0097] ΔQ total ≤ ΔQ max ;

[0098] In the formula, ΔQ total is the change in comprehensive energy consumption after each iteration optimization, and ΔQ max is the maximum allowable change value to avoid unstable energy efficiency caused by excessive adjustment.

[0099] Optionally, a sample acquisition module is constructed to obtain the historical operation data of the air conditioning equipment in the target building space and the building outdoor environment parameters corresponding to the time of the historical operation data as sample data. The sample data is processed for missing data identification, outlier identification, and elimination of inconsistent data according to existing data preprocessing methods. By obtaining the historical operation data of the air conditioning equipment, relevant thresholds for related ventilation energy consumption constraints are determined, such as the maximum air conditioning energy consumption limit and the maximum allowable change value.

[0100] The historical operation data of the air conditioning equipment includes: the operation status of the indoor unit of the air conditioner and the internal temperature of the building, and the outdoor environment parameters include: the outdoor temperature of the building and the solar radiation intensity.

[0101] In the building self-optimizing thermal engineering method of the present invention, the self-optimizing algorithm is constructed based on the particle swarm algorithm. The step of using the self-optimizing algorithm to solve the objective function based on the constraint conditions to obtain the thermal engineering parameters that meet the building heat load requirements specifically includes:

[0102] Map the objective function to the solution space, and form a particle population with all solutions of the objective function, with each solution as a particle;

[0103] On the basis of avoiding particle collisions, move the particles to the optimal position among adjacent particles and update the particle positions;

[0104] Calculate the fitness value of the particles, set a fitness threshold, form a new particle swarm with the particles that meet the fitness threshold and continuously iterate to the maximum number of iterations. Select the particle with the largest fitness value from the particle swarm after the last iteration is completed as the result of solving the objective function, and use the thermal engineering parameters corresponding to the particle with the largest fitness value as the thermal engineering parameters that meet the building heat load requirements.

[0105] Among them, the learning factor used for particle position update is dynamically adjusted.

[0106] Optionally, a self-optimizing algorithm model is constructed to search for variable parameters in the objective function and constraint conditions, obtain the optimal parameters, and obtain the optimal solution of the objective function to achieve the self-optimizing energy-saving goal of green building thermal engineering.

[0107] The specific steps of the self-optimizing algorithm model are as follows:

[0108] First, map the objective function to the solution space, and form a particle population with all solutions of the objective function, with each solution as a particle. Random parameters are generated through Gaussian mapping, and the specific formula is as follows:

[0109]

[0110] In the formula, z j+1Represents the (j + 1)-th random number, where j represents the ordinal number of the random number. The Gaussian random number is used as the position to initialize the particle swarm, i.e., Z 0 ={z 1 ,z 2 ,…,z J}, J represents the number of particles, that is, the size of the initial particle population. Set the maximum number of iterations k, the initial velocity of the particle V 0 , and the inertia weight ω.

[0111] On the premise of avoiding particle collisions, move the particle to the best position among adjacent particles. The specific expression is:

[0112]

[0113] In the formula, M(j, best) represents the process of the particle moving from the current position to the optimal position in the j-th iteration, β represents a random number in the range of [0, 1], represents the optimal position of the particle after the j-th iteration, Z j represents the position of the particle after the j-th iteration.

[0114] To avoid conflicts in particle behavior, update the particle position:

[0115] V j+1 =ωV j +a 1 b 1 (Pb j -Z j )+a 2 b 2 (gb j -Z j )+M(j, best);

[0116] Z j+1 =Z j +V j+1 ;

[0117] In the formula, V j+1 represents the velocity of the particle after the (j + 1)-th iteration, a 1 and a 2 respectively represent the learning factors, b 1 and b 2 respectively represent random numbers in the range of [0, 1], Pb j represents the extreme value point of the current particle, gb j represents the extreme value point of the particle

[0118] before iteration, Pb j has a larger value, stronger global optimization ability, and weaker local optimization ability, gb jThe value is small, the global optimization ability in all 5 games is weak, and the local optimization ability is strong, Z j+1 represents the position of the particle after the (j + 1)-th iteration.

[0119] Calculate the fitness value of the particle:

[0120] f = F(Z j+1 );

[0121] In the formula, F represents the fitness function. Set a fitness threshold, and remove the particles that do not meet the fitness threshold after each iteration. The new particle swarm composed of the particles that meet the fitness threshold is used for the next round of iteration until the maximum number of iterations is completed. Select the particle with the largest fitness value from the particle swarm after the last iteration is completed, which is the optimal solution of the objective function.

[0122] In order to achieve the self-optimizing energy saving of building thermal engineering, the learning factor is dynamically adjusted to keep the building thermal engineering

[0123] parameters always in a self-optimizing dynamic balance. The specific adjustment method is as follows:

[0124]

[0125] In the formula, respectively represent the adjusted learning factors, a max and a min respectively represent the maximum and minimum values of the learning factor.

[0126] According to the optimal solution of the objective function, the self-optimizing energy-saving thermal engineering parameters that meet the building heat load requirements are obtained, that is, the optimal state values of air-conditioning operation. The control module controls the air-conditioning according to the optimal state values, adjusts it to the optimal state, and the energy consumption analysis module analyzes the current building energy consumption situation. The judgment module judges whether it is the best energy-saving state. If not, the self-optimization module performs optimization and adjustment again to achieve the self-optimizing energy-saving goal of green building thermal engineering.

[0127] The particle swarm algorithm adopted in this embodiment is different from the traditional particle swarm algorithm. In the traditional particle swarm algorithm, the velocity of the particle is composed of an inertia term, a guiding term of the individual optimal position, and a guiding term of the global optimal position. However, this fixed update formula may sometimes lead to "over-exploration"

[0128] or "over-exploitation" of the particles, resulting in algorithm instability.

[0129] In the improved particle swarm optimization algorithm, the velocity update formula for particle movement optimizes the particle movement process by introducing Gaussian random numbers M(j, best). Through Gaussian mapping, random perturbations can be generated, making the particle update more adaptable and flexible. By dynamically adjusting the velocity of the particles, the problems of local optimum and particle conflict are avoided, thus improving the optimization effect.

[0130] In the traditional particle swarm optimization algorithm, the learning factors (usually the acceleration factors a 1 and a 2 ) are fixed, which may lead to an imbalance between the exploration and exploitation capabilities during the particle search process. Dynamically adjusting the learning factors can enhance the global optimization ability of the particles and enable the algorithm to explore and exploit more effectively in the search space, thus better avoiding the local optimum.

[0131] In the improved particle swarm optimization algorithm, the learning factors are dynamically adjusted with the number of iterations. Specifically, as the iteration process progresses, the values of a 1 and a 2 gradually transition from larger values to smaller values. This adjustment enables the particles to explore the search space more in the initial stage of the algorithm and focus on searching for more accurate solutions in the later stage, improving the convergence speed and accuracy.

[0132] The building self-optimizing thermal system provided by the present invention will be described below. The building self-optimizing thermal system described below can be mutually referred to the building self-optimizing thermal method described above.

[0133] The building self-optimizing thermal system includes a construction module 201, a determination module 202, and an output module 203;

[0134] The construction module 201 is used to construct the heat dissipation function of the building envelope of the target building and the heat consumption function of heating and ventilation of the target building based on the building structure information and outdoor environment information of the target building;

[0135] Building energy-saving design involves a wide range of aspects, mainly including two aspects: one is the energy consumption problem during the normal function of the building, that is, reasonably and effectively using energy on the premise of meeting people's comfortable thermal environment, that is, energy saving of the heating and ventilation system; the other is the insulation and heat insulation problem of the building envelope, that is, the reasonable use and correct arrangement of high-efficiency insulation and heat insulation materials to reduce the impact of the external environment on the indoor thermal environment, that is, energy saving of the envelope system.

[0136] Optionally, parameters related to building energy conservation, such as the building's shape coefficient, heat transfer coefficient and thermal inertia index of the roof and exterior wall, window-to-wall area ratio, air tightness, heat transfer coefficient of partition walls and floor slabs, annual heating power consumption, annual air conditioning power consumption, shading coefficient, etc., are used as thermal parameters to construct a heat dissipation function of the building envelope structure containing parameters for the target building and a heating and ventilation heat consumption function for the target building, so as to construct a comprehensive thermal model of the target building from two aspects of the building's heating and ventilation and maintenance structure systems.

[0137] It is understandable that constructing the heat dissipation function of the building envelope structure of the target building also requires collecting the building structure information of the target building, which includes the size and material of each external surface of the building; constructing the heating and ventilation heat consumption function of the target building also requires collecting the outdoor environment information of the target building.

[0138] Optionally, an information acquisition module is constructed to collect the above information. For example, the thermal environment of the building is analyzed and outdoor environmental information is collected as a meteorological data source. The outdoor environmental information includes information such as temperature and solar radiation.

[0139] A determination module 202 is used to construct an objective function that considers ventilation energy consumption and air conditioning energy consumption based on the heat dissipation function of the building envelope and the heating and ventilation heat consumption function, and to determine constraint conditions;

[0140] In order to achieve the energy conservation and emission reduction goals of green buildings, it is necessary to reduce the heating energy consumption of the target buildings, while ensuring that the internal thermal environment of the buildings is within the human comfort range. Therefore, it is necessary to construct a self-optimizing energy-saving objective function for green buildings, and determine the constraints based on the factors considered when constructing the objective function to achieve a dynamic balance of the building thermal environment.

[0141] Specifically, among the factors affecting thermal parameters, each factor has an impact on building energy consumption, and there are situations where various factors interact with each other. In a specific embodiment, the shape coefficient refers to the ratio of the surface area of ​​the building in contact with the outdoor atmosphere to the volume enclosed by the outer surface. The shape coefficient has a significant impact on building energy consumption; the smaller the shape coefficient, the smaller the surface area corresponding to the unit building area, and the smaller the heat transfer loss of the external envelope structure. Therefore, from the perspective of building energy conservation, the shape coefficient should be controlled at a lower level, but the shape coefficient is also closely related to building shape, plane layout, lighting and ventilation and other conditions. If the shape coefficient limit is set too small, it will restrict the creativity of architects, resulting in rigid building shape, difficult plane layout, and even damage to building functions.

[0142] Therefore, based on the above considerations, the heat dissipation function Q can be determined according to the target building. S and heat dissipation function Q WConstruct the target building by using the thermal parameters specifically used at that time and the target of the target building's thermal system, and determine the corresponding constraint conditions.

[0143] On this basis, in this embodiment, when constructing the objective function, ventilation energy consumption and air-conditioning energy consumption are also considered, and a construction method of the objective function is proposed as follows:

[0144] minQ total =Q S +Q W +Q vent +Q cool ;

[0145] In the formula, the objective function Q total represents the comprehensive energy consumption of the target building, including various parts such as heat dissipation of the building envelope, heating and ventilation energy consumption, and air-conditioning energy consumption; Q vent is the ventilation energy consumption, indicating the energy loss caused by building ventilation; Q cool is the air-conditioning energy consumption, indicating the energy consumed by the air-conditioning system when maintaining a comfortable temperature inside the building.

[0146] On this basis, solve the objective function, that is, obtain Q total with the minimum value of Q S and Q W .

[0147] The output module 203 is used to solve the objective function based on the constraint conditions by using a self-optimization algorithm, and obtain the thermal parameters that meet the building heat load requirements.

[0148] Optionally, the self-optimization algorithm can be a particle swarm algorithm, an ant colony algorithm, a genetic algorithm, an adaptive learning algorithm, etc.

[0149] Using a self-optimization algorithm, solve the objective function under the given constraint conditions, and take the optimal solution for a set of thermal parameters as the thermal parameters that meet the building heat load requirements.

[0150] It can be understood that different self-optimization algorithms are selected, and the definition of the optimal solution is also different, which is specifically determined according to the selected self-optimization algorithm. Solving the objective function can obtain the values of the heat dissipation function of the building envelope and the heating and ventilation heat consumption function of the target building with dynamic balance optimization, and then determine the values of the thermal parameters as variables therein.

[0151] The present invention analyzes building energy consumption from the heating and ventilation energy consumption analysis part and the building envelope energy consumption analysis part to ensure the comprehensiveness of the analysis, providing an accurate data basis for subsequent energy-saving optimization measures. Taking the air-conditioning operation mode in the building as the research object of energy-saving optimization, a self-optimizing energy-saving objective function is constructed, enabling energy conservation and environmental protection in the internal environment of green buildings while achieving human comfort, and taking into account high efficiency, low cost, and green sustainable development.

[0152] Figure 3 An example of the physical structure diagram of an electronic device is shown as Figure 3 shown. The electronic device may include: a processor 310, a communications interface 320, a memory 330, and a communication bus 340. Among them, the processor 310, the communications interface 320, and the memory 330 communicate with each other through the communication bus 340. The processor 310 can call the logical instructions in the memory 330 to execute the building self-optimizing thermal engineering method, which includes: constructing a heat dissipation function of the building envelope of the target building and a heat consumption function of heating and ventilation of the target building based on the building structure information and outdoor environment information of the target building; constructing an objective function considering ventilation energy consumption and air-conditioning energy consumption based on the heat dissipation function of the building envelope and the heat consumption function of heating and ventilation, and determining the constraint conditions; using a self-optimizing algorithm to solve the objective function based on the constraint conditions to obtain the thermal engineering parameters that meet the building heat load requirements.

[0153] In addition, when the logical instructions in the above-mentioned memory 330 are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art or part of this technical solution can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The foregoing storage medium includes: USB flash drives, mobile hard disks, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks, or optical disks and other various media that can store program codes.

[0154] On the other hand, the present invention also provides a computer program product, which includes a computer program. The computer program can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the building self-optimizing thermal engineering method provided by the above-mentioned various methods. The method includes: based on the building structure information and outdoor environment information of the target building, constructing a heat dissipation function of the building envelope structure of the target building and a heat consumption function of heating, ventilation and air conditioning of the target building; based on the heat dissipation function of the building envelope structure and the heat consumption function of heating, ventilation and air conditioning, constructing an objective function considering ventilation energy consumption and air conditioning energy consumption, and determining constraint conditions; using a self-optimizing algorithm to solve the objective function based on the constraint conditions to obtain thermal engineering parameters that meet the building heat load requirements.

[0155] In another aspect, the present invention also provides a non-transitory computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, it realizes the building self-optimizing thermal engineering method provided by the above-mentioned various methods. The method includes: based on the building structure information and outdoor environment information of the target building, constructing a heat dissipation function of the building envelope structure of the target building and a heat consumption function of heating, ventilation and air conditioning of the target building; based on the heat dissipation function of the building envelope structure and the heat consumption function of heating, ventilation and air conditioning, constructing an objective function considering ventilation energy consumption and air conditioning energy consumption, and determining constraint conditions; using a self-optimizing algorithm to solve the objective function based on the constraint conditions to obtain thermal engineering parameters that meet the building heat load requirements.

[0156] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place, or may be distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art can understand and implement it without creative efforts.

[0157] Through the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus a necessary general hardware platform, and of course, also by hardware. Based on such an understanding, the essence of the above technical solution, or the part that contributes to the prior art, can be embodied in the form of a software product. The computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0158] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A building self-optimization thermal method, characterized in that: include: Based on the building structure information and outdoor environment information of the target building, the heat dissipation function of the building envelope structure of the target building and the heating and ventilation heat consumption function of the target building are constructed; Based on the heat dissipation function of the building envelope and the heating and ventilation heat consumption function, an objective function considering ventilation energy consumption and air conditioning energy consumption is constructed, and constraint conditions are determined; A self-optimization algorithm is used to solve the objective function based on the constraint conditions to obtain thermal parameters that meet the building heat load demand.

2. The building self-optimization thermal method according to claim 1, characterized in that: The step of constructing the heat dissipation function of the building envelope structure of the target building and the heating and ventilation heat consumption function of the target building specifically includes: The building surface of the target building is divided into a plurality of grid units, and the heat dissipation function of the building envelope is constructed based on the divided grid units: In the formula, Q S represents the heat dissipation of the building envelope, K represents the overall heat transfer coefficient of the target building, Q u represents the heat transfer of a single grid unit formed by the u-th material division of the target building; n u represents the number of grid cells occupied by the u-th material, S represents the area of ​​each grid cell, and ΔT represents the temperature difference between the inner and outer surfaces of the building.

3. The building self-optimization thermal method according to claim 1, characterized in that: The step of constructing the heat dissipation function of the building envelope structure of the target building and the heating and ventilation heat consumption function of the target building specifically includes: The building surface of the target building is divided into a plurality of grid units, and the heating and ventilation heat consumption function is constructed based on the divided grid units: Q W =α×[WWR×K win +(1-WWR)×K m ]×HDD 18 ; In the formula, Q W represents the annual heating heat consumption per unit area of ​​the building envelope, WWR represents the window-to-wall ratio, α is the adjustment parameter, and K win represents the heat transfer coefficient of the window, K win Denotes the average heat transfer coefficient of the exterior wall, HDD 18 Indicates the number of heating degree days when the indoor heating temperature is 18 degrees Celsius.

4. The building self-optimization thermal method according to claim 2, characterized in that: The overall heat transfer coefficient of the target building is calculated as follows: In the formula, k u represents the heat transfer coefficient of the u-th material, n represents the number of material categories on the exterior surface of the target building, k in and k out Respectively represent the heat transfer coefficient inside and outside the target building, L u is the thickness of the u-th material.

5. The building self-optimization thermal method according to claim 1, characterized in that: The constraints include: heat load demand constraints, envelope structure thermal performance constraints, ventilation energy consumption constraints, air conditioning energy consumption constraints, and energy efficiency regulation and stability constraints.

6. The building self-optimization thermal method according to any one of claims 1 to 5, characterized in that: The self-optimization algorithm is constructed based on a particle swarm algorithm. The step of using the self-optimization algorithm to solve the objective function based on the constraint conditions to obtain thermal parameters that meet the building heat load demand specifically includes: Mapping the objective function to a solution space, forming all solutions of the objective function into a particle population, with each solution serving as a particle; On the basis of avoiding particle conflicts, the particles are moved to the optimal position among the adjacent particles and the particle positions are updated; Calculate the fitness value of the particle, set the fitness threshold, form a new particle group with particles that meet the fitness threshold, and iterate continuously until the maximum number of iterations. Select the particle with the largest fitness value from the particle group completed in the last iteration as the result of solving the objective function, and use the thermal parameters corresponding to the particle with the largest fitness value as the thermal parameters that meet the building heat load requirements. Among them, the learning factor used when updating the particle position is dynamically adjusted.

7. A building self-optimizing thermal system, characterized in that: include: A construction module, used to construct a heat dissipation function of a building envelope structure of a target building and a heating and ventilation heat consumption function of the target building based on building structure information and outdoor environment information of the target building; A determination module, used to construct an objective function that considers ventilation energy consumption and air conditioning energy consumption based on the heat dissipation function of the building envelope and the heating and ventilation heat consumption function, and to determine constraint conditions; The output module is used to adopt a self-optimization algorithm to solve the objective function based on the constraint conditions to obtain thermal parameters that meet the building heat load demand.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the building self-optimization thermal method as described in any one of claims 1 to 6 is implemented.

9. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the building self-optimization thermal method according to any one of claims 1 to 6 is implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the building self-optimization thermal method according to any one of claims 1 to 6 is implemented.

Images