CN106599467B - Design method for building structure in severe cold region based on multi-objective optimization algorithm - Google Patents
Design method for building structure in severe cold region based on multi-objective optimization algorithm Download PDFInfo
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Abstract
The invention relates to the technical field of building construction, in particular to a design method of a building construction in a severe cold region based on a multi-objective optimization algorithm. The method comprises the steps of establishing an external wall structure multi-objective optimization system model, carrying out evolutionary computation on decision parameters under the guidance of performance indexes by utilizing parameter relation between the decision parameters and constraint conditions, and automatically screening an optimal scheme. The method can realize the energy conservation and environmental protection of the whole building, greatly optimizes the building structure and improves the whole quality of the building.
Description
Technical Field
The invention relates to the technical field of building construction, in particular to a design method of a building construction in a severe cold region based on a multi-objective optimization algorithm.
Background
Northeast as a typical severe cold area, as a cradle for industrial development in China, under the current technical conditions, the industrial structure still mainly gives priority to industry, the technology is relatively backward, the energy efficiency is not high, a large amount of mineral resources such as coal, petroleum and the like are required to be consumed every year, and huge carbon emission is generated, which is very unfavorable for the completion of the emission reduction task promised in China. As areas with more carbon emission, the emission reduction pressure in northeast severe cold areas of China is higher. And a large amount of buildings in severe cold regions are constructed and used along with huge energy consumption and carbon emission, so that research and calculation on the carbon emission of the buildings have important influence on building energy conservation.
In annual air-conditioning heating energy consumption of public buildings in severe cold regions, about 50% of the energy consumption is caused by heat transfer of an outer enclosure structure. According to the statistics of the related technology, the proportion of the heat dissipation and heat loss of each part of the enclosure structure is as follows: the heat transfer loss of the wall structure accounts for about 60 to 70 percent; the heat transfer loss of the door and window is about 20-30%; the heat transfer loss of the roof accounts for about 10 percent. Therefore, the energy-saving design of the outer wall is very important for reducing the energy consumption of the building.
The structural mode of the outer wall structure determines the heat transfer performance of the outer wall, is a key factor influencing the indoor thermal environment and directly influences the energy consumption of building heating and air conditioning. Therefore, the heat preservation and insulation performance of the building outer wall is the key of building energy consumption control. The wall body is used as the main part of the building outer enclosing structure and has the effects of bearing, heat preservation, moisture protection, heat insulation and the like. The energy conservation of the outer enclosure wall is an important link of building energy conservation, and the development and utilization of the outer wall energy conservation technology and the optimization of the wall structure are main ways for realizing the building energy conservation. In severe cold areas, the heat preservation requirement in winter is fully considered, and heat protection in summer is also considered in partial areas, so that an outer wall structure with a small heat transfer coefficient is selected.
Although the external wall heat insulation technology is widely applied to the external wall of the cold-region building, the prior art cannot perform comprehensive search of space solution set aiming at selection of the type of the external wall structure and control of different levels of thickness of the external wall structure, so that optimization of energy conservation is achieved. By combining a multi-objective evolutionary algorithm, the optimization calculation can be performed on different levels of thicknesses of the building external wall structure by taking the building energy consumption and the carbon emission as optimization targets, and an optimal solution set with the minimum energy consumption and carbon emission is obtained. Therefore, the optimal design of the building outer wall structure in the severe cold region based on the multi-objective evolutionary algorithm has an important influence on the energy-saving effect of the building in the severe cold region in China.
Disclosure of Invention
In order to solve the technical problems in the prior art, the invention provides a design method of a building structure in a severe cold region based on a multi-objective optimization algorithm
A severe cold region building structure design method based on a multi-objective optimization algorithm comprises the following steps:
the first step is as follows: aiming at the construction type of the building outer wall in the severe cold area, establishing an outer wall mathematical model with controllable construction parameters corresponding to the construction type;
the second step is that: building thermal performance analysis building models are built according to the construction types of building outer walls in severe cold regions, and project information integrated by the building thermal performance analysis building models comprises building types, project positions, thermal properties of building envelope materials, building operation schedules, heating ventilation and air conditioning system types and ventilation rates;
the third step: combining climate data, and establishing a multi-objective evolutionary algorithm module by taking the annual energy consumption and carbon emission of the building thermal performance analysis building model in the second step as an envelope optimization target;
the fourth step: and performing optimization design on the external wall structure of the severe cold area building by using the multi-objective evolutionary algorithm module in the third step, and constructing the multi-objective optimization module of the external wall structure according to an optimization design result.
Preferably, the construction types in the first step comprise three types of external wall internal thermal insulation, external wall sandwich thermal insulation and external wall external thermal insulation.
Further, carrying out decision parameter and constraint condition parameterization modeling on the external wall mathematical model with controllable structural parameters in the first step, and specifically comprising the following steps:
a1: determining a constraint condition range of the external wall mathematical model with controllable construction parameters in the first step; the constraint condition range is as follows: in the heat-insulating layer, the thickness of the EPS plate is not less than 90mm, the thickness of the XPS plate is not less than 40mm, and the thickness of the polyurethane foam plastic is not less than 40 mm. In the structure layer, the thicknesses of the concrete hollow blocks, the ceramsite concrete blocks and the reinforced concrete shear wall are all not less than 200 mm; taking the construction thickness of the building outer wall as a decision parameter;
a2: establishing a driving association between a visual data module and a construction parameter by using a parametric modeling technology; and determining a specific constraint value within the constraint range in A1;
a3: establishing a relation between the decision parameters and the optimized parameters of the decision parameters and the specific constraint condition values in A2, wherein the relation is a mathematical model of the decision parameters.
Further, the decision parameter mathematical model in a3 is subjected to integrated construction of a performance optimization target and decision parameters, and the specific steps are as follows:
b1: importing climate data into the decision parameter mathematical model by using an energy consumption analysis platform, and calculating annual energy consumption of the decision parameter mathematical model;
b2: drawing a carbon footprint of the decision parameter mathematical model in a whole life cycle by using a carbon emission analysis platform;
b3: and calculating the carbon emission of the decision parameter mathematical model by using the carbon footprint in B2, and taking the annual energy consumption and carbon emission of the decision parameter mathematical model as the optimization target of the performance of the exterior wall of the building.
Further, the fourth step of using the multi-objective evolutionary algorithm module to carry out optimization design on the building exterior wall structure in the severe cold region comprises the specific steps of:
c1: introducing annual energy consumption and carbon emission of the decision parameter mathematical model into the multi-objective evolutionary algorithm module in the third step, and establishing an adaptive function model;
c2: performing evolutionary computation on the decision parameter by using the adaptive function model in C1 and taking annual energy consumption and carbon emission of the decision parameter mathematical model as a building outer wall performance optimization target to obtain an evolutionary computation result;
c3: importing the evolutionary computation result in the step C2 into a decision parameter mathematical model, and performing iterative computation to obtain an iterative result;
c4: and (4) screening out the optimal scheme of the building outer wall structure in the severe cold region with the minimum annual energy consumption and carbon emission through a genetic optimization algorithm module Optimo according to the iteration result in the C3, and finishing the optimal design.
Preferably, in the first step, the external wall mathematical model with controllable construction parameters is established by taking annual energy consumption and carbon emission as optimization targets, and a structural layer (x) is used in the three construction typesSTR) Insulating layer (x)INS) And a surface layer (x)SF) Establishing an external wall mathematical model with controllable construction parameters by taking different types of mutual collocation and thickness of each layer as decision parameters, wherein the external wall mathematical model with controllable construction parametersThe wall mathematical model is built as follows:
Min Z1(X)＝EC(X)
Min Z2(X)＝C(X)
make it satisfy
xSTR{1,...I},(I＝3)
xINS{1,...J},(J＝3)
xSF{1,...K},(K＝5)
xSTRt{200,...+∞}
xINSt (XPS, polyurethane foam){40,...+∞}
xINSt(EPS){90,...+∞}
xSURt{10,...+∞}
X＝{xSTR，xINS，xSF}
Wherein X represents a set of decision parameters; EC represents annual energy consumption; c represents carbon emission; x is the number ofSTRRepresenting the material category of the structural layer; x is the number ofINSShowing the material category of the heat-insulating layer; x is the number ofSURRepresenting the material category of the surface layer; i represents the upper limit value of the material class of the structural layer; j represents the upper limit value of the material class of the heat-insulating layer; k represents the upper limit value of the material class of the surface layer; x is the number ofSTRtRepresenting a structural layer thickness range; x is the number ofINStRepresenting the thickness range of the heat preservation layer; x is the number ofSURtIndicating the thickness range of the facing layer.
Preferably, the building thermal performance analysis building model in the second step is established by the following steps:
e1: the construction parameter controllable outer wall mathematical model in the first step is used for defining project positions, building types, the number of people using, the thermal properties of building envelope materials, heating ventilation and air conditioning system types, building operation schedules and ventilation rates; importing climate data into the project location;
e2: performing annual energy consumption simulation calculation by using the external wall mathematical model with controllable structural parameters to obtain an annual energy consumption simulation value;
e3: respectively carrying out simulation calculation on the carbon emission of the building construction stage, the carbon emission of the building use stage and the carbon emission of the building dismantling stage to obtain carbon emission simulation values;
e4: and combining the annual energy consumption simulation value and the carbon emission simulation value to form a building thermal performance analysis building model.
Preferably, the specific process of performing annual energy consumption simulation calculation by using the external wall mathematical model with controllable structural parameters in E2 is as follows:
e2.1: calculating the heating heat consumption of each unit of the building according to the heating heat consumption model of each unit of the building under the living condition, wherein the heating heat consumption model of each unit of the building under the living condition is as follows:
e2.2: calculating the heating heat consumption of each unit of the building according to the heating heat consumption model of each unit of the building under the condition of no people living, wherein the heating heat consumption model of each unit of the building under the condition of no people living is as follows:
in the formula qhm-building unit heating heat consumption, W/m2；Qhm-detecting the total heat supply MJ measured at the building heat inlet for the duration; q. q.sIH-heating of the interior of the building per unit building area, W/m2；ti-average indoor temperature, deg.c, for a single whole room; t is te-calculating the outdoor average temperature in the heating period at deg.c; t is tia-detecting the building indoor average temperature, deg.c, over a duration; t is tea-detecting the average temperature outside the chamber, deg.c, for a duration; a. the0-total building heating area of building, m2；Hr-detecting the duration, h; 278-unit conversion factor.
Preferably, the process of simulating and calculating the carbon emission of the building construction stage, the carbon emission of the building use stage and the carbon emission of the building dismantling stage respectively according to the E3 is as follows:
e3.1: establishing a carbon emission model of a construction stage, and calculating the carbon emission of the stage according to the carbon emission model of the construction stage, wherein the carbon emission model of the construction stage is as follows:
Econ＝Econ，1+Econ，2+Econ，3
in the formula: econ，1CO for transporting building materials from factory to construction site2eqAmount in kgCO2eq；Econ，2CO for construction during building construction2eqAmount in kgCO2eq；Econ，3CO generation for property changes in construction land2eqAmount in kgCO2eq；
In the formula: wi，jThe mass of the ith material transported by the jth mode is in kg; FTjTransporting goods of unit mass and unit distance, the j-th mode transporting emission factor is given in kgCO2eq/(km·kg)；LjThe distance the ith material is transported by the jth mode is in km.
Econ，2＝A×(FLb-FLa)
In the formula: a is the land area of the floor area of the building, and the unit is m2。FLb，FLaCarbon emission factors of different properties of land before and after construction are respectively expressed by the following units: kgCO2eq/m2。
Econ，3＝Y×S
In the formula: y is the construction carbon emission intensity of a building in unit area, and the unit is kgCO2eq/m2(ii) a S is the building area in m2；
E3.2: establishing a carbon emission model of a building use stage, and calculating the carbon emission of the stage according to the carbon emission model of the building use stage, wherein the carbon emission model of the building use stage is as follows:
Eopr＝Eopr，1+Eopr，2
in the formula: eopr，1CO generated for operation energy consumption of heating ventilation, air conditioning, lighting and other equipment2eqThe discharge amount is in kg; eopr，2CO escaping for refrigerant in building use stage2eqThe discharge amount is in kg;
in the formula: eopr，1Carbon emission CO of heating ventilation air conditioning equipment and lighting equipment for building consumption energy2eqAmount in kg; a. theiIs the area of a building of type i, in m2. The building type can be residential buildings, small and medium-sized public buildings, large-sized public buildings and state organs; t is the design service life of the building, and the unit is year; m is the annual average energy consumption of a building in unit area, and the unit is as follows: KWh/m2Year; EFiThe unit is the equivalent carbon dioxide emission factor of the i-type building energy, and the unit is as follows: CO 22eq/(KWh)
Eopr，2＝(Ml+Ma)×n×GWP
In the formula: m1 is the leakage quantity of the refrigerant in the using stage of the refrigeration equipment, and the unit is kg per unit; maThe unit of the discarded amount of the refrigerant after the refrigeration equipment is discarded is kg/station; n is the number of refrigeration equipment, and the unit is as follows: a stage;
e3.3: establishing a carbon emission model of a building demolition stage, and calculating the carbon emission of the stage according to the carbon emission model of the building demolition stage, wherein the carbon emission model of the building demolition stage is as follows:
Edis＝Edis，1+Edis，2+Edis，3
Edisthe total carbon emission in kgCO at the demolition stage of the building2eq；Edis，1Carbon emission for building demolition in kgCO2eq；Edis，2Carbon emission for construction waste transportation, with the unit of kgCO2eq；Edis，3For disposal of construction wasteCarbon emission in kgCO2eq。
Edis，1＝Econ，3×10％
In the formula Econ，3Carbon emission in kgCO for building construction in building construction stage2eq；
In the formula: wijThe unit of the mass of the ith construction waste transported in the jth transportation mode is kg; FTjTransporting goods of unit mass and unit distance, and the j transport mode emission factor is given in kgCO2eq/(km·kg)；LjThe distance the ith material is transported by the jth mode is in km.
Mj is the amount of the jth building rubbish, and the unit is kg; PM (particulate matter)f、PM1、PMrRespectively the percentage of the jth building garbage in landfill, incineration and recovery; EFf、EF1、EFrRespectively is a carbon emission factor of incineration, landfill and recovery of the jth building rubbish, and the unit is kgCO2eq/kg。
Preferably, the fourth step is a specific process of building the multi-objective optimization module for constructing the exterior wall structure, and the specific process comprises the following steps:
f1: importing the annual energy consumption analog value and the carbon emission analog value into a multi-target evolution algorithm module for analysis;
f2: feeding back the analysis result of F1 to the decision parameters through a genetic algorithm in the module for iterative computation to obtain a solution set which simultaneously takes annual energy consumption and carbon emission as targets;
f3: automatically screening Pareto (Pareto) optimal solutions in the solution set, namely solutions with minimum annual energy consumption and carbon emission;
f4: and automatically searching the decision parameters corresponding to the pareto solution, namely the exterior wall construction parameters, and completing the establishment of the multi-objective optimization module.
The invention has the beneficial effects that: the traditional design mode has a plurality of limitations in dealing with complexity problems, such as the structure type and the structure thickness of the outer wall of a building, the influence on the energy consumption and the carbon emission of the building, and a plurality of factors influence the energy-saving effect of the building in a complex relation. The invention uses a multi-objective evolutionary algorithm to convert the simulation calculation results of building energy consumption and carbon emission into logic control of the design process of the building outer wall structure, and obtains the optimized building outer wall structure under the condition that the energy consumption and the carbon emission are simultaneously minimized. The energy-saving building design of the outer wall structure is the most effective means for saving energy.
Drawings
FIG. 1 is a schematic diagram of the correlation between the external thermal insulation structure type and the structure parameters of the external wall.
FIG. 2 is a schematic diagram showing the correlation between the type and the construction parameters of the external wall sandwich insulation structure of the present invention.
FIG. 3 is a schematic diagram showing the correlation between the type of the thermal insulation structure and the structural parameters in the exterior wall according to the present invention.
Detailed Description
The present invention will be further described with reference to the following specific examples, but the present invention is not limited to these examples.
Example 1:
the invention provides a severe cold region building structure design method based on a multi-objective optimization algorithm, which comprises the following steps:
a severe cold region building structure design method based on a multi-objective optimization algorithm comprises the following steps:
the first step is as follows: aiming at the construction type of the building outer wall in the severe cold area, establishing an outer wall mathematical model with controllable construction parameters corresponding to the construction type;
the second step is that: building thermal performance analysis building models are built according to the construction types of building outer walls in severe cold regions, and project information integrated by the building thermal performance analysis building models comprises building types, project positions, thermal properties of building envelope materials, building operation schedules, heating ventilation and air conditioning system types and ventilation rates;
the third step: combining climate data, and establishing a multi-objective evolutionary algorithm module by taking the annual energy consumption and carbon emission of the building thermal performance analysis building model in the second step as an envelope optimization target; the climate data may be obtained by a local meteorological department.
The fourth step: and performing optimization design on the external wall structure of the severe cold area building by using the multi-objective evolutionary algorithm module in the third step, and constructing the multi-objective optimization module of the external wall structure according to an optimization design result.
The construction types in the first step comprise three types of external wall internal heat insulation, external wall sandwich heat insulation and external wall external heat insulation.
Further, carrying out decision parameter and constraint condition parameterization modeling on the external wall mathematical model with controllable structural parameters in the first step, and specifically comprising the following steps:
a1: determining a constraint condition range of the external wall mathematical model with controllable construction parameters in the first step; the constraint condition range is as follows: in the heat-insulating layer, the thickness of the EPS plate is not less than 90mm, the thickness of the XPS plate is not less than 40mm, and the thickness of the polyurethane foam plastic is not less than 40 mm. In the structure layer, the thicknesses of the concrete hollow blocks, the ceramsite concrete blocks and the reinforced concrete shear wall are all not less than 200 mm; meanwhile, taking the construction thickness of the building outer wall as a decision parameter;
a2: establishing driving association of a visual data module and construction parameters according to different construction types by using a parametric modeling technology, wherein the driving association relationship is shown in figures 1-3; and determining a specific constraint value within the constraint range in A1; the visual data module is embodied in the existing building analysis software, or can be subjected to simulation according to building technical knowledge through the existing simulation software;
a3: establishing a relation between the decision parameters and the optimized parameters of the construction thickness of the building outer wall and the specific constraint condition values in A2, wherein the constraint condition values are specified in the drive association process of A2, and the relation is a decision parameter mathematical model.
Further, the decision parameter mathematical model in a3 is subjected to integrated construction of a performance optimization target and decision parameters, and the specific steps are as follows:
b1: importing climate data into the decision parameter mathematical model by using an energy consumption analysis platform, and calculating annual energy consumption of the decision parameter mathematical model;
b2: drawing a carbon footprint of the decision parameter mathematical model in a whole life cycle by using a carbon emission analysis platform;
b3: and calculating the carbon emission of the decision parameter mathematical model by using the carbon footprint in B2, and taking the annual energy consumption and carbon emission of the decision parameter mathematical model as the optimization target of the performance of the exterior wall of the building.
The energy consumption analysis platform and the carbon emission analysis platform are software platforms, such as Green building studio and SimaPro, and have the functions of energy consumption analysis and carbon emission analysis;
further, the fourth step of using the multi-objective evolutionary algorithm module to optimally design the external wall structure of the severe cold region building comprises the specific steps of:
c1: introducing annual energy consumption and carbon emission of the decision parameter mathematical model into the multi-objective evolutionary algorithm module in the third step, and establishing an adaptive function model;
c2: performing evolutionary computation on the decision parameter by using the adaptive function model in C1 and taking annual energy consumption and carbon emission of the decision parameter mathematical model as a building outer wall performance optimization target to obtain an evolutionary computation result;
c3: importing the evolutionary computation result in the step C2 into a decision parameter mathematical model, and performing iterative computation to obtain an iterative result;
c4: and (4) screening out the optimal scheme of the building outer wall structure in the severe cold region with the minimum annual energy consumption and carbon emission through a genetic optimization algorithm module Optimo according to the iteration result in the C3, and finishing the optimal design.
In the first step, the external wall mathematical model with controllable construction parameters is established by taking annual energy consumption and carbon emission as optimization targets, and in the three construction types, a structural layer (x) is usedSTR) Insulating layer (x)INS) And a surface layer (x)SF) Different categories of mutual matchingAnd establishing an external wall mathematical model with controllable construction parameters by taking the thickness of each layer as a decision parameter, wherein the establishment process of the external wall mathematical model with controllable construction parameters is as follows:
Min Z1(X)＝EC(X)
Min Z2(X)＝C(X)
make it satisfy
xSTR{1,...I},(I＝3)
xINS{1,...J},(J＝3)
xSF{1,...K},(K＝5)
xSTRt{200,...+∞}
xINSt (XPS, polyurethane foam){40,...+∞}
xINSt(EPS){90,...+∞}
xSURt{10,...+∞}
X＝{xSTR，xINS，xSF}
Wherein X represents a set of decision parameters; EC represents annual energy consumption; c represents carbon emission; x is the number ofSTRRepresenting the material category of the structural layer; x is the number ofINSShowing the material category of the heat-insulating layer; x is the number ofSURRepresenting the material category of the surface layer; i represents the upper limit value of the material class of the structural layer; j represents the upper limit value of the material class of the heat-insulating layer; k represents the upper limit value of the material class of the surface layer; x is the number ofSTRtRepresenting a structural layer thickness range; x is the number ofINStRepresenting the thickness range of the heat preservation layer; x is the number ofSURtIndicating the thickness range of the facing layer.
The building thermal performance analysis building model building method comprises the following steps:
e1: the construction parameter controllable outer wall mathematical model in the first step is used for defining project positions, building types, the number of people using, the thermal properties of building envelope materials, heating ventilation and air conditioning system types, building operation schedules and ventilation rates; importing climate data into the project location; the project position, the building type, the number of people using, the thermal property of the building envelope material, the type of the heating, ventilating and air conditioning system, the building operation schedule and the ventilation rate can be specifically designed and appointed according to the building design requirement;
e2: performing annual energy consumption simulation calculation by using the external wall mathematical model with controllable structural parameters to obtain an annual energy consumption simulation value;
e3: respectively carrying out simulation calculation on the carbon emission of the building construction stage, the carbon emission of the building use stage and the carbon emission of the building dismantling stage to obtain carbon emission simulation values;
e4: and combining the annual energy consumption simulation value and the carbon emission simulation value to form a building thermal performance analysis building model.
The specific process of applying the external wall mathematical model with controllable structural parameters to carry out annual energy consumption simulation calculation in E2 is as follows:
e2.1: calculating the heating heat consumption of each unit of the building according to the heating heat consumption model of each unit of the building under the living condition, wherein the heating heat consumption model of each unit of the building under the living condition is as follows:
e2.2: calculating the heating heat consumption of each unit of the building according to the heating heat consumption model of each unit of the building under the condition of no people living, wherein the heating heat consumption model of each unit of the building under the condition of no people living is as follows:
in the formula qhm-building unit heating heat consumption, W/m2；Qhm-detecting the total heat supply MJ measured at the building heat inlet for the duration; q. q.sIH-heating of the interior of the building per unit building area, W/m2；ti-average indoor temperature, deg.c, for a single whole room; t is te-calculating the outdoor average temperature in the heating period at deg.c; t is tia-detecting the building indoor average temperature, deg.c, over a duration; t is tea-detecting the average temperature outside the chamber, deg.c, for a duration; a. the0-total building heating area of building, m2；Hr-detecting the duration, h; 278-unit conversion factor.
Wherein, the process of respectively carrying out simulation calculation on the carbon emission of the building construction stage, the carbon emission of the building use stage and the carbon emission of the building dismantling stage according to E3 is as follows:
e3.1: establishing a carbon emission model of a construction stage, and calculating the carbon emission of the stage according to the carbon emission model of the construction stage, wherein the carbon emission model of the construction stage is as follows:
Econ＝Econ，1+Econ，2+Econ，3
in the formula: econ，1CO for transporting building materials from factory to construction site2eqAmount in kgCO2eq；Econ，2CO for construction during building construction2eqAmount in kgCO2eq；Econ，3CO generation for property changes in construction land2eqAmount in kgCO2eq；
In the formula: wi，jThe mass of the ith material transported by the jth mode is in kg; FTjTransporting goods of unit mass and unit distance, the j-th mode transporting emission factor is given in kgCO2eq/(km·kg)；LjThe distance the ith material is transported by the jth mode is in km.
Econ，2＝A×(FLb-FLa)
In the formula: a is the land area of the floor area of the building, and the unit is m2。FLb，FLaCarbon emission factors of different properties of land before and after construction are respectively expressed by the following units: kgCO2eq/m2。
Econ，3＝Y×S
In the formula: y is the construction carbon emission intensity of a building in unit area, and the unit is kgCO2eq/m2(ii) a S is the area of the buildingBit is m2；
E3.2: establishing a carbon emission model of a building use stage, and calculating the carbon emission of the stage according to the carbon emission model of the building use stage, wherein the carbon emission model of the building use stage is as follows:
Eopr＝Eopr，1+Eopr，2
in the formula: eopr，1CO generated for operation energy consumption of heating ventilation, air conditioning, lighting and other equipment2eqThe discharge amount is in kg; eopr，2CO escaping for refrigerant in building use stage2eqThe discharge amount is in kg;
in the formula: eopr，1Carbon emission CO of heating ventilation air conditioning equipment and lighting equipment for building consumption energy2eqAmount in kg; a. theiIs the area of a building of type i, in m2. The building type can be residential buildings, small and medium-sized public buildings, large-sized public buildings and state organs; t is the design service life of the building, and the unit is year; m is the annual average energy consumption of a building in unit area, and the unit is as follows: KWh/m2Year; EFiThe unit is the equivalent carbon dioxide emission factor of the i-type building energy, and the unit is as follows: CO 22eq/(KWh)
Eopr，2＝(Ml+Ma)×n×GWP
In the formula: m1 is the leakage quantity of the refrigerant in the using stage of the refrigeration equipment, and the unit is kg per unit; maThe unit of the discarded amount of the refrigerant after the refrigeration equipment is discarded is kg/station; n is the number of refrigeration equipment, and the unit is as follows: a stage;
e3.3: establishing a carbon emission model of a building demolition stage, and calculating the carbon emission of the stage according to the carbon emission model of the building demolition stage, wherein the carbon emission model of the building demolition stage is as follows:
Edis＝Edis，1+Edis，2+Edis，3
Edisthe total carbon emission in kgCO at the demolition stage of the building2eq；Edis，1Carbon emission for building demolition in kgCO2eq；Edis，2Carbon emission for construction waste transportation, with the unit of kgCO2eq；Edis，3Carbon emission for building garbage treatment with the unit of kgCO2eq。
Edis，1＝Econ，3×10％
In the formula Econ，3Carbon emission in kgCO for building construction in building construction stage2eq；
In the formula: wijThe unit of the mass of the ith construction waste transported in the jth transportation mode is kg; FTjTransporting goods of unit mass and unit distance, and the j transport mode emission factor is given in kgCO2eq/(km·kg)；LjThe distance the ith material is transported by the jth mode is in km.
Mj is the amount of the jth building rubbish, and the unit is kg; PM (particulate matter)f、PM1、PMrRespectively the percentage of the jth building garbage in landfill, incineration and recovery; EFf、EF1EFr are carbon emission factors of incineration, landfill and recovery of the jth building rubbish, and the unit is kgCO2eq/kg。
The fourth step is that the specific process of building the multi-objective optimization module for the exterior wall structure is as follows:
f1: importing the annual energy consumption analog value and the carbon emission analog value into a multi-target evolution algorithm module for analysis;
f2: feeding back the analysis result of F1 to the decision parameters through a genetic algorithm in the module for iterative computation to obtain a solution set which simultaneously takes annual energy consumption and carbon emission as targets;
f3: automatically screening Pareto (Pareto) optimal solutions in the solution set, namely solutions with minimum annual energy consumption and carbon emission;
f4: and automatically searching the decision parameters corresponding to the pareto solution, namely the exterior wall construction parameters, and completing the establishment of the multi-objective optimization module.
The building design process in severe cold areas is a very complex process, not only needs to consider the rationality of each index of the building, but also needs to consider the influence of the building on the external environment, therefore, various factors influence the performance index of the building and the energy-saving effect of the building in a complex relationship in the building design process. The traditional building design method has many limitations on the treatment process of the complexity problem of building design, for example, the influence of the collocation of the traditional building design method on the building outer wall structure type and the structure thickness on the annual energy consumption and carbon emission of the building cannot give the optimal reasonable analysis and the optimal combination result. In the building design process, the design is usually performed only by calculation and analysis of part of indexes, and in the design, the combination of the experience of engineers and the indexes after simple calculation is mainly relied on. Meanwhile, in view of the severe pressure on the current environment, in the face of the increasing practical situations of urban construction and various public buildings and houses at present, how to achieve energy conservation and environmental protection of the buildings to the greatest extent is a technical problem which is always eagerly solved but is not successful all the time.
The invention provides a design method of a building structure in a severe cold region based on a multi-objective algorithm, which comprises the following steps: 1. the technical bias that the traditional design is designed by taking building experience as a main joint part building index calculation and analysis is overcome, the annual energy consumption and carbon emission of the building are simulated and calculated in advance according to the building construction requirements in advance by combining with the local climate, environmental requirements and peripheral arrangement of the building, and the optimized building outer wall structure is obtained under the condition that the simulated and calculated effect is converted into the logic control energy consumption and carbon emission in the building outer wall structure design process and the minimum energy consumption and carbon emission are simultaneously achieved, so that a solid design theoretical numerical value foundation is laid for the building design; 2. can be directed to differentThe construction type of the building is designed in a pertinence manner, and the pertinence optimization design of the actual requirements of the building is really realized; 3. the design is carried out by using multi-objective optimization, so that the labor cost and the time cost are greatly saved, and meanwhile, the accuracy of the optimization design is improved; 4. with a structural layer (x)STR) Insulating layer (x)INS) And a surface layer (x)SF) The different types of mutual collocation and the thicknesses of all layers are decision parameters, and the buildings are designed by taking annual energy consumption and carbon emission as optimization targets, so that the energy consumption of the buildings and the optimization design of the whole carbon emission from construction, use to demolition of the buildings are realized, the energy consumption of the buildings and the whole carbon emission from construction, use to demolition of the buildings are reduced to a great extent, and the energy conservation and environmental protection in the true sense are realized; 5. because the method provided by the invention uses the structural layer (x)STR) Insulating layer (x)INS) And a surface layer (x)SF) Different types of mutual collocation and each layer thickness are decision-making parameters, and the buildings are designed by taking annual energy consumption and carbon emission as optimization targets, so that the heat insulation performance of the buildings is completely ensured while the buildings are energy-saving and environment-friendly, the collocation between the heat insulation performance of the buildings and the building cost saving and the energy-saving and environment-friendly is optimized to a great extent, and the overall quality of the buildings is improved from the perspective of environmental requirements and the perspective of resident requirements.
Although the present invention has been described with reference to the preferred embodiments, it should be understood that various changes and modifications can be made therein by those skilled in the art without departing from the spirit and scope of the invention as defined in the appended claims.
Claims (8)
1. A severe cold region building structure design method based on a multi-objective optimization algorithm is characterized by comprising the following steps:
the first step is as follows: aiming at the construction type of the building outer wall in the severe cold area, establishing an outer wall mathematical model with controllable construction parameters corresponding to the construction type;
the second step is that: building thermal performance analysis building models are built according to the construction types of building outer walls in severe cold regions, and project information integrated by the building thermal performance analysis building models comprises building types, project positions, thermal properties of building envelope materials, building operation schedules, heating ventilation and air conditioning system types and ventilation rates;
the third step: combining climate data, and establishing a multi-objective evolutionary algorithm module by taking the annual energy consumption and carbon emission of the building thermal performance analysis building model in the second step as an envelope optimization target;
the fourth step: performing optimization design on the external wall structure of the severe cold region building by using the multi-objective evolutionary algorithm module in the third step, and constructing an external wall structure multi-objective optimization module according to an optimization design result;
the method comprises the following steps of carrying out decision parameter and constraint condition parameterization modeling on the external wall mathematical model with controllable construction parameters in the first step, and specifically comprises the following steps:
a1: determining a constraint condition range of the external wall mathematical model with controllable construction parameters in the first step; the constraint condition range is as follows: in the heat-insulating layer, the thickness of the EPS plate is not less than 90mm, the thickness of the XPS plate is not less than 40mm, and the thickness of the polyurethane foam plastic is not less than 40 mm; in the structure layer, the thicknesses of the concrete hollow blocks, the ceramsite concrete blocks and the reinforced concrete shear wall are all not less than 200 mm; taking the construction thickness of the building outer wall as a decision parameter;
a2: establishing a driving association between a visual data module and a construction parameter by using a parametric modeling technology; and determining a specific constraint value within the constraint range in A1;
a3: establishing a relation between the decision parameters and the optimized parameters of the decision parameters and the specific constraint condition values in A2, wherein the relation is a decision parameter mathematical model;
and (2) performing integrated construction of a performance optimization target and decision parameters on the decision parameter mathematical model in A3, wherein the method specifically comprises the following steps:
b1: importing climate data into the decision parameter mathematical model by using an energy consumption analysis platform, and calculating annual energy consumption of the decision parameter mathematical model;
b2: drawing a carbon footprint of the decision parameter mathematical model in a whole life cycle by using a carbon emission analysis platform;
b3: and calculating the carbon emission of the decision parameter mathematical model by using the carbon footprint in B2, and taking the annual energy consumption and carbon emission of the decision parameter mathematical model as the optimization target of the performance of the exterior wall of the building.
2. The method for designing the building structure in the severe cold region according to claim 1, wherein the structure types in the first step include three types of external wall internal insulation, external wall sandwich insulation and external wall external insulation.
3. The method for designing the building structure in the severe cold region according to claim 1, wherein the fourth step of the multi-objective evolutionary algorithm module is to perform the optimization design on the external wall structure of the severe cold region by the specific steps of:
c1: introducing annual energy consumption and carbon emission of the decision parameter mathematical model into the multi-objective evolutionary algorithm module in the third step, and establishing an adaptive function model;
c2: performing evolutionary computation on the decision parameter by using the adaptive function model in C1 and taking annual energy consumption and carbon emission of the decision parameter mathematical model as a building outer wall performance optimization target to obtain an evolutionary computation result;
c3: importing the evolutionary computation result in the step C2 into a decision parameter mathematical model, and performing iterative computation to obtain an iterative result;
c4: and (4) screening out the optimal scheme of the building outer wall structure in the severe cold region with the minimum annual energy consumption and carbon emission through a genetic optimization algorithm module Optimo according to the iteration result in the C3, and finishing the optimal design.
4. The method for designing the building structure in the severe cold region according to claim 2, wherein in the first step, the external wall mathematical model with controllable structural parameters is established by taking annual energy consumption and carbon emission as optimization targets, and the external wall mathematical model with controllable structural parameters is established in the following process:
Min Z1(X)＝EC(X)
Min Z2(X)＝C(X)
make it satisfy
xSTR{1,...I},(I＝3)
xINS{1,...J},(J＝3)
xSUR{1,...K},(K＝5)
xSTRt{200,...+∞}
xINSt (XPS, polyurethane foam){40,...+∞}
xINSt(EPS){90,...+∞}
xSURt{10,...+∞}
X＝{xSTR，xINS，xSUR}
Wherein Z is1Representing an outer wall mathematical model based on annual energy consumption; z2The method is an outer wall mathematical model based on carbon emission; x represents a decision parameter set; EC represents annual energy consumption; c represents carbon emission; x is the number ofSTRRepresenting the material category of the structural layer; x is the number ofINSShowing the material category of the heat-insulating layer; x is the number ofSURRepresenting the material category of the surface layer; i represents the upper limit value of the material class of the structural layer; j represents the upper limit value of the material class of the heat-insulating layer; k represents the upper limit value of the material class of the surface layer; x is the number ofSTRtRepresenting a structural layer thickness range; x is the number ofINStRepresenting the thickness range of the heat preservation layer; x is the number ofSURtIndicating the thickness range of the facing layer.
5. The method for designing the building structure in the severe cold region according to claim 2, wherein the building thermal performance analysis building model in the second step is established by:
e1: the construction parameter controllable outer wall mathematical model in the first step is used for defining project positions, building types, the number of people using, the thermal properties of building envelope materials, heating ventilation and air conditioning system types, building operation schedules and ventilation rates; importing climate data into the project location;
e2: performing annual energy consumption simulation calculation by using the external wall mathematical model with controllable structural parameters to obtain an annual energy consumption simulation value;
e3, respectively carrying out simulation calculation on the carbon emission of the building construction stage, the carbon emission of the building use stage and the carbon emission of the building dismantling stage to obtain carbon emission simulation values;
e4: and combining the annual energy consumption simulation value and the carbon emission simulation value to form a building thermal performance analysis building model.
6. The method for designing the building structure in the severe cold region according to claim 5, wherein the specific process of performing annual energy consumption simulation calculation by using the external wall mathematical model with controllable structural parameters in E2 is as follows:
e2.1: calculating the heating heat consumption of each unit of the building according to the heating heat consumption model of each unit of the building under the living condition, wherein the heating heat consumption model of each unit of the building under the living condition is as follows:
e2.2: calculating the heating heat consumption of each unit of the building according to the heating heat consumption model of each unit of the building under the condition of no people living, wherein the heating heat consumption model of each unit of the building under the condition of no people living is as follows:
in the formula qhmBuilding heating heat consumption per unit is W/square meter; qhm-detecting the total heat supply MJ measured at the building heat inlet for the duration; q. q.sIHThe interior of a building in unit building area is heated, and the square meter is W/square meter; t is ti-average indoor temperature, deg.c, for a single whole room; t is te-calculating the outdoor average temperature in the heating period at deg.c; t is tia-detecting the building indoor average temperature, deg.c, over a duration; t is tea-detecting the average temperature outside the chamber, deg.c, for a duration; a. the0Building total heating building area and square meter; hr-detecting the duration, h; 278-unit conversion factor.
7. The method for designing a building structure in a severe cold region according to claim 5, wherein the simulation calculation of the carbon emission in the building construction stage, the carbon emission in the building use stage and the carbon emission in the building dismantling stage, respectively, of E3 is as follows:
e3.1: establishing a carbon emission model of a construction stage, and calculating the carbon emission of the stage according to the carbon emission model of the construction stage, wherein the carbon emission model of the construction stage is as follows:
Econ＝Econ,1+Econ,2+Econ,3
in the formula: econ,1CO for transporting building materials from factory to construction site2eqAmount in kgCO2eq；Econ,2CO for construction during building construction2eqAmount in kgCO2eq；Econ,3CO generation for property changes in construction land2eqAmount in kgCO2eq；
E3.2: establishing a carbon emission model of a building use stage, and calculating the carbon emission of the stage according to the carbon emission model of the building use stage, wherein the carbon emission model of the building use stage is as follows:
Eopr＝Eopr,1+Eopr,2
in the formula: eopr,1CO generated for operation energy consumption of heating ventilation, air conditioning and lighting equipment2eqThe discharge amount is in kg; eopr,2CO escaping for refrigerant in building use stage2eqThe discharge amount is in kg;
e3.3: establishing a carbon emission model of a building demolition stage, and calculating the carbon emission of the stage according to the carbon emission model of the building demolition stage, wherein the carbon emission model of the building demolition stage is as follows:
Edis＝Edis,1+Edis,2+Edis,3
Edisthe total carbon emission in kgCO at the demolition stage of the building2eq；Edis,1Carbon emission for building demolition in kgCO2eq；Edis,2Carbon emission for construction waste transportation, with the unit of kgCO2eq；Edis,3Carbon emission for building garbage treatment with the unit of kgCO2eq。
8. The design method of the building structure in the severe cold region according to claim 7, wherein the fourth step is that the specific process of building the multi-objective optimization module for constructing the exterior wall structure is as follows:
f1: importing the annual energy consumption analog value and the carbon emission analog value into a multi-target evolution algorithm module for analysis;
f2: feeding back the analysis result of F1 to the decision parameters through a genetic algorithm in the module for iterative computation to obtain a solution set which simultaneously takes annual energy consumption and carbon emission as targets;
f3: automatically screening pareto optimal solutions in the solution set, namely solutions with minimum annual energy consumption and carbon emission;
f4: and automatically searching decision parameters corresponding to the pareto optimal solution, namely the exterior wall construction parameters, and completing the establishment of the multi-objective optimization module.
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