CN116757364A - BIM technology-based carbon emission evaluation method and system - Google Patents
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Abstract
The invention relates to a carbon emission assessment method and system based on BIM technology, and belongs to the technical field of carbon emission assessment. The method comprises the following steps: s1, dividing a carbon emission stage of a life cycle of a building; s2, collecting data of each stage of a building; s3, building a BIM model; s4, simulation; s5, carbon emission calculation: calculating carbon emission of the construction stage, the use stage and the dismantling stage respectively through simulation results; the construction stage and the dismantling stage are calculated through energy consumption in the construction process, and the use stage is calculated through energy consumption and human activities in the operation process; s6, evaluating carbon emission; s7, state monitoring and feedback. According to the invention, the whole life cycle of the building is divided into different stages according to the evaluation requirement for calculation and evaluation, the data provided by the BIM model is utilized to construct the neural network model for evaluating the carbon discharge generated by artificial activities, so that the more comprehensive and accurate evaluation of carbon discharge is realized.
Description
Technical Field
The invention belongs to the technical field of carbon emission evaluation, and particularly relates to a carbon emission evaluation method and system based on a BIM technology.
Background
With global warming and frequent occurrence of extreme weather, meteorological problems have become a global topic of attention, and carbon control and carbon reduction have become a human consensus. The research finds that the carbon emission produced by the building industry is highest, so that the carbon reduction and carbon control in the building industry are not slow, and the basis of carbon emission assessment is the measurement and calculation of the carbon emission.
The building information model (Building Information Modeling, BIM for short) contains various engineering data of building projects, and is widely applied in the building industry at present. The BIM technology in the present stage provides comprehensive technical support for the construction from the front-stage planning design to the construction operation through a digital model, and the combination of the BIM technology and the green construction design becomes one of important tools for carbon emission assessment, can provide powerful support for environmental protection, energy conservation and emission reduction work, improves the working efficiency of the construction design stage, optimizes the design scheme, and is a necessary flow and technical means in the transformation of the construction industry.
While BIM can provide a number of benefits in building carbon displacement calculations, it also has its limitations. In practical application, the data involved in the BIM model is not comprehensive, and more accurate calculation cannot be performed on factors in some use stages, such as weather changes, population number changes and the like.
Disclosure of Invention
In order to solve the problems in the prior art, the invention provides a carbon emission assessment method and a system based on BIM technology, which are characterized in that the whole life cycle of a building is divided into different stages according to assessment requirements for calculation and assessment, a neural network model is constructed by utilizing data provided by a BIM model to estimate carbon discharge generated by artificial activities, and the carbon emission is assessed more comprehensively and accurately by combining the mapping relation between each influence factor and carbon emission.
The aim of the invention can be achieved by the following technical scheme:
the invention provides a carbon emission assessment method based on BIM technology, which comprises the following steps:
s1, dividing a carbon emission stage of a life cycle of a building: according to the stage characteristics of the carbon emission of the whole life cycle of the building, dividing a carbon emission key stage for researching the building;
s2, collecting data of each stage of the building: collecting various data of a building construction stage, a use stage and a dismantling stage;
s3, building a BIM model: building a three-dimensional model of a building by using BIM software, and integrating the acquired data of each stage of the building into the model;
s4, simulation: the BIM model is imported into energy simulation software to perform simulation, and a simulation result is obtained by simulating the energy consumption condition and human activities in the building;
s5, carbon emission calculation: calculating carbon emission of the construction stage, the use stage and the dismantling stage respectively through simulation results; the construction stage and the dismantling stage are calculated through energy consumption in the construction process, and the use stage is calculated through energy consumption and human activities in the operation process;
s6, evaluating carbon emission: evaluating the carbon emission condition of the building by comparing with the carbon emission standard of the country or region according to the carbon emission calculation result;
s7, state monitoring and feedback: in the using stage of the building, the data such as energy consumption and artificial activities of the building are monitored through the BIM model, and feedback is carried out through the staged carbon emission evaluation.
Further, in the step S1, the carbon emission phase of the building life cycle is divided into: a construction stage, a use stage and a dismantling stage;
wherein the carbon emissions of the build phase include those generated by consumption of building materials and use of construction tools; the carbon emission of the using stage comprises the energy consumption and the carbon emission caused by artificial activities of the building in the operation and maintenance process; carbon emissions from the demolition stage include carbon emissions from waste recovery and use of construction equipment.
Further, in the step S2, the data of the building construction stage includes building design, construction drawing, construction process record, equipment energy data and maintenance record; the using stage data comprise energy consumption of an energy supply system, meteorological data, water resource utilization, waste treatment, maintenance data and human activity data; the demolition phase data includes work process records and equipment energy data.
Further, in the step S4, the energy consumption includes the energy consumption of lighting, socket, air conditioner, power, gas, fuel oil and renewable energy sources; artificial activities include people flow conditions, activity areas, people activity patterns, crowdedness and time allocation.
Further, in the step S5, the process of calculating the carbon emission by the energy consumption is estimated by using a neural network model according to the carbon emission standard established by the country or region.
Further, in the step S5, estimating the carbon emissions of the artificial activities through the neural network model includes the steps of:
s51, screening carbon discharge influence factors obtained through a BIM model;
s52, constructing a neural network;
s53, estimating the carbon emission of the artificial activity.
Further, the carbon displacement influencing factors include meteorological conditions, personnel flow, activity types, equipment states, equipment efficiency, equipment life, use behaviors, energy prices and management regimes.
Further, in the step S51, the screening of the carbon emission influencing factors adopts a regression model, which specifically includes data standardization and construction of carbon emission Y t And (5) an influence factor linear regression model and determining a final selected variable.
Further, in the step S52, the neural network is constructed, which specifically includes the following steps:
s521, carrying out normalization processing on sample data;
s522, determining a neural network structure;
s523, training the neural network.
The invention also provides a carbon emission evaluation system based on BIM technology, which is applied to the carbon emission evaluation method based on BIM technology, and comprises a carbon emission data acquisition unit, a carbon emission database unit, a carbon emission calculation unit and a carbon emission estimation unit, wherein:
the carbon emission data acquisition unit is used for acquiring data of each stage of a building;
the carbon emission database unit is used for storing collected data of each stage of the building;
the carbon emission calculating unit is used for calculating carbon emission generated by energy consumption of each stage of the whole life cycle of the building;
the carbon discharge estimation unit is used for estimating carbon discharge generated by artificial activities at each stage of the whole life cycle of the building.
The beneficial effects of the invention are as follows:
(1) The whole life cycle of the building is divided into different stages according to the evaluation requirement for calculation and evaluation, and the carbon discharge capacity is acquired in different modes according to the characteristics of carbon discharge in each stage, so that the evaluation of carbon discharge is more comprehensively and accurately carried out.
(2) In the using stage of the building, the carbon discharge capacity of the stage is calculated through two aspects of energy consumption and artificial activities, a neural network model is constructed based on comprehensive and accurate data provided by a BIM model to estimate the carbon discharge capacity generated by the artificial activities, and a large number of influencing factors are integrated, so that the efficiency and accuracy of carbon discharge evaluation are further improved.
Drawings
The present invention is further described below with reference to the accompanying drawings for the convenience of understanding by those skilled in the art.
FIG. 1 is a schematic diagram showing the steps of a carbon emission estimation method according to the present invention;
FIG. 2 is a schematic diagram of a carbon emission evaluation system according to the present invention.
Detailed Description
In order to further describe the technical means and effects adopted by the invention for achieving the preset aim, the following detailed description is given below of the specific implementation, structure, characteristics and effects according to the invention with reference to the attached drawings and the preferred embodiment.
Referring to fig. 1, a carbon emission assessment method based on BIM technology includes the following steps:
s1, dividing a carbon emission stage of a life cycle of a building: according to the stage characteristics of the carbon emission of the whole life cycle of the building, the important stage of carbon emission for researching the building is divided.
It should be noted that, in order to understand the influence of each stage on the carbon emission, it is beneficial to make corresponding energy-saving and emission-reducing measures in a targeted manner, and meanwhile, the carbon emission of the building is divided into a construction stage, a use stage and a demolition stage by dividing the carbon emission of the building life cycle into stages for guiding design and construction. Wherein the carbon emissions of the construction stage mainly include the consumption of building materials and carbon emissions generated by the use of construction tools; the carbon emission in the using stage mainly comprises the carbon emission caused by energy consumption and artificial activities in the operation and maintenance process of the building, such as the processes of energy supply system, transportation, garbage disposal, maintenance and equipment use and the like; the carbon emissions of the demolition stage mainly include those generated by the recycling of waste materials and the use of construction tools.
It is understood that the energy supply system includes heating, cooling, lighting, ventilation, and the like. The use of equipment, including various equipment, furniture, appliances, etc. within a building, particularly the use of electronic equipment, such as computers, displays, printers, etc., can produce carbon emissions.
S2, collecting data of each stage of the building: various data of a building construction stage, a use stage and a demolition stage are collected. Specifically, the data of the building construction stage comprises building design, construction drawing, construction process record, equipment energy data, maintenance record and the like; the using stage data comprise energy consumption of an energy supply system, meteorological data, water resource utilization, waste treatment, maintenance data, artificial activity data and the like; the demolition phase data includes construction process records, equipment energy data, and the like.
Specifically, the human activity data includes building usage records for residential, office, business, etc. uses, such as personnel access time, room usage, fenestration and ventilation, and temperature settings.
It should be noted that, the data acquisition sources of each stage of the building include monitoring equipment, an energy metering system, designers, information provided by material suppliers and construction units, and the like, and the data acquisition sources are mainly used for automatic acquisition, uploading by maintenance personnel, database synchronization, and the like in the using stage.
It is understood that the data, such as the sensor and the metering instrument, which are acquired in real time by the monitoring device, are acquired automatically; uploading maintenance personnel, namely, maintenance records, fault diagnosis reports and the like; database synchronization is specifically accomplished by interworking with databases within the enterprise.
S3, building a BIM model: building a three-dimensional model of the building using BIM software and integrating the collected data of each stage of the building into the model.
It can be appreciated that by integrating the data of each stage of the building into the BIM model, the accuracy and authenticity of the model can be improved, thereby better understanding the actual situation of the building. And in the using stage, the BIM model is used for managing and updating the data, so that richer and more accurate data are provided for the calculation and evaluation of the carbon emission.
S4, simulation: and importing the BIM model into energy simulation software to perform simulation, and obtaining a simulation result by simulating the energy consumption condition and human activities in the building.
The energy consumption includes lighting, socket, air conditioner, power, gas, fuel oil, renewable energy, and so on. The artificial activities include people flow conditions, activity areas, personnel activity modes, crowdedness, time allocation and the like. The BIM technology can integrate, simulate and simulate various data related to human activities in the building, manage energy, analyze space utilization and monitor traffic.
It can be appreciated that the carbon emission evaluation system of the embodiment performs integrity and stage-by-stage evaluation according to the carbon emission division stage of the building life cycle, for example, the construction stage and the demolition stage can perform integrity evaluation, so as to help a designer select an optimal emission reduction scheme to reduce the carbon emission of the whole life cycle of the building; the use stage is used for stage evaluation, so that the current carbon emission condition can be known at any time, the management scheme can be adjusted in time, and the carbon emission control of the whole life cycle can be realized. The simulation of human activities is mainly based on the use phase.
S5, carbon emission calculation: the carbon emissions of the construction stage, the use stage and the demolition stage are calculated respectively from the simulation results. The construction stage and the demolition stage are calculated through energy consumption in the construction process, and the use stage is calculated through energy consumption and human activities in the operation process.
Specifically, the process of calculating the carbon discharge through energy consumption is estimated by adopting a neural network model according to the carbon discharge standard established by the country or region and the process of calculating the carbon discharge through artificial activities.
It should be noted that, during the use stage of the building, the carbon emission generated by the artificial activity is a non-negligible part, and because the source of the carbon emission generated by the artificial activity is very complex and the influence factors are many, the embodiment adopts the neural network model to estimate the carbon emission generated by the artificial activity, so as to accurately and efficiently estimate the carbon emission amount of the artificial activity during the use stage of the building.
Specifically, estimating the carbon emissions of the human activity through the neural network model comprises the steps of:
s51, screening carbon discharge influence factors obtained through a BIM model, wherein the carbon discharge influence factors specifically comprise meteorological conditions, personnel flow, activity types, equipment states, equipment efficiency, equipment service life, use behaviors, energy prices, management systems and the like.
It can be understood that the data of the carbon emission influencing factors are derived from the data and the simulation result acquired by the BIM model, and in order to ensure the accuracy of the carbon emission estimation in the later period, enough data needs to be acquired for the periodic carbon emission estimation. In a general carbon emission prediction model, the data acquisition of influencing factors is difficult, such as socioeconomic data, business data, atmospheric environment data, geospatial data and the like, and the data acquisition of influencing factors is more visual, convenient and efficient based on a BIM model.
In this embodiment, screening of carbon emission influencing factors is accomplished by regression modeling, and 9 variables influencing carbon emission are selected, specifically including meteorological conditions x 1 Flow x of personnel 2 Type x of activity 3 Device state x 4 Efficiency x of the plant 5 Device life x 6 Usage behavior x 7 Price x of energy 8 And management system x 9 . Specifically, the regression model screening step includes:
s511, data standardization: the method comprises the steps of carrying out center standardization processing on original data, wherein the specific standardized data are as follows:
x′＝(x-μ)/σ；
wherein x is the original data, mu is the original data mean value, and sigma is the variance;
s512, constructing a linear regression model of carbon emission Yt influencing factors:
Y t ＝β+β 1 x 1t +β 2 x 2t +…+β 9 x 9t +ε t ；
wherein beta is 1 …β 9 Is the regression coefficient of the influencing factor, epsilon t is a series of interference terms conforming to the standard normal distribution, and beta is a constant.
S513, determining a final selected variable: all solutions are obtained by n iterations of the normalized data, and the best model is determined using the red pool information criterion. The red pool model describes the accuracy of the model by adding penalty terms to the likelihood function, and from the red pool minimum A, the best model can be determined from a series of different models, i.e
A＝n ln(R/n)+2d；
Wherein d is the number of parameters and d=9, n is the number of observations (i.e. the number of iterations), R is the sum of squares of the residuals, and the calculation method is:
wherein y is i As a result of the fact that the value,is an estimated value.
The model fits best when the red pool information value a reaches a minimum value during the iteration. And selecting the iteration result as a variable screening basis, and selecting a final variable.
S52, constructing a neural network, which specifically comprises the following steps:
s521, normalizing sample data: the data is mapped to [0,1] using a zero-mean normalization approach.
S522, determining a neural network structure: the variables screened by the regression model are used as input layer nodes of the neural network, the output layer nodes are carbon emission, and the calculation formula of the hidden layer node number is as follows:
where N is the number of input layer nodes (i.e., the number of screening variables), M is the number of output layer nodes, and the constant a ranges from 0, 10.
S523, training a neural network: setting the error target to 10 -8 Training the neural network.
S53, estimating the carbon emission of artificial activities: and inputting the test sample into the constructed neural network, estimating the carbon discharge amount, and performing inverse normalization processing on the output result to obtain a carbon discharge amount estimated value.
S6, evaluating carbon emission: according to the carbon emission calculation result, the carbon emission condition of the building is evaluated by comparing with the national or regional carbon emission standard, and emission reduction measures and optimization schemes are formulated in a targeted manner so as to reduce the carbon emission of the building.
S7, state monitoring and feedback: in the using stage of the building, the BIM is used for monitoring the energy consumption, artificial activities and other data of the building, and the emission reduction measures and the optimization scheme are timely adjusted through the staged carbon emission evaluation.
The invention also provides a carbon emission evaluation system based on BIM technology, which comprises a carbon emission data acquisition unit, a carbon emission database unit, a carbon emission calculation unit and a carbon emission estimation unit, wherein:
the carbon emission data acquisition unit is used for acquiring data of each stage of a building, and acquiring the data through the modes of importing a building model, inputting management personnel, acquiring monitoring equipment, sharing an existing database and the like.
The carbon emission database unit is used for storing the collected data of each stage of the building, and is convenient for the evaluation of the carbon emission in the later stage and the calling of the data.
The carbon emission calculating unit is used for calculating carbon emission generated by energy consumption of each stage of the whole life cycle of the building.
The carbon discharge estimation unit is used for estimating carbon discharge generated by artificial activities at each stage of the whole life cycle of the building.
According to the invention, the whole life cycle of the building is divided into different stages according to the evaluation requirement for calculation and evaluation, the carbon discharge capacity is obtained in different modes according to the characteristics of carbon discharge in each stage, and the calculation of the carbon discharge capacity is more comprehensively and accurately carried out by a targeted evaluation mode. In particular, in the using stage of a building, the carbon discharge of the stage is calculated through two aspects of energy consumption and artificial activities, more carbon discharge influence factors are further comprehensively considered, meanwhile, the carbon discharge generated by the artificial activities is estimated by constructing a neural network model based on comprehensive and accurate data provided by a BIM model, and the efficiency and accuracy of carbon discharge estimation are further improved through integrating a large number of influence factors.
The present invention is not limited to the above embodiments, but is capable of modification and variation in detail, and other modifications and variations can be made by those skilled in the art without departing from the scope of the present invention.
Claims (10)
1. A carbon emission assessment method based on BIM technology is characterized in that: the method comprises the following steps:
s1, dividing a carbon emission stage of a life cycle of a building: according to the stage characteristics of the carbon emission of the whole life cycle of the building, dividing a carbon emission key stage for researching the building;
s2, collecting data of each stage of the building: collecting various data of a building construction stage, a use stage and a dismantling stage;
s3, building a BIM model: building a three-dimensional model of a building by using BIM software, and integrating the acquired data of each stage of the building into the model;
s4, simulation: the BIM model is imported into energy simulation software to perform simulation, and a simulation result is obtained by simulating the energy consumption condition and human activities in the building;
s5, carbon emission calculation: calculating carbon emission of the construction stage, the use stage and the dismantling stage respectively through simulation results; the construction stage and the dismantling stage are calculated through energy consumption in the construction process, and the use stage is calculated through energy consumption and human activities in the operation process;
s6, evaluating carbon emission: evaluating the carbon emission condition of the building by comparing with the carbon emission standard of the country or region according to the carbon emission calculation result;
s7, state monitoring and feedback: in the using stage of the building, the energy consumption and the artificial activity data of the building are monitored through a BIM model, and feedback is carried out through staged carbon emission evaluation.
2. The carbon emission assessment method based on the BIM technique according to claim 1, wherein: in the step S1, the carbon emission phase of the building life cycle is divided into: a construction stage, a use stage and a dismantling stage;
wherein the carbon emissions of the build phase include those generated by consumption of building materials and use of construction tools; the carbon emission of the using stage comprises the energy consumption and the carbon emission caused by artificial activities of the building in the operation and maintenance process; carbon emissions from the demolition stage include carbon emissions from waste recovery and use of construction equipment.
3. The carbon emission assessment method based on the BIM technique according to claim 1, wherein: in the step S2, the data of the building construction stage includes building design, construction drawing, construction process record, equipment energy data and maintenance record; the using stage data comprise energy consumption of an energy supply system, meteorological data, water resource utilization, waste treatment, maintenance data and human activity data; the demolition phase data includes work process records and equipment energy data.
4. The carbon emission assessment method based on the BIM technique according to claim 1, wherein: in the step S4, the energy consumption includes the energy consumption of lighting, socket, air conditioner, power, fuel gas, fuel oil and renewable energy sources; artificial activities include people flow conditions, activity areas, people activity patterns, crowdedness and time allocation.
5. The carbon emission assessment method based on the BIM technique according to claim 1, wherein: in the step S5, the process of calculating the carbon emission by the energy consumption is estimated by using a neural network model according to the carbon emission standard established by the country or region.
6. The carbon emission assessment method based on the BIM technique according to claim 5, wherein: in the step S5, estimating the carbon emissions of the artificial activities through the neural network model includes the steps of:
s51, screening carbon discharge influence factors obtained through a BIM model;
s52, constructing a neural network;
s53, estimating the carbon emission of the artificial activity.
7. The carbon emission assessment method based on the BIM technique according to claim 6, wherein: the carbon emission influencing factors comprise meteorological conditions, personnel flow, activity types, equipment states, equipment efficiency, equipment service life, use behaviors, energy prices and management systems.
8. The carbon emission assessment method based on the BIM technique according to claim 7, wherein: in step S51, the screening of the carbon emission influencing factors adopts a regression model, which specifically includes data standardization, construction of a linear regression model of the carbon emission influencing factors, and determination of final selected variables.
9. The carbon emission assessment method based on the BIM technique according to claim 8, wherein: in the step S52, a neural network is constructed, which specifically includes the following steps:
s521, carrying out normalization processing on sample data;
s522, determining a neural network structure;
s523, training the neural network.
10. A carbon emission assessment system based on BIM technology, applied to a carbon emission assessment method based on BIM technology as claimed in any one of claims 1 to 9, wherein: the device comprises a carbon emission data acquisition unit, a carbon emission database unit, a carbon displacement calculation unit and a carbon displacement estimation unit, wherein:
the carbon emission data acquisition unit is used for acquiring data of each stage of a building;
the carbon emission database unit is used for storing collected data of each stage of the building;
the carbon emission calculating unit is used for calculating carbon emission generated by energy consumption of each stage of the whole life cycle of the building;
the carbon discharge estimation unit is used for estimating carbon discharge generated by artificial activities at each stage of the whole life cycle of the building.
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20140126639A (en) * | 2013-04-23 | 2014-10-31 | 인하대학교 산학협력단 | A calculation method for carbon dioxide emissions in the all stage of life of the road |
| CN113435054A (en) * | 2021-07-06 | 2021-09-24 | 天津水泥工业设计研究院有限公司 | Carbon emission assessment method and system based on digital twin model |
| CN114925874A (en) * | 2022-03-28 | 2022-08-19 | 中国科学院深圳先进技术研究院 | Carbon emission pre-judging method and device based on BERT neural network model |
| CN115630434A (en) * | 2022-12-25 | 2023-01-20 | 国网上海能源互联网研究院有限公司 | Building carbon emission prediction method and device based on multi-agent simulation |
-
2023
- 2023-06-28 CN CN202310771776.2A patent/CN116757364A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20140126639A (en) * | 2013-04-23 | 2014-10-31 | 인하대학교 산학협력단 | A calculation method for carbon dioxide emissions in the all stage of life of the road |
| CN113435054A (en) * | 2021-07-06 | 2021-09-24 | 天津水泥工业设计研究院有限公司 | Carbon emission assessment method and system based on digital twin model |
| CN114925874A (en) * | 2022-03-28 | 2022-08-19 | 中国科学院深圳先进技术研究院 | Carbon emission pre-judging method and device based on BERT neural network model |
| CN115630434A (en) * | 2022-12-25 | 2023-01-20 | 国网上海能源互联网研究院有限公司 | Building carbon emission prediction method and device based on multi-agent simulation |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117541272A (en) * | 2024-01-09 | 2024-02-09 | 小象飞羊(北京)科技有限公司 | Method, system, equipment and storage medium for determining digital engineering carbon emission data |
| CN117541272B (en) * | 2024-01-09 | 2024-03-29 | 小象飞羊(北京)科技有限公司 | Method, system, equipment and storage medium for determining digital engineering carbon emission data |
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