CN112668802B - Construction carbon emission prediction method based on design parameters - Google Patents
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Abstract
The invention discloses a construction carbon emission prediction method based on design parameters, which comprises the following steps: s1, calculating the carbon emission of each finished engineering project in the construction process and the carbon emission of each finished engineering project in the construction process in unit volume, and constructing a construction process carbon emission data set in unit volume; s2, extracting design parameters corresponding to the finished engineering project, and constructing a design parameter data set; s3, establishing a construction process carbon emission prediction model based on design parameters; s4, judging the accuracy of the carbon emission prediction model in the construction process, and selecting the model with the minimum mean square error of the data outside the bag as a final prediction model; and S5, predicting the construction carbon emission through the final prediction model. The method can be used in the engineering project design stage, provides a predicted value of carbon emission in the construction process for designers, and assists the designers to reduce the carbon emission in the construction process by optimizing design parameters in the design stage.
Description
Technical Field
The invention relates to the technical field of building design optimization, in particular to a construction carbon emission prediction method based on design parameters.
Background
The total value of the building industry in China is rapidly increased from 27259.3 million yuan in 2010 to 70904.3 million yuan in 2019, which accounts for 7.2 percent of the total value of domestic production, and the total energy consumption of the building industry is increased from 5872.16 million tons of standard coal in 2011 to 8685 million tons of standard coal in 2018. Carbon emission generated by engineering construction in China and countries along the line becomes one of the main factors influencing regional climate change. In each stage of engineering project implementation, the building design plays a critical role in carbon emission of the whole life cycle of the building, and the structural form and material selection of the building are determined, and the construction process adopted in the construction stage is indirectly influenced. Therefore, making optimal early design decisions at the building design stage can greatly reduce the carbon emissions for the full life cycle of the building.
Over the past decade, although scholars developed many tools regarding carbon emissionsAnd validation systems, such as the Life cycle analytical evaluation method (Wooley et al, 1997), the Life cycle assessment model ((R))
1998; tukker, 2000), environmental performance assessment (CIB, 1998), environmental assessment of the uk architecture institute (BREEAM) (uk architecture institute, 1998), chinese hong kong architecture environmental assessment (HKBEAM) (CET, 1999), etc., but these tools and systems are mostly used to evaluate the environmental impact of building design, building materials and building processes, and further modification and validation are required to apply these methods to construction process carbon emission prediction.
In addition, in the existing research on carbon emission generated in the construction process in the design stage, the carbon emission generated in the construction process of the building is obtained by taking the carbon emission generated in the construction process of the unit building area in the past literature as reference and multiplying the carbon emission by the building area of the existing designed building. However, the method only considers the influence of the building area on the carbon emission in the construction process, and does not consider the influence of other factors related to the building design on the carbon emission in the construction process, so that the carbon emission result is not fully and accurately predicted. Meanwhile, the calculation method does not consider the relation between the design parameters and the carbon emission in the building construction process, so that the carbon emission in the construction process can not be reduced by optimizing the related design parameters. Because the calculation of the carbon emission in the construction of the construction project is very complex, besides the influence factors such as construction machinery, manpower and related materials, the calculation also relates to the complex construction process, construction process and material transportation process, and at present, no method capable of comprehensively, systematically, accurately and conveniently predicting the carbon emission in the construction process of the engineering project exists in the early design stage.
Disclosure of Invention
The invention aims to provide a construction carbon emission prediction method based on design parameters, which can be used in the engineering project design stage, provides a carbon emission prediction value in the construction process for designers through setting of different design parameters, and assists the designers to reduce the carbon emission in the construction process through optimizing the design parameters in the design stage.
In order to achieve the purpose, the technical scheme provided by the invention is as follows:
a construction carbon emission prediction method based on design parameters comprises the following steps:
s1, calculating the carbon emission of each finished engineering project in the construction process and the carbon emission of each finished engineering project in the construction process of unit volume according to the engineering quota and the engineering quantity list of the finished engineering projects, and constructing a construction process carbon emission data set of unit volume;
s2, determining a design parameter list influencing carbon emission in the construction process according to the design specification and the previous research literature, extracting design parameters corresponding to the finished engineering project, and constructing a design parameter data set;
s3, searching the incidence relation between the design parameters and the carbon emission of the construction process by using a random forest algorithm according to the construction process carbon emission data set of the unit volume constructed in the step S1 and the design parameter data set of the corresponding engineering project constructed in the step S2, and establishing a construction process carbon emission prediction model based on the design parameters;
s4, judging the accuracy of the carbon emission prediction model in the construction process, and selecting the model with the minimum mean square error of the data outside the bag as a final prediction model;
and S5, predicting the construction carbon emission through the final prediction model.
Further, the step S1 is based on the project quota, the resources consumed in the construction process of each finished project are obtained through the project amount list of the finished project, the carbon emission factor method is utilized to calculate the carbon emission amount of the construction process of each finished project, the carbon emission amount mainly relates to the carbon emission amount generated by fuel oil consumption or electric power energy consumption of construction machinery, and the unit is kgCO 2 eq；
Wherein CE is carbon emission, CV, of engineering project construction process j For total work of construction activity jVolume, CM ij Number of machine shifts consumed by machine i to complete a unit construction activity j, C energy The amount of fuel or electric energy consumed for 1 mechanical shift, EF f/e A carbon emission factor that is a fuel or electrical energy source; in the formula, CV j From engineering volume inventory, CM ij And C energy Can be found in engineering quota, and various countries and related documents can also publish EF in different regions f/e The specific value of (c).
Further, the step S2 classifies various design parameters related to each type of engineering project in the design stage according to the design specifications and the previous research literature, and determines a design parameter list affecting the carbon emission in the construction process, including parameters including a base area X1, an above-ground building area X2, an underground building area X3, a building height X4, an excavation volume X5, an above-ground number of floors X6, and an underground number of floors X7.
Further, the specific process of establishing the construction process carbon emission prediction model based on the design parameters in the step S3 includes:
s3-1, determining model parameter values, including the minimum terminal node number nodal size of each tree, the total number ntree of trees in forest and the feature variable number mtry of randomly selected trees;
s3-2, evaluating the importance of design parameters which may influence the carbon emission in the construction process through a random forest algorithm, and sequencing the design parameters from high to low according to the importance;
and S3-3, deleting the design parameters with the lowest importance indexes one by one according to the importance sequence of the design parameters in the step S3-2 to obtain a carbon emission prediction model in the construction process.
Further, when the importance of design parameters which may affect the carbon emission in the construction process is evaluated through a random forest algorithm in the step S3-2, the evaluation is determined according to a displacement variable importance measurement method, which describes the mean square error of the data outside the bag before and after the variable displacement, that is, the mean square error of the data outside the bag before and after the variable displacement of each tree is calculated, and then the mean square error difference of each tree is added, except for the total tree amount;
the larger the change of the mean square error before and after the variable displacement is, the more important the variable is, and the more sensitive the model result is to the variable; the calculation formula is as follows:
wherein, VI (X) j ) Represents X j The importance of the variable, j is more than or equal to 1 and less than or equal to mtry; VI (X) j ) The higher the value, the greater the importance of the variable;
represents a replacement variable X j The mean square error of the out-of-bag data of the last t tree,
represents a replacement variable X j Mean square error of the out-of-bag data of the first t-th tree.
Further, the calculation formula of the mean square error of the data outside the bag is as follows:
wherein n is t The total number of samples in the test sample set for the t-th tree,
carbon emission prediction, y, for the kth test sample of the t-tree calculated using a prediction model tk A carbon emission prediction value calculated using a quota-based carbon emission prediction method for the kth test sample of the t-th tree.
Further, said step S4 is performed by determining the coefficient R 2 To judge the accuracy of the carbon emission prediction model in the construction process and determine the coefficient R 2 Equal to the ratio of the variation caused by the predictors in the regression model to the total variation of the actual subset:
in the above formula, N is the total number of samples in the training sample subset, y i For the carbon emission prediction values calculated using the quota-based carbon emission prediction method in the training sample subset,
for all training sample subsets y i Is determined by the average value of (a),
predicting the construction carbon emission of the ith sample subset obtained by utilizing a random forest regression model;
R 2 the closer to 1, the higher the prediction accuracy of the construction process carbon emission prediction model.
Further, in the step S4, the mean square error index of the data outside the bag is used to verify the carbon emission prediction model in the construction process based on the design parameters, and the model with the minimum mean square error of the data outside the bag is selected as the final prediction model.
Compared with the prior art, the principle and the advantages of the scheme are as follows:
1. a carbon emission prediction model generated in the construction process of an engineering project is constructed through a random forest algorithm, so that a designer can be helped to calculate the carbon emission possibly generated in the construction process in the design stage, the boundary of a considered building carbon emission system is perfected, the result of the building design is more credible, the carbon emission is reduced from the perspective of the whole life cycle of the building, and the method has important significance for protecting the environment.
2. The random forest model measures the importance of the variables, analyzes the influence and difference of different design factors on the carbon emission in the construction process, and can help designers to know the key design factors influencing the carbon emission in the construction process.
3. The random forest model establishes the relation between the design parameters and the carbon emission in the construction process, and can help designers to optimize the design parameters in the design stage, so that the carbon emission generated by building materials, building construction and building operation is reduced.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the services required to be used in the embodiments or the prior art descriptions will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is also possible for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
Fig. 1 is a schematic flow chart of a construction carbon emission prediction method based on design parameters for constructing a prediction model according to the present invention.
Detailed Description
The invention will be further illustrated with reference to specific examples:
the construction carbon emission prediction method based on the design parameters comprises the following steps:
s1, based on the project quota, obtaining resources consumed in the construction process of each finished project through a project amount list of the finished project, and calculating the carbon emission amount of the construction process of each finished project by using a carbon emission factor method, wherein the carbon emission amount mainly relates to the carbon emission amount generated by fuel oil consumption or electric power energy consumption of construction machinery and is given in the unit of kgCO 2 eq；
(1) In the formula, CE is carbon emission, CV, of engineering project construction process j Total workload for construction campaign j, CM ij Number of machine shifts consumed by machine i to complete a unit construction activity j, C energy Quantity of fuel or electric energy consumed for 1 mechanical shift, EF f/e A carbon emission factor that is a fuel or an electrical energy source; in the formula, CV j From engineering quantity inventory, CM ij And C energy Can be found in the project quota, and different regions EF are published in various countries and related documents f/e The specific value of (a);
in order to enable the calculated carbon emission of the buildings with different structure types in the engineering project construction process to be comparable under different design parameters, calculating the carbon emission of the construction process in unit volume of each finished engineering project (the total carbon emission of one engineering project is divided by the volume of the construction project) on the basis of obtaining the carbon emission of the construction process of each finished engineering project, and constructing a construction process carbon emission data set in unit volume;
s2, determining a design parameter list influencing carbon emission in the construction process according to the design specification and the previous research literature, extracting design parameters corresponding to the finished engineering project, and constructing a design parameter data set;
determining a design parameter list influencing carbon emission in the construction process, wherein the design parameter list comprises parameters including a base area X1, an above-ground building area X2, an underground building area X3, a building height X4, an excavation volume X5, an above-ground layer number X6 and an underground layer number X7.
S3, as shown in the figure 1, according to the unit volume construction process carbon emission data set constructed in the step S1 and the design parameter data set of the corresponding engineering project constructed in the step S2, searching the incidence relation between the design parameters and the construction process carbon emission by using a random forest algorithm, and establishing a construction process carbon emission prediction model based on the design parameters;
the random forest algorithm adopts a random and replacement method to extract samples from the data set, training sample subsets are established, and the number of the samples in each training sample subset is the same; establishing a decision tree by using the training sample subset when the Mean Square Error (MSE) of the prediction model is less than 10 -6 Each tree will stop growing.
(2) Where N is the total number of samples in the subset of training samples, y i To train the predicted values of carbon emissions in the subset of samples calculated using the quota-based carbon emission prediction method (i.e. equation (1)),
for all training samplesY in this subset i Average value of (d);
the method specifically comprises the following steps:
s3-1, determining model parameter values, including the minimum terminal node number nodal size of each tree, the total number ntree of trees in forest and the feature variable number mtry of randomly selected trees;
s3-2, evaluating the importance of design parameters which possibly influence the carbon emission in the construction process through a random forest algorithm, and sequencing the design parameters from high to low according to the importance;
when the importance evaluation is carried out on design parameters which may influence the carbon emission in the construction process through a random forest algorithm, the importance evaluation is determined according to a displacement variable importance measurement method, the importance evaluation describes the mean square error of the data outside the bags before and after the variable displacement, namely the mean square error of the data outside the bags before and after the variable displacement of each tree is calculated, and then the mean square error difference values of each tree are added, and the sum is divided by the total tree quantity;
the larger the change of the mean square error before and after the variable displacement is, the more important the variable is, and the more sensitive the model result is to the variable; the calculation formula is as follows:
(3) In the formula, VI (X) j ) Represents X j The importance of the variable, j is more than or equal to 1 and less than or equal to mtry; VI (X) j ) The higher the value, the greater the importance of the variable;
represents a replacement variable X j The mean square error of the out-of-bag data of the last t tree,
represents a replacement variable X j Mean square error of the data outside the bag of the first t-th tree;
the calculation formula of the mean square error of the data outside the bag is as follows:
(4) In, n t The total number of samples in the test sample set for the t-th tree,
carbon emission prediction, y, for the kth test sample of the t-tree calculated using a prediction model tk A carbon emission prediction value calculated for a kth test sample of the t-th tree using a quota-based carbon emission prediction method;
and S3-3, deleting the design parameters with the lowest importance indexes one by one according to the importance sequence of the design parameters in the step S3-2 to obtain a carbon emission prediction model in the construction process.
S4, passing the decisive coefficient R 2 To judge the accuracy of the carbon emission prediction model in the construction process and determine the coefficient R 2 Equal to the ratio of the variation caused by the predictors in the regression model to the total variation of the actual subset:
(5) Where N is the total number of samples in the subset of training samples, y i For the carbon emission prediction values calculated using the quota-based carbon emission prediction method in the training sample subset,
for all subsets of training samples y i Is determined by the average value of (a) of (b),
predicting the construction carbon emission of the ith sample subset obtained by utilizing a random forest regression model;
R 2 the closer to 1, the higher the prediction accuracy of the construction process carbon emission prediction model.
The prediction model has good prediction capability not only on known data, but also on unknown data, so that the prediction model has strong adaptability and good generalization capability.
Therefore, the mean square error index of the data outside the bag is used for verifying the carbon emission prediction model based on the design parameters in the construction process, and the model with the minimum mean square error of the data outside the bag is selected as the final prediction model.
And S5, predicting the construction carbon emission through the final prediction model.
The above-mentioned embodiments are merely preferred embodiments of the present invention, and the scope of the present invention is not limited thereto, so that variations based on the shape and principle of the present invention should be covered within the scope of the present invention.
Claims (5)
1. A construction carbon emission prediction method based on design parameters is characterized by comprising the following steps:
s1, calculating the carbon emission of each finished engineering project in the construction process and the carbon emission of each finished engineering project in the construction process of unit volume according to the engineering quota and the engineering quantity list of the finished engineering projects, and constructing a construction process carbon emission data set of unit volume;
s2, determining a design parameter list influencing carbon emission in the construction process according to the design specification and the previous research literature, extracting design parameters corresponding to the finished engineering project, and constructing a design parameter data set;
s3, according to the unit volume construction process carbon emission data set constructed in the step S1 and the design parameter data set of the corresponding engineering project constructed in the step S2, searching the incidence relation between the design parameters and the construction process carbon emission by using a random forest algorithm, and establishing a construction process carbon emission prediction model based on the design parameters;
s4, judging the accuracy of the carbon emission prediction model in the construction process, and selecting the model with the minimum mean square error of the data outside the bag as a final prediction model;
s5, predicting the construction carbon emission through the final prediction model;
the specific process of establishing the construction process carbon emission prediction model based on the design parameters in the step S3 comprises the following steps:
s3-1, determining model parameter values, including the minimum terminal node number nodal size of each tree, the total number ntree of trees in forest and the feature variable number mtry of randomly selected trees;
s3-2, evaluating the importance of design parameters which may influence the carbon emission in the construction process through a random forest algorithm, and sequencing the design parameters from high to low according to the importance;
s3-3, deleting the design parameters with the lowest importance indexes one by one according to the importance sequence of the design parameters in the step S3-2 to obtain a carbon emission prediction model in the construction process;
when the importance of design parameters which may influence the carbon emission in the construction process is evaluated through a random forest algorithm in the step S3-2, the evaluation is determined according to a replacement variable importance measurement method, the mean square error of the data outside the bags before and after the variable replacement is described, namely the mean square error of the data outside the bags before and after the variable replacement of each tree is calculated, and then the mean square error difference values of each tree are added and divided by the total tree amount;
the larger the change of the mean square error before and after the variable displacement is, the more important the variable is, and the more sensitive the model result is to the variable; the calculation formula is as follows:
wherein, VI (X) j ) Represents X j The importance of the variable, j is more than or equal to 1 and less than or equal to mtry; VI (X) j ) The higher the value, the greater the importance of the variable;
represents a replacement variable X j The mean square error of the out-of-bag data of the last t tree,
represents a replacement variable X j Mean square error of the data outside the bag of the first tth tree;
the calculation formula of the mean square error of the data outside the bag is as follows:
wherein n is t The total number of samples in the test sample set for the t-th tree,
carbon emission prediction value, y, calculated for the kth test sample of the t-tree using the prediction model tk A carbon emission prediction value calculated using a quota-based carbon emission prediction method for the kth test sample of the t-th tree.
2. The construction carbon emission prediction method based on design parameters as claimed in claim 1, wherein the step S1 is based on engineering quota, the resources consumed in the construction process of each finished engineering project are obtained through the project amount list of the finished engineering project, and the carbon emission factor method is utilized to calculate the carbon emission amount in the construction process of each finished engineering project, mainly related to the carbon emission amount generated by fuel oil consumption or electric energy consumption of construction machinery, and the unit is kgCO 2 eq；
Wherein CE is carbon emission, CV, of engineering project construction process j For the total workload of construction campaign j, CM ij Number of machine shifts consumed by machine i to complete unit construction activity j, C energy The amount of fuel or electric energy consumed for 1 mechanical shift, EF f/e A carbon emission factor that is a fuel or electrical energy source; in the formula, CV j From engineering volume inventory, CM ij And C energy Can be found in engineering quota, and various governments and related documents can also publish EF in different regions f/e The specific value of (c).
3. The method for predicting carbon emission in construction based on design parameters according to claim 1, wherein in the step S2, various design parameters related to each type of engineering project in the design stage are classified according to design specifications and previous research documents, and a design parameter list influencing carbon emission in the construction process is determined, wherein the design parameter list comprises parameters including a base area X1, an above-ground building area X2, an underground building area X3, a building height X4, an excavation volume X5, an above-ground floor number X6 and an underground floor number X7.
4. The method for predicting construction carbon emission based on design parameters as claimed in claim 1, wherein the step S4 is implemented by determining a coefficient R 2 To judge the accuracy of the carbon emission prediction model in the construction process and determine the coefficient R 2 Equal to the ratio of the variation caused by the predictor to the total variation of the actual subset in the regression model:
in the above formula, N is the total number of samples in the training sample subset, y i For the carbon emission prediction values calculated using the quota-based carbon emission prediction method in the training sample subset,
for all subsets of training samples y i Is determined by the average value of (a) of (b),
predicting the construction carbon emission of the ith sample subset obtained by utilizing a random forest regression model;
R 2 the closer to 1, the higher the prediction accuracy of the construction process carbon emission prediction model.
5. The method for predicting carbon emission in construction based on design parameters as claimed in claim 1, wherein the step S4 verifies the carbon emission prediction model in construction based on design parameters by using mean square error index of data outside the bag, and selects the model with the minimum mean square error of data outside the bag as the final prediction model.
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