A carbon emission reduction management method for engineering projects

Abstract

The present invention discloses a carbon emission reduction management method for engineering projects, belonging to the technical field of carbon emission management, and comprising the following steps: S1, obtaining carbon emission sources in the usage stage of the engineering project to be managed; S2, generating a source matrix and a transformation matrix according to the carbon emissions of the carbon emission sources in the usage stage, and calculating a carbon emission impact factor; S3, obtaining the ontology situation of the engineering project to be managed, and determining the self-impact factor of the engineering project to be managed; S4, determining the predicted carbon emissions of the engineering project to be managed according to the self-impact factor of the engineering project to be managed and the carbon emission impact factors of all carbon emission sources in the usage stage. The predicted carbon emissions of the present invention can provide an important reference for the long-term energy conservation and emission reduction planning of buildings, help to formulate emission reduction targets that meet the requirements of sustainable development, avoid excessive or insufficient emission reduction, and achieve the best balance of energy conservation and emission reduction.

Description

Technical Field

[0001] The present invention belongs to the technical field of carbon emission management, and particularly relates to a carbon emission reduction management method for engineering projects. Background Art

[0002] With the increasingly severe global climate change, reducing greenhouse gas emissions, especially carbon dioxide emissions, has become a major issue of common concern in the international community. As one of the main sources of energy consumption and carbon emissions, the importance of carbon emission reduction in the construction field is self-evident. During the use stage of buildings, a large amount of carbon emissions are caused by factors such as energy consumption (such as heating, cooling, and lighting), aging and replacement of building materials, and waste treatment during operation and maintenance.

[0003] Traditionally, building carbon emission reduction management often relies on measures such as late-stage energy-saving renovation or the use of low-carbon building materials. Although these methods are effective, they lack the flexibility of early planning and dynamic management. In order to more effectively control and manage carbon emissions during the use stage of buildings, it is necessary to predict the carbon emissions of building projects.

[0004] In the prior art, the prediction and evaluation of building carbon emissions mostly rely on static real-time data collection. Even when using neural networks for prediction, the prediction accuracy is relatively low, and the dynamic changes during the building use process are not considered. In addition, the in-depth study of the relationship between the characteristics of the building itself (such as structure, materials, and insulation performance) and carbon emissions is also relatively insufficient.

[0005] Therefore, it is particularly important to develop a method that can comprehensively consider various carbon emission sources, the characteristics of the building itself, and its dynamic changes during the use stage of buildings, so as to accurately predict and effectively manage building carbon emissions. Summary of the Invention

[0006] In order to solve the above problems, the present invention provides a carbon emission reduction management method for engineering projects.

[0007] The technical solution of the present invention is as follows: A carbon emission reduction management method for engineering projects includes the following steps:

[0008] S1. Obtain the carbon emission sources during the use stage of the engineering project to be managed;

[0009] S2. Generate a source matrix and a transformation matrix based on the carbon emissions during the use stage of the carbon emission sources, and calculate the carbon emission impact factor;

[0010] S3. Obtain the ontology situation of the engineering project to be managed, and determine the self-impact factor of the engineering project to be managed;

[0011] S4. Determine the predicted carbon emissions of the engineering project to be managed based on the self - impact factor of the engineering project to be managed and the carbon emission impact factors of all carbon emission sources during the usage stage.

[0012] Further, S2 includes the following sub - steps:

[0013] S21. Determine the operating equipment corresponding to the carbon emission sources during the usage stage of the engineering project to be managed;

[0014] S22. Generate a source matrix for the carbon emission sources according to the carbon emissions of each operating equipment in the carbon emission sources during the usage stage;

[0015] S23. Generate a transformation matrix according to the maximum and minimum carbon emissions of each operating equipment in the carbon emission sources during the usage stage;

[0016] S24. Calculate the carbon emission impact factor according to the source matrix and the transformation matrix of the carbon emission sources.

[0017] Possible carbon emission sources of the engineering project to be managed include fuel combustion, power consumption, and vehicle transportation.

[0018] Power consumption: As the main carbon emission source during the building operation stage, the corresponding operating equipment includes lighting, air - conditioning, elevators, and heating, etc. These equipment all consume electricity, and the production of electricity (especially thermal power) will generate a large amount of carbon dioxide emissions.

[0019] Fuel combustion: Some buildings may use fossil fuels such as natural gas for heating and cooking, etc. The combustion process of these energy sources will directly generate carbon dioxide emissions; for buildings with a regional centralized cooling and heating system, their energy comes from the centralized supply of external thermal power plants or energy stations; the fuel consumption corresponding to carbon emissions during the steam production process needs to be allocated to each building.

[0020] Vehicle transportation: The fuel or electricity consumed for cargo transportation (such as building material distribution and garbage removal) within the building project also belongs to the indirect emissions caused by building operation.

[0021] The beneficial effects of the above - mentioned further solution are as follows: In the present invention, the source matrix presents all carbon emissions in the form of specific values, and the carbon emission differences between different equipment can be analyzed. The transformation matrix provides the maximum and minimum values of carbon emissions during the operation of each equipment, analyzes the fluctuation range of carbon emissions, including the carbon emission situation in some extreme cases (such as when the equipment is fully loaded or unloaded), and provides a basis for predicting risks. The carbon emission impact factor comprehensively considers the information of the source matrix and the transformation matrix, and provides a comprehensive assessment of the overall carbon emission situation of the engineering project.

[0022] Further, S22 includes the following sub - steps;

[0023] S221. Obtain the average carbon emissions of each operating device corresponding to the carbon emission source during the usage stage, and generate a carbon emission dataset of the carbon emission source;

[0024] S222. Cluster the carbon emission dataset of the carbon emission source to determine the average value of each operating device in the corresponding clustering cluster;

[0025] S223. Sort the average values of each operating device in the corresponding clustering cluster from small to large, and generate a source matrix.

[0026] The beneficial effect of the above further solution is: In the present invention, by calculating the average value of the carbon emissions of the operating device, a representative value can be obtained, representing the carbon emission level of the operating device during long-term use. Cluster analysis determines the characteristic values of each operating device, reflecting the carbon emission levels of the devices in each clustering cluster, as comprehensive information of the carbon emission source.

[0027] Further, in S223, the source matrix has the following expression:

[0028] ;

[0029] In the formula, represents the average value of the 1st operating device in the corresponding clustering cluster after sorting from small to large, represents the average value of the carbon emissions of the 1st operating device during the usage stage, represents the average value of the 2nd operating device in the corresponding clustering cluster after sorting from small to large, represents the average value of the carbon emissions of the 2nd operating device during the usage stage, represents the th average value of the operating device in the corresponding clustering cluster after sorting from small to large, represents the th average value of the carbon emissions of the operating device during the usage stage, represents constructing a diagonal matrix, represents an identity matrix.

[0030] Further, S23 includes the following sub-steps:

[0031] S231. Extract the maximum carbon emissions of each operating device corresponding to the carbon emission source during the usage stage, and generate a first characteristic column vector;

[0032] S232. Extract the minimum carbon emissions of each operating device corresponding to the carbon emission source during the usage stage, and generate a second characteristic column vector;

[0033] S233. Use the covariance matrix between the first eigenvector and the second eigenvector as the transformation matrix.

[0034] The beneficial effects of the above further solution are as follows: In the present invention, by extracting the maximum carbon emissions, the peak emission situation of each operating device can be reflected. The minimum carbon emissions represent the emission level of the device under optimal operating conditions. The covariance matrix can reflect the extreme value situation of the carbon emissions of all operating devices and can achieve dimensionality reduction processing of the data. Using the sum of the maximum singular value of the source matrix and the maximum singular value of the transformation matrix as the carbon emission impact factor can comprehensively reflect the changes in the emission characteristics of the carbon emission source.

[0035] Further, in S24, use the sum of the maximum singular value of the source matrix and the maximum singular value of the transformation matrix as the carbon emission impact factor of the carbon emission source.

[0036] Further, in S3, the self - impact factor The calculation formula is:

[0037] ;

[0038] In the formula, represents the outer - surface heat transfer coefficient of the th wall - body grid of the engineering project to be managed, represents the inner - surface heat transfer coefficient of the th wall - body grid of the engineering project to be managed, represents the number of wall - body grids of the engineering project to be managed.

[0039] The beneficial effects of the above further solution are as follows: In the present invention, the insulation performance of the wall has an important impact on the energy consumption and carbon emissions of the building. Since there are many wall - bodies in the building, grid division is required. The outer - surface heat transfer coefficient and the inner - surface heat transfer coefficient respectively reflect the heat - exchange ability between the wall and the external environment and the indoor environment. The magnitudes of these two coefficients directly affect the absorption, storage, and release of heat by the wall, and thus affect the energy consumption and carbon emissions of the building. δ is an index that combines the outer - and inner - surface heat transfer coefficients and the number of wall grids, used to quantify the heat - exchange characteristics of the wall and evaluate the contribution of the wall to the overall energy consumption and carbon emissions of the building.

[0040] Further, S4 includes the following sub - steps:

[0041] S41. Construct a carbon emission trend function based on the self - impact factor of the engineering project to be managed and the carbon emission impact factors of all carbon emission sources during the usage stage;

[0042] S42. Use the carbon emission trend function to determine the predicted carbon emissions of the engineering project to be managed.

[0043] The beneficial effects of the above further solution are as follows: In the present invention, when constructing the carbon emission trend function, the self-influence factors of the engineering projects to be managed and the carbon emission influence factors of all carbon emission sources in the use stage are considered, which not only cover the building characteristics of the construction projects themselves, but also include the carbon emissions of each source during the operation process.

[0044] Further, in S41, the carbon emission trend function has the following expression:

[0045] ;

[0046] In the formula, represents the total carbon emissions of the th operating device corresponding to the th carbon emission source of the engineering project to be managed in the use stage, represents the number of operating devices corresponding to the th carbon emission source of the engineering project to be managed in the use stage, represents the number of carbon emission sources of the engineering project to be managed in the use stage, represents the self-influence factor of the engineering project to be managed, represents the carbon emission influence factor of the th carbon emission source of the engineering project to be managed in the use stage.

[0047] The beneficial effects of the present invention are as follows:

[0048] (1) By identifying the carbon emission sources of a building in the use stage, the present invention can clearly determine the main contributing devices participating in carbon emissions. The source matrix and transformation matrix can quantify the emission characteristics and mutual relationships of each carbon emission source, providing a basis for carbon emission prediction;

[0049] (2) By calculating the carbon emission influence factors, the present invention comprehensively considers the carbon emission situation of the engineering project to be managed throughout the use stage, and considering the self-influence factor of the building reflects the thermal performance and energy consumption characteristics of the building itself, which is an important factor that cannot be ignored when formulating emission reduction measures, and can more accurately predict carbon emissions;

[0050] (3) The prediction of carbon emissions by the present invention can provide an important reference for the long-term energy conservation and emission reduction planning of buildings, help formulate emission reduction targets that meet the requirements of sustainable development, avoid excessive or insufficient emission reduction, and achieve the best balance of energy conservation and emission reduction. Description of the Drawings

[0051] Figure 1 is a flowchart of the carbon emission reduction management method for engineering projects. Detailed Embodiments

[0052] The embodiments of the present invention will be further described below in conjunction with the accompanying drawings.

[0053] As Figure 1 shown, the present invention provides a carbon emission reduction management method for engineering projects, including the following steps:

[0054] S1. Obtain the carbon emission sources of the engineering project to be managed during the usage phase;

[0055] S2. Generate a source matrix and a transformation matrix based on the carbon emissions of the carbon emission sources during the usage phase, and calculate the carbon emission impact factor;

[0056] S3. Obtain the ontology situation of the engineering project to be managed, and determine the self-impact factor of the engineering project to be managed;

[0057] S4. Determine the predicted carbon emissions of the engineering project to be managed based on the self-impact factor of the engineering project to be managed and the carbon emission impact factors of all carbon emission sources during the usage phase.

[0058] In the embodiments of the present invention, S2 includes the following sub-steps:

[0059] S21. Determine the operating equipment corresponding to the carbon emission sources of the engineering project to be managed during the usage phase;

[0060] S22. Generate a source matrix for the carbon emission sources according to the carbon emissions of each operating equipment in the carbon emission sources during the usage phase;

[0061] S23. Generate a transformation matrix according to the maximum and minimum carbon emissions of each operating equipment in the carbon emission sources during the usage phase;

[0062] S24. Calculate the carbon emission impact factor according to the source matrix and the transformation matrix of the carbon emission sources.

[0063] Possible carbon emission sources of the engineering project to be managed include fuel combustion, electricity consumption, and vehicle transportation.

[0064] Electricity consumption: As the main carbon emission source during the building operation phase, the corresponding operating equipment includes lighting, air conditioning, elevators, heating, etc. These devices all consume electricity, and the production of electricity (especially thermal power) will generate a large amount of carbon dioxide emissions.

[0065] Fuel combustion: Some buildings may use fossil fuels such as natural gas for heating and cooking, etc. The combustion process of these energy sources will also directly generate carbon dioxide emissions; for buildings with a regional centralized cooling and heating system, their energy comes from the centralized supply of external thermal power plants or energy stations; the carbon emissions corresponding to the fuel consumption during the steam production process need to be allocated to each building.

[0066] Vehicle transportation: Fuel or electricity consumed to transport goods within a construction project (such as building material distribution and waste removal) is also considered indirect emissions caused by construction operations.

[0067] In the present invention, the source matrix presents all carbon emissions in the form of specific numerical values, and can analyze the differences in carbon emissions between different equipment. The transformation matrix provides the maximum and minimum values ​​of carbon emissions from the operation of each device, analyzes the fluctuation range of carbon emissions, and includes carbon emissions in certain extreme cases (such as full or empty equipment), providing a basis for predicted risks. The carbon emission impact factor comprehensively considers the information of the source matrix and the transformation matrix, and provides a comprehensive assessment of the overall carbon emissions of the project.

[0068] In the embodiment of the present invention, S22 includes the following sub-steps:

[0069] S221, obtaining the average carbon emissions of each operating equipment corresponding to the carbon emission source during the use phase, and generating a carbon emission data set of the carbon emission source;

[0070] S222, clustering the carbon emission data set of the carbon emission source, and determining the average value of each operating device in the corresponding cluster;

[0071] S223, sorting the average values ​​of each running device in the corresponding cluster from small to large, and generating a source matrix.

[0072] In the present invention, by calculating the mean of the carbon emissions of the operating equipment, a representative value can be obtained, which represents the carbon emission level of the operating equipment during long-term use. Cluster analysis determines the characteristic value of each operating equipment, reflecting the carbon emission level of the equipment in each cluster, as comprehensive information of the carbon emission source.

[0073] In the embodiment of the present invention, in S223, the source matrix The expression is:

[0074] ;

[0075] In the formula, It represents the average value of the first running device in the corresponding cluster after sorting from small to large. It represents the average carbon emission of the first operating equipment in the use phase after sorting from small to large. It represents the average value of the second running device in the corresponding cluster after sorting from small to large. It represents the average carbon emission of the second operating equipment in the use phase after sorting from small to large. Indicates the first The average value of the running devices in the corresponding cluster, Indicates the first The average carbon emission of each operating device during the usage stage denotes constructing a diagonal matrix denotes the identity matrix

[0076] In the embodiment of the present invention, S23 includes the following sub-steps:

[0077] S231: Extract the maximum carbon emissions of each operating device corresponding to the carbon emission source during the usage stage to generate a first characteristic column vector;

[0078] S232: Extract the minimum carbon emissions of each operating device corresponding to the carbon emission source during the usage stage to generate a second characteristic column vector;

[0079] S233: Use the covariance matrix between the first characteristic column vector and the second characteristic column vector as the transformation matrix.

[0080] In the present invention, by extracting the maximum carbon emissions, the peak emission situation of each operating device can be reflected. The minimum carbon emissions represent the emission level of the device under optimal operating conditions. The covariance matrix can reflect the extreme value situation of the carbon emissions of all operating devices and can achieve data dimensionality reduction processing. Taking the sum of the maximum singular value of the source matrix and the maximum singular value of the transformation matrix as the carbon emission impact factor can comprehensively reflect the change in the emission characteristics of the carbon emission source. Both the first characteristic column vector and the second characteristic column vector contain several numerical values, and the covariance matrix can be in the form of 2×2. The element in the first row and the first column is the variance of all elements of the first characteristic column vector, the element in the second row and the second column is the variance of all elements of the second characteristic column vector, the element in the first row and the second column is the covariance between the elements of the two column vectors, and the element in the second row and the first column can be the same as the element in the first row and the second column.

[0081] In the embodiment of the present invention, in S24, take the sum of the maximum singular value of the source matrix and the maximum singular value of the transformation matrix as the carbon emission impact factor of the carbon emission source.

[0082] In the embodiment of the present invention, in S3, the self-influence factor The calculation formula is:

[0083] ;

[0084] In the formula, denotes the outer surface heat transfer coefficient of the th wall body grid of the engineering project to be managed, denotes the inner surface heat transfer coefficient of the th wall body grid of the engineering project to be managed, denotes the number of wall body grids of the engineering project to be managed.

[0085] In the present invention, the thermal insulation performance of the wall has an important impact on the energy consumption and carbon emissions of the building. Since there are numerous wall bodies in the building, it is necessary to perform grid division. The external surface heat transfer coefficient and the internal surface heat transfer coefficient respectively reflect the heat exchange capabilities between the wall and the external environment and the indoor environment. The magnitudes of these two coefficients directly affect the absorption, storage, and release of heat by the wall, and thus affect the energy consumption and carbon emissions of the building. δ is an index that comprehensively considers the external and internal surface heat transfer coefficients and the number of wall grids, and is used to quantify the heat transfer characteristics of the wall and evaluate the contribution of the wall to the overall energy consumption and carbon emissions of the building.

[0086] In an embodiment of the present invention, S4 includes the following sub-steps:

[0087] S41. Construct a carbon emission trend function based on the self-influence factor of the engineering project to be managed and the carbon emission influence factors of all carbon emission sources during the usage stage;

[0088] S42. Determine the predicted carbon emissions of the engineering project to be managed using the carbon emission trend function.

[0089] In the present invention, when constructing the carbon emission trend function, the self-influence factor of the engineering project to be managed and the carbon emission influence factors of all carbon emission sources during the usage stage are considered, which not only covers the building characteristics of the construction project itself but also includes the carbon emissions from each source during the operation process.

[0090] In an embodiment of the present invention, in S41, the carbon emission trend function has the following expression:

[0091] ;

[0092] In the formula, represents the total carbon emissions of the th operating device corresponding to the th carbon emission source during the usage stage of the engineering project to be managed, represents the number of operating devices corresponding to the th carbon emission source during the usage stage of the engineering project to be managed, represents the number of carbon emission sources during the usage stage of the engineering project to be managed, represents the self-influence factor of the engineering project to be managed, represents the carbon emission influence factor of the th carbon emission source during the usage stage of the engineering project to be managed.

[0093] Those of ordinary skill in the art will realize that the embodiments described herein are provided to assist the reader in understanding the principles of the present invention, and it should be understood that the scope of protection of the present invention is not limited to such specific statements and embodiments. Those of ordinary skill in the art can make various other specific deformations and combinations that do not depart from the essence of the present invention based on the technical revelations disclosed in the present invention, and these deformations and combinations are still within the scope of protection of the present invention.

Claims

1. A carbon emission reduction management method for engineering projects, characterized in that, Including the following steps: S1. Obtain the carbon emission sources of the engineering project to be managed during the usage stage; S2. Generate a source matrix and a transformation matrix based on the carbon emissions of the carbon emission sources during the usage stage, and calculate the carbon emission impact factor; S3. Obtain the ontology situation of the engineering project to be managed and determine the self-impact factor of the engineering project to be managed; S4. Determine the predicted carbon emissions of the engineering project to be managed based on the self-impact factor of the engineering project to be managed and the carbon emission impact factors of all carbon emission sources during the usage stage; The said S2 includes the following sub-steps: S21. Determine the operating equipment corresponding to the carbon emission sources during the usage stage of the engineering project to be managed; S22. Generate a source matrix for the carbon emission sources according to the carbon emissions of each operating equipment in the carbon emission sources during the usage stage; S23. Generate a transformation matrix according to the maximum and minimum carbon emissions of each operating equipment in the carbon emission sources during the usage stage; S24. Calculate the carbon emission impact factor according to the source matrix and the transformation matrix of the carbon emission sources; The said S22 includes the following sub-steps; S221. Obtain the average value of the carbon emissions of each operating equipment corresponding to the carbon emission sources during the usage stage and generate a carbon emission data set of the carbon emission sources; S222. Cluster the carbon emission data set of the carbon emission sources and determine the average value of each operating equipment in the corresponding clustering cluster; S223. Sort the average values of each operating equipment in the corresponding clustering cluster from small to large and generate a source matrix; In the said S223, the expression of the source matrix A is: A = diag((K1 + CE mean_1 ), (K2 + CE mean_2 ), …, (K J + CE mean_J )) + I; Wherein, K1 represents the average value of the first operating device sorted from small to large in the corresponding clustering cluster, and CE mean_1 represents the average carbon emission of the first operating device sorted from small to large during the usage stage, K2 represents the average value of the second operating device sorted from small to large in the corresponding clustering cluster, and CE mean_2 represents the average carbon emission of the second operating device sorted from small to large during the usage stage, K J represents the average value of the Jth operating device sorted from small to large in the corresponding clustering cluster, and CE mean_J represents the average carbon emission of the Jth operating device sorted from small to large during the usage stage, diag(·) represents constructing a diagonal matrix, and I represents an identity matrix; The said S23 includes the following sub-steps: S231. Extract the maximum carbon emissions of each operating equipment corresponding to the carbon emission sources during the usage stage and generate a first eigen-column vector; S232. Extract the minimum carbon emissions of each operating equipment corresponding to the carbon emission sources during the usage stage and generate a second eigen-column vector; S233. Take the covariance matrix between the first eigen-column vector and the second eigen-column vector as the transformation matrix; In the said S24, the sum of the maximum singular value of the source matrix and the maximum singular value of the transformation matrix is used as the carbon emission impact factor of the carbon emission sources; In the said S3, the calculation formula of the self-impact factor δ is: where d out_g represents the external surface heat transfer coefficient of the g-th wall body grid of the engineering project to be managed, and d in_g represents the internal surface heat transfer coefficient of the g-th wall body grid of the engineering project to be managed, and G represents the number of wall body grids of the engineering project to be managed.

2. The carbon emission reduction management method for engineering projects according to claim 1, wherein The said S4 includes the following sub-steps: S41. Construct a carbon emission trend function based on the self-impact factor of the engineering project to be managed and the carbon emission impact factors of all carbon emission sources during the usage stage; S42. Use the carbon emission trend function to determine the predicted carbon emissions of the engineering project to be managed.

3. The carbon emission reduction management method for engineering projects according to claim 2, characterized in that, In the said S41, the expression of the carbon emission trend function f is: where CE r_k represents the total carbon emissions of the k-th operating device corresponding to the r-th carbon emission source during the use phase of the engineering project to be managed, K represents the number of operating devices corresponding to the r-th carbon emission source during the use phase of the engineering project to be managed, R represents the number of carbon emission sources during the use phase of the engineering project to be managed, δ represents the self-influence factor of the engineering project to be managed, and W represents the carbon emission influence factor of the r-th carbon emission source during the use phase of the engineering project to be managed.

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