Building Carbon Emission Prediction Analysis Method, System, Terminal and Medium

Abstract

The present invention discloses a method, system, terminal and medium for predicting and analyzing building carbon emissions, relating to the technical field of carbon emission analysis. The key points of its technical solution are as follows: A data balance model is established based on the types of consumed energy and the types of energy-using systems; the carbon emission data is input into the data balance model to obtain the balance coefficients between each type of energy-using system and each type of consumed energy; a balance change curve varying with time is established based on the balance coefficients, and the predicted balance coefficients for the future period are intercepted; the type of data with the smallest volatility is selected as the reference value, and the predicted values for each type of consumed energy and each type of energy-using system are obtained by combining the predicted balance coefficients; the predicted values are input into the carbon emission calculation model to obtain the predicted carbon emission values. The present invention can accurately and reliably analyze the carbon emission prediction results under the influence of factors such as human activities, and is applicable to the carbon emission analysis of target objects in different spatial and temporal environments.

Description

Technical Field

[0001] The present invention relates to the technical field of carbon emission analysis, and more specifically, it relates to a method, system, terminal and medium for predicting and analyzing building carbon emissions. Background Art

[0002] Carbon emission is a general term or abbreviation for greenhouse gas emissions. The main gas in greenhouse gases is carbon dioxide, so the term "carbon" is used as a representative. In various industries, the construction industry consumes approximately 30%-40% of the world's energy and emits almost 30% of the world's greenhouse gases. Therefore, it is very necessary to effectively monitor and analyze building carbon emissions to provide basic data for achieving the carbon emission reduction goal.

[0003] In the prior art, the relatively common carbon emission monitoring technology is distributed monitoring. By accurately monitoring a single target object, such as an individual, a unit enterprise, a single building, etc., and then summarizing the monitoring results, the carbon emission situation of a single region or country can be obtained. To ensure the timeliness of carbon emission analysis, the prior art has recorded that data modeling is carried out for each target and combined with the existing activity data to predict and analyze the carbon emission results in the future. However, carbon emission is the result of human-led behavior. Due to various factors such as seasons, the alternation of off-seasons and peak seasons in the industry, industry nature, and geographical conditions, the results of human-led behavior vary in different regions. If the above-mentioned various factors are considered and designed to be added to the existing data prediction model, the computational complexity of the prediction results will inevitably increase significantly, and the application difficulty will be great.

[0004] Therefore, how to research and design a method, system, terminal and medium for predicting and analyzing building carbon emissions that can overcome the above defects is an urgent problem for us to solve at present. Summary of the Invention

[0005] To solve the deficiencies in the prior art, the purpose of the present invention is to provide a method, system, terminal and medium for predicting and analyzing building carbon emissions, which can accurately and reliably analyze the carbon emission prediction results under the influence of factors such as human activities, and is applicable to the carbon emission analysis of target objects in different spatial and temporal environments.

[0006] The above technical object of the present invention is achieved through the following technical solutions:

[0007] In the first aspect, a method for predicting and analyzing building carbon emissions is provided, including the following steps:

[0008] Establish a data balance model that correlates various carbon emission data based on the type of consumed energy and the type of energy-using system;

[0009] Input the carbon emission data in multiple historical periods into the data balance model to obtain the balance coefficients between various energy consumption system types and various energy consumption types in the corresponding historical periods;

[0010] Based on the balance coefficients of the same type in multiple historical periods, establish a balance change curve that changes over time, and intercept the predicted balance coefficients for the future period from the balance change curve;

[0011] Select the type data with the smallest volatility from various energy consumption types and energy consumption system types as the benchmark reference value, and analyze and obtain the predicted values of various energy consumption types and energy consumption system types in combination with each predicted balance coefficient;

[0012] Input the predicted values of various energy consumption types and energy consumption system types into the carbon emission calculation model to obtain the predicted carbon emission values.

[0013] Further, the calculation formula of the data balance model is specifically:

[0014]

[0015] Among them, P(b,a) represents the balance coefficient between the energy consumption system type b and the energy consumption type a; E a represents the consumption corresponding to the energy consumption type a; E b represents the consumption corresponding to the energy consumption system type b; E i represents the consumption of the i-th energy consumption type; n represents the number of energy consumption types; E j represents the consumption of the j-th energy consumption system type; m represents the number of energy consumption system types.

[0016] Further, the analysis process of the predicted value is specifically:

[0017] Establish a verification matrix based on all the predicted balance coefficients;

[0018] Based on the fluctuation situation of all the predicted balance coefficients in the verification matrix, perform correction analysis on the predicted balance coefficients in the corresponding row to obtain the first correction coefficient of the predicted balance coefficients in the corresponding row;

[0019] Based on the fluctuation situation of all the predicted balance coefficients in the verification matrix, perform correction analysis on the predicted balance coefficients in the corresponding column to obtain the second correction coefficient of the predicted balance coefficients in the corresponding column;

[0020] Comprehensively correct the corresponding predicted balance coefficients according to the first correction coefficient and the second correction coefficient to obtain the final balance coefficient;

[0021] Solve and obtain the predicted values of various energy consumption system types and energy consumption types according to the benchmark reference value and the final balance coefficient.

[0022] Further, the specific calculation formula of the first correction coefficient is as follows:

[0023]

[0024] where P k,g represents the k-th predicted balance coefficient in the g-th row of the parity-check matrix; x k,g represents the first correction coefficient corresponding to the k-th predicted balance coefficient in the g-th row; Q represents the number of columns in the parity-check matrix; H represents the number of rows in the parity-check matrix.

[0025] Further, the specific calculation formula of the final balance coefficient is as follows:

[0026] P Z = P 0 (1 + 0.5X 1 + 0.5X 2 )

[0027] where P Z represents the final balance coefficient; P 0 represents the predicted balance coefficient; X 1 represents the first correction coefficient; X 2 represents the second correction coefficient.

[0028] Further, the balance change curve is constructed by using the least squares method.

[0029] Further, the specific calculation formula of the carbon emission calculation model is as follows:

[0030]

[0031] where represents the actual carbon emissions of the target building within a fixed period; E i represents the consumption of the i-th type of energy consumption; F i represents the carbon emission factor of the i-th type of energy consumption; n represents the number of types of energy consumption; C p represents the carbon reduction amount of greening; E i(j) represents the consumption of the i-th type of energy of the j-th type of energy-using system; R i(j) represents the amount of the i-th type of energy provided by the renewable energy system consumed by the j-th type of energy-using system; m represents the number of types of energy-using systems.

[0032] In a second aspect, a building carbon emission prediction and analysis system is provided, including:

[0033] A model construction module, configured to establish a data balance model for associating various carbon emission data based on the types of energy consumption and the types of energy-using systems;

[0034] A balance calculation module for inputting carbon emission data in multiple historical periods into a data balance model to obtain balance coefficients between various energy consumption system types and various energy consumption types in the corresponding historical periods;

[0035] A balance prediction module for establishing a balance change curve varying with time based on balance coefficients of the same type in multiple historical periods and intercepting the predicted balance coefficients for future periods from the balance change curve;

[0036] A prediction analysis module for selecting the type data with the smallest volatility from various energy consumption types and energy consumption system types as the reference value and analyzing to obtain the predicted values of various energy consumption types and energy consumption system types in combination with the predicted balance coefficients;

[0037] A prediction calculation module for inputting the predicted values of various energy consumption types and energy consumption system types into a carbon emission calculation model to obtain the predicted carbon emission values.

[0038] In a third aspect, a computer terminal is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the building carbon emission prediction analysis method described in any item of the first aspect is implemented.

[0039] In a fourth aspect, a computer-readable medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the building carbon emission prediction analysis method described in any item of the first aspect can be implemented.

[0040] Compared with the prior art, the present invention has the following beneficial effects:

[0041] 1. For the building carbon emission prediction analysis method proposed by the present invention, the balance coefficients between various energy consumption system types and various energy consumption types are analyzed through a data balance model, and the predicted balance coefficients for future periods are obtained according to the non-linear fitting method. Based on at least one relatively stable data and the determined predicted balance coefficients, the carbon emission prediction results under the influence of factors such as human activities can be accurately and reliably analyzed, which is applicable to the carbon emission analysis of target objects in different spatial and temporal environments;

[0042] 2. The present invention corrects the predicted balance coefficients from two dimensions of energy consumption system types and energy consumption types, effectively reducing the prediction error. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] The drawings described herein are used to provide a further understanding of the embodiments of the present invention, form a part of this application, and do not limit the embodiments of the present invention. In the drawings:

[0044] Figure 1 is the flowchart in the embodiment of the present invention;

[0045] Figure 2 is the system block diagram in the embodiment of the present invention. Specific Embodiment

[0046] To make the objectives, technical solutions and advantages of the present invention more clear and understandable, the present invention will be further described in detail below in conjunction with embodiments and drawings. The illustrative embodiments of the present invention and their descriptions are only used to explain the present invention and are not intended to limit the present invention.

[0047] Embodiment 1: A method for predicting and analyzing building carbon emissions, as Figure 1 shown, includes the following steps:

[0048] S1: Establish a data balance model that correlates various carbon emission data based on the types of energy consumed and the types of energy-using systems;

[0049] S2: Input the carbon emission data in multiple historical periods into the data balance model to obtain the balance coefficients between various energy-using system types and various energy consumption types in the corresponding historical periods;

[0050] S3: Use the least squares method to establish a balance change curve that changes with time based on the balance coefficients of the same category in multiple historical periods, and intercept the predicted balance coefficients for the future period from the balance change curve;

[0051] S4: Select the type data with the smallest volatility from various energy consumption types and energy-using system types as the reference value, and analyze the predicted values of various energy consumption types and energy-using system types in combination with the predicted balance coefficients;

[0052] S5: Input the predicted values of various energy consumption types and energy-using system types into the carbon emission calculation model to obtain the predicted carbon emission values.

[0053] The present invention takes into account the influence of external factors such as human dominance on carbon emission data, and represents it by the correlation relationship between various data. That is, the balance coefficients between various energy-using system types and various energy consumption types are analyzed through a data balance model, and the predicted balance coefficients for the future period are obtained according to the non-linear fitting method. Based on at least one relatively stable data and the determined predicted balance coefficients, the carbon emission prediction results under the influence of factors such as human activities can be accurately and reliably analyzed, and it is applicable to the carbon emission analysis of target objects in different spatial and temporal environments.

[0054] In this embodiment, the calculation formula of the data balance model is specifically:

[0055]

[0056] Among them, P(b,a) represents the balance coefficient between the energy utilization system type b and the consumed energy type a; E a represents the consumption corresponding to the consumed energy type a; E b represents the consumption corresponding to the energy utilization system type b; E i represents the consumption of the i-th consumed energy type; n represents the number of consumed energy types; E j represents the consumption of the j-th energy utilization system type; m represents the number of energy utilization system types.

[0057] In addition, the data balance model can be constructed from the overall distribution of all energy utilization system types and all consumed energy types, or from a single energy utilization system type corresponding to all consumed energy types, or from a single consumed energy type corresponding to all energy utilization system types, not limited to a single energy utilization system type corresponding to a single consumed energy type.

[0058] The analysis process of the predicted values is specifically as follows: Based on all predicted balance coefficients, a verification matrix of size n×m is established; according to the fluctuation of all predicted balance coefficients in the verification matrix, the predicted balance coefficients of the corresponding rows are corrected and analyzed to obtain the first correction coefficient of the predicted balance coefficients of the corresponding rows; according to the fluctuation of all predicted balance coefficients in the verification matrix, the predicted balance coefficients of the corresponding columns are corrected and analyzed to obtain the second correction coefficient of the predicted balance coefficients of the corresponding columns; according to the first correction coefficient and the second correction coefficient, the corresponding predicted balance coefficients are comprehensively corrected to obtain the final balance coefficient; according to the benchmark reference value and the final balance coefficient, the predicted values of each energy utilization system type and consumed energy type are solved.

[0059] In this embodiment, the calculation formula principles of the first correction coefficient and the second correction coefficient are the same. Taking the first correction coefficient as an example, its specific calculation formula is:

[0060]

[0061] Among them, P k,g represents the k-th predicted balance coefficient in the g-th row of the verification matrix; x k,g represents the first correction coefficient corresponding to the k-th predicted balance coefficient in the g-th row; Q represents the number of columns in the verification matrix; H represents the number of rows in the verification matrix.

[0062] In this embodiment, the specific calculation formula of the final balance coefficient is:

[0063] P Z =P 0 (1 + 0.5X 1 + 0.5X 2 )

[0064] Among them, P Z represents the final balance coefficient; P 0 represents the predicted balance coefficient; X 1 represents the first correction coefficient; X 2 represents the second correction coefficient.

[0065] In this embodiment, the calculation formula of the carbon emission calculation model is specifically:

[0066]

[0067] Among them, represents the actual carbon emissions of the target building within a fixed period; E i represents the consumption of the i-th energy consumption type; F i represents the carbon emission factor of the i-th energy consumption type; n represents the number of energy consumption types; C p represents the carbon reduction amount of greening; E i(j) represents the consumption of the i-th type of energy of the j-th energy-using system type; R i(j) represents the amount of the i-th type of energy provided by the renewable energy system consumed by the j-th energy-using system type; m represents the number of energy-using system types.

[0068] It should be noted that the carbon emission calculation model can also adopt a model for analyzing according to specific activities, not limited to the above model for overall analysis.

[0069] Embodiment 2: A building carbon emission prediction and analysis system, which is used to implement the analysis method described in Embodiment 1, as Figure 2 shown, including a model construction module, a balance calculation module, a balance prediction module, a prediction analysis module, and a prediction calculation module.

[0070] Among them, the model construction module is used to establish a data balance model for associating various carbon emission data based on the energy consumption type and the energy-using system type; the balance calculation module is used to input the carbon emission data in multiple historical periods into the data balance model to obtain the balance coefficients between each energy-using system type and each energy consumption type in the corresponding historical periods; the balance prediction module is used to establish a balance change curve that changes with time based on the balance coefficients of the same type in multiple historical periods, and intercept the predicted balance coefficients for the future period from the balance change curve; the prediction analysis module is used to select the type data with the smallest volatility from each energy consumption type and energy-using system type as the reference value, and analyze and obtain the predicted values of each energy consumption type and energy-using system type in combination with each predicted balance coefficient; the prediction calculation module is used to input the predicted values of each energy consumption type and energy-using system type into the carbon emission calculation model to obtain the predicted carbon emission value.

[0071] Working principle: The present invention analyzes the balance coefficients between various energy consumption system types and various energy consumption types through a data balance model, and obtains the predicted balance coefficients for the future period according to the non-linear fitting method. Based on at least one relatively stable data and the determined predicted balance coefficients, it can accurately and reliably analyze the carbon emission prediction results under the influence of factors such as human activities, and is applicable to the carbon emission analysis of target objects in different spatial and temporal environments; in addition, the present invention corrects the predicted balance coefficients from two dimensions of energy consumption system types and energy consumption types, effectively reducing the prediction error.

[0072] Those skilled in the art should understand that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0073] The present application is described with reference to the flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or block in the flowchart and / or block diagram can be implemented by computer program instructions, and the combination of the processes and / or blocks in the flowchart and / or block diagram can also be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate for implementing in the process Figure 1 one process or multiple processes and / or blocks Figure 1 a device for the functions specified in one block or multiple blocks.

[0074] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory generate a manufactured article including an instruction device, and the instruction device implements in the process Figure 1 one process or multiple processes and / or blocks Figure 1 a device for the functions specified in one block or multiple blocks.

[0075] These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process. Thus, the instructions executed on the computer or other programmable device provide for implementing in the process Figure 1One process or multiple processes and / or boxes Figure 1 Steps of functions specified in one box or multiple boxes.

[0076] In the above specific embodiments, the purpose, technical solution and beneficial effects of the present invention have been further described in detail. It should be understood that the above are only specific embodiments of the present invention and are not used to limit the protection scope of the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. Building carbon emission prediction and analysis method, Characterized in that, It includes the following steps: Establish a data balance model that correlates various carbon emission data based on the type of consumed energy and the type of energy - using system; Input the carbon emission data in multiple historical periods into the data balance model to obtain the balance coefficients between each type of energy - using system and each type of consumed energy in the corresponding historical periods; Establish a balance change curve that changes with time based on the balance coefficients of the same type in multiple historical periods, and intercept the predicted balance coefficients for the future period from the balance change curve; Select the type data with the smallest volatility from each type of consumed energy and energy - using system as the reference value, and analyze and obtain the predicted values of each type of consumed energy and energy - using system in combination with each predicted balance coefficient; The analysis process of the predicted value is specifically as follows: Establish a calibration matrix based on all the predicted balance coefficients; Perform correction analysis on the predicted balance coefficients of the corresponding rows according to the fluctuation conditions of all the predicted balance coefficients in the calibration matrix to obtain the first correction coefficient of the predicted balance coefficients of the corresponding rows; The specific calculation formula of the first correction coefficient is: where P k,g represents the k-th predicted balance coefficient in the g-th row of the check matrix; x k,g represents the first correction coefficient corresponding to the k-th predicted balance coefficient in the g-th row; Q represents the number of columns in the check matrix; H represents the number of rows in the check matrix; Perform correction analysis on the predicted balance coefficients of the corresponding columns according to the fluctuation conditions of all the predicted balance coefficients in the calibration matrix to obtain the second correction coefficient of the predicted balance coefficients of the corresponding columns; Perform comprehensive correction on the corresponding predicted balance coefficients according to the first correction coefficient and the second correction coefficient to obtain the final balance coefficient; Solve and obtain the predicted values of each type of energy - using system and consumed energy according to the reference value and the final balance coefficient; Input the predicted values of each type of consumed energy and energy - using system into the carbon emission calculation model to obtain the predicted value of carbon emissions.

2. The building carbon emission prediction and analysis method according to claim 1, Characterized in that, The specific calculation formula of the data balance model is: Among them, P(b,a) represents the balance coefficient between the energy utilization system type b and the consumed energy type a; E a represents the consumption corresponding to the consumed energy type a; E b represents the consumption corresponding to the energy utilization system type b; E i represents the consumption of the i-th consumed energy type; n represents the number of consumed energy types; E j represents the consumption of the j-th energy utilization system type; m represents the number of energy utilization system types.

3. The building carbon emission prediction and analysis method according to claim 1, Characterized in that, The specific calculation formula of the final balance coefficient is: P Z = P 0 (1 + 0.5X 1 + 0.5X 2 ) Among them, P Z represents the final balance coefficient; P 0 represents the predicted balance coefficient; X 1 represents the first correction coefficient; X 2 represents the second correction coefficient.

4. The building carbon emission prediction and analysis method according to claim 1, Characterized in that, The balance change curve is constructed using the least - squares method.

5. The building carbon emission prediction and analysis method according to claim 1, Characterized in that, The specific calculation formula of the carbon emission calculation model is: Among them, represents the actual carbon emissions of the target building within a fixed period; E i represents the consumption of the i-th type of energy consumed; F i represents the carbon emission factor of the i-th type of energy consumed; n represents the number of types of energy consumed; C p represents the carbon reduction amount by greening; E i(j) represents the consumption of the i-th type of energy of the j-th type of energy-using system; R i(j) represents the amount of the i-th type of energy consumed by the j-th type of energy-using system provided by the renewable energy system; m represents the number of types of energy-using systems.

6. A building carbon emission prediction and analysis system, which applies the building carbon emission prediction and analysis method according to any one of claims 1 - 5, Characterized in that, It includes: A model construction module, which is used to establish a data balance model that correlates various carbon emission data based on the type of consumed energy and the type of energy - using system; A balance calculation module, which is used to input the carbon emission data in multiple historical periods into the data balance model to obtain the balance coefficients between each type of energy - using system and each type of consumed energy in the corresponding historical periods; A balance prediction module, which is used to establish a balance change curve that changes with time based on the balance coefficients of the same type in multiple historical periods, and intercept the predicted balance coefficients for the future period from the balance change curve; A prediction analysis module, configured to select the type data with the smallest volatility from each energy consumption type and energy-using system type as a reference value, and analyze the predicted values of each energy consumption type and energy-using system type in combination with the predicted balance coefficients of each; A prediction calculation module, configured to input the predicted values of each energy consumption type and energy-using system type into a carbon emission calculation model to obtain a predicted carbon emission value.

7. A computer terminal, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein, when the processor executes the program, it implements the building carbon emission prediction analysis method described in any one of claims 1-5.

8. A computer-readable medium, having a computer program stored thereon, wherein, when the computer program is executed by the processor, it can implement the building carbon emission prediction analysis method described in any one of claims 1-5.

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