CN111445060B - Community carbon emission monitoring and predicting system and method - Google Patents
Community carbon emission monitoring and predicting system and method Download PDFInfo
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Abstract
The invention discloses a community carbon emission monitoring and predicting system and method. The carbon emission monitoring module collects energy consumption activity data of community electricity, gas, liquefied petroleum gas, gasoline, diesel oil and the like and solid waste activity data of garbage, waste water and the like, calculates the carbon emission of the community by using an emission factor method, and realizes the monitoring function of community carbon emission; the monitoring module collects the consumption expenditure of the residents per capita, the number of the residents in the community and the consumption price index input parameters of the residents in the local area, the carbon emission prediction module firstly conducts dimensionless on the input parameters, and then the parameters subjected to conversion processing are input to the improved support vector machine for training and modeling, so that the function of predicting the carbon emission of the community is achieved. The method and the device can help to comprehensively and accurately monitor each carbon emission source in the boundary range of the community, predict and obtain future carbon emission of the community, and provide basis for formulating carbon emission reduction measures for the community.
Description
Technical Field
The invention relates to the field of carbon dioxide emission management, in particular to a community carbon emission monitoring and predicting system and method.
Background
Climate warming is a major environmental problem facing the world, and most scientists consider carbon dioxide released by human activities to be the most important greenhouse gas causing global warming. At present, china is in the rapid industrialization and urbanization process, but the urbanization process is accelerated, the content of carbon dioxide in the atmosphere is continuously increased, and the environmental burden is further increased.
As a residential living gathering area, the carbon emission of residential life tends to increase gradually. With the increase of the economic level of China, the domestic energy consumption of residents and the generation amount of wastes such as garbage, waste water and the like are increased at an accelerated speed. Accurate accounting and prediction of carbon emission are the prerequisites for establishing targeted energy-saving and carbon-reducing measures. The reduction of energy consumption of living energy and the effective utilization of resources are more the result of scientific and technological means or education and the change of behavior modes of people, and the unit bearing the responsibility is naturally the community.
However, at present, research on carbon emission characteristics and low carbon development at home and abroad mainly focuses on the urban scale, and most of the research on carbon emission characteristics and carbon emission accounting at the small scale of residential areas is less in the research on social and economic development data of departments, such as mangu in thailand, toronto in canada and the like, and at home, such as beijing, shanghai, guangzhou and the like. It can be seen that there is a great potential for energy-saving and carbon-reduction in communities, but there is a lack of a corresponding evaluation calculation method for accounting for carbon emissions in communities. The carbon emission of communities cannot be mastered, and the development of related energy-saving technical transformation in communities is difficult. The method systematizes the community carbon emission accounting method, can identify the community carbon emission source, and ensures the availability of the community carbon emission activity data, thereby quantifying the carbon emission level of the community, predicting the community carbon emission, mining the energy-saving and carbon-reducing potential of the community, and promoting the development of the community to a green, low-carbon and sustainable direction.
Disclosure of Invention
In order to solve the above technical problems, an object of the present invention is to provide a community carbon emission monitoring and predicting system, which implements automatic monitoring of community carbon emission and prediction of future carbon emission. The system is used for solving the problem of difficult accounting of the carbon emission of the community, can measure the low-carbon construction level of the community, and provides reference for formulating energy-saving and carbon-reducing measures of the community.
In order to realize the purpose of the invention, the technical scheme adopted by the invention is as follows:
a community carbon emission monitoring and predicting system comprises a carbon emission monitoring module and a carbon emission predicting module;
the carbon emission monitoring module comprises a data acquisition module and a data analysis module, wherein the data acquisition module is used for acquiring the community carbon emission activity data and input parameters required by carbon emission prediction, and the input parameters comprise the consumption expenditure of per capita residents, the number of the community resident regular population and the consumption price index of local residents;
the data acquisition module comprises a sensor, a data acquisition unit and a storage server, wherein the sensor is connected with a carbon emission source of a community, the sensor transmits the community carbon emission activity data measured from the carbon emission source to the data acquisition unit, and the data acquisition unit transmits the community carbon emission activity data to the storage server for storage to form storage data;
the data analysis module is used for reading the storage data from the storage server and comprehensively analyzing analysis information in the storage server, and the data analysis module is used for converting the community carbon emission activity data into carbon emission and summarizing the carbon emission activity data to generate the total community carbon emission;
the carbon emission prediction module carries out non-dimensionalization processing on the input parameters and predicts the carbon emission of the community by using an improved support vector machine;
the carbon emission prediction module and the analysis module are both installed in a terminal computer.
Preferably, the analysis information includes a community carbon emission boundary range, a community building area, carbon emission unit identification information, a community population number, a community carbon emission amount, a household carbon emission amount, and a human carbon emission amount.
Preferably, the community carbon emission activity data includes residential area carbon emission data, community public area carbon emission data, community garbage disposal carbon emission data and wastewater disposal carbon emission data, the residential area carbon emission data includes residential area electricity consumption and carbon emission data, residential area natural gas consumption and carbon emission data, the community public area carbon emission data includes community electricity consumption and carbon emission data, community natural gas consumption and carbon emission data, community gasoline consumption and carbon emission data, community diesel consumption and carbon emission data, and community consumption and other energy carbon emission data, and the data does not include carbon emission of a community production part; the community carbon emission activity data is converted into a community carbon emission total amount through the data analysis module, and the community carbon emission total amount calculation formula is as follows:
wherein:
-total community carbon emissions; e Residential area -carbon emissions from residential areas; e Community public area -carbon emissions from community public areas; e Garbage collection -carbon emission from community waste disposal; e Waste water -carbon emissions from wastewater treatment; e Electric power -residential areas and community public areas consume electricity and carbon emissions; e Natural gas Consumption of carbon emissions of natural gas in residential and community communities areas, E Diesel oil -community consumption of diesel carbon emissions; e Gasoline (gasoline) -community consumption of gasoline carbon emissions; e Others The community consumes carbon emissions of other energy sources besides natural gas, diesel oil and gasoline; the above units are ton carbon dioxide (tCO) 2 )。
Preferably, the community carbon emission activity data is transmitted in an online transmission mode or an offline transmission mode, the online transmission mode comprises an Ethernet or wireless transmission mode, and the offline transmission mode comprises mobile terminal photographing uploading and data recording.
Preferably, the carbon emission prediction module comprises an improved support vector machine prediction model module, and the total amount of community carbon emission and the input parameters subjected to non-dimensionalization are input into the improved support vector machine prediction model module;
and optimizing a least square support vector machine model through a genetic algorithm to obtain an improved support vector machine in the improved support vector machine prediction model module, and predicting the carbon emission of the community through the optimized improved support vector machine.
The influence (elimination of dimension) of big data and small data in three parameters of the consumption expenditure of the average population, the number of the permanent population of the community population and the consumption price index of the local population and the difference influence of variation size can be eliminated by using the dispersion standardization method.
Preferably, the kernel function in the support vector machine is RBF, i.e.
The optimization of the support vector machine is converted into the optimization of the penalty factor C of the support vector machine and the variance sigma of the sum function.
The invention also aims to provide a method for monitoring and predicting the carbon emission of the community, which comprises the following steps:
step 2, collecting the community historical carbon emission through a carbon emission monitoring module, and carrying out dimensionless operation on the input parameter data by adopting a deviation standardization method, wherein the calculation formula for carrying out dimensionless operation on the per-capita resident consumption expense, the community resident regular population number and the local resident consumption price index is as follows: x' i ＝(x i -x min )/(x max -x min ) Wherein i =1,2, … m, m is the number of the parameter x; x is the number of min And x max Expressed respectively as the minimum and maximum values, x ', of a parameter x' i For non-dimensionalized input parameter data, x i The current actual input parameter data is passed through;
and step 3: inputting the analyzed and calculated total carbon emission amount of the community and the input parameters subjected to dimensionless into an improved support vector machine prediction model in a carbon emission prediction module, optimizing a least square support vector machine model through a genetic algorithm to obtain an improved support vector machine in the improved support vector machine prediction model module, and predicting the total carbon emission amount of the community through the optimized improved support vector machine.
Preferably, the community carbon emission activity data includes residential area carbon emission data, community public area carbon emission data, community garbage disposal carbon emission data and wastewater disposal carbon emission data, the residential area carbon emission data includes residential area electricity consumption and carbon emission data, residential area natural gas consumption and carbon emission data, the community public area carbon emission data includes community electricity consumption and carbon emission data, community natural gas consumption and carbon emission data, community gasoline consumption and carbon emission data, community diesel consumption and carbon emission data, community consumption and other energy carbon emission data, and does not include carbon emission of a community production part; the community carbon emission activity data is converted into the total community carbon emission amount through the data analysis module, and on the basis of obtaining the community carbon emission activity data, the community carbon emission monitoring and predicting system calculates community carbon emission by adopting an emission factor method, wherein the community carbon emission comprises the carbon emission amount of residential areas, the carbon emission amount of public areas of communities, the carbon emission amount of community garbage disposal, and the data amount of wastewater disposal carbon emission, and does not contain the production carbon emission in the community;
the community carbon emission total amount calculation formula is as follows:
wherein:
-total community carbon emissions; e Residential area -carbon emissions from residential areas; e Community public area -carbon emissions from community public areas; e Garbage collection -carbon emission from community waste disposal; e Waste water -carbon emissions from wastewater treatment; e Electric power -residential areas and community public areas consume electricity and carbon emissions; e Natural gas Consumption of carbon emissions of natural gas in residential and community communities areas, E Diesel oil -community consumption of gasoline carbon emissions; e Gasoline (gasoline) -community consumption of diesel carbon emissions; e Others -the carbon emission of other combustible energy sources than natural gas, diesel oil and gasoline are consumed by communities; the above units are ton carbon dioxide (tCO) 2 )；
Preferably, the collecting of the community carbon emission activity data adopts the following steps:
step (1), collecting the power and natural gas consumption of residential areas and community public areas through one of online transmission and offline transmission;
wherein the online transmission mode comprises:
the community carbon emission monitoring module is in butt joint with a power supply station and a natural gas company system, and respectively collects real-time electricity consumption and gas consumption of each resident user and a community public area through a data collector;
or data transmission devices are installed in electric meters and natural gas meters in residential and community public areas, and real-time electricity consumption and gas consumption are transmitted in an Ethernet or GPRS mode;
the off-line transmission mode is as follows: the method comprises the following steps that residents or property management personnel take pictures or directly record electric quantity and gas quantity data by mobile terminal equipment and periodically and respectively upload the electric quantity and the gas quantity data to a power data acquisition unit and a gas data acquisition unit in a data acquisition unit, and carbon emission data generated by consuming electric quantity and carbon emission data generated by consuming gas in residential areas and community public areas are completed;
step (2), collecting carbon emission data of gasoline, diesel oil and other combustible energy in a community public area in an off-line mode, and periodically counting consumption by community property managers and uploading the consumption to corresponding collection units; periodically counted once a week or once a month,
weighing garbage in residential areas and public areas of communities by using a weighing machine, recording garbage treatment modes, and uploading the garbage to a garbage data acquisition unit in a data acquisition unit; the waste water production of resident residential area and community public area is measured through the flowmeter, records the waste water treatment mode simultaneously to upload to the waste water data acquisition unit in the data collection station.
Preferably, the prediction model selects RBF as the kernel function of the support vector machine, i.e.
Converting the optimization problem of the support vector machine into an optimization problem of a penalty factor C of the support vector machine and a variance sigma of a sum function, and improving the support vector machine by utilizing a genetic algorithm comprises the following steps:
step a: determining the value ranges of the penalty factor C and the variance sigma of the sum function, wherein the value ranges of the two parameters are nonzero positive numbers;
step b: selecting the mean error E of the training sample as the fitness calculation function of the genetic algorithm as
Wherein l is the number of samples, y i (i =1,2, …, l) is the true value of the ith sample, y i ' (i =1,2, …, l) is the predicted value of the ith sample;
step c: randomly generating a penalty factor C and a variance sigma of a sum function in a value range to generate an initial training population;
step d: substituting the initialized population into a support vector machine for training, and calculating a fitness function value;
step e: copying, crossing and mutating chromosomes with high fitness by using a genetic algorithm to obtain a new population, judging whether the prediction precision meets the requirement, and stopping if so; otherwise, returning to the step d for recalculation;
step f: and after training is finished, obtaining an optimal penalty factor C and the variance sigma of the sum function, and substituting the optimal penalty factor C and the variance sigma of the sum function into the data of the support vector machine prediction test sample.
Compared with the prior art, the invention has the beneficial technical effects that:
1. the invention can solve the problem of difficulty in collecting the carbon emission activity data of the community: the method determines the collection mode of each carbon emission activity data of the community, and ensures the availability of the data in an online mode and an offline mode;
2. the invention can solve the problem of complex carbon emission accounting of the community: the community property manager or the residence committee manager lacks a community carbon emission accounting methodology, so that the community carbon emission accounting difficulty is high;
3. the invention can solve the problem of inaccurate prediction of community carbon emission: a support vector machine is optimized by using a genetic algorithm to obtain a community carbon emission prediction model, so that the prediction precision is high;
4. the invention can solve the problem of low pertinence of the establishment of community carbon emission reduction measures: according to the method, the carbon emission source of the community is identified, and the future carbon emission trend of the community is predicted, so that technical support is provided for the community mining energy-saving and carbon-reducing measures, and the development of low-carbon community construction is facilitated.
Drawings
FIG. 1 is a schematic diagram of the overall architecture of the community carbon emission monitoring and prediction system of the present invention;
FIG. 2 is a schematic diagram of a community carbon emission monitoring module according to the present invention;
FIG. 3 is a schematic diagram of a community carbon emission prediction architecture of the present invention;
FIG. 4 is a flow chart of the community carbon emission prediction for the genetic algorithm optimized support vector machine of the present invention;
FIG. 5 is a graph of the community carbon emissions training results of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail with reference to the following embodiments, but the scope of the present invention is not limited to the following embodiments.
System embodiment
A community carbon emission monitoring and predicting system comprises a carbon emission monitoring module and a carbon emission predicting module; the monitoring range of the community carbon emission comprises non-production carbon emission of resident life, business and the like, and the carbon emission caused by production activities in the community is not considered, such as industrial production and the like, but third industrial carbon emission activities such as commercial facilities and the like in the community range are included; if the house building in the community contains production activities, only carbon emission caused by living energy is considered in principle; in the case that the energy for production and the energy for life cannot be distinguished, the relevant data may include the energy for production according to a conservative principle.
As shown in fig. 2, the carbon emission monitoring module includes a data acquisition module and a data analysis module, the data acquisition module is configured to acquire the community carbon emission activity data and input parameters required by carbon emission prediction, where the input parameters include the consumption expenditure of per capita residents, the number of the community residents in the daily life population, and the consumption price index of the local residents;
the data acquisition module comprises a sensor, a data acquisition unit and a storage server, wherein the sensor is connected with a carbon emission source of a community, the sensor transmits the community carbon emission activity data measured from the carbon emission source to the data acquisition unit, and the data acquisition unit transmits the community carbon emission activity data to the storage server for storage to form storage data; the sensor mainly comprises an electric energy meter, a natural gas flowmeter, a sewage flowmeter and a weighing apparatus meter.
The data analysis module is used for reading the storage data from the storage server and comprehensively analyzing analysis information in the storage server, and the data analysis module is used for converting the community carbon emission activity data into carbon emission and summarizing the carbon emission activity data to generate the total community carbon emission;
the carbon emission prediction module carries out non-dimensionalization processing on the input parameters and predicts the carbon emission of the community by using an improved support vector machine;
the carbon emission prediction module and the analysis module are both installed in a terminal computer.
Before the community carbon emission monitoring system is implemented in a community, the community carbon emission source needs to be identified, the most appropriate collection mode of each carbon emission activity data is determined, and basic information such as community building area, frequent population and the like is collected. The number of the data collectors and the storage capacity of the storage server are determined according to the scale of the community, and a foundation is provided for designing a reasonable system architecture. The analysis information comprises community carbon emission boundary range, community building area, carbon emission unit identification information, community standing population number, community carbon emission amount, household carbon emission amount and per-capita carbon emission amount.
As shown in fig. 1 and 3, the community carbon emission activity data includes residential carbon emission data, community public carbon emission data, community garbage disposal carbon emission data, and wastewater disposal carbon emission data, the residential carbon emission data includes residential electricity consumption and carbon emission data, residential natural gas consumption and carbon emission data, and the community public carbon emission data includes community electricity consumption and carbon emission data, community natural gas consumption and carbon emission data, community gasoline consumption and carbon emission data, community diesel consumption and carbon emission data, and community consumption and other energy carbon emission data, which do not include carbon emission of a community production part; the community carbon emission activity data is converted into a community carbon emission total amount through the data analysis module, and the community carbon emission total amount calculation formula is as follows:
wherein:
-total community carbon emissions; e Residential area -carbon emissions from residential areas; e Community public area -carbon emissions from community public areas; e Garbage collection -carbon emission from community waste disposal; e Waste water -carbon emissions from wastewater treatment; e Electric power -residential areas and community public areas consume electricity and carbon emissions; e Natural gas Consumption of carbon emissions of natural gas in residential and community communities areas, E Diesel oil -community consumption of diesel carbon emissions; e Gasoline (gasoline) -community consumption of gasoline carbon emissions; e Others The community consumes carbon emissions of other energy sources besides natural gas, diesel oil and gasoline; the above units are ton carbon dioxide (tCO) 2 )。
The community carbon emission activity data is transmitted in an online transmission mode or an offline transmission mode, the online transmission mode comprises an Ethernet or wireless transmission mode, and the offline transmission mode comprises mobile terminal photographing uploading and data inputting.
The carbon emission prediction module comprises an improved support vector machine prediction model module, and the community total carbon emission and the input parameters subjected to dimensionless transformation are input into the improved support vector machine prediction model module; and optimizing a least square support vector machine model through a genetic algorithm to obtain an improved support vector machine in the improved support vector machine prediction model module, and predicting the carbon emission of the community through the optimized improved support vector machine.
The kernel function in the support vector machine is RBF, i.e.
The optimization of the support vector machine is converted into the optimization of the support vector machine penalty factor C and the variance sigma of the sum function.
Method embodiment
step 2, collecting the community historical carbon emission through a carbon emission monitoring module, and carrying out dimensionless transformation on the input parameter data by adopting a deviation standardization method, wherein the calculation formula for carrying out dimensionless transformation on the per-capita resident consumption expense, the community resident regular population number and the local resident consumption price index is as follows: x' i ＝(x i -x min )/(x max -x min ) Wherein i =1,2, … m, m is the number of the parameter x; x is the number of min And x max Expressed as the minimum and maximum values, x ', respectively, of the parameter x' i For non-dimensionalized input parameter data, x i The current actual input parameter data is passed through;
and step 3: inputting the analyzed and calculated total carbon emission amount of the community and the input parameters subjected to dimensionless transformation into an improved support vector machine prediction model in a carbon emission prediction module, optimizing a least square support vector machine model through a genetic algorithm to obtain an improved support vector machine in the improved support vector machine prediction model module, and predicting the total carbon emission amount of the community through the optimized improved support vector machine.
Preferably, the community carbon emission activity data includes residential area carbon emission data, community public area carbon emission data, community garbage disposal carbon emission data and wastewater disposal carbon emission data, the residential area carbon emission data includes residential area electricity consumption and carbon emission data and residential area natural gas consumption and carbon emission data, the community public area carbon emission data includes community electricity consumption and carbon emission data, community natural gas consumption and carbon emission data, community gasoline consumption and carbon emission data, community diesel consumption and carbon emission data and community consumption and other energy carbon emission data, and the community carbon emission activity data does not include carbon emission of a community production part; the community carbon emission activity data is converted into the total community carbon emission amount through the data analysis module, and on the basis of obtaining the community carbon emission activity data, the community carbon emission monitoring and predicting system calculates community carbon emission by adopting an emission factor method, wherein the community carbon emission comprises the carbon emission amount of residential areas, the carbon emission amount of public areas of communities, the carbon emission amount of community garbage disposal, and the data amount of wastewater disposal carbon emission, and does not contain the production carbon emission in the community;
the total community carbon emission calculation formula is as follows:
wherein:
-total community carbon emissions; e Residential area -carbon emissions from residential areas; e Community public area -carbon emissions from community public areas; e Garbage collection -carbon emission from community waste disposal; e Waste water -carbon emissions from wastewater treatment; e Electric power -residential areas and community public areas consume electricity and carbon emissions; e Natural gas Consumption of carbon emissions of natural gas in residential and community communities areas, E Diesel oil -community consumption of diesel carbon emissions; e Gasoline (R) and its preparation method -community consumption of gasoline carbon emissions; e Others The community consumes carbon emissions of other energy sources besides natural gas, diesel oil and gasoline; the above units are ton carbon dioxide (tCO) 2 )；
Preferably, the collecting of the community carbon emission activity data adopts the following steps:
step (1), collecting the power and natural gas consumption of residential areas and community public areas through one of online transmission and offline transmission;
wherein the online transmission mode comprises:
the community carbon emission monitoring module is in butt joint with a power supply station and a natural gas company system, and respectively collects real-time electricity consumption and gas consumption of each resident user and a community public area through a data collector;
or data transmission devices are installed on electricity meters and natural gas meters in residential and community public areas, and real-time electricity consumption and gas consumption are transmitted in an Ethernet or GPRS mode;
the off-line transmission mode is as follows: the method comprises the following steps that residents or property management personnel take pictures or directly record electric quantity and gas quantity data by mobile terminal equipment and periodically and respectively upload the electric quantity and the gas quantity data to a power data acquisition unit and a gas data acquisition unit in a data acquisition unit, and carbon emission data generated by consuming electric quantity and carbon emission data generated by consuming gas in residential areas and community public areas are completed;
acquiring carbon emission data of gasoline, diesel oil and other energy sources in a community public area in an off-line mode, and periodically counting consumption by community property managers and uploading the consumption to corresponding acquisition units; the regular period is counted once a week or once a month,
weighing garbage in residential areas and public areas of communities by using a weighing machine, recording garbage treatment modes, and uploading the garbage to a garbage data acquisition unit in a data acquisition unit; the waste water production of resident residential area and community public area is measured through the flowmeter, records the waste water treatment mode simultaneously to upload to the waste water data acquisition unit in the data collection station.
A cell is selected as an embodiment of the system of the present invention, and the community is continuously monitored for 4 years, and the carbon emission monitoring data of the community 2018 is shown in the following table 1:
TABLE 1 Community 2018 carbon emission monitoring data
And (3) adopting a dispersion standardization method to input parameters: the consumption expenditure of the residents per capita, the number of the resident residents in the community and the consumption price index of the local residents are dimensionless. The influence (elimination of dimension) of big data and small data in three parameters of the consumption expenditure of the average population, the number of the permanent population of the community population and the consumption price index of the local population and the difference influence of variation size can be eliminated by using the dispersion standardization method. Monitoring data of communities 2015, 2016 and 2017 are used as training samples, and monitoring data of 2018 are used as testing data.
As shown in FIG. 4, the training sample set is defined as { (x) ij ,y ij ) , i =1,2, ·, l; j =1,2,3}, where x ij (x ij ∈R) J =1,2,3 represents the consumption expenditure of the average population, the number of the permanent population of the community and the consumption price index of the residents in the current place respectively, which are input vectors of the ith training sample. y is ij e.R is the corresponding output value.
The linear regression function established in the high-dimensional feature space is set as follows:
f(x)＝wφ(x)+b
introducing relaxation variables xi i And
the regression function of the support vector machine can be converted to a mathematical formula to find the w and b problems as follows:
wherein C is a penalty factor, the larger C is the penalty larger for the sample with the training error larger than epsilon, epsilon specifies the error requirement of the regression function, and the smaller C is the error of the regression function.
Introducing Lagrangian function, converting the above formula into dual form:
wherein, K (x) i ,x j )＝φ(x i )φ(x j ) Is a kernel function. The regression function of the support vector machine can be converted to:
and the converted regression function f (x) is a data output layer of the support vector machine, and the prediction output of the carbon emission of the community is finished after the regression function is carried out.
The prediction model selects RBF as the kernel function of the support vector machine, i.e.
Converting the optimization problem of the support vector machine into an optimization problem of a penalty factor C of the support vector machine and a variance sigma of a sum function, and improving the support vector machine by using a genetic algorithm comprises the following steps:
step a: determining the value ranges of the penalty factor C and the variance sigma of the sum function, wherein the value ranges of the two parameters are [0.01,500];
step b: selecting the mean error E of the training sample as the fitness calculation function of the genetic algorithm as
Wherein l is the number of samples, y i (i =1,2, …, l) is the true value of the ith sample, y i ' (i =1,2, …, l) is the predicted value of the ith sample;
step c: randomly generating a penalty factor C and a variance sigma of a sum function in a value range to generate an initial training population;
step d: substituting the initialized population into a support vector machine for training, and calculating a fitness function value;
step e: calculating the fitness: copying, crossing and mutating chromosomes with high fitness by using a genetic algorithm to obtain a new population, judging whether the prediction precision meets the requirement, and stopping if so; otherwise, returning to the step d for recalculation;
step f: and after training is finished, obtaining the optimal penalty factor C and the variance sigma of the sum function, and substituting the optimal penalty factor C and the variance sigma of the sum function into a support vector machine to predict data of the test sample.
The step d specifically comprises the following steps: and decoding to obtain a penalty factor C and the variance sigma of the sum function, assigning the penalty factor C and the variance sigma of the sum function to a support vector machine, training a model by using a training sample, testing a network by using a test sample, and then testing errors.
The samples of the communities 2015, 2016 and 2017 are used for training, the predicted carbon emission value is set as an abscissa, the true carbon emission value is set as an ordinate, and fitting is carried out, as shown in fig. 5. As can be seen from FIG. 5, the fitting correlation coefficient between the predicted result and the true value reaches 0.996, which shows that a better fitting result can be realized by using the method of optimizing the support vector machine by using the genetic algorithm.
And inputting the test samples of 2018 into the established community carbon emission prediction model to obtain a community carbon emission prediction result. In order to verify the prediction accuracy of the established model, in this example, the BP neural network and the support vector machine are used for prediction at the same time, and the comparison result is shown in table 2 below. As can be seen from the table, the improved support vector machine provided by the invention predicts the carbon emission of the community more accurately than other methods, and the method has practicability and effectiveness for predicting the carbon emission of the community.
TABLE 2 comparison table of carbon emission prediction by BP neural network and support vector machine
| 2018 years old | Improving support vector machine% | Support vector machine/%) | BP neural network/ |
| 1 month | 0.13 | -4.59 | 7.39 |
| 2 month | -0.84 | 1.02 | -5.93 |
| 3 month | 0.93 | 1.16 | 8.41 |
| 4 month | 0.47 | -1.55 | -8.67 |
| Month 5 | -0.64 | 3.08 | -7.61 |
| 6 month | -0.25 | -4.98 | 6.36 |
| 7 month | 0.61 | -2.18 | -3.73 |
| 8 month | -0.77 | 1.54 | 6.35 |
| 9 month | 0.33 | 5.12 | -7.98 |
| 10 month | 0.22 | -0.77 | -8.30 |
| 11 month | -0.40 | -5.43 | 8.79 |
| 12 month | 0.50 | 5.51 | -8.73 |
The carbon emission monitoring module in the community carbon emission monitoring and predicting system collects community power, gas, liquefied petroleum gas, gasoline, diesel oil and other energy consumption activity data and garbage, waste water and other solid waste activity data, calculates the carbon emission of a community by using an emission factor method, and realizes the monitoring function of community carbon emission; the carbon emission prediction module collects input parameters such as per capita resident consumption expenditure, community resident common population quantity, local resident consumption price index and the like through the monitoring module, the prediction module firstly carries out dimensionless on the input parameters, and then the converted parameters are input to the improved support vector machine for training and modeling, so that the function of predicting the community carbon emission is realized. The method can solve the problem of difficulty in acquiring the carbon emission activity data of the community, is beneficial to comprehensively and accurately monitoring each carbon emission source in the boundary range of the community, predicts and obtains the future carbon emission of the community, provides a basis for the community to formulate the carbon emission reduction measures, and promotes the development of the low-carbon community work.
Variations and modifications to the above-described embodiments may occur to those skilled in the art, in light of the above teachings and teachings. Therefore, the present invention is not limited to the specific embodiments disclosed and described above, and some modifications and variations of the present invention should fall within the scope of the claims of the present invention. Furthermore, although specific terms are employed herein, they are used in a generic and descriptive sense only and not for purposes of limitation.
Claims (6)
1. A community carbon emission monitoring and predicting system is characterized by comprising a carbon emission monitoring module and a carbon emission predicting module;
the carbon emission monitoring module comprises a data acquisition module and a data analysis module, wherein the data acquisition module is used for acquiring community carbon emission activity data and input parameters required by carbon emission prediction, and the input parameters comprise the consumption expenditure of per capita residents, the number of the community resident permanent population and the consumption price index of local residents;
the data acquisition module comprises a sensor, a data acquisition unit and a storage server, wherein the sensor is connected with a carbon emission source of a community, the sensor transmits the community carbon emission activity data measured from the carbon emission source to the data acquisition unit, and the data acquisition unit transmits the community carbon emission activity data to the storage server for storage to form storage data;
the data analysis module is used for reading the storage data from the storage server and comprehensively analyzing the analysis information in the storage server, and the data analysis module is used for converting the community carbon emission activity data into carbon emission and summarizing the carbon emission activity data to generate the total community carbon emission;
the carbon emission prediction module carries out non-dimensionalization processing on the input parameters and predicts the community carbon emission by using an improved support vector machine; carrying out dimensionless on the input parameter data by adopting a dispersion standardization method, wherein the calculation formula for carrying out dimensionless on the per-person resident consumption expense, the community resident permanent population number and the local resident consumption price index is as follows: x is the number of i '＝(x i -x min )/(x max -x min ) Wherein i =1,2, … m, m is input parameter x i The number of (2); x is the number of min And x max Respectively expressed as input parameters x i Minimum and maximum values of, x' i For non-dimensionalized input parameter data, x i Is a warpInputting parameter data of the current reality;
the carbon emission prediction module and the analysis module are both installed in a terminal computer;
the analysis information comprises a community carbon emission boundary range, a community building area, carbon emission unit identification information, a community permanent population number, a community carbon emission amount, a household carbon emission amount and a human carbon emission amount;
the carbon emission prediction module comprises an improved support vector machine prediction model module, and the community total carbon emission and the input parameters subjected to non-dimensionalization are input into the improved support vector machine prediction model module;
optimizing a least square support vector machine model through a genetic algorithm to obtain an improved support vector machine in the improved support vector machine prediction model module, and predicting the carbon emission of the community through the optimized improved support vector machine;
the steps of improving the support vector machine by using the genetic algorithm are as follows:
a, step a: determining the value ranges of the penalty factor C and the variance sigma of the kernel function, wherein the value ranges of the two parameters are nonzero positive numbers;
step b: selecting the mean error E of the training sample as the fitness calculation function of the genetic algorithm as
Wherein l is the number of samples, y i (i =1,2, …, l) is the true value of the ith sample, y i ' (i =1,2, …, l) is the predicted value for the ith sample;
step c: randomly generating a penalty factor C and a variance sigma of a kernel function in a value range to generate an initial training population;
step d: substituting the initialized population into a support vector machine for training, and calculating a fitness function value; the method specifically comprises the following steps: decoding to obtain a penalty factor C and a variance sigma of a kernel function, assigning the penalty factor C and the variance sigma of the kernel function to a support vector machine, training a model by using a training sample, testing a network by using a test sample, and then testing an error;
step e: copying, crossing and mutating chromosomes with high fitness by using a genetic algorithm to obtain a new population, judging whether the prediction precision meets the requirement, and stopping if so; otherwise, returning to the step d for recalculation;
step f: and after training is finished, obtaining an optimal penalty factor C and the variance sigma of the kernel function, and substituting the optimal penalty factor C and the variance sigma into the data of the support vector machine prediction test sample.
2. The community carbon emission monitoring and prediction system of claim 1, wherein the community carbon emission activity data comprises residential carbon emission data, community public area carbon emission data, community garbage disposal carbon emission data, and wastewater disposal carbon emission data, the residential carbon emission data comprises residential electricity consumption generated carbon emission data and residential natural gas consumption generated carbon emission data, and the community public area carbon emission data comprises community electricity consumption carbon emission data, community natural gas consumption carbon emission data, community gasoline consumption carbon emission data, community diesel consumption carbon emission data, and community consumption other energy carbon emission data; the community carbon emission activity data is converted into a community carbon emission total amount through the data analysis module, and the community carbon emission total amount calculation formula is as follows:
wherein:
-total community carbon emissions; e Residential area -carbon emissions from residential areas; e Community public area -carbon emissions in community public areas; e Garbage collection -carbon emission from community waste disposal; e Waste water -carbon emissions from wastewater treatment; e Electric power Consumption of electricity and carbon emission in residential areas and community public areasAn amount; e Natural gas Consumption of carbon emissions of natural gas in residential and community areas, E Diesel oil -community consumption of diesel carbon emissions; e Gasoline (gasoline) -community consumption of gasoline carbon emissions; e Others (C) The community consumes carbon emissions of other energy sources besides natural gas, diesel oil and gasoline; the unit of each parameter in the community carbon emission total amount calculation formula is ton carbon dioxide (tCO) 2 )。
3. The community carbon emission monitoring and predicting system according to claim 1, wherein the community carbon emission activity data is transmitted in two ways, namely online transmission or offline transmission, the online transmission comprises an Ethernet or wireless transmission mode, and the offline transmission comprises mobile terminal photographing and uploading and data recording.
4. A community carbon emission monitoring and predicting method is characterized by comprising the following steps:
step 1, acquiring input parameters required by community carbon emission activity data and carbon emission prediction through a carbon emission monitoring module, wherein the input parameters comprise average people consumption expenditure, community resident regular population quantity and local resident consumption price index; the carbon emission monitoring module comprises a data acquisition module and a data analysis module, the data acquisition module is used for acquiring input parameters required by community carbon emission activity data and carbon emission prediction, the data acquisition module comprises a sensor, a data acquisition unit and a storage server, the sensor is connected with a carbon emission source of a community, the sensor transmits the community carbon emission activity data measured from the carbon emission source to the data acquisition unit, and the data acquisition unit transmits the community carbon emission activity data to the storage server for storage to form storage data; the data analysis module is used for reading the storage data from the storage server and comprehensively analyzing analysis information in the storage server, and the data analysis module is used for converting the community carbon emission activity data into carbon emission and summarizing the carbon emission activity data to generate the total community carbon emission; the analysis information comprises community carbon emission boundary range, community building area, carbon emission unit identification information, community standing population number, community carbon emission, household carbon emission and per-capita carbon emission;
step 2, collecting the community historical carbon emission through a carbon emission monitoring module, and carrying out dimensionless operation on the input parameter data by adopting a deviation standardization method, wherein the calculation formula for carrying out dimensionless operation on the per-capita resident consumption expense, the community resident regular population number and the local resident consumption price index is as follows: x' i ＝(x i -x min )/(x max -x min ) Wherein i =1,2, … m, m is input parameter x i The number of (2); x is the number of min And x max Respectively expressed as input parameters x i Minimum and maximum values of, x' i For non-dimensionalized input parameter data, x i The current actual input parameter data is passed through;
and 3, step 3: inputting the analyzed and calculated total carbon emission amount of the community and the input parameters subjected to dimensionless transformation into an improved support vector machine prediction model in a carbon emission prediction module, optimizing a least square support vector machine model through a genetic algorithm to obtain an improved support vector machine in the improved support vector machine prediction model module, and predicting the total carbon emission amount of the community through the optimized improved support vector machine;
the steps of improving the support vector machine by using the genetic algorithm are as follows:
step a: determining the value ranges of the penalty factor C and the variance sigma of the kernel function, wherein the value ranges of the two parameters are nonzero positive numbers;
step b: selecting the mean error E of the training sample as the fitness calculation function of the genetic algorithm as
Wherein l is the number of samples, y i (i =1,2, …, l) is the true value of the ith sample, y i ' (i =1,2, …, l) is the predicted value of the ith sample;
step c: randomly generating a penalty factor C and a variance sigma of a kernel function in a value range to generate an initial training population;
step d: substituting the initialized population into a support vector machine for training, and calculating a fitness function value; the method specifically comprises the following steps: decoding to obtain a penalty factor C and a variance sigma of a kernel function, assigning the penalty factor C and the variance sigma of the kernel function to a support vector machine, training a model by using a training sample, testing a network by using a test sample, and then testing an error;
step e: copying, crossing and mutating chromosomes with high fitness by using a genetic algorithm to obtain a new population, judging whether the prediction precision meets the requirement, and stopping if so; otherwise, returning to the step d for recalculation;
step f: and after training is finished, obtaining an optimal penalty factor C and the variance sigma of the kernel function, and substituting the optimal penalty factor C and the variance sigma into the data of the support vector machine prediction test sample.
5. The method of claim 4, wherein the monitoring and predicting of the carbon emissions of the community,
the community carbon emission activity data comprises residential area carbon emission data, community public area carbon emission data, community garbage disposal carbon emission data and wastewater disposal carbon emission data, the residential area carbon emission data comprises residential area electricity consumption and carbon emission data and residential area natural gas consumption and carbon emission data, and the community public area carbon emission data comprises community electricity consumption and carbon emission data, community natural gas consumption and carbon emission data, community gasoline consumption and carbon emission data, community diesel consumption and carbon emission data and community consumption other energy carbon emission data; the community carbon emission activity data is converted into a community carbon emission total amount through the data analysis module, and the community carbon emission total amount calculation formula is as follows:
wherein:
-total community carbon emissions; e Residential area -carbon emissions from residential areas; e Community public area -carbon emissions in community public areas; e Garbage collection -carbon emission from community waste disposal; e Waste water -carbon emissions from wastewater treatment; e Electric power -the residential areas and the community public areas consume electricity and carbon emission; e Natural gas Consumption of carbon emission from natural gas in residential and community areas, E Diesel oil -community consumption of diesel carbon emissions; e Gasoline (gasoline) -community consumption of gasoline carbon emissions; e Others The community consumes carbon emissions of other energy sources besides natural gas, diesel oil and gasoline; the unit of each parameter in the community carbon emission total amount calculation formula is ton carbon dioxide (tCO) 2 )。
6. The method for monitoring and predicting the carbon emission of the community as claimed in claim 5, wherein the step of collecting the activity data of the carbon emission of the community comprises the following steps:
step (1), collecting the power and natural gas consumption of residential areas and community public areas through one of online transmission and offline transmission;
wherein the online transmission mode comprises:
the community carbon emission monitoring module is in butt joint with a power supply bureau and a natural gas company system, and respectively collects real-time electricity consumption and gas consumption of each resident user and a community public area through a data collector;
or data transmission devices are installed on electricity meters and natural gas meters in residential and community public areas, and real-time electricity consumption and gas consumption are transmitted in an Ethernet or GPRS mode;
the off-line transmission mode is as follows: the method comprises the following steps that resident or property management personnel adopt mobile terminal equipment to take pictures or directly input electric quantity and gas quantity data, and periodically and respectively upload the electric quantity data and the gas quantity data to a power data acquisition unit and a natural gas data acquisition unit in a data acquisition device, so that the collection of carbon emission data generated by consuming electric quantity and carbon emission data generated by consuming natural gas in resident residential areas and community public areas is completed;
acquiring carbon emission data of gasoline, diesel oil and other energy sources in a community public area in an off-line mode, and periodically counting consumption by community property managers and uploading the consumption to corresponding acquisition units;
weighing garbage in residential areas and public areas of communities by using a weighing machine, recording garbage treatment modes, and uploading the garbage to a garbage data acquisition unit in a data acquisition unit; the waste water production of resident residential area and community public area is measured through the flowmeter, records the waste water treatment mode simultaneously to upload to the waste water data acquisition unit in the data collection station.
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Families Citing this family (29)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
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| CN114202191A (en) * | 2021-12-09 | 2022-03-18 | 特斯联科技集团有限公司 | Service robot carbon emission accounting method |
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| CN114399110B (en) * | 2022-01-14 | 2024-11-01 | 国网福建省电力有限公司营销服务中心 | Ceramic industry carbon emission prediction method based on electric power data |
| CN114124730A (en) * | 2022-01-27 | 2022-03-01 | 北京冠邦开源智能系统工程技术有限公司 | Communication method, device and system |
| CN114626627A (en) * | 2022-03-28 | 2022-06-14 | 王大成 | Monitoring and early warning system for carbon emission in area |
| CN114819716A (en) * | 2022-05-20 | 2022-07-29 | 江苏中电通智能科技有限公司 | Carbon emission data analysis method based on big data technology |
| CN115081315B (en) * | 2022-06-06 | 2024-10-22 | 苏州科技大学 | Method and system for constructing community built environment space model for low-carbon travel |
| CN115408831A (en) * | 2022-07-15 | 2022-11-29 | 大地资源有限公司 | System and method for simulating and predicting carbon emissions |
| CN115829081B (en) * | 2022-10-19 | 2024-06-11 | 华中科技大学 | Urban traffic carbon emission prediction method based on support vector regression model |
| CN115879638B (en) * | 2022-12-30 | 2023-06-09 | 东北石油大学 | Carbon emission prediction method for oilfield transfer station system |
| CN115994625B (en) * | 2023-03-17 | 2023-06-13 | 中环洁集团股份有限公司 | Pavement garbage prediction method and system and intelligent terminal |
| CN116026418B (en) * | 2023-03-23 | 2023-09-22 | 山东石油化工学院 | Carbon emission checking on-line monitoring system for petrochemical industry |
| CN116186120B (en) * | 2023-04-18 | 2023-07-18 | 山西维云数智科技有限公司 | Energy data acquisition and analysis database system and data storage method |
| CN116187788A (en) * | 2023-05-04 | 2023-05-30 | 江苏智能低碳科技发展有限公司 | Application platform of carbon management algorithm for factory |
| CN116933983B (en) * | 2023-09-19 | 2024-01-23 | 江西财经大学 | Low-carbon emission data monitoring system and method |
| CN117743785B (en) * | 2023-12-08 | 2024-09-20 | 广东埃文低碳科技股份有限公司 | Greenhouse gas emission control method and system |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107045672A (en) * | 2017-03-27 | 2017-08-15 | 广州能源检测研究院 | A kind of evaluation method of community's low carbon levels |
| CN110334821A (en) * | 2019-07-09 | 2019-10-15 | 广东埃文低碳科技股份有限公司 | A kind of community service system of mild areas chain |
| CN110414801A (en) * | 2019-07-08 | 2019-11-05 | 中国科学院地理科学与资源研究所 | A method of the assessment indirect energy-consuming carbon emission of urban and rural residents |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108427845A (en) * | 2018-03-16 | 2018-08-21 | 中南大学 | A kind of Pb-Zn deposits mining process carbon emission short term prediction method |
| CN109727000A (en) * | 2019-01-22 | 2019-05-07 | 浙江爱立美能源科技有限公司 | Carbon emission amount management system for monitoring and method |
-
2020
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Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107045672A (en) * | 2017-03-27 | 2017-08-15 | 广州能源检测研究院 | A kind of evaluation method of community's low carbon levels |
| CN110414801A (en) * | 2019-07-08 | 2019-11-05 | 中国科学院地理科学与资源研究所 | A method of the assessment indirect energy-consuming carbon emission of urban and rural residents |
| CN110334821A (en) * | 2019-07-09 | 2019-10-15 | 广东埃文低碳科技股份有限公司 | A kind of community service system of mild areas chain |
Non-Patent Citations (1)
| Title |
|---|
| "Combining a Genetic Algorithm and Support Vector";inying Li et al.;《Energies》;20171002;第1-17页 * |
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