CN117033927A - Subway station carbon emission real-time monitoring prediction analysis method and prediction analysis method - Google Patents
Subway station carbon emission real-time monitoring prediction analysis method and prediction analysis method Download PDFInfo
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Abstract
The invention relates to the technical field of carbon emission calculation, in particular to a subway station carbon emission real-time monitoring, predicting and analyzing method, which comprises the following steps of S1, determining a plurality of carbon emission sources and a plurality of carbon sink sources in each carbon emission range; step S2, energy consumption data and carbon sink source characteristic data in a monitoring period are obtained; s3, dividing the carbon emission source into a stable carbon source and a dynamic carbon source; step S4, calculating the actual carbon emission amount in the analysis time period according to the total carbon sink amount and the total carbon emission amount; step S5, fitting a carbon emission prediction function of a dynamic carbon source in a unit detection period, and fitting a carbon sink prediction function in the unit detection period; step S6, calculating and predicting total carbon emission according to the carbon emission of the stable carbon source, the carbon emission of the dynamic carbon source and the carbon sink prediction amount; s7, performing out-of-standard analysis on the predicted total carbon emission; according to the invention, the accuracy of carbon emission prediction is improved by analyzing the actual data in the actual carbon emission boundary range.
Description
Technical Field
The invention relates to the technical field of carbon emission calculation, in particular to a subway station carbon emission real-time monitoring, predicting and analyzing method.
Background
The urban rail transit system has dual properties of economy and service functions in urban development, and is an important symbol of urban development. In recent years, chinese traffic infrastructure such as highways, airports, railways, ports, buses, subways, etc. are rapidly developed to meet various travel and logistics transportation demands. The subway is an important component for building green city and developing urban green traffic as a convenient, clean and efficient trip mode, is rapidly developed in recent years, is used as a main commuting tool for urban citizens, is most rapidly developed in the whole rail transit system, and occupies extremely important positions in terms of large passenger flow, high frequency and large social influence surface.
However, with the rapid development of urban rail transit systems, environmental problems caused by carbon emission are increasingly prominent, wherein the carbon emission problems caused by the operation of the urban rail transit systems are particularly prominent, beijing is taken as an example, the network scale 727 km is operated in the early 2020, and the total annual electricity consumption is 21.9 hundred million kilowatt-hours, wherein the energy consumption of the station ventilation and air conditioning system, the lighting equipment, the escalator and other equipment accounts for more than 80% of the total station energy consumption, and the space for realizing energy conservation and consumption reduction from the station aspect is huge. Therefore, how to reduce the emission of greenhouse gases of various types of urban passenger transportation systems on the premise of meeting the comfort requirements of passengers to the maximum extent, thereby reducing the influence on the urban and even global environments, has been developed as a core problem of the urban passenger transportation systems. In order to solve the core problem, firstly, a sound carbon emission monitoring management system is established so as to analyze and evaluate the carbon emission state of the building, at present, the related standard construction of the carbon emission monitoring and evaluating of the building in China is just started, the quantitative analysis method and the evaluation system are still imperfect, and matched calculation tools are lacked, so that the development of the quantitative work of the carbon emission of the building of the subway station is hindered, and therefore, the carbon emission monitoring and predicting analysis method of the subway station is necessary to be designed to meet the carbon emission requirement of the building of the subway station and can generate positive influence on solving the global climate problem.
Chinese patent publication No. CN114707774a discloses a method and apparatus for predicting carbon emission based on transportation, for solving the problems: the dynamic random time-varying characteristics of the existing urban traffic network cannot provide reasonable route travel time ranges for users in real time well and provide optimal traffic and transportation routes. The method comprises the following steps: determining a carbon emission element; accounting is carried out on the carbon emission factors to obtain transportation carbon emission factors; determining the total amount of transportation carbon emission according to the transportation carbon emission factor; carrying out stabilization correction on the total carbon emission amount of transportation to obtain an optimal decision; performing stable optimization on the optimal decision, and establishing an optimized comprehensive autoregressive moving average model; and establishing a carbon emission change curve through the final prediction result output by the optimized comprehensive autoregressive moving average model so as to conveniently plan a transportation path according to the carbon emission change curve.
Therefore, in the above technical scheme, the traffic route in transportation is planned, and for a single transportation junction such as a comprehensive subway station, due to the large difference between the design scheme and the energy utilization condition of each subway station, the prior art cannot quantitatively analyze, so that the analysis and statistics of the carbon emission of the subway station are inaccurate, and the problems that the carbon emission reduction object and the emission reduction strategy cannot achieve the corresponding emission reduction effect are caused
Disclosure of Invention
Therefore, the invention provides a real-time monitoring, predicting and analyzing method and a predicting and analyzing method for carbon emission of a subway station, which are used for solving the problem that in the prior art, quantitative analysis cannot be carried out on the carbon emission of the subway station, so that the analysis and statistics of the carbon emission of the subway station are inaccurate.
In order to achieve the above purpose, in one aspect, the present invention provides a method for monitoring, predicting and analyzing carbon emissions of a subway station in real time, including:
step S1, determining a carbon emission boundary of a subway station, determining a carbon emission monitoring range according to a physical boundary of the subway station, dividing a carbon emission scene of the subway station into an environmental carbon emission range and a structural carbon emission range, and determining a plurality of carbon emission sources and a plurality of carbon sink sources in each carbon emission range;
s2, acquiring the environmental temperature of each monitoring time point in the monitoring period, the energy data corresponding to each carbon emission source and the carbon sink source characteristic data;
step S3, determining a unit monitoring period and each monitoring time point according to the utilization/productivity period of each carbon emission source and each carbon sink source, determining the type of the carbon emission source according to the variation of energy data corresponding to the carbon emission source in each unit monitoring period, and dividing the carbon emission source into a stable carbon source and a dynamic carbon source;
Step S4, when analyzing the carbon emission, calculating the total carbon emission according to the energy data corresponding to each carbon emission source monitored in the analysis time period, calculating the total carbon sink according to the carbon neutralization data in the analysis time period, and calculating the actual carbon emission in the analysis time period according to the total carbon sink and the total carbon emission;
step S5, determining the average carbon emission of the stable carbon source in the monitoring time period, fitting a carbon emission prediction function of the dynamic carbon source in the unit detection period according to the carbon emission of the dynamic carbon source in the monitoring time period, and fitting a carbon sink prediction function in the unit detection period according to the temperature data and the illumination data;
step S6, determining a predicted time period, dividing the predicted time period into a plurality of statistical time periods according to a unit monitoring period, respectively calculating the carbon emission amount of the stable carbon source, the carbon emission amount of the dynamic carbon source and the carbon sink predicted amount in each statistical time period, and calculating the predicted total carbon emission amount;
s7, performing out-of-standard analysis on the predicted total carbon emission, and determining an adjustment mode of the dynamic carbon source according to the difference value between the predicted total carbon emission and an expected standard;
the duration of the monitoring period is longer than that of the unit monitoring period.
Further, the carbon emission source is an object for directly or indirectly generating carbon emission by using energy, and comprises various temperature regulation systems in the environmental carbon emission range and a water supply and drainage system, a lighting system and an elevator system in the structural carbon emission range;
the carbon sink source is an object for directly or indirectly utilizing renewable energy sources to generate electric energy or heat energy, and carbon absorption capacity is larger than carbon emission capacity, and comprises a solar domestic hot water system, a ground source heat pump system and a photovoltaic power generation system.
Further, the carbon sink characteristic data includes energy supply data corresponding to an energy generation amount of a carbon sink source generating energy, and carbon neutralization data corresponding to a carbon sink amount of a carbon sink source absorbing carbon content in a carbon neutralization process;
the carbon neutralization data includes vegetation type, vegetation area, and corresponding carbon sink factors.
Further, in the step S3, the monitoring module determines the type of the single carbon emission source according to the variation of the energy of use data corresponding to the carbon emission source during each unit monitoring period;
for a single carbon emission source, if the difference value of the average value of the energy consumption data during any two unit monitoring periods is larger than a preset variation difference value standard, the monitoring module judges that the single carbon emission source is a dynamic carbon source;
If the difference value of the average value of the energy consumption data of any two unit monitoring periods is smaller than or equal to a preset variation difference value standard, the monitoring module judges that the single carbon emission source is a stable carbon source;
wherein, the difference takes the absolute value after the difference is made.
Further, in the step S4, the calculation module calculates actual energy data of each carbon emission source by subtracting the energy supplemented by the corresponding carbon sink source from the energy consumed by each carbon emission source, and calculates the total carbon emission amount by multiplying the actual energy data by the corresponding carbon emission factor;
calculation of total carbon sink c from carbon sink source absorption neutralized carbon content data P ；
The actual carbon emissions are determined by the following formula:
wherein E is i EF as practical energy data for a single carbon emission source i Carbon emission factor, c, for a single carbon emission source p Is the total carbon sink.
Further, in step S5, the fitting module fits the carbon emission prediction function of the dynamic carbon source in the unit detection period according to the carbon emission amount of the dynamic carbon source in the monitoring period and the independent variable function of the dynamic carbon source;
the fitting module fits a carbon sink prediction function in a unit detection period according to temperature data, illumination data and an independent variable function of a carbon sink source in a monitoring time period;
The independent variable of the dynamic carbon source is the maximum characteristic variable which causes the change of the energy consumption data of the carbon source of the dynamic carbon source, and the independent variable function of the dynamic carbon source is a function taking the maximum characteristic variable as the independent variable and the energy consumption data as the dependent variable;
the independent variable of the carbon sink source is a carbon sink characteristic variable which causes the change of carbon sink characteristic data of the carbon sink source, and the independent variable of the carbon sink source is a function taking the carbon sink characteristic variable as the independent variable and taking the carbon sink characteristic data as the dependent variable.
Further, in step S6, the prediction analysis module divides the predicted time period into a plurality of statistical time periods according to the correspondence between the predicted time period and the unit monitoring period, wherein:
if the predicted time period is within a single unit monitoring period, the predicted time period is not required to be divided;
and if at least two time points in the prediction time period respectively correspond to different unit monitoring periods, dividing the prediction time period according to dividing points of each adjacent unit monitoring period.
Further, in step S6, the prediction analysis module obtains prediction data corresponding to the prediction time period, and calculates a carbon emission amount and a carbon sink predicted amount of the dynamic carbon source in each statistical time period according to the carbon emission prediction function and the carbon sink prediction function, respectively;
The predicted data includes an independent variable predicted value of the dynamic carbon source, an independent variable predicted value of the carbon sink source, an ambient temperature predicted value, and an illumination data predicted value.
Further, in the step S7, the prediction analysis module compares the predicted total carbon emission with a standard carbon emission amount to determine whether the carbon emission exceeds a standard in a predicted period of time;
if the predicted total carbon emission amount is larger than the carbon emission standard amount, the prediction analysis module judges that the carbon emission in the predicted time period exceeds the standard;
the predictive analysis module determines an adjustment mode of the dynamic carbon source according to a difference value between the predicted total carbon emission and an expected standard, and comprises the following steps:
under a first condition, the predictive analysis module determines an energy conversion amount of the elevated carbon sink source;
under a second condition, the prediction analysis module judges to reduce the energy consumption of the dynamic carbon source;
the first condition is that the carbon sink predicted amount is smaller than or equal to any corresponding historical carbon summary amount, and the second condition is that the carbon sink predicted amount is larger than any corresponding historical carbon summary amount.
On the other hand, the invention provides an analysis system adopting the subway station carbon emission real-time monitoring, predicting and analyzing method, which comprises the following steps:
The boundary identification module is used for determining a carbon emission boundary of the subway station, determining a carbon emission monitoring range according to the physical boundary of the subway station, dividing a carbon emission scene of the subway station into an environmental carbon emission range and a structural carbon emission range, and determining a plurality of carbon emission sources and a plurality of carbon sink sources in each carbon emission range;
the monitoring module is connected with the boundary identification module and used for acquiring the external environment temperature of the subway station, the application energy data corresponding to each carbon emission source and the carbon sink source characteristic data;
the calculation module is connected with the monitoring module and is used for calculating actual energy data of each carbon emission source by subtracting the energy supplemented by the corresponding carbon sink source from the energy consumed by each carbon emission source and calculating the total carbon emission according to the multiplication of the actual energy data and the corresponding carbon emission factor;
the fitting module is connected with the monitoring module and the calculating module and is used for fitting a carbon emission prediction function of the dynamic carbon source in the unit detection period according to the carbon emission amount of the dynamic carbon source and the independent variable function of the dynamic carbon source in the monitoring period and fitting a carbon sink prediction amount in the unit detection period according to the temperature data, the illumination data and the independent variable function of the carbon sink source in the monitoring period;
The prediction analysis module is respectively connected with the boundary recognition module, the monitoring module, the calculation module and the fitting module, and is used for dividing the prediction time period into a plurality of statistical time periods according to the corresponding relation between the prediction time period and the unit monitoring period, calculating the predicted total carbon emission amount according to the carbon emission amount of the dynamic carbon source and the predicted carbon sink amount in each statistical time period, performing out-of-standard analysis on the predicted total carbon emission amount, and determining the adjustment mode of the dynamic carbon source according to the difference value between the predicted total carbon emission amount and the expected standard under the out-of-standard condition.
Compared with the prior art, the method has the beneficial effects that the method carries out data statistics on the carbon emission sources and the carbon sink sources in the subway station, intuitively displays the neutralization effect of the green energy source on the carbon emission through the data, effectively recognizes the carbon emission characteristics of a single subway station by combining with the environmental factors of the subway station to obtain the dynamic carbon source, predicts the total carbon emission according to the change of the dynamic carbon source, and improves the accuracy of carbon emission prediction.
Further, the carbon emission can be influenced by judging the dynamic carbon source of the single subway station and the corresponding independent variable of the dynamic carbon source, so that the change of the carbon emission of the subway station is effectively obtained, and the accuracy of the carbon emission prediction is further improved.
Furthermore, the method for quantitatively calculating the carbon emission data to obtain the total carbon emission is provided by the method for real-time monitoring, predicting and analyzing the carbon emission of the subway station, so that low-carbon technology innovation and industrialization are promoted, the method is also a low-carbon technology application scene, and the method is gradually copied and popularized to other fields of economy and society, and has good social benefit and economic benefit;
further, the method and the system can be used for predicting and analyzing the carbon discharge of the subway station in a future period by carrying out linear or nonlinear fitting on the known data and summarizing the change rule of the historical carbon discharge data, so as to provide basis for slowing down global climate warming and scientifically making carbon reaching peaks and carbon neutralization path measures.
Furthermore, aiming at the defect that the existing civil green building technology system is difficult to be fully applied, the invention provides a monitoring and evaluating method suitable for green operation of urban rail transit stations, which is favorable for improving the green and health performances of the current subways, and supporting the high-level construction and management of urban rail transit in China by combining the current new requirements on safety and humane performances.
Drawings
FIG. 1 is a schematic diagram of steps of a method for monitoring, predicting and analyzing carbon emission of a subway station in real time according to an embodiment of the invention;
FIG. 2 is a schematic diagram of an analysis system according to an embodiment of the present invention.
Detailed Description
In order that the objects and advantages of the invention will become more apparent, the invention will be further described with reference to the following examples; it should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
Preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are merely for explaining the technical principles of the present invention, and are not intended to limit the scope of the present invention.
It should be noted that, in the description of the present invention, terms such as "upper," "lower," "left," "right," "inner," "outer," and the like indicate directions or positional relationships based on the directions or positional relationships shown in the drawings, which are merely for convenience of description, and do not indicate or imply that the apparatus or elements must have a specific orientation, be constructed and operated in a specific orientation, and thus should not be construed as limiting the present invention.
Furthermore, it should be noted that, in the description of the present invention, unless explicitly specified and limited otherwise, the terms "mounted," "connected," and "connected" are to be construed broadly, and may be either fixedly connected, detachably connected, or integrally connected, for example; can be mechanically or electrically connected; can be directly connected or indirectly connected through an intermediate medium, and can be communication between two elements. The specific meaning of the above terms in the present invention can be understood by those skilled in the art according to the specific circumstances.
For ease of understanding, the following description is made:
the environmental carbon emission mainly refers to a system for providing a whole or partial temperature regulation system for a building in a building area and a system for providing a cold source and a heat source for the environmental regulation outside the area; the structural carbon emission mainly refers to carbon discharge generated in the running process of a system related to illumination and elevator in a building area, water supply and drainage functions, renewable energy sources, building carbon sinks and the like; the carbon emission of the energy consumption terminal mainly refers to carbon discharge generated by daily operation of the energy consumption terminal existing in a building.
Referring to fig. 1, which is a schematic step diagram of a method for real-time monitoring, predicting and analyzing carbon emission of a subway station according to an embodiment of the present invention, the present invention provides a method for real-time monitoring, predicting and analyzing carbon emission of a subway station, including:
step S1, determining a carbon emission boundary of a subway station, determining a carbon emission monitoring range according to a physical boundary of the subway station, dividing a carbon emission scene of the subway station into an environmental carbon emission range and a structural carbon emission range, and determining a plurality of carbon emission sources and a plurality of carbon sink sources in each carbon emission range;
s2, acquiring the environmental temperature of each monitoring time point in the monitoring period, the energy data corresponding to each carbon emission source and the carbon sink source characteristic data; and acquiring basic information of each carbon emission source, and acquiring the range of carbon sink activity and the number of carbon sink sources, wherein the basic information comprises the number, type, working parameters, service life data and the like of the carbon emission sources, and the basic information also comprises the types, areas, growth periods and the like of greenbelts or vegetation of the carbon sink.
Step S3, determining a unit monitoring period and each monitoring time point according to the utilization/productivity period of each carbon emission source and each carbon sink source, determining the type of the carbon emission source according to the variation of energy data corresponding to the carbon emission source in each unit monitoring period, and dividing the carbon emission source into a stable carbon source and a dynamic carbon source;
step S4, when analyzing the carbon emission, calculating the total carbon emission according to the energy data corresponding to each carbon emission source monitored in the analysis time period, calculating the total carbon sink according to the carbon neutralization data in the analysis time period, and calculating the actual carbon emission in the analysis time period according to the total carbon sink and the total carbon emission;
step S5, determining the average carbon emission of the stable carbon source in the monitoring time period, fitting a carbon emission prediction function of the dynamic carbon source in the unit detection period according to the carbon emission of the dynamic carbon source in the monitoring time period, and fitting a carbon sink prediction function in the unit detection period according to the temperature data and the illumination data;
step S6, determining a predicted time period, dividing the predicted time period into a plurality of statistical time periods according to a unit monitoring period, respectively calculating the carbon emission amount of the stable carbon source, the carbon emission amount of the dynamic carbon source and the carbon sink predicted amount in each statistical time period, and calculating the predicted total carbon emission amount;
S7, performing out-of-standard analysis on the predicted total carbon emission, and determining an adjustment mode of the dynamic carbon source according to the difference value between the predicted total carbon emission and an expected standard;
the duration of the monitoring period is longer than that of the unit monitoring period.
Specifically, the carbon emission source is an object for directly or indirectly generating carbon emission using an energy source, including individual temperature regulation systems within the environmental carbon emission range and water supply and drainage systems, lighting systems, and elevator systems within the structural carbon emission range;
the carbon sink source is an object for directly or indirectly utilizing renewable energy sources to generate electric energy or heat energy, and carbon absorption capacity is larger than carbon emission capacity, and comprises a solar domestic hot water system, a ground source heat pump system and a photovoltaic power generation system.
Specifically, the carbon sink characteristic data includes energy supply data corresponding to an energy generation amount of a carbon sink source generating energy, and carbon neutralization data corresponding to a carbon sink amount of a carbon sink source absorbing carbon content in a carbon neutralization process;
the carbon neutralization data includes vegetation type, vegetation area, and corresponding carbon sink factors.
Specifically, in the step S3, the monitoring module determines the type of the single carbon emission source according to the variation of the energy of use data corresponding to the carbon emission source during each unit monitoring period;
For a single carbon emission source, if the difference value of the average value of the energy consumption data during any two unit monitoring periods is larger than a preset variation difference value standard, the monitoring module judges that the single carbon emission source is a dynamic carbon source;
if the difference value of the average value of the energy consumption data of any two unit monitoring periods is smaller than or equal to a preset variation difference value standard, the monitoring module judges that the single carbon emission source is a stable carbon source;
wherein, the difference takes the absolute value after the difference is made.
The energy consumption data of the single carbon emission source comprises out-of-limit energy consumption data and renewable energy source generation energy consumption data.
It will be appreciated that the unit monitoring period and each monitoring time point are determined according to the usage/productivity period of each carbon emission source and each carbon sink source, and in general, since the carbon sink source includes the utilization of clean energy such as light energy, wind energy, heat energy and solar energy, the least common multiple of the usage period of such clean energy is in units of year, so that the unit monitoring period is set to be 1 year more reasonably, and as for the determination of each monitoring time point, the unit monitoring period can be set according to the required monitoring precision and prediction precision, and the more the monitoring time points in the unit monitoring period are, the higher the monitoring precision is.
Specifically, in the step S4, the calculation module calculates actual energy data of each carbon emission source by subtracting the energy supplemented by the corresponding carbon sink source from the energy consumed by each carbon emission source, and calculates the total carbon emission amount by multiplying the actual energy data by the corresponding carbon emission factor;
calculation of total carbon sink c from carbon sink source absorption neutralized carbon content data P ；
The actual carbon emissions are determined by the following formula:
wherein E is i EF as practical energy data for a single carbon emission source i Carbon emission factor, c, for a single carbon emission source p Is the total carbon sink.
Specifically, in step S5, the fitting module fits the carbon emission prediction function of the dynamic carbon source in the unit detection period according to the carbon emission amount of the dynamic carbon source in the monitoring period and the independent variable function of the dynamic carbon source;
the fitting module fits a carbon sink prediction function in a unit detection period according to temperature data, illumination data and an independent variable function of a carbon sink source in a monitoring time period;
the independent variable of the dynamic carbon source is the largest characteristic variable which causes the change of the energy consumption data of the dynamic carbon source, and the independent variable function of the dynamic carbon source is a function taking the largest characteristic variable as the independent variable and the energy consumption data as the dependent variable;
The independent variable of the carbon sink source is a carbon sink characteristic variable which causes the change of carbon sink characteristic data of the carbon sink source, and the independent variable of the carbon sink source is a function taking the carbon sink characteristic variable as the independent variable and taking the carbon sink characteristic data as the dependent variable.
It can be understood that, for example, the dynamic carbon source is exemplified by a temperature adjustment system, the variation of the energy consumption data of the number of the load-bearing personnel of the subway station is the largest, so that the number of the load-bearing personnel is the largest characteristic variable, then the independent variable function of the dynamic carbon source is the personnel as the independent variable, the energy consumption data of the temperature adjustment system is the function of the dependent variable, the function relationship is established between the function and the carbon emission, and the fitted carbon emission prediction function of the dynamic carbon source which is established by the number of the load-bearing personnel as the independent variable and the carbon emission as the dependent variable is obtained; similarly, the principle of the fitting mode of the carbon sink prediction function is the same, and the description is omitted here.
Specifically, in step S6, the prediction analysis module divides the prediction time period into a plurality of statistical time periods according to the correspondence between the prediction time period and the unit monitoring period, where:
if the predicted time period is within a single unit monitoring period, the predicted time period is not required to be divided;
And if at least two time points in the prediction time period respectively correspond to different unit monitoring periods, dividing the prediction time period according to dividing points of each adjacent unit monitoring period.
Specifically, in step S6, the prediction analysis module obtains prediction data corresponding to the prediction time period, and calculates a carbon emission amount and a carbon sink predicted amount of the dynamic carbon source in each statistical time period according to the carbon emission prediction function and the carbon sink prediction function, respectively;
the predicted data includes an independent variable predicted value of the dynamic carbon source, an independent variable predicted value of the carbon sink source, an ambient temperature predicted value, and an illumination data predicted value.
Specifically, in the step S7, the prediction analysis module determines whether the carbon emission exceeds the standard in the predicted period of time according to the comparison between the predicted total carbon emission and the standard carbon emission;
if the predicted total carbon emission amount is larger than the carbon emission standard amount, the prediction analysis module judges that the carbon emission in the predicted time period exceeds the standard;
the predictive analysis module determines an adjustment mode of the dynamic carbon source according to a difference value between the predicted total carbon emission and an expected standard, and comprises the following steps:
Under a first condition, the predictive analysis module determines an energy conversion of the carbon sink source to be increased, including adjusting the self-variable data of the carbon sink source to increase the carbon sink amount of the carbon sink source, such as adjusting a green land area or adjusting a type of planted vegetation to a vegetation type capable of higher carbon absorption;
under a second condition, the predictive analysis module determines to reduce the energy consumption of the dynamic carbon source, including reducing the independent variable of the dynamic carbon source to reduce the energy consumption data of the dynamic carbon source, for example, under the same external environment condition, increasing the indoor temperature set value of the refrigeration equipment, and reducing the energy consumption.
The first condition is that the carbon sink predicted amount is smaller than or equal to any corresponding historical carbon summary amount, and the second condition is that the carbon sink predicted amount is larger than any corresponding historical carbon summary amount.
Referring to fig. 2, the invention provides an analysis system adopting the method for monitoring, predicting and analyzing carbon emission of a subway station in real time, which comprises:
the boundary identification module is used for determining a carbon emission boundary of the subway station, determining a carbon emission monitoring range according to the physical boundary of the subway station, dividing a carbon emission scene of the subway station into an environmental carbon emission range and a structural carbon emission range, and determining a plurality of carbon emission sources and a plurality of carbon sink sources in each carbon emission range;
The monitoring module is connected with the boundary identification module and used for acquiring the external environment temperature of the subway station, the application energy data corresponding to each carbon emission source and the carbon sink source characteristic data;
the calculation module is connected with the monitoring module and is used for calculating actual energy data of each carbon emission source by subtracting the energy supplemented by the corresponding carbon sink source from the energy consumed by each carbon emission source and calculating the total carbon emission according to the multiplication of the actual energy data and the corresponding carbon emission factor;
the fitting module is connected with the monitoring module and the calculating module and is used for fitting a carbon emission prediction function of the dynamic carbon source in the unit detection period according to the carbon emission amount of the dynamic carbon source and the independent variable function of the dynamic carbon source in the monitoring period and fitting a carbon sink prediction amount in the unit detection period according to the temperature data, the illumination data and the independent variable function of the carbon sink source in the monitoring period;
the prediction analysis module is respectively connected with the boundary recognition module, the monitoring module, the calculation module and the fitting module, and is used for dividing the prediction time period into a plurality of statistical time periods according to the corresponding relation between the prediction time period and the unit monitoring period, calculating the predicted total carbon emission amount according to the carbon emission amount of the dynamic carbon source and the predicted carbon sink amount in each statistical time period, performing out-of-standard analysis on the predicted total carbon emission amount, and determining the adjustment mode of the dynamic carbon source according to the difference value between the predicted total carbon emission amount and the expected standard under the out-of-standard condition.
It will be appreciated that the boundary identification module is for a carbon emission source at the operating stage of a subway station building, the carbon emission source identification involving identification within the range of environmental carbon emissions and structural carbon emissions.
The monitoring module is mainly used for monitoring and counting the energy utilization information of the carbon emission source by utilizing the function of sensor networking.
The computing module is used for computing and determining the carbon emission of the carbon emission and the carbon sink in the building range determined by the boundary recognition module so as to be convenient for predicting, analyzing and processing the overall carbon emission of the building; the carbon emission calculation is specifically performed aiming at the energy conditions of a heating ventilation air conditioner, domestic hot water, illumination, an elevator, renewable energy sources and a building carbon sink system in the operation process of a subway station.
The prediction analysis module mainly comprises a data communication module, a data analysis module and a data analysis module; the communication module is accessed to the Internet through a mobile cellular network and sends data to the Internet for transmission; the data analysis module analyzes and functionally fits the energy consumption data obtained by the monitoring module, and stores the data in a database of the server; the data analysis module is mainly used for acquiring data from a server and analyzing carbon emission in different area ranges, and the fitting mode of the function can be obtained through common technical means such as machine learning, fuzzy algorithm and the like in the prior art.
The monitoring module is used for acquiring basic data of carbon emission and carbon sink activities in the range of the subway station building so as to be used for the calculation module to carry out operation processing.
The monitoring content of environmental carbon emission comprises: based on different seasons or environmental temperatures, acquiring the temperature of the input and output sides of the temperature regulating system under different seasons or environmental temperatures by adopting a fixed period measurement mode; and acquiring equipment operation parameters, medium transfer quantity and energy consumption in the operation process of the temperature regulation system by adopting an accumulated measurement mode.
Specifically, the monitoring of structural carbon emissions includes: based on the total number of the current lighting equipment and the pump equipment, randomly selecting not less than 10% -30% of the total number to monitor, wherein the monitoring range comprises the average power and the accumulated running time of the equipment.
Specifically, the monitoring content of the carbon emission of the energy consumption terminal comprises: the number of different energy consuming devices, the operating power of the devices, the average operating time of the terminals.
Specifically, the monitoring content for the carbon sink includes: the method based on periodic sampling monitors the plant carbon sequestration amount measuring element and the method based on cumulative monitoring monitors the plant total amount change value.
In practice, the following is used:
the carbon emissions of the subway station environment include greenhouse gas emissions C generated by the use of refrigerants r Refrigerant charge amount m capable of passing through the apparatus r Life y of the device e Is related to the global warming potential GWP of the refrigerant r Multiplication calculation is carried out to obtain;
the carbon emission calculation of the subway station building structure comprises the functions of water supply and drainage in the area, illumination and elevator, renewable energy sources and carbon emission generated in the running process of a system related to building carbon sink;
the energy consumption of the hot water system is measured by the actual annual hot water delivery of subway station buildingsSubtracting the hot water heat Q provided by the solar energy system s Finally dividing the annual average efficiency eta of the heat source of the hot water system w To obtain annual energy consumption E of the hot water system w Annual energy consumption E of hot water system w Multiplying the carbon emission factor of the energy source to obtain the carbon emission of the hot water system;
when the lighting system does not have an optoelectronic automatic control system, the energy consumption calculation method is that the energy consumption calculation is obtained by multiplying the product of the lighting power density, the lighting area and the lighting time in the building range of the railway station by 365 days a year and the lighting power density of an emergency lamp and the building area of the subway station, and finally the carbon emission of the lighting system is obtained by multiplying the energy consumption of the lighting system by the corresponding carbon emission factor;
The energy consumption calculation method of the elevator system comprises the steps of obtaining the sum of energy consumption during elevator operation and energy consumption during elevator standby time, and finally multiplying the energy consumption of the elevator system by corresponding carbon emission factors to obtain the carbon emission of the elevator system;
the renewable energy system comprises a solar domestic hot water system, a ground source heat pump system and a photovoltaic power generation system;
the energy consumption of the solar domestic hot water system passes through the heat collectorArea A c Annual average solar irradiance J T Heat loss rate eta L Average heat collection efficiency eta of heat collector cd Calculating and acquiring;
annual energy generation of the photovoltaic system is achieved through annual solar radiation illuminance I of the surface of the photovoltaic cell and conversion efficiency K of the photovoltaic cell E Loss efficiency K of photovoltaic system s And photovoltaic system photovoltaic panel clear area A p Calculating and obtaining, namely subtracting the photovoltaic power generation amount from all the power consumption in the building range of the subway station to obtain corresponding actual power consumption, and finally multiplying the corresponding carbon emission factor to obtain actual carbon emission generated by the power system;
the subway station building carbon sink is obtained by multiplying all kinds of green lands in the subway station building range by corresponding carbon sink factors.
The calculation formula may be expressed by the following formula:
the calculation method of the total carbon emission during the operation of the subway station is expressed as the following formula:
in the above formula, C M Represents the carbon emission amount per building area (kgC 02/m) of the subway station building operation stage 2 )；E i The annual energy consumption (unit/a) of the ith class of subway station is represented; EF (electric F) i Representing the carbon emission factor of the i-th energy source, and taking a value according to the reference field standard; e (E) i,j An i-th energy consumption amount (unit/a) representing a j-th system; ER (ER) i,j Indicating that the class j system consumes class i energy (units/a) provided by the renewable energy system; i represents the energy types of building consumption terminals, including electric power, fuel gas, petroleum, municipal heating power and the like; j represents the type of building energy system, including heating and air conditioning, lighting, domestic hot water system, etc.; c P Representation of the groundAnnual carbon reduction of the carbon sink system of the green land of the iron station building (kgC 02/a); y represents a building design lifetime (a); a represents the building area (m) 2 )；
The calculation of the environmental carbon emission of the subway station comprises the emission of greenhouse gases generated by using a refrigerant, and the calculation should be carried out according to the following formula:
C r representing the amount of carbon emissions (tC 02 e/a) produced by the building using the refrigerant; r represents the refrigerant type; m is m r Indicating the refrigerant charge (kg/table) of the apparatus; y is e Indicating the service life (a) of the device; GWP r A global warming potential value representing the refrigerant r;
the carbon emission calculation of the subway station building structure comprises carbon emission generated in the running process of systems related to illumination, water supply and drainage functions, renewable energy sources, building carbon sinks and the like in an area;
specifically, the calculation of annual heat consumption of the building life hot water is expressed as the following calculation according to the actual running condition of the building:
Q r ＝TQ rp
in the above formula, Q r Represents annual heat consumption (kwh/a) of domestic hot water; q (Q) rp Mean heat consumption per hour (kW/h) of domestic hot water; t represents the number of hours (h) of annual domestic hot water use; m represents the number of units calculated with water; q r The water consumption quota (L/man) of the hot water is determined according to the current national standard GB50555 of the civil architecture Water-saving design Standard; ρ r Represents the hot water density (kg/L); t is t r Designing the temperature (DEG C) of hot water; t is t 1 Representing the design cold water temperature (deg.c);
specifically, the energy consumption of the building domestic hot water system is calculated according to the following formula:
wherein E is w Represents annual energy consumption (kwh/a) of the domestic hot water system; q (Q) r Represents annual heat consumption of domestic hot water (kWh/a); q (Q) s Representing the heat of domestic hot water (kWh/a) provided by the solar system; η (eta) r The energy consumption of the water heating system, the heat loss of the pipeline, the secondary circulation of the domestic hot water and the heat loss (%) of the storage are shown; η (eta) w Annual average efficiency (%) of heat source of domestic hot water system;
Q r ＝TQ rp
Q r representing annual heat consumption of domestic hot water; q (Q) rp The average heat consumption of domestic hot water in hours is shown; t represents the number of hours of use of annual domestic hot water; m represents the number of units calculated with water; q r Water for hot water is rated; ρ r Representing the hot water density; t is t r Designing the temperature of hot water; t is t 1 Representing the design cold water temperature;
specifically, the method for calculating the energy consumption of the illumination and elevator system can calculate the energy consumption when the illumination system does not have the photoelectric automatic control system according to the following formula:
wherein E is 1 Represents the annual energy consumption (kwh/a) of the lighting system; p (P) i,j Represents the jth day i room illumination power density value (W/m 2 )；A i Represents the i-th room illumination area (m 2 )；t i,j Indicating the jth day i room lighting time (h); p (P) p Indicating the power density (W/m) of emergency lamp illumination 2 ) The method comprises the steps of carrying out a first treatment on the surface of the A represents the building area (m) 2 )。
Specifically, the elevator system energy consumption should be calculated as follows:
wherein E is e Represents annual elevator energy consumption (kWh/a); p specific energy consumption (mWh/kgm); t is t a Representing the average number of operating hours (h) of an elevator year; v represents the elevator speed (m/s); w represents the rated load capacity (kg) of the elevator; e (E) s Represents the energy consumption (W) of the elevator during standby; t is t s Average number of standby hours (h) in an elevator year;
the renewable energy system comprises a solar domestic hot water system, a ground source heat pump system and a photovoltaic power generation system, and the energy calculation mode is expressed as the following formula:
Specifically, the energy provided by the solar water heating system can be calculated as follows:
wherein Q is s,a Represents the annual energy (kwh) of a solar water heating system; a is that c Solar collector area (m) 2 )；J T Represents the annual average solar irradiance (MJ/m) on the lighting surface of a solar collector 2 )；η cd Average heat collection efficiency (%) of the heat collector based on the total area; η (eta) L Heat loss rate (%) of the pipe and the heat storage device;
the annual energy production of a photovoltaic system can be calculated as follows:
E pv ＝IK E (1-K s )A p
wherein E is pv Annual energy production (kWh) for photovoltaic systems; i is the annual solar radiation illuminance (kwh/m) of the photovoltaic cell surface 2 )；K E Conversion efficiency (%) for photovoltaic cell; k (K) s Loss efficiency (%) for photovoltaic system; a is that p Photovoltaic system photovoltaic panel clear area (m 2 )；
Specifically, the subway station building carbon sink amount is calculated as follows:
s represents a green field type or a planting mode or a plant type; a is that s Represents the area of the s-th green land/planting mode/plant, and the unit is square meter (m 2); EF (electric F) s Carbon sink factor, expressed as kg carbon dioxide per square meter (kgCO 2/m 2), for the s-th green land/planting mode/plant;
according to the calculation results of the steps, calculating the carbon emission amount of the unit building area of the subway station building in the operation stage;
C M representing the carbon emission amount of a unit building area in the building operation stage of the subway station; e (E) i The annual energy consumption of the ith class of subway stations is represented; EF (electric F) i A carbon emission factor representing a class i energy source; e (E) i,j An i-th energy consumption amount representing a j-th system; ER (ER) i,j Indicating that the j-th system consumes the i-th energy provided by the renewable energy system; i represents the energy types of building consumption terminals, including electric power, fuel gas, petroleum, municipal heating power and the like; j represents the type of building energy system, including heating and air conditioning, lighting, domestic hot water system, etc.; c P Representing the annual carbon reduction of a subway station building green land carbon sink system; y represents the life of the building design; a represents the building area of the subway station.
Thus far, the technical solution of the present invention has been described in connection with the preferred embodiments shown in the drawings, but it is easily understood by those skilled in the art that the scope of protection of the present invention is not limited to these specific embodiments. Equivalent modifications and substitutions for related technical features may be made by those skilled in the art without departing from the principles of the present invention, and such modifications and substitutions will be within the scope of the present invention.
The foregoing description is only of the preferred embodiments of the invention and is not intended to limit the invention; various modifications and variations of the present invention will be apparent to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. The method for monitoring, predicting and analyzing the carbon emission of the subway station in real time is characterized by comprising the following steps of:
step S1, determining a carbon emission boundary of a subway station, determining a carbon emission monitoring range according to a physical boundary of the subway station, dividing a carbon emission scene of the subway station into an environmental carbon emission range and a structural carbon emission range, and determining a plurality of carbon emission sources and a plurality of carbon sink sources in each carbon emission range;
s2, acquiring the environmental temperature of each monitoring time point in the monitoring period, the energy data corresponding to each carbon emission source and the carbon sink source characteristic data;
step S3, determining a unit monitoring period and each monitoring time point according to the utilization/productivity period of each carbon emission source and each carbon sink source, determining the type of the carbon emission source according to the variation of energy data corresponding to the carbon emission source in each unit monitoring period, and dividing the carbon emission source into a stable carbon source and a dynamic carbon source;
step S4, when analyzing the carbon emission, calculating the total carbon emission according to the energy data corresponding to each carbon emission source monitored in the analysis time period, calculating the total carbon sink according to the carbon neutralization data in the analysis time period, and calculating the actual carbon emission in the analysis time period according to the total carbon sink and the total carbon emission;
Step S5, determining the average carbon emission of the stable carbon source in the monitoring time period, fitting a carbon emission prediction function of the dynamic carbon source in the unit detection period according to the carbon emission of the dynamic carbon source in the monitoring time period, and fitting a carbon sink prediction function in the unit detection period according to the temperature data and the illumination data;
step S6, determining a predicted time period, dividing the predicted time period into a plurality of statistical time periods according to a unit monitoring period, respectively calculating the carbon emission amount of the stable carbon source, the carbon emission amount of the dynamic carbon source and the carbon sink predicted amount in each statistical time period, and calculating the predicted total carbon emission amount;
s7, performing out-of-standard analysis on the predicted total carbon emission, and determining an adjustment mode of the dynamic carbon source according to the difference value between the predicted total carbon emission and an expected standard;
the duration of the monitoring period is longer than that of the unit monitoring period.
2. The method according to claim 1, wherein the carbon emission source is a target for directly or indirectly generating carbon emission by using energy, and comprises a respective temperature regulation system in the environmental carbon emission range and a water supply and drainage system, a lighting system and an elevator system in the structural carbon emission range;
The carbon sink source is an object for directly or indirectly utilizing renewable energy sources to generate electric energy or heat energy, and carbon absorption capacity is larger than carbon emission capacity, and comprises a solar domestic hot water system, a ground source heat pump system and a photovoltaic power generation system.
3. The method according to claim 2, wherein the characteristic data of carbon sink includes energy supply data corresponding to energy generation amount of a carbon sink source generating energy and carbon neutralization data corresponding to carbon sink amount of a carbon sink source absorbing carbon content during carbon neutralization;
the carbon neutralization data includes vegetation type, vegetation area, and corresponding carbon sink factors.
4. The method according to claim 3, wherein in the step S3, the monitoring module determines the type of the single carbon emission source according to the amount of change of the application energy data corresponding to the carbon emission source during each unit monitoring period;
for a single carbon emission source, if the difference value of the average value of the energy consumption data during any two unit monitoring periods is larger than a preset variation difference value standard, the monitoring module judges that the single carbon emission source is a dynamic carbon source;
if the difference value of the average value of the energy consumption data of any two unit monitoring periods is smaller than or equal to a preset variation difference value standard, the monitoring module judges that the single carbon emission source is a stable carbon source;
Wherein, the difference takes the absolute value after the difference is made.
5. The method according to claim 4, wherein in the step S4, the calculation module calculates actual energy data of each carbon emission source by subtracting the energy supplemented by the corresponding carbon sink source from the energy consumed by each carbon emission source, and calculates the total carbon emission amount by multiplying the actual energy data by the corresponding carbon emission factor;
calculating the total carbon sink amount cP through carbon sink source absorption and neutralization carbon content data;
the actual carbon emissions are determined by the following formula:
wherein E is i EF as practical energy data for a single carbon emission source i Carbon emission factor, c, for a single carbon emission source p Is the total carbon sink.
6. The method according to claim 5, wherein in step S5, the fitting module fits a carbon emission prediction function of the dynamic carbon source in a unit detection period according to the carbon emission amount of the dynamic carbon source and an independent variable function of the dynamic carbon source in a monitoring period;
the fitting module fits a carbon sink prediction function in a unit detection period according to temperature data, illumination data and an independent variable function of a carbon sink source in a monitoring time period;
The independent variable of the dynamic carbon source is the maximum characteristic variable which causes the change of the energy consumption data of the carbon source of the dynamic carbon source, and the independent variable function of the dynamic carbon source is a function taking the maximum characteristic variable as the independent variable and the energy consumption data as the dependent variable;
the independent variable of the carbon sink source is a carbon sink characteristic variable which causes the change of carbon sink characteristic data of the carbon sink source, and the independent variable of the carbon sink source is a function taking the carbon sink characteristic variable as the independent variable and taking the carbon sink characteristic data as the dependent variable.
7. The method according to claim 4, wherein in step S6, the prediction analysis module divides the predicted time period into a plurality of statistical time periods according to the correspondence between the predicted time period and the unit monitoring period, wherein:
if the predicted time period is within a single unit monitoring period, the predicted time period is not required to be divided;
and if at least two time points in the prediction time period respectively correspond to different unit monitoring periods, dividing the prediction time period according to dividing points of each adjacent unit monitoring period.
8. The method according to claim 4, wherein in step S6, the prediction analysis module obtains prediction data corresponding to the prediction time period, and calculates a carbon emission amount and a carbon sink predicted amount of the dynamic carbon source in each statistical time period according to the carbon emission prediction function and the carbon sink prediction function, respectively;
The predicted data includes an independent variable predicted value of the dynamic carbon source, an independent variable predicted value of the carbon sink source, an ambient temperature predicted value, and an illumination data predicted value.
9. The method according to claim 4, wherein in step S7, the prediction analysis module compares the predicted total carbon emission with a standard carbon emission amount to determine whether the carbon emission exceeds a standard for a predicted period of time;
if the predicted total carbon emission amount is larger than the carbon emission standard amount, the prediction analysis module judges that the carbon emission in the predicted time period exceeds the standard;
the predictive analysis module determines an adjustment mode of the dynamic carbon source according to a difference value between the predicted total carbon emission and an expected standard, and comprises the following steps:
under a first condition, the predictive analysis module determines an energy conversion amount of the elevated carbon sink source;
under a second condition, the prediction analysis module judges to reduce the energy consumption of the dynamic carbon source;
the first condition is that the carbon sink predicted amount is smaller than or equal to any corresponding historical carbon summary amount, and the second condition is that the carbon sink predicted amount is larger than any corresponding historical carbon summary amount.
10. The method for real-time monitoring, predicting and analyzing carbon emission in subway stations according to any one of claims 1 to 9, wherein the method adopts an analysis system comprising:
the boundary identification module is used for determining a carbon emission boundary of the subway station, determining a carbon emission monitoring range according to the physical boundary of the subway station, dividing a carbon emission scene of the subway station into an environmental carbon emission range and a structural carbon emission range, and determining a plurality of carbon emission sources and a plurality of carbon sink sources in each carbon emission range;
the monitoring module is connected with the boundary identification module and used for acquiring the external environment temperature of the subway station, the application energy data corresponding to each carbon emission source and the carbon sink source characteristic data;
the calculation module is connected with the monitoring module and is used for calculating actual energy data of each carbon emission source by subtracting the energy supplemented by the corresponding carbon sink source from the energy consumed by each carbon emission source and calculating the total carbon emission according to the multiplication of the actual energy data and the corresponding carbon emission factor;
the fitting module is connected with the monitoring module and the calculating module and is used for fitting a carbon emission prediction function of the dynamic carbon source in the unit detection period according to the carbon emission amount of the dynamic carbon source and the independent variable function of the dynamic carbon source in the monitoring period and fitting a carbon sink prediction amount in the unit detection period according to the temperature data, the illumination data and the independent variable function of the carbon sink source in the monitoring period;
The prediction analysis module is respectively connected with the boundary recognition module, the monitoring module, the calculation module and the fitting module, and is used for dividing the prediction time period into a plurality of statistical time periods according to the corresponding relation between the prediction time period and the unit monitoring period, calculating the predicted total carbon emission amount according to the carbon emission amount of the dynamic carbon source and the predicted carbon sink amount in each statistical time period, performing out-of-standard analysis on the predicted total carbon emission amount, and determining the adjustment mode of the dynamic carbon source according to the difference value between the predicted total carbon emission amount and the expected standard under the out-of-standard condition.
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