CN113962468A - Energy consumption monitoring and statistics-based energy consumption carbon emission management method and system - Google Patents
Energy consumption monitoring and statistics-based energy consumption carbon emission management method and system Download PDFInfo
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Abstract
The invention discloses a management method, a system and a device for monitoring and counting energy consumption carbon emission based on energy consumption, wherein the method comprises the following steps: acquiring energy consumption and a corresponding emission coefficient to obtain the total amount of historical carbon emission in each period; obtaining the variation intensity based on the total carbon emission in two continuous time periods; establishing a change intensity prediction model through the change intensity set, and predicting the change intensity of the next time period and the current time period; measuring and calculating the total carbon emission amount in the next time period; and establishing a carbon emission early warning model, and analyzing the predicted total carbon emission amount in the next time period through the emission early warning model to obtain an early warning result. The carbon emission can be monitored in a timing or real-time manner, and the result can be ensured to be accurate enough; the carbon emission amount of the next stage can be predicted, the predicted total carbon emission amount of the next period is analyzed based on the carbon emission early warning model, and the energy utilization scheme can be optimized by combining carbon emission indexes, so that the carbon emission amount is reduced really.
Description
Technical Field
The invention relates to the technical field of carbon emission management, in particular to a method and a system for managing energy consumption carbon emission based on energy consumption monitoring and statistics.
Background
Carbon emissions refer to the average greenhouse gas emissions generated during the production, transportation, use and recovery of the product. The dynamic carbon emission refers to the amount of greenhouse gas emitted per unit of goods, and different dynamic carbon emissions exist among different batches of the same product.
At present, more and more enterprises or related units pay attention to the carbon emission, but for most enterprises, the carbon emission statistics is not conscious; while most of the companies involved in this regard calculate the total carbon emissions over a certain period of time by means of less accurate statistics, the former is less accurate and the latter is less accurate. And part of enterprises can ask a third party to identify and count the carbon emission condition of the enterprise by a statistical organization, and the data is relatively accurate, but the emission of the enterprise can be counted only in a short time range, so that normalized statistics can not be realized, and reasonable arrangement of carbon emission indexes can not be made. Because the statistics are not accurate enough, the total carbon emission or related systems in a certain period of time can not be clearly estimated.
In a word, especially, the carbon emission of each enterprise cannot be accurately counted or normally counted, the planning of the carbon indexes cannot be well acted and monitored, and the carbon emission cannot be accurately predicted.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides a method and a system for managing the carbon emission based on energy consumption monitoring and statistics.
In order to solve the technical problem, the invention is solved by the following technical scheme:
a management method for monitoring and counting energy consumption carbon emission based on energy consumption comprises the following steps:
acquiring the energy consumption and the corresponding emission coefficient of the area to be managed in each time period to obtain the total historical carbon emission amount in each time period;
obtaining the variation intensity based on the total carbon emission in two continuous time periods;
establishing a change intensity prediction model through the change intensity set, and predicting the change intensity of the next time period and the current time period based on the change intensity of the current time period and the change intensity of the previous time period;
measuring and calculating the total carbon emission amount of the next time period according to the change intensity of the next time period and the current time period;
and establishing a carbon emission early warning model, and analyzing the predicted total carbon emission amount in the next time period through the emission early warning model to obtain an early warning result.
As an implementation manner, the energy consumption and the corresponding emission coefficient of the area to be managed in each period include the following steps:
acquiring a corresponding emission coefficient according to the energy type in the area to be managed;
acquiring meter reading corresponding to the energy type of the area to be managed in each time period;
based on the emission coefficients and the meter readings, the total amount of carbon emissions at each stage is obtained by the following formula:
wherein p isiAnd biIndicating that the energy type obtains a corresponding emission coefficient, i indicating the number of energy types, C indicating the total energy consumption amount,
Nat presentIndicating the corresponding meter reading, N, of the energy type at the current time intervalTo the lastAnd indicating the meter reading corresponding to the energy type in the last period.
As an implementation manner, the variation intensity is obtained by a preset variation intensity algorithm, which includes:
wherein Y is the variation intensity, XAt presentFor adjusting the weight value in the current time interval, AAt presentIs the value of the total carbon emission in the current time period，XTo the lastFor adjusting the weight value in the last time interval, ATo the lastIs the value of the total carbon emission in the preceding period, DFirst of allRepresents a first adjustment value, which is a value given due to a deviation in the current period, DSecond oneRepresents a second adjustment value, Z, which is a value given due to the deviation in the previous periodAt presentRepresenting the baseline carbon emissions data, Z, of the enterprise over the current time periodTo the lastAnd the data of the benchmark carbon emission of the enterprise in the last period is shown, Z represents the total emission, and the total emission is the total emission of the enterprise allowed in the period.
As an implementation, the carbon emission early warning model includes at least three models:
the first model is a blind measurement early warning model, the blind measurement early warning model is established based on enterprise reference carbon emission data pre-distributed in each stage, and the total carbon emission amount in the next time period is compared with the blind measurement early warning model to obtain early warning information;
the second stage is a rolling early warning model, the known total carbon emission amount of each stage is collected, the blind measurement early warning model is corrected by combining the causal relationship to obtain the rolling early warning model, and the total carbon emission amount in the next time period is compared with the rolling early warning model to obtain early warning information;
and the third stage is a law early warning model, the known total carbon emission amount of each stage is collected to form a stable database, the law early warning model is established based on the stable database and attribution analysis, and the total carbon emission amount of the next time period is compared with the law early warning model to obtain early warning information.
As an implementation manner, the establishment of the law pre-warning model based on the stable database and the attribution analysis at least includes the following three manners:
time series method: judging the trend rule of the data of the total carbon emission amount of each stage, and summarizing and sequencing the trend rule according to the trend rule, wherein the trend rule at least comprises a periodic rule, a trend rule, an incident rule, an input rule and a classification rule;
and (3) artificial intervention adjustment: carrying out manual intervention adjustment according to historical experience, various encountered events and force ineligibility factors;
a causal relationship prediction method comprises the following steps: and finding out a key influence factor to adjust based on the data of the total carbon emission of each stage.
As an implementation, the method further comprises the following steps:
and analyzing the early warning result, and optimizing and adjusting the energy utilization frame by combining the energy utilization condition of the next time period, so that the sum of the total carbon emission amount of each time period in the period is within the total emission amount of the enterprise allowed in the period.
A management system for monitoring and counting energy consumption carbon emission based on energy consumption comprises an acquisition calculation module, a first prediction module, a second calculation module and a comparison early warning module;
the acquisition and calculation module is used for acquiring the energy consumption and the corresponding emission coefficient of the area to be managed in each time period to obtain the total historical carbon emission amount in each time period;
the first calculation module is used for obtaining the variation intensity based on the total carbon emission amount in two continuous time periods;
the first prediction module is used for establishing a change strength prediction model through the change strength set and predicting the change strength of the next time period and the current time period based on the change strength of the current time period and the change strength of the previous time period;
the second calculation module is used for calculating the total carbon emission amount of the next time period according to the change intensity of the next time period and the current time period;
and the comparison early warning module is used for establishing a carbon emission early warning model, and analyzing the predicted total carbon emission amount in the next time period through the emission early warning model to obtain an early warning result.
As an implementable embodiment, the obtaining calculation module is configured to: acquiring a corresponding emission coefficient according to the energy type in the area to be managed;
acquiring meter reading corresponding to the energy type of the area to be managed in each time period;
based on the emission coefficients and the meter readings, the total amount of carbon emissions at each stage is obtained by the following formula:
wherein p isiAnd biIndicating that the energy type obtains a corresponding emission coefficient, i indicating the number of energy types, C indicating the total energy consumption amount,
Nat presentIndicating the corresponding meter reading, N, of the energy type at the current time intervalTo the lastAnd indicating the meter reading corresponding to the energy type in the last period.
A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the method steps of:
acquiring the energy consumption and the corresponding emission coefficient of the area to be managed in each time period to obtain the total historical carbon emission amount in each time period;
obtaining the variation intensity based on the total carbon emission in two continuous time periods;
establishing a change intensity prediction model through the change intensity set, and predicting the change intensity of the next time period and the current time period based on the change intensity of the current time period and the change intensity of the previous time period;
measuring and calculating the total carbon emission amount of the next time period according to the change intensity of the next time period and the current time period;
and establishing a carbon emission early warning model, and analyzing the predicted total carbon emission amount in the next time period through the emission early warning model to obtain an early warning result.
A carbon emission management device based on energy usage monitoring statistics, comprising a memory, a processor and a computer program stored in the memory and executable on the processor, the processor when executing the computer program implementing the method steps as follows:
acquiring the energy consumption and the corresponding emission coefficient of the area to be managed in each time period to obtain the total historical carbon emission amount in each time period;
obtaining the variation intensity based on the total carbon emission in two continuous time periods;
establishing a change intensity prediction model through the change intensity set, and predicting the change intensity of the next time period and the current time period based on the change intensity of the current time period and the change intensity of the previous time period;
measuring and calculating the total carbon emission amount of the next time period according to the change intensity of the next time period and the current time period;
and establishing a carbon emission early warning model, and analyzing the predicted total carbon emission amount in the next time period through the emission early warning model to obtain an early warning result.
Due to the adoption of the technical scheme, the invention has the remarkable technical effects that: by adopting the technical scheme of the invention, the carbon emission can be monitored in a timing or real-time manner, and the result can be ensured to be accurate enough; the carbon emission amount of the next stage can be predicted, the predicted total carbon emission amount of the next period is analyzed based on the carbon emission early warning model, and the energy utilization scheme can be optimized by combining carbon emission indexes, so that the carbon emission amount is reduced really.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
FIG. 1 is a schematic flow diagram of the process of the present invention;
FIG. 2 is a schematic flow chart of one embodiment of the method of the present invention;
FIG. 3 is a schematic diagram of the system of the present invention;
FIG. 4 is a schematic diagram of one embodiment of a carbon emissions forewarning model;
fig. 5 is a schematic diagram of another embodiment of a carbon emissions forewarning model.
Detailed Description
The present invention will be described in further detail with reference to examples, which are illustrative of the present invention and are not to be construed as being limited thereto.
At present, global warming is achieved, the concentration of carbon dioxide is increased year by year, and the management of carbon emission is more and more emphasized in various countries. The carbon emission is concerned by more and more enterprises or related units due to the large amount of countries and enterprises, but most enterprises are not conscious of the carbon emission; while most of the companies involved in this regard calculate the total carbon emissions over a certain period of time by means of less accurate statistics, the former is less accurate and the latter is less accurate. And part of enterprises can ask a third party to identify and count the carbon emission condition of the enterprise by a statistical organization, and the data is relatively accurate, but the emission of the enterprise can be counted only in a short time range, so that normalized statistics can not be realized, and reasonable arrangement of carbon emission indexes can not be made. Because the statistics are not accurate enough, the total carbon emission or related systems in a certain period of time can not be clearly estimated.
In a word, especially, the carbon emission of each enterprise cannot be accurately counted or normally counted, the planning of the carbon indexes cannot be well acted and monitored, and the carbon emission cannot be accurately predicted. Therefore, the present application provides a management method capable of calculating the carbon emission of energy consumption more accurately, referring to the following embodiments.
Example 1:
a management method for monitoring and counting energy consumption carbon emission based on energy consumption comprises the following steps:
s100, acquiring the energy consumption and the corresponding emission coefficient of the area to be managed in each time period to obtain the total historical carbon emission amount in each time period;
s200, obtaining the variation intensity based on the total carbon emission amount in two continuous time periods;
s300, establishing a change intensity prediction model through the change intensity set, and predicting the change intensity of the next time period and the current time period based on the change intensity of the current time period and the change intensity of the previous time period;
s400, calculating the total carbon emission amount of the next time period according to the change intensity of the next time period and the current time period;
s500, establishing a carbon emission early warning model, and analyzing the predicted total carbon emission amount in the next time period through the emission early warning model to obtain an early warning result.
By the method, the carbon emission can be monitored in a timing or real-time manner, and the result can be ensured to be accurate enough; the carbon emission amount of the next stage can be predicted, the predicted total carbon emission amount of the next period is analyzed based on the carbon emission early warning model, and the energy utilization scheme can be optimized by combining carbon emission indexes, so that the carbon emission amount is reduced really.
In one embodiment, the energy consumption and the corresponding emission coefficient of the area to be managed in each period in step S100 includes the following steps:
s110, acquiring a corresponding emission coefficient according to the energy type in the area to be managed;
s120, obtaining meter reading corresponding to the energy type of the area to be managed in each time period;
s130, obtaining the total carbon emission amount of each stage through the following formula based on the emission coefficient and the meter reading:
wherein p isiAnd biIndicating that the energy type obtains a corresponding emission coefficient, i indicating the number of energy types, C indicating the total energy consumption amount,
Nat presentIndicating the corresponding meter reading, N, of the energy type at the current time intervalTo the lastAnd indicating the meter reading corresponding to the energy type in the last period.
In the industry, the carbon emission coefficient represents the amount of carbon emission generated by a unit energy source during the combustion or use of each energy source, and generally, the carbon emission coefficient of a certain energy source is considered to be constant during the use. It is accurate relatively to say that it is accurate to calculate the total amount of carbon emission through carbon emission coefficient, and the emission coefficient that each kind of energy type corresponds is different, and it is little to gather carbon emission amount meaning in the very short time, consequently can be through regularly through data acquisition equipment acquisition energy strapping table numerical reading of modes such as timing task, because every strapping table unit probably is inconsistent in this process, consequently can unify the numerical value of converting the reading into standard unit before calculating the total amount of carbon emission.
In this embodiment, the carbon emission of the energy is summarized as the production, transportation and combustion of the energy, the energy type has two emission coefficients respectively representing the carbon emission coefficient generated in the use process and the carbon emission coefficient when the combustion phenomenon occurs, the carbon emission coefficient when the combustion phenomenon does not occur like electricity is used, the carbon emission occurs when the combustion phenomenon occurs with combustible oil, and p is defined as the carbon emission coefficient generated per unit of energy production and transportation, and the carbon emission coefficient per unit of energy production and transportation is respectively pi＝p1，p2，p3.., the coefficients corresponding to respective energy types; b is defined as the coefficient of carbon emission per unit of energy combustion, and b is the coefficient of carbon emission per unit of energy combustioni＝b1，b2，b3.., the coefficients corresponding to respective energy types, i representing the number of energy types; definition C denotes total energy consumption, NAt presentIndicating the corresponding meter reading, N, of the energy type at the current time intervalTo the lastRepresenting the meter reading corresponding to the energy type in the previous period, defining T as the numerical reading acquisition time of the energy meter, and respectively setting the acquisition time as Ti＝T1，T2，T3.., the difference between every two collection times is each time interval, therefore, the total carbon emission of each stage is obtained by the following formula:
in fact, the carbon emission amount of each enterprise or each campus in a period is regulated, the total carbon emission amount monitored in each period cannot be directly predicted from the total carbon emission amount in each period to the total carbon emission amount in the next period, since the total carbon emission amount allowed for the enterprise in the period is a fixed value, the total carbon emission amount is adjusted according to the total carbon emission amount in each period and combined with the benchmark carbon emission data of the enterprise in each period, the variation intensity is predicted after the adjustment, and the total carbon emission amount allowed in the next period is predicted according to the variation intensity, therefore, in one embodiment, the variation intensity is obtained by a preset variation intensity algorithm, and the method includes:
wherein Y is the variation intensity, XAt presentFor adjusting the weight value in the current time interval, AAt presentIs the value of the total carbon emission in the current time period, XTo the lastFor adjusting the weight value in the last time interval, ATo the lastIs the value of the total carbon emission in the preceding period, DFirst of allRepresents a first adjustment value, which is a value given due to a deviation in the current period, DSecond oneRepresents a second adjustment value, Z, which is a value given due to the deviation in the previous periodAt presentRepresenting the baseline carbon emissions data, Z, of the enterprise over the current time periodTo the lastAnd the data of the benchmark carbon emission of the enterprise in the last period is shown, Z represents the total emission, and the total emission is the total emission of the enterprise allowed in the period.
In this embodiment, XAt presentAnd XTo the lastAnd may be any number other than 0, both of which may vary with time and the total amount of carbon emissions per period. The traditional mode is to predict the data of the next time interval through measured or calculated data, so that the total emission of the enterprises allowed in a period is not considered, and if the period is one year and the time interval is one quarter, the total emission of the enterprises allowed in the period is a fixed value which is not allowed to be broken through, and cannot exceed a large range even if the total emission is broken through, so that enterprises are provided with enterprisesThe total carbon emission amount in each time interval is preset according to each season, in an actual situation, the total carbon emission amount in each time interval is calculated, the total carbon emission amount in the next time interval is expected to be predicted and is also associated with the enterprise reference carbon emission data in each time interval in the period, therefore, an adjusting weight value needs to be added, the change intensity is obtained, and the total carbon emission amount in the next time interval is measured.
In another embodiment, referring to fig. 4, the carbon emission early warning model includes at least three models, and the three models actually include three stages, namely a blind measurement stage, a rolling stage and a law stage, and the three stages are summarized to obtain three corresponding models, namely a blind measurement early warning model, a rolling early warning model and a law early warning model: the first model is a blind measurement early warning model, the blind measurement early warning model is established based on enterprise reference carbon emission data pre-distributed in each stage, and the total carbon emission amount in the next time period is compared with the blind measurement early warning model to obtain early warning information; the second stage is a rolling early warning model, the known total carbon emission amount of each stage is collected, the blind measurement early warning model is corrected by combining the causal relationship to obtain the rolling early warning model, and the total carbon emission amount in the next time period is compared with the rolling early warning model to obtain early warning information; and the third stage is a law early warning model, the known total carbon emission amount of each stage is collected to form a stable database, the law early warning model is established based on the stable database and attribution analysis, and the total carbon emission amount of the next time period is compared with the law early warning model to obtain early warning information.
In the process of establishing the early warning model, the following two aspects are mainly considered: the rolling prediction early warning model is adopted under the condition without big data, as shown in fig. 4, which comprises three steps: 1) in the blind test stage, a large amount of data is not used as a reference basis, and only qualitative judgment can be performed due to the fact that blind test is performed through business experience; 2) in the rolling stage, the qualitative prediction mismatching can be immediately corrected along with the inflow of data, and the causal relationship prediction is taken into consideration; 3) and in the regular stage, the data are stabilized, and the next prediction is carried out by using the regular data simulation.
In one embodiment, referring to fig. 5, after the data volume is increased to a sufficient amount and stabilized, a law pre-warning model is established based on the stabilized database and the attribution analysis, which includes at least the following three ways:
time series method: judging the trend rule of the data of the total carbon emission amount of each stage, and summarizing and sequencing the trend rule according to the trend rule, wherein the trend rule at least comprises a periodic rule, a trend rule, an incident rule, an input rule and a classification rule; and (3) artificial intervention adjustment: carrying out manual intervention adjustment according to historical experience, various encountered events and force ineligibility factors; a causal relationship prediction method comprises the following steps: and finding out a key influence factor to adjust based on the data of the total carbon emission of each stage.
That is, at least a time series method, a human intervention adjustment method and a causal relationship prediction method are adopted for establishing the law early warning model, and data trends with strong regularity, namely a periodic law, a trend law, an event law, an input law and a classification law, are summarized; when an incident and an irresistance factor are subjected to manual intervention adjustment, namely manual adjustment is carried out; and finding out a key influence factor to adjust based on the data of the total carbon emission of each stage. By combining the three modes, the law early warning model is more stable and accurate, and after the law early warning model is established, the law early warning model can be updated all the time along with the gradual increase of data.
In all the above embodiments, the method further comprises the following steps:
and after the early warning result is obtained, analyzing the early warning result, and optimizing and adjusting the energy utilization frame by combining the energy utilization condition of the next time period, so that the sum of the total carbon emission amount of each time period in the period is within the total emission amount of the enterprise allowed in the period.
Example 2:
a carbon emission management system based on energy consumption monitoring and statistics is shown in FIG. 3 and comprises an acquisition calculation module 100, a first calculation module 200, a first prediction module 300, a second calculation module 400 and a comparison and early warning module 500;
the obtaining and calculating module 100 is configured to obtain the energy consumption amount and the corresponding emission coefficient of the area to be managed in each time period, and obtain the total amount of historical carbon emission in each time period;
the first calculation module 200 is used for obtaining the variation intensity based on the total carbon emission amount in two continuous time periods;
the first prediction module 300 is configured to establish a change strength prediction model through a change strength set, and predict change strengths of a next time period and a current time period based on the change strengths of the current time period and a previous time period;
the second calculating module 400 is configured to measure and calculate the total carbon emission amount in the next period according to the variation intensity in the next period and the current period;
the comparison early warning module 500 is configured to establish a carbon emission early warning model, and analyze the predicted total carbon emission amount in the next time period through the emission early warning model to obtain an early warning result.
In one embodiment, the acquisition computation module 100 is configured to:
acquiring a corresponding emission coefficient according to the energy type in the area to be managed;
acquiring meter reading corresponding to the energy type of the area to be managed in each time period;
based on the emission coefficients and the meter readings, the total amount of carbon emissions at each stage is obtained by the following formula:
wherein p isiAnd biIndicating that the energy type obtains a corresponding emission coefficient, i indicating the number of energy types, C indicating the total energy consumption amount,
Nat presentIndicating the corresponding meter reading, N, of the energy type at the current time intervalTo the lastAnd indicating the meter reading corresponding to the energy type in the last period.
Example 3:
a computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the method steps of:
s100, acquiring the energy consumption and the corresponding emission coefficient of the area to be managed in each time period to obtain the total historical carbon emission amount in each time period;
s200, obtaining the variation intensity based on the total carbon emission amount in two continuous time periods;
s300, establishing a change intensity prediction model through the change intensity set, and predicting the change intensity of the next time period and the current time period based on the change intensity of the current time period and the change intensity of the previous time period;
s400, calculating the total carbon emission amount of the next time period according to the change intensity of the next time period and the current time period;
s500, establishing a carbon emission early warning model, and analyzing the predicted total carbon emission amount in the next time period through the emission early warning model to obtain an early warning result.
Example 4:
in one embodiment, the energy consumption based monitoring and statistics energy consumption carbon emission management device is provided, and can be a server or a mobile terminal. The energy consumption monitoring and statistics-based carbon emission management device comprises a processor, a memory, a network interface and a database which are connected through a system bus. Wherein the processor of the carbon emission management device based on energy consumption monitoring statistics is configured to provide computing and control capabilities. The memory of the management device for monitoring and counting the carbon emission based on the energy consumption comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The database stores all data of the energy consumption-based monitoring statistical energy consumption carbon emission management device. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to implement a method for carbon emission management based on energy usage monitoring statistics.
The embodiments in the present specification are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, apparatus, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention has been described with reference to flowchart illustrations and/or block diagrams of methods, terminal devices (systems), and computer program products according to the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing terminal to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing terminal, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing terminal to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing terminal to cause a series of operational steps to be performed on the computer or other programmable terminal to produce a computer implemented process such that the instructions which execute on the computer or other programmable terminal provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
It should be noted that:
reference in the specification to "one embodiment" or "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the invention. Thus, the appearances of the phrase "one embodiment" or "an embodiment" in various places throughout this specification are not necessarily all referring to the same embodiment. In addition, it should be noted that the specific embodiments described in the present specification may differ in the shape of the components, the names of the components, and the like. All equivalent or simple changes of the structure, the characteristics and the principle of the invention which are described in the patent conception of the invention are included in the protection scope of the patent of the invention. Various modifications, additions and substitutions for the specific embodiments described may be made by those skilled in the art without departing from the scope of the invention as defined in the accompanying claims.
Claims (10)
1. A management method for monitoring and counting energy consumption carbon emission based on energy consumption is characterized by comprising the following steps:
acquiring the energy consumption and the corresponding emission coefficient of the area to be managed in each time period to obtain the total historical carbon emission amount in each time period;
obtaining the variation intensity based on the total carbon emission in two continuous time periods;
establishing a change intensity prediction model through the change intensity set, and predicting the change intensity of the next time period and the current time period based on the change intensity of the current time period and the change intensity of the previous time period;
measuring and calculating the total carbon emission amount of the next time period according to the change intensity of the next time period and the current time period;
and establishing a carbon emission early warning model, and analyzing the predicted total carbon emission amount in the next time period through the emission early warning model to obtain an early warning result.
2. The energy consumption monitoring-based energy consumption carbon emission management method according to claim 1, wherein the energy consumption and the corresponding emission coefficient of the area to be managed in each period comprise the following steps:
acquiring a corresponding emission coefficient according to the energy type in the area to be managed;
acquiring meter reading corresponding to the energy type of the area to be managed in each time period;
based on the emission coefficients and the meter readings, the total amount of carbon emissions at each stage is obtained by the following formula:
wherein p isiAnd biIndicating that the energy type obtains a corresponding emission coefficient, i indicating the number of energy types, C indicating the total energy consumption amount,
Nat presentIndicating the corresponding meter reading, N, of the energy type at the current time intervalTo the lastAnd indicating the meter reading corresponding to the energy type in the last period.
3. The energy consumption monitoring statistics-based carbon emission management method according to claim 1, wherein the intensity of change is obtained by a preset intensity of change algorithm, comprising:
wherein Y is the variation intensity, XAt presentFor the current time periodInner adjustment weight value, AAt presentIs the value of the total carbon emission in the current time period, XTo the lastFor adjusting the weight value in the last time interval, ATo the lastIs the value of the total carbon emission in the preceding period, DFirst of allRepresents a first adjustment value, which is a value given due to a deviation in the current period, DSecond oneRepresents a second adjustment value, Z, which is a value given due to the deviation in the previous periodAt presentRepresenting the baseline carbon emissions data, Z, of the enterprise over the current time periodTo the lastAnd the data of the benchmark carbon emission of the enterprise in the last period is shown, Z represents the total emission, and the total emission is the total emission of the enterprise allowed in the period.
4. The energy consumption monitoring statistics energy consumption carbon emission management method according to claim 1, wherein the carbon emission early warning model comprises at least three models:
the first model is a blind measurement early warning model, the blind measurement early warning model is established based on enterprise reference carbon emission data pre-distributed in each stage, and the total carbon emission amount in the next time period is compared with the blind measurement early warning model to obtain early warning information;
the second stage is a rolling early warning model, the known total carbon emission amount of each stage is collected, the blind measurement early warning model is corrected by combining the causal relationship to obtain the rolling early warning model, and the total carbon emission amount in the next time period is compared with the rolling early warning model to obtain early warning information;
and the third stage is a law early warning model, the known total carbon emission amount of each stage is collected to form a stable database, the law early warning model is established based on the stable database and attribution analysis, and the total carbon emission amount of the next time period is compared with the law early warning model to obtain early warning information.
5. The energy consumption monitoring and statistics energy consumption carbon emission management method according to claim 4, wherein the establishment of the law pre-warning model based on the stable database and the attribution analysis at least comprises the following three ways:
time series method: judging the trend rule of the data of the total carbon emission amount of each stage, and summarizing and sequencing the trend rule according to the trend rule, wherein the trend rule at least comprises a periodic rule, a trend rule, an incident rule, an input rule and a classification rule;
and (3) artificial intervention adjustment: carrying out manual intervention adjustment according to historical experience, various encountered events and force ineligibility factors;
a causal relationship prediction method comprises the following steps: and finding out a key influence factor to adjust based on the data of the total carbon emission of each stage.
6. The energy consumption monitoring-based statistical energy consumption carbon emission management method according to claim 1, further comprising the steps of:
and analyzing the early warning result, and optimizing and adjusting the energy utilization frame by combining the energy utilization condition of the next time period, so that the sum of the total carbon emission amount of each time period in the period is within the total emission amount of the enterprise allowed in the period.
7. A management system for monitoring and counting energy consumption carbon emission based on energy consumption is characterized by comprising an acquisition calculation module, a first prediction module, a second calculation module and a comparison early warning module;
the acquisition and calculation module is used for acquiring the energy consumption and the corresponding emission coefficient of the area to be managed in each time period to obtain the total historical carbon emission amount in each time period;
the first calculation module is used for obtaining the variation intensity based on the total carbon emission amount in two continuous time periods;
the first prediction module is used for establishing a change strength prediction model through the change strength set and predicting the change strength of the next time period and the current time period based on the change strength of the current time period and the change strength of the previous time period;
the second calculation module is used for calculating the total carbon emission amount of the next time period according to the change intensity of the next time period and the current time period;
and the comparison early warning module is used for establishing a carbon emission early warning model, and analyzing the predicted total carbon emission amount in the next time period through the emission early warning model to obtain an early warning result.
8. The energy consumption monitoring statistics energy consumption carbon emission management system of claim 7, wherein the acquisition calculation module is configured to: acquiring a corresponding emission coefficient according to the energy type in the area to be managed;
acquiring meter reading corresponding to the energy type of the area to be managed in each time period;
based on the emission coefficients and the meter readings, the total amount of carbon emissions at each stage is obtained by the following formula:
wherein p isiAnd biIndicating that the energy type obtains a corresponding emission coefficient, i indicating the number of energy types, C indicating the total energy consumption amount,
Nat presentIndicating the corresponding meter reading, N, of the energy type at the current time intervalTo the lastAnd indicating the meter reading corresponding to the energy type in the last period.
9. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the method steps of one of claims 1 to 6.
10. A management device for carbon emission based on energy consumption monitoring statistics, comprising a memory, a processor and a computer program stored in said memory and executable on said processor, characterized in that said processor, when executing said computer program, implements the method steps according to any of claims 1 to 6.
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