CN116228277A - Price fluctuation analysis method, device, equipment and storage medium for carbon trade market - Google Patents
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Abstract
The invention discloses a price fluctuation analysis method, a device, equipment and a storage medium for a carbon trade market, which comprise the following steps: acquiring influence factor data of relative carbon trade market price in a set time period in the power industry and historical operation data of a power grid system; according to the influence factor data and the historical operation data, carrying out price fluctuation analysis model optimization to obtain a price fluctuation hierarchical causal analysis model taking the electricity consumption as one of analysis parameters; and taking the electricity consumption data in the historical operation data as one of input data, inputting the price fluctuation hierarchical causal analysis model, and obtaining an output carbon market price fluctuation characteristic analysis result. According to the technical scheme, the accuracy of the price fluctuation feature analysis of the carbon trade market is effectively improved, and the problem that the accuracy of the price fluctuation analysis method of the existing carbon trade market is low after the current carbon trade market is incorporated into the power industry is solved.
Description
Technical Field
The invention relates to the technical field of electric power, in particular to a price fluctuation analysis method, a price fluctuation analysis device, price fluctuation analysis equipment and a storage medium for a carbon trade market.
Background
The price of the carbon trade market influences the whole scale of the carbon trade market, and by accurate analysis of the price fluctuation of the carbon trade market, investors can be promoted to actively participate in the carbon financial market trade and avoid carbon market risks to provide a beneficial reference. Therefore, how to accurately analyze price fluctuations in the carbon trade market is a problem that needs to be solved.
At present, the related research of price fluctuation analysis of a carbon market mainly focuses on the influence of the rules of the carbon market on the price fluctuation of the carbon market, including the setting of the total carbon emission amount, the distribution mode of the carbon emission amount and the like. For example, studying the influence of the free quota allocation proportion of the carbon market on the price fluctuation of the carbon market; taking a setting method of the total carbon quota as a variable, considering the influence of reducing the total carbon quota year by year in proportion, reducing the total carbon quota year by year in equal quantity on the price fluctuation of the carbon market, and the like. However, the analysis results obtained by the analysis methods have low matching degree with actual price fluctuation of the carbon trade market, and the accuracy of the analysis results is poor.
Disclosure of Invention
The invention provides a price fluctuation analysis method, a device, equipment and a storage medium for a carbon trade market, which are used for obtaining a price fluctuation hierarchical causal model by optimizing analysis parameters, determining a price fluctuation analysis result of the carbon trade market according to the price fluctuation causal model, and effectively improving the accuracy of price fluctuation feature analysis of the carbon trade market so as to solve the problem that the accuracy of the price fluctuation analysis method of the existing carbon trade market is low after the current carbon trade market is brought into the power industry.
In a first aspect, an embodiment of the present disclosure provides a price fluctuation analysis method for a carbon trade market, including:
acquiring influence factor data of relative carbon trade market price in a set time period in the power industry and historical operation data of a power grid system;
according to the influence factor data and the historical operation data, carrying out price fluctuation analysis model optimization to obtain a price fluctuation hierarchical causal analysis model taking the electricity consumption as one of analysis parameters;
and taking the electricity consumption data in the historical operation data as one of input data, inputting the price fluctuation hierarchical causal analysis model, and obtaining an output carbon market price fluctuation characteristic analysis result.
In a second aspect, embodiments of the present disclosure provide a price fluctuation analysis apparatus for a carbon trade market, including:
the data acquisition module is used for acquiring influence factor data of relative carbon trade market price in a set time period in the power industry and historical operation data of the power grid system;
the analysis model acquisition module is used for optimizing a price fluctuation analysis model according to the influence factor data and the historical operation data to obtain a price fluctuation hierarchical causal analysis model taking the electricity consumption as one of analysis parameters;
And the analysis result acquisition module is used for taking the electricity consumption data in the historical operation data as one of the input data, inputting the price fluctuation hierarchical causal analysis model, and obtaining the output carbon market price fluctuation characteristic analysis result.
In a third aspect, an embodiment of the present disclosure provides an electronic device, including:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,,
the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the price fluctuation analysis method of the carbon trade market provided by the embodiment of the first aspect described above.
In a fourth aspect, embodiments of the present disclosure provide a computer readable storage medium storing computer instructions for causing a processor to execute the method for analyzing price fluctuation of a carbon trade market provided in the above first aspect.
According to the price fluctuation analysis method, device, equipment and storage medium for the carbon trade market, the influence factor data of the relative carbon trade market price in a set time period in the power industry and the historical operation data of a power grid system are obtained; according to the influence factor data and the historical operation data, carrying out price fluctuation analysis model optimization to obtain a price fluctuation hierarchical causal analysis model taking the electricity consumption as one of analysis parameters; and taking the electricity consumption data in the historical operation data as one of input data, inputting the price fluctuation hierarchical causal analysis model, and obtaining an output carbon market price fluctuation characteristic analysis result. According to the technical scheme, the price fluctuation hierarchical causal model is obtained by optimizing the analysis parameters, and the carbon market price fluctuation analysis result is determined according to the price fluctuation causal model. Key factors influencing national carbon market price are stripped from complex variables, and the reasons and rules of carbon market price fluctuation are analyzed, so that the comprehensive performance is high; and the historical operation data of the power grid system is combined for analysis, so that the actual situation of the national carbon market construction stage is met. The accuracy of the price fluctuation feature analysis of the carbon trade market is effectively improved, and the problem that the accuracy of the existing price fluctuation analysis method of the carbon trade market is low after the current carbon trade market is incorporated into the power industry is solved.
It should be understood that the description in this section is not intended to identify key or critical features of the embodiments of the invention or to delineate the scope of the invention. Other features of the present invention will become apparent from the description that follows.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a method for price fluctuation analysis of a carbon trade market according to an embodiment of the present invention;
FIG. 2 is a flow chart of a method for analyzing price fluctuation of a carbon trade market according to a second embodiment of the present invention;
FIG. 3 is a schematic diagram of a price fluctuation analysis device for a carbon trade market according to a third embodiment of the present invention;
fig. 4 is a schematic structural diagram of an electronic device according to a fourth embodiment of the present invention.
Detailed Description
In order that those skilled in the art will better understand the present invention, a technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.
It should be noted that the terms "first," "second," and "object" in the description of the present invention and the claims and the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the invention described herein may be implemented in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
Example 1
Fig. 1 is a flowchart of a method for analyzing price fluctuation of a carbon trade market according to an embodiment of the present invention, where the method may be implemented by a price fluctuation analysis device of a carbon trade market, and may be implemented in hardware and/or software.
In this example, the factors influencing the fluctuation of the carbon market are improved over existing studies in terms of data selection, analysis scenario and analysis steps. According to PYTHON software, the price fluctuation analysis model is optimized, the price fluctuation hierarchical causal analysis model based on the electricity consumption data is obtained, and the price fluctuation analysis of the carbon trade market is completed.
As shown in fig. 1, the method includes:
s101, acquiring influence factor data of relative carbon trade market price in a set time period in the power industry and historical operation data of a power grid system.
In the present embodiment, the influence factor data may be understood as data capable of influencing price fluctuation of the carbon trade market. Historical operation data can be understood as data such as electricity consumption generated by the operation of the grid system in a historical time.
Specifically, the method comprises the steps of obtaining influence factor data which can influence price fluctuation of a carbon trade market in a set time period in the power industry, and data such as electricity consumption generated by running of a power grid system in historical time. The set time period is determined according to the data representation and the actual demand of the carbon trade market, for example, may be 2 years, which is not limited in this embodiment. The historical operation data may be operation data within a set time period, or may be all operation data since the market for carbon trade was released, which is not limited in this embodiment.
And S102, optimizing a price fluctuation analysis model according to the influence factor data and the historical operation data to obtain a price fluctuation hierarchical causal analysis model taking the electricity consumption as one of analysis parameters.
In this embodiment, the analysis parameters may be understood as the parameter data having the most influence on the carbon market price fluctuation feature in the influence factor data and the historical operation data. The price fluctuation hierarchical causal analysis model can be understood as a model for determining the price fluctuation characteristics of the carbon market and obtaining the analysis results of the price fluctuation characteristics of the carbon market, and consists of time sequence variables, electric power variables and macroscopic variables.
Specifically, the influence factor data and the historical operation data are screened, the self-variable data with the most obvious influence on the price fluctuation of the carbon trade market in the influence factor data and the historical operation data is determined to be the electricity consumption, the electricity consumption is determined to be an analysis parameter, the analysis parameter is optimized for the original price fluctuation analysis model, and the price fluctuation hierarchical causal analysis model taking the electricity consumption as one of the analysis parameters is obtained.
S103, taking the electricity consumption data in the historical operation data as one of the input data, inputting the data into a price fluctuation hierarchical causal analysis model, and obtaining an output carbon market price fluctuation characteristic analysis result.
In this embodiment, the electricity consumption data may be understood as data that the electricity consumption object consumes active electric energy in the historical operation of the grid system. The analysis result of the price fluctuation feature of the carbon market can be understood as the output dependent variable result according to the formula or equation that the electricity consumption data is taken as the independent variable and is input into the price fluctuation level causal analysis model.
Specifically, the electricity consumption data in the historical operation data is used as one of the input data, the input data is input into the optimized price fluctuation level causal analysis model, a carbon market price fluctuation characteristic analysis result is obtained according to an equation or a formula of the price fluctuation characteristic of the carbon trade market corresponding to the price fluctuation level causal analysis model, and a carbon price measuring and calculating result of the national carbon market is determined.
In the embodiment, the influence factor data of the relative carbon trade market price in a set time period in the power industry and the historical operation data of the power grid system are obtained; according to the influence factor data and the historical operation data, carrying out price fluctuation analysis model optimization to obtain a price fluctuation level causal analysis model taking the electricity consumption as one of analysis parameters; and taking the electricity consumption data in the historical operation data as one of the input data, inputting the input data into a price fluctuation hierarchical causal analysis model, and obtaining an output carbon market price fluctuation characteristic analysis result. According to the technical scheme, the price fluctuation hierarchical causal model is obtained by optimizing the analysis parameters, and the carbon market price fluctuation analysis result is determined according to the price fluctuation causal model. Key factors influencing national carbon market price are stripped from complex variables, and the reasons and rules of carbon market price fluctuation are analyzed, so that the comprehensive performance is high; and the historical operation data of the power grid system is combined for analysis, so that the actual situation of the national carbon market construction stage is met. The accuracy of the price fluctuation feature analysis of the carbon trade market is effectively improved, and the problem that the accuracy of the existing price fluctuation analysis method of the carbon trade market is low after the current carbon trade market is incorporated into the power industry is solved.
As a first alternative embodiment of the embodiments, on the basis of the above embodiments, the first alternative embodiment further optimizes and increases:
and carrying out applicability analysis on the price fluctuation hierarchical causal analysis model by combining the output carbon market price fluctuation characteristic analysis result with actual price fluctuation data.
In the present embodiment, the actual price fluctuation data can be understood as actual carbon price data since the market for carbon trade was opened.
Specifically, in order to improve the accuracy of carbon market price fluctuation measurement and calculation, the method and the device realize measurement of carbon market price fluctuation through electricity consumption, and can accurately measure, and compared with the current price fluctuation analysis model, the improvement rate is more than 5%.
Example two
Fig. 2 is a flowchart of a price fluctuation analysis method for a carbon trade market according to a second embodiment of the present invention, where any of the foregoing embodiments is further optimized, and the method may be applicable to a case of analyzing price fluctuation of a carbon trade market, and the method may be performed by a price fluctuation analysis device for a carbon trade market, and the device may be implemented in hardware and/or software.
As shown in fig. 2, the method includes:
S201, obtaining influence factor data extracted according to a set influence dimension in a set time period from the power industry, wherein the set influence dimension comprises economy, policy, environment and energy.
In this embodiment, the economic impact dimension includes a domestic production total (Gross Domestic Product, GDP), an industrial procurement manager index, a stock market index, etc.; policy-influencing dimensions include power production, power consumption, power supply, national carbon market trading volume, etc.; the environmental impact dimension comprises extremely cold air temperature days, air temperature, precipitation, wind speed, national carbon emission and the like. The energy influence dimension comprises the variables such as price of power coal, price of petroleum, price of natural gas, price of electricity, international price of carbon and the like.
Specifically, the influence factor data extracted according to the set economy, policy, environment and energy dimension in the set time period is obtained from the power industry, and it can be understood that the more the data is collected, the more accurate the price fluctuation analysis of the corresponding carbon trade market.
S202, collecting historical operation data in a set time period from a power grid system, wherein the historical operation data comprises power consumption of the whole society, power consumption of the whole industry and power consumption of the eight industries.
In this embodiment, the total social electricity consumption can be understood as a summary of the electricity consumption in all the electricity consumption fields of the first, second, third industries, etc. The power consumption of the whole industry can be understood as the summary of the power consumption of all industries such as industry, agriculture, business and other industries. The electricity consumption of eight industries can be understood as the electricity consumption summary of eight high energy consumption industries of electricity generation, petrifaction, chemical industry, building materials, steel, nonferrous metals, papermaking and domestic civil aviation.
Specifically, in order to provide detailed data support for the application, power consumption data of all-society power consumption, all-industry power consumption and eight-industry power consumption within a set time period are collected from a power grid system.
It will be appreciated that price volatility analysis of the carbon trade market is accomplished based on independent and dependent variables. The independent variables can be understood as input data of an original price fluctuation analysis model, and can be understood as input data of an optimized price fluctuation level causal analysis model, and the input data can comprise data of economic, policy, environment and energy influence dimensions in influence factor data, and power consumption data of whole society power consumption, whole industry power consumption and eight major industry power consumption in historical operation data. The dependent variable can be understood as output data of an original price fluctuation analysis model, and can also be understood as output data of an optimized price fluctuation hierarchical causal analysis model, and the output data is national carbon price, including online average price and online average price change rate of a national carbon market.
S203, performing primary feature screening of an original price fluctuation analysis model on the influence factor data and the historical operation data through a Delphi method.
In this embodiment, the original price fluctuation analysis model may be understood as an initial model that is not parameter-optimized for price fluctuation analysis of the carbon trade market according to the original analysis parameters.
In this embodiment, the original price fluctuation analysis model is optimized for the first time by the delta film method. Specifically, weight assignment and parameter screening are carried out on each set influence dimension in influence factor data and each power consumption data in historical operation data according to a Delphi method, so that screening results are obtained, wherein the screening results are as follows: the key impact parameter of price fluctuation of the carbon trade market is historical operation data. Therefore, the historical operation data is used as an optimization parameter after primary feature screening is carried out on the original price fluctuation analysis model.
S204, performing secondary feature screening of a price fluctuation analysis model on the historical operation data through correlation analysis and causality analysis.
In the present embodiment, the correlation between the independent variable and the dependent variable of the price fluctuation feature of the carbon trade market, that is, the correlation between the input data and the output data of the price fluctuation analysis model is determined by correlation analysis; causality between independent variables and dependent variables of price fluctuation characteristics of the carbon trade market, namely causality between input data and output data of a price fluctuation analysis model, is determined through causality analysis. After correlation and causality analysis are carried out on the historical operation data, the historical operation data serving as an independent variable and the national carbon price serving as the dependent variable are screened according to the correlation analysis result and the causality analysis result, the optimization parameter of the independent variable is determined to be the electricity consumption data in the historical operation data, and the optimization parameter of the dependent variable is the online average price change rate in the national carbon price. And completing the second optimization of the price fluctuation analysis model.
It is understood that the primary feature screening is screening for the independent variable of the price fluctuation analysis model, and the secondary feature screening is screening for the independent variable and the dependent variable of the price fluctuation analysis model.
S205, obtaining a price fluctuation hierarchical causal analysis model taking the electricity consumption as one of analysis parameters according to the feature screening result.
In this embodiment, the optimized independent variable parameter (analysis parameter) is determined to be the power consumption according to the primary feature screening and the secondary feature screening. Therefore, a price fluctuation analysis model after screening and optimizing analysis parameters is obtained, and the price fluctuation hierarchical causal analysis model taking the electricity consumption as one of the analysis parameters is obtained.
S206, taking the electricity consumption data in the historical operation data as one of the input data, inputting the data into a price fluctuation hierarchical causal analysis model, and obtaining an output carbon market price fluctuation characteristic analysis result.
In the embodiment, the influence factor data extracted according to the set influence dimension in the set time period is obtained from the power industry, wherein the set influence dimension comprises economy, policy, environment and energy; collecting historical operation data in a set time period from a power grid system, wherein the historical operation data comprises power consumption of the whole society, power consumption of the whole industry and power consumption of the eight industries; performing primary feature screening of an original price fluctuation analysis model on influence factor data and historical operation data through a Delphi method; performing secondary feature screening of a price fluctuation analysis model on historical operation data through correlation analysis and causality analysis; obtaining a price fluctuation hierarchical causal analysis model taking the electricity consumption as one of analysis parameters according to the feature screening result; and taking the electricity consumption data in the historical operation data as one of the input data, inputting the input data into a price fluctuation hierarchical causal analysis model, and obtaining an output carbon market price fluctuation characteristic analysis result. By adopting the technical scheme, the original price fluctuation analysis model is subjected to characteristic screening twice, the screened analysis parameters are determined, a new price fluctuation hierarchical causal analysis model is determined according to the screened analysis parameters, the characteristic analysis result of the price fluctuation of the carbon market is obtained, the influence of various internal and external variables is fully considered, the influence factors of the price fluctuation of carbon market related products such as economic factors, policy factors, environmental factors and energy factors of a carbon emission source are accurately grasped, key factors influencing the price of the national carbon market are stripped from complex variables, and the reason and rule of the price fluctuation of the carbon market are analyzed, so that the comprehensive performance is high; and the historical operation data of the power grid system is combined for analysis, so that the actual situation of the national carbon market construction stage is met. The accuracy of price fluctuation analysis of the carbon trade market is effectively guaranteed, and a more accurate carbon price prediction result can be obtained according to predicted requirements.
As a first optional embodiment of the embodiments, on the basis of the above embodiments, the specific step of adding a secondary feature screening of the price fluctuation analysis model to the historical operation data through correlation analysis and causality analysis is further optimized, including:
a1 According to the normal distribution judgment result of the historical operation data, adopting a corresponding correlation analysis method to perform correlation analysis on the historical operation data.
In this embodiment, the normal distribution determination result may be understood as a result of determining whether the historical operating data is in normal distribution, including whether the historical operating data is in normal distribution and whether the historical operating data is in abnormal distribution.
Specifically, before correlation analysis is performed on the historical operation data, it is required to determine whether the historical operation data is normally distributed, and a K-S (Kolmogorov-Smirnov) algorithm is used to obtain a normal distribution determination result of the historical operation data. If the historical operation data meets normal distribution, carrying out correlation analysis on the historical operation data by adopting a Pearson correlation analysis method; and if the historical operation data does not meet the normal distribution, carrying out correlation analysis on the historical operation data by adopting a Spekerman correlation analysis method. The K-S algorithm is a test method for comparing the frequency distribution f (x) with the theoretical distribution g (x) or two observed value distributions. Comparing the cumulative frequency distribution of the sample data with a specific theoretical distribution, if the difference between the cumulative frequency distribution and the theoretical distribution is small, the sample distribution can be deduced to be taken from a specific distribution, such as a normal distribution.
The K-S normal distribution test analysis results are shown in the following Table 1:
as shown in the table above, in the three time periods of carbon market starting up to date, annual performance and 2 months before completion of performance, neither the online average price nor the online average price change rate passes the normal distribution test, the significance is <0.05, and further pearson correlation analysis cannot be performed only by the normal distribution test of historical operation data, so that the spearman correlation analysis is required for secondary screening.
Specifically, in order to detect whether the power consumption of the whole society and the national carbon price have a nonlinear relation before and after the performance period, the spearman correlation analysis is further carried out. The spearman correlation analysis is an analysis technique that measures the dependence of two variables, and is pearson correlation of a class variable. The spearman correlation coefficient can be calculated according to the difference between the peer-to-peer numbers of two rows of paired grades, and the calculation formula is as follows:
d i ＝rg(X i )-rg(Y i )
wherein X and Y are two groups of data respectively, X represents historical operation data (independent variable), and Y represents national carbon price (dependent variable); rg (X) i ) X represents i Is of the order of rg (Y) i ) Represents Y i Is a grade of (2); d, d i Is X i 、Y i Level differences between. The corresponding spearman (rank) correlation coefficient can be obtained by:
Wherein: r is (r) s Between-1 and 1; n represents what number of data is in each set of data. The spearman correlation coefficient indicates the correlation direction of X and Y. If Y tends to increase as X increases, the Szechwan correlation coefficient is positive. If Y tends to decrease as X increases, the Szechwan correlation coefficient is negative. As X and Y get closer to a perfect monotonic correlation, the spearman correlation coefficient increases in absolute value.
And carrying out correlation analysis on the historical operation data and the national carbon price according to the spearman correlation coefficient, wherein the spearman correlation analysis result of the historical operation data on the national carbon price is shown in the following table 2:
in Table 2, the correlation coefficient is positive, indicating a positive correlation between the independent and dependent variables; the correlation coefficient is negative, indicating a negative correlation between the independent variable and the dependent variable. According to the absolute value of the correlation coefficient, the historical operation data can be determined to meet the conditions that the correlation of national carbon price is more than 0.05 and the significance is less than 0.05, wherein the correlation of the total social electricity consumption in the historical operation data and the national carbon price is stronger, the electricity consumption in the whole industry is more acceptable, and the electricity consumption in the eight industries is less than the electricity consumption in the eight industries. Specifically, near the running-off day, the positive correlation between the total social electricity consumption and the national carbon price can be expressed by a substantially linear correlation, and the closer to the running-off day, the more obvious the positive correlation between the total social electricity consumption and the national carbon price is.
b1 Performing stationarity test on the historical operation data, and performing causal relationship analysis on the historical operation data passing the stationarity test by adopting a glaring test method.
In this embodiment, before performing causality analysis on the historical operation data, whether the historical operation data and the national carbon price have stationarity is checked, and if so, causality analysis can be performed on the historical operation data passing the stationarity check by adopting a glaring check method; if the historical operating data does not have stationarity, carrying out same-order difference, such as first-order difference, on the historical operating data and the national carbon price, and determining the data with stationarity after difference so as to ensure that the historical operating data and the national carbon price meet the same-order coordination, wherein the data after difference has stationarity. The difference is the difference between yesterday data and today data. Specifically, a unit root checking method is adopted, and the independent variable and the dependent variable are checked for stability by taking a standard that a unit root is not existed in a lag operator polynomial equation of the time sequence, namely a stable sequence. The results of the stability test analysis of historical operating data on national carbon prices are shown in table 3 below:
the P value is a significance value, and when the P value is smaller than a preset threshold value, the data has stability, namely the data passes the stability test. If the P value cannot be met and is smaller than the preset threshold value, the corresponding data is required to be subjected to first-order difference, and whether the data after difference is stable or not is judged. In table 3, "/" indicates that the P value is smaller than the preset threshold, and the preset threshold may be 0.1.
In this embodiment, in the stage of starting the carbon market up to now, the p value after the first order difference is less than 0.1, which indicates that the electricity consumption of the whole society and the online average price are subject to the same order coordination, and the electricity consumption of the whole industry and the electricity consumption of the eight industries are respectively subject to the same order coordination with the online average price change rate. And 2 months before the completion of the implementation, the power consumption of the whole society, the power consumption of the whole industry and the power consumption of the eighth industry are respectively matched with the online average price change rate in a same order. And further carrying out causal relationship analysis on the data meeting the stationarity and the homonymy coordination according to the Granges test method.
Specifically, the causal relationship is defined from the prediction perspective, that is, if X is the glauca cause of Y, the change of X should precede the change of Y, so when making Y regression on other variables, if the past or hysteresis value of X is included to significantly improve the prediction of Y, then X can be considered as the glauca cause of Y. Through the graininess causal relation test, whether the electricity consumption is the leading cause of the national carbon price can be judged, and further, the causal analysis is carried out. The results of the analysis of the historic operating data on the gralanjie cause and effect relationship test of the national carbon price are shown in table 4:
in table 2, the number of days lagged indicates causality between the independent variable and the dependent variable, i.e., causality between the historical operating data and the national carbon price, and for example, the number of days lagged is 2, which indicates that the independent variable X (historical operating data) of today has an effect on the dependent variable Y (national carbon price) after 2 days. "/" indicates that the significance is greater than 0.05, there is no causality between the independent and dependent variables.
c1 And (3) carrying out secondary feature screening of the price fluctuation analysis model according to the correlation analysis result and the causal relationship analysis result.
In this example, in combination with the spearman correlation analysis and stationarity test results, the following conclusions were obtained: firstly, the power consumption and online average price of the whole society have strong correlation and strong causal relationship at the stage of starting the carbon market to date; secondly, the power consumption and the online average price change rate of the whole society have strong correlation and strong causal relationship 2 months before the completion of the performance; thirdly, the power consumption and the online average price change rate in the whole industry have strong correlation and strong causal relationship in the carbon market starting stage. Among the three strong association relationships, the first one is not in line with common sense and is discarded; the third correlation is inferior to the second one (the pearson/spearman correlation coefficient expressed in the third correlation is small), and is therefore discarded. Therefore, the strong association relation detection result that the power consumption of the whole society and the online average price change rate are 2 months before the completion of the implementation is finally reserved.
As a second optional embodiment of the embodiments, on the basis of the above embodiments, the optimizing further includes, before obtaining the price fluctuation hierarchical causal analysis model using the electricity consumption as one of the analysis parameters according to the feature screening result:
a2 According to the historical operation data and the scene analysis strategy, performing scene analysis on the price fluctuation analysis model.
In this embodiment, the scenario analysis policy may be understood as a policy for performing scenario analysis on the price fluctuation analysis model according to a set time period of the national carbon trade market, and by combining the causality and the correlation of the historical operation data and the national carbon price.
Illustratively, the national carbon market has about 2 years of each performance cycle. The trade volume and price of the national carbon market which begin to date show staged characteristics, namely, the characteristics of small trade volume and infrequent price fluctuation exist in the non-nearby performance cut-off days of 7-10 months in 2021 and 1-6 months in 2022; the transaction amount is concentrated and the carbon price level is increased in the period of about the expiration date of 11 to 12 months in 2021.
Specifically, based on the fact that the carbon market presents different operation modes in the two periods, the fluctuation characteristics of the carbon market are analyzed and measured in different stages for accurately grasping different operation rules of the carbon market in the two periods. Specifically, the last two months of the year of performance may be considered as the upcoming track-cut days, and the other months of each two years may be considered as non-upcoming track-cut days. Based on the above analysis, it is concluded that as the selected time period is reduced and gradually approaches the performance cut-off day, the positive correlation between the electricity consumption and the national carbon price is highlighted, and the positive correlation between the electricity consumption and the national carbon price in the whole society is most prominent in the approach to the performance period.
The analysis results of the correlation between the historical operation data and the national carbon number obtained by analyzing the date data and the national carbon number of each of the historical operation data from 11/1/12/30 of 2021 in the near-covered period are shown in table 5:
b2 Obtaining electricity consumption data affecting the carbon trade market in the power industry from the historical operation data according to the scene analysis result.
Specifically, according to the determination in the scene analysis result, the independent variable and the dependent variable with strong association relation in the price wave analysis of the carbon trade market are respectively the total social electricity consumption and the online average price change rate, the correlation coefficient of the total social electricity consumption and the online average price change rate is 0.25, the stationary inspection p value is 0.1, the analysis result of the Granges causality analysis is obvious, and the hysteresis period between the total social electricity consumption and the online average price change rate is 1 day. Accordingly, the data of the total social electricity consumption is determined as the electricity consumption data affecting the carbon trade market.
As a third optional embodiment of the embodiments, on the basis of the above embodiments, the specific step of obtaining a price fluctuation hierarchical causal analysis model using electricity consumption as one of analysis parameters according to feature screening results is further added in an optimized manner, and the method includes:
a3 A first screening result of the primary feature screening and a second screening result of the secondary feature screening are obtained as feature screening results.
In this embodiment, the first screening result may be understood as a screening result of primary feature screening, the influencing factor data in the independent variable is screened out, and only the historical operation data is reserved as the independent variable to perform secondary feature screening. The second screening result can be understood as a screening result of the secondary feature screening, and the whole social electricity consumption in the historical operation data is taken as an independent variable, and the online average price change rate in the national carbon price is taken as an independent variable.
Specifically, according to the first screening result with the independent variable as the historical operation data obtained by the primary characteristic screening, the secondary characteristic screening is continuously carried out based on the first screening result, the second screening result with the independent variable as the whole society electricity consumption and the on-line average price change rate is obtained, and the second screening result is determined to be the characteristic screening result.
b3 A new parameter equation of the price fluctuation analysis model is constructed based on the feature screening result.
In the embodiment, the original price fluctuation analysis model is subjected to parameter optimization according to the feature screening result, and a new parameter equation based on the fact that the electricity consumption of the whole society is an independent variable and the online average price change rate is an independent variable is obtained.
c3 Solving a parameter equation according to a preset parameter optimization condition to obtain the electricity consumption used as an analysis parameter in the parameter equation.
In this embodiment, the new parameter equation is determined according to the total social electricity consumption and the online average price change rate, and the equation can be expressed as Y 2 ＝A 1 X 2 +B 1 Wherein Y is 2 X is a causal carbon price measuring and calculating result corresponding to online average price change rate 2 Is the electricity consumption of the whole society, A 1 B is the slope of the linear relation between the electricity consumption of the whole society and the causal carbon valence 1 Intercept of linear relation between electricity consumption and causal carbon valence of the whole society; a is that 1 And B is connected with 1 Based on the whole society in a large amount of history dataThe linear relation between the power consumption and the causal carbon valence is determined and is a predetermined value.
Specifically, substituting the total social electricity consumption, solving a parameter equation according to a preset slope and intercept to obtain a solving result of the parameter equation, and calculating a causal price-talking measuring result Y 2 Electricity consumption as an analysis parameter.
d3 According to the electricity consumption as the analysis parameter and other analysis parameters adopted in advance, obtaining an optimized price fluctuation hierarchical causal analysis model through given linear weighted combination information.
In this embodiment, the new parameter equation is composed of the electricity consumption and other analysis parameters, including the carbon price measurement results corresponding to the three variables of the time sequence variable, the electricity variable and the macroscopic variable. The electricity consumption is a carbon price measuring result corresponding to the electric power variable, and other analysis parameters are carbon price measuring results corresponding to the time sequence variable and the macroscopic variable. The time sequence variable is daily carbon price, and the macroscopic variables comprise international carbon price, energy price, GDP level, temperature difference, carbon emission and carbon related search index. The linear weighted combination information is information that time sequence variable, electric power variable and macroscopic variable are weighted and summed according to the weight corresponding to each time sequence variable, electric power variable and macroscopic variable, and an equation corresponding to the optimized price fluctuation level causal analysis model is Y=lambda 1 Y1+λ 2 Y 2 +λ 3 Y 3 Wherein Y is the final measurement result of the carbon number, Y 1 For the calculation result of carbon price for calculating daily carbon price based on ARIMA model, Y 2 As the result of measuring and calculating the carbon price of the electricity consumption, Y 3 Lambda as a result of calculation of carbon price for economy, policy, environment and energy parameters based on ARIMAX model 1 Is ARIMA carbon number weighting coefficient lambda 2 Lambda is the causal carbon valence weight coefficient 3 Is ARIMAX carbon number weight coefficient.
Specifically, according to the carbon price measurement result Y corresponding to the time sequence variable 1 Carbon price measuring and calculating result Y corresponding to electric power variable 2 Carbon price measurement result Y corresponding to macroscopic variable 3 By a given Y 1 、Y 2 And Y 3 Respectively corresponding carbon valence weight coefficient lambda 1 、λ 2 And lambda (lambda) 3 The equation of the optimized price fluctuation level causal analysis model is determined as Y=lambda 1 Y1+λ 2 Y 2 +λ 3 Y 3 And obtaining an optimized price fluctuation hierarchical causal analysis model.
According to the technical scheme, the historical time sequence factors and the relevant influence factors of the fluctuation characteristics of the carbon market are more completely considered, so that the accuracy of carbon price measurement and calculation is effectively improved, and the analysis accuracy of the price fluctuation of the carbon trade market is ensured.
Example III
Fig. 3 is a schematic structural diagram of a price fluctuation analysis device for a carbon trade market according to a third embodiment of the present invention. As shown in fig. 3, the apparatus includes:
The data acquisition module 31 is configured to acquire factor data of influence of a relative carbon market price in a set time period in the power industry and historical operation data of the power grid system;
an analysis model acquisition module 32, configured to perform price fluctuation analysis model optimization according to the influence factor data and the historical operation data, so as to obtain a price fluctuation hierarchical causal analysis model using electricity consumption as one of analysis parameters;
and the analysis result acquisition module 33 is used for taking the electricity consumption data in the historical operation data as one of the input data, inputting the price fluctuation hierarchical causal analysis model, and obtaining the output carbon market price fluctuation characteristic analysis result.
According to the price fluctuation analysis device of the carbon market adopted by the technical scheme, the price fluctuation hierarchical causal model is obtained by optimizing analysis parameters, and the price fluctuation analysis result of the carbon market is determined according to the price fluctuation causal model. Key factors influencing national carbon market price are stripped from complex variables, and the reasons and rules of carbon market price fluctuation are analyzed, so that the comprehensive performance is high; and the historical operation data of the power grid system is combined for analysis, so that the actual situation of the national carbon market construction stage is met. The accuracy of the price fluctuation feature analysis of the carbon trade market is effectively improved, and the problem that the accuracy of the existing price fluctuation analysis method of the carbon trade market is low after the current carbon trade market is incorporated into the power industry is solved.
Optionally, the analysis model acquisition module 32 includes:
the first feature screening unit is used for carrying out primary feature screening of an original price fluctuation analysis model on the influence factor data and the historical operation data through a Delphi method;
the second feature screening unit is used for carrying out secondary feature screening of the price fluctuation analysis model on the historical operation data through correlation analysis and causality analysis;
and the analysis model obtaining unit is used for obtaining a price fluctuation hierarchical causal analysis model taking the electricity consumption as one of analysis parameters according to the characteristic screening result.
Optionally, the second feature screening unit is specifically configured to:
according to the normal distribution judgment result of the historical operation data, adopting a corresponding correlation analysis method to perform correlation analysis on the historical operation data;
performing stability test on the historical operation data, and performing causal relationship analysis on the historical operation data passing the stability test by adopting a Grangel test method;
and carrying out secondary feature screening of the price fluctuation analysis model according to the correlation analysis result and the causal relationship analysis result.
Optionally, the analysis model acquisition module 32 is specifically configured to:
Before a price fluctuation hierarchical causal analysis model taking electricity consumption as one of analysis parameters is obtained according to a feature screening result, scene analysis is carried out on the price fluctuation analysis model according to the historical operation data and a scene analysis strategy;
and obtaining electricity consumption data affecting a carbon trade market in the electric power industry from the historical operation data according to the scene analysis result.
Optionally, the analysis model obtaining unit is specifically configured to:
acquiring a first screening result of primary feature screening and a second screening result of secondary feature screening as feature screening results;
constructing a new parameter equation of the price fluctuation analysis model based on the feature screening result;
solving the parameter equation according to a preset parameter optimization condition to obtain the electricity consumption serving as an analysis parameter in the parameter equation;
and obtaining an optimized price fluctuation hierarchical causal analysis model through given linear weighted combination information according to the electricity consumption serving as an analysis parameter and other analysis parameters adopted in advance.
Optionally, the data acquisition module 31 is specifically configured to:
obtaining influence factor data extracted according to set influence dimensions in a set time period from the power industry, wherein the set influence dimensions comprise economy, policy, environment and energy;
Historical operation data in the set time period is collected from the power grid system, wherein the historical operation data comprises the power consumption of the whole society, the power consumption of the whole industry and the power consumption of the eight industries.
Optionally, the price fluctuation analysis device of the carbon trade market further includes:
and the applicability analysis module is used for carrying out applicability analysis on the price fluctuation hierarchical causal analysis model by combining the output carbon market price fluctuation characteristic analysis result with actual price fluctuation data.
The price fluctuation analysis device for the carbon trade market provided by the embodiment of the invention can execute the price fluctuation analysis method for the carbon trade market provided by any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the execution method.
Example IV
Fig. 4 shows a schematic diagram of an electronic device 40 that may be used to implement an embodiment of the invention. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. Electronic equipment may also represent various forms of mobile devices, such as personal digital processing, cellular telephones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the inventions described and/or claimed herein.
As shown in fig. 4, the electronic device 40 includes at least one processor 41, and a memory communicatively connected to the at least one processor 41, such as a Read Only Memory (ROM) 42, a Random Access Memory (RAM) 43, etc., in which the memory stores a computer program executable by the at least one processor, and the processor 41 may perform various suitable actions and processes according to the computer program stored in the Read Only Memory (ROM) 42 or the computer program loaded from the storage unit 48 into the Random Access Memory (RAM) 43. In the RAM 43, various programs and data required for the operation of the electronic device 40 may also be stored. The processor 41, the ROM 42 and the RAM 43 are connected to each other via a bus 44. An input/output (I/O) interface 45 is also connected to bus 44.
Various components in electronic device 40 are connected to I/O interface 45, including: an input unit 46 such as a keyboard, a mouse, etc.; an output unit 47 such as various types of displays, speakers, and the like; a storage unit 48 such as a magnetic disk, an optical disk, or the like; and a communication unit 49 such as a network card, modem, wireless communication transceiver, etc. The communication unit 49 allows the electronic device 40 to exchange information/data with other devices via a computer network, such as the internet, and/or various telecommunication networks.
The processor 41 may be various general and/or special purpose processing components with processing and computing capabilities. Some examples of processor 41 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various processors running machine learning model algorithms, digital Signal Processors (DSPs), and any suitable processor, controller, microcontroller, etc. The processor 41 performs the various methods and processes described above, such as the price fluctuation analysis method of the carbon trade market.
In some embodiments, the price fluctuation analysis method of the carbon trade market may be implemented as a computer program tangibly embodied on a computer-readable storage medium, such as the storage unit 48. In some embodiments, part or all of the computer program may be loaded and/or installed onto the electronic device 40 via the ROM 42 and/or the communication unit 49. When the computer program is loaded into the RAM 43 and executed by the processor 41, one or more steps of the price fluctuation analysis method of the carbon trade market described above may be performed. Alternatively, in other embodiments, the processor 41 may be configured to perform the price volatility analysis method of the carbon trade market in any other suitable manner (e.g., by means of firmware).
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuit systems, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), systems On Chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs, the one or more computer programs may be executed and/or interpreted on a programmable system including at least one programmable processor, which may be a special purpose or general-purpose programmable processor, that may receive data and instructions from, and transmit data and instructions to, a storage system, at least one input device, and at least one output device.
A computer program for carrying out methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the computer programs, when executed by the processor, cause the functions/acts specified in the flowchart and/or block diagram block or blocks to be implemented. The computer program may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.
In the context of the present invention, a computer-readable storage medium may be a tangible medium that can contain, or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. The computer readable storage medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. Alternatively, the computer readable storage medium may be a machine readable signal medium. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
To provide for interaction with a user, the systems and techniques described here can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) through which a user can provide input to the electronic device. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic input, speech input, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a background component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such background, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), blockchain networks, and the internet.
The computing system may include clients and servers. The client and server are typically remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, also called a cloud computing server or a cloud host, and is a host product in a cloud computing service system, so that the defects of high management difficulty and weak service expansibility in the traditional physical hosts and VPS service are overcome.
It should be appreciated that various forms of the flows shown above may be used to reorder, add, or delete steps. For example, the steps described in the present invention may be performed in parallel, sequentially, or in a different order, so long as the desired results of the technical solution of the present invention are achieved, and the present invention is not limited herein.
The above embodiments do not limit the scope of the present invention. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives are possible, depending on design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of the present invention.
Claims (10)
1. A method of price volatility analysis of a carbon trade market, comprising:
acquiring influence factor data of relative carbon trade market price in a set time period in the power industry and historical operation data of a power grid system;
according to the influence factor data and the historical operation data, carrying out price fluctuation analysis model optimization to obtain a price fluctuation hierarchical causal analysis model taking the electricity consumption as one of analysis parameters;
And taking the electricity consumption data in the historical operation data as one of input data, inputting the price fluctuation hierarchical causal analysis model, and obtaining an output carbon market price fluctuation characteristic analysis result.
2. The method according to claim 1, wherein the optimizing the price fluctuation analysis model based on the influence factor data and the historical operation data to obtain the price fluctuation hierarchical causal analysis model using the electricity consumption as one of the analysis parameters comprises:
performing primary feature screening of an original price fluctuation analysis model on the influence factor data and the historical operation data through a Delphi method;
performing secondary feature screening of the price fluctuation analysis model on historical operation data through correlation analysis and causality analysis;
and obtaining a price fluctuation hierarchical causal analysis model taking the electricity consumption as one of analysis parameters according to the feature screening result.
3. The method of claim 2, wherein said performing secondary feature screening of said price volatility analysis model on historical operating data by correlation analysis and causality analysis comprises:
according to the normal distribution judgment result of the historical operation data, adopting a corresponding correlation analysis method to perform correlation analysis on the historical operation data;
Performing stability test on the historical operation data, and performing causal relationship analysis on the historical operation data passing the stability test by adopting a Grangel test method;
and carrying out secondary feature screening of the price fluctuation analysis model according to the correlation analysis result and the causal relationship analysis result.
4. The method of claim 2, further comprising, prior to obtaining the price fluctuation hierarchical causal analysis model using electricity usage as one of the analysis parameters based on the feature screening result:
according to the historical operation data and a scene analysis strategy, performing scene analysis on the price fluctuation analysis model;
and obtaining electricity consumption data influencing the market price of carbon transaction in the electric power industry from the historical operation data according to the scene analysis result.
5. The method according to claim 2, wherein the obtaining a price fluctuation hierarchical causal analysis model using electricity consumption as one of analysis parameters based on the feature screening result comprises:
acquiring a first screening result of primary feature screening and a second screening result of secondary feature screening as feature screening results;
constructing a new parameter equation of the price fluctuation analysis model based on the feature screening result;
Solving the parameter equation according to a preset parameter optimization condition to obtain the electricity consumption serving as an analysis parameter in the parameter equation;
and obtaining an optimized price fluctuation hierarchical causal analysis model through given linear weighted combination information according to the electricity consumption serving as an analysis parameter and other analysis parameters adopted in advance.
6. The method according to claim 1, wherein the obtaining the influence factor data of the relative carbon market price in the set time period in the power industry and the historical operation data of the power grid system includes:
obtaining influence factor data extracted according to set influence dimensions in a set time period from the power industry, wherein the set influence dimensions comprise economy, policy, environment and energy;
historical operation data in the set time period is collected from the power grid system, wherein the historical operation data comprises the power consumption of the whole society, the power consumption of the whole industry and the power consumption of the eight industries.
7. The method of any one of claims 1-6, further comprising:
and carrying out applicability analysis on the price fluctuation hierarchical causal analysis model by combining the output carbon market price fluctuation characteristic analysis result with actual price fluctuation data.
8. A price fluctuation analyzing apparatus for a carbon trade market, comprising:
the data acquisition module is used for acquiring influence factor data of relative carbon trade market price in a set time period in the power industry and historical operation data of the power grid system;
the analysis model acquisition module is used for optimizing a price fluctuation analysis model according to the influence factor data and the historical operation data to obtain a price fluctuation hierarchical causal analysis model taking the electricity consumption as one of analysis parameters;
and the analysis result acquisition module is used for taking the electricity consumption data in the historical operation data as one of the input data, inputting the price fluctuation hierarchical causal analysis model, and obtaining the output carbon market price fluctuation characteristic analysis result.
9. An electronic device, comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,,
the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform a price volatility analysis method of a carbon trade market according to any one of claims 1-7.
10. A computer readable storage medium storing computer instructions for causing a processor to execute the price volatility analysis method of the carbon trade market of any one of claims 1-7.
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