CN114723173A - Natural gas load fluctuation symmetry analysis method and system based on GARCH model - Google Patents

Natural gas load fluctuation symmetry analysis method and system based on GARCH model Download PDF

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CN114723173A
CN114723173A CN202210480168.1A CN202210480168A CN114723173A CN 114723173 A CN114723173 A CN 114723173A CN 202210480168 A CN202210480168 A CN 202210480168A CN 114723173 A CN114723173 A CN 114723173A
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边根庆
孙世冲
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Abstract

The invention discloses a method and a system for analyzing fluctuation symmetry of gas load for natural gas based on a GARCH model, which are characterized in that firstly, missing values of an obtained natural gas data set are filled and abnormal values are removed: then, dividing the natural gas data set according to year to form comparative analysis on time span; and then, carrying out stationarity check on the natural gas data set, then carrying out ARCH effect check on the natural gas data set, and finally carrying out fluctuation symmetry analysis based on a GARCH model on the natural gas data set which passes the ARCH effect check to obtain an analysis result.

Description

Natural gas load fluctuation symmetry analysis method and system based on GARCH model
Technical Field
The invention belongs to the field of data analysis, and particularly relates to a method and a system for analyzing natural gas load fluctuation symmetry based on a GARCH model.
Background
As a clean and efficient energy source, the natural gas not only has lower greenhouse gas, sulfur dioxide and particulate matter emission than coal and petroleum, but also can effectively make up for the defects that wind energy and solar energy are not easy to store and unstable in supply. The countries in the world develop low-carbon economy, clean energy utilization and reduce atmospheric emission. Under the large background of transformation of global energy consumption structures, natural gas is favored and valued by all countries in the world, the large trend of replacing high-carbon and high-pollution coal by natural gas is irreversible, and the natural gas plays a more important role in global economic development and energy consumption structures.
The existing natural gas load prediction method comprises a linear regression analysis method, a time series method and a grey system theory, but most of the methods are models based on linear data prediction, however, the natural gas load has significant non-linear characteristics such as periodicity, uncertainty and volatility, and a common mathematical model hardly meets the prediction requirement, so that the existing prediction method cannot meet the increasingly huge and complex data prediction requirement in reality, and needs to try to explore the characteristics and rules of the natural gas load to provide accurate reference data for the prediction of the natural gas load.
Disclosure of Invention
Aiming at the problems in the prior art, the invention provides a method and a system for analyzing the fluctuation of the natural gas load, wherein the method is based on a GARCH model and is used for analyzing the fluctuation symmetry characteristics of a natural gas load sequence.
The invention is realized by the following technical scheme:
a natural gas load fluctuation symmetry analysis method based on a GARCH model comprises the following steps:
step 1, dividing an acquired natural gas consumption load data set of a historical time period into a plurality of subdata sets according to a preset rule;
step 2, respectively carrying out stability verification on the natural gas load sequence on the plurality of subdata sets obtained in the step 1;
step 3, performing ARCH effect verification on the subdata sets subjected to stationarity verification in the step 2 respectively;
and 4, performing mobility analysis on the sub data set verified by the ARCH effect by adopting an autoregressive conditional variance model, and predicting the gas load for natural gas according to the analysis result.
Preferably, in step 1, the natural gas usage load data set is divided into a plurality of sub data sets according to a set time period.
Preferably, the stability of the natural gas load sequence in the sub-data set is checked by using an ADF (automatic feeder) unit root checking method in the step 2;
the ADF inspection method is characterized in that a hysteresis difference item of a dependent variable yt is added into a regression equation to control the correlation of a high-order sequence, the probability value of a natural gas load sequence of a sub data set is obtained according to the regression equation, and the probability value is compared with a set probability value to determine the stationarity of the natural gas load sequence.
Preferably, the equation expression of the ADF root-mean-square method is as follows:
Figure BDA0003619975320000021
Figure BDA0003619975320000022
in the formula utIs the residual, betatAs a time trend term, ytFor random perturbation terms, r is a coefficient.
Preferably, the ARCH-LM method is adopted in the step 3 to carry out ARCH effect check on the subdata sets.
Preferably, the ARCH effect checking method of the ARCH-LM method is as follows:
and performing preliminary estimation on the sub data sets by adopting a least square method, establishing an estimation equation according to the preliminary estimation value, obtaining regression coefficients corresponding to the natural gas sequences of the sub data sets according to the estimation equation, and determining the ARCH effect of the sub data sets according to the regression coefficients.
Preferably, the expression of the estimation equation is as follows:
Figure BDA0003619975320000031
in the formula, σtFor the current fluctuation rate, epsilontWhite noise, μ, independently identically distributed with zero mean unit variancetFor the current residual, ai、rjAre coefficients.
Preferably, the expression of the autoregressive conditional heteroscedasticity model in step 4 is as follows:
Figure BDA0003619975320000032
in the formula, σtFor the current fluctuation rate, epsilontWhite noise, μ, independently identically distributed with zero mean unit variancetIs the current residual.
Preferably, the missing values are filled and the abnormal values are deleted from the acquired natural gas data set before step 1 is executed.
A system of a natural gas load fluctuation symmetry analysis method based on a GARCH model comprises,
the sub data set module is used for dividing the acquired natural gas load data set of the historical time period into a plurality of sub data sets according to a preset rule;
the stability checking module is used for performing stability checking on the natural gas load sequence on the sub-data sets;
the effect checking module is used for carrying out ARCH effect checking on the subdata set checked by the stationarity checking module;
and the analysis module is used for performing volatility symmetry analysis on the subdata set verified by the effect verification module by adopting an autoregressive condition variance model and predicting the gas load for the natural gas according to an analysis result.
Compared with the prior art, the invention has the following beneficial technical effects:
the invention discloses a method for analyzing the symmetry of gas load fluctuation for natural gas based on a GARCH model, which is characterized in that a natural gas data set is divided according to year to form comparative analysis on time span; and then, carrying out stability verification on the natural gas data set, carrying out ARCH effect verification on the natural gas data set, and finally, carrying out fluctuation symmetry analysis based on a GARCH model on the natural gas data set subjected to ARCH effect verification, and carrying out natural gas load prediction according to an analysis result.
Further, before performing volatility analysis of the GARCH model, filling missing values and removing abnormal values of the acquired natural gas data set: and filling the missing values by a mean filling method, and removing possible abnormal values by a K-nearest neighbor method, so that the analysis is more accurate.
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FIG. 1 is a flowchart of the GARCH model volatility analysis of the present invention;
FIG. 2 is a schematic view of the fluctuation of a natural gas usage train according to the present invention;
FIG. 3 is a diagram showing the analysis results of the GARCH model of the present invention.
Detailed Description
The present invention will now be described in further detail with reference to the attached drawings, which are illustrative, but not limiting, of the present invention.
Referring to fig. 1 to 3, a method for analyzing the fluctuation symmetry of gas load for natural gas based on a GARCH model includes the following steps:
step 1, preprocessing the acquired natural gas data set.
Specifically, the preprocessing comprises filling missing values and deleting abnormal values; filling the missing values by using a mean interpolation method, and detecting and removing abnormal values by using a K-means algorithm;
during data collection or transmission, a missing condition may occur. For example, the data consumption of the user is lost due to damage or abnormality of the acquisition terminal of the natural gas meter, which results in failure of partial data acquisition, or loss or damage of the data file during data transmission. Therefore, missing value padding is also an important step, in which the missing rate of some users is high, and for the users with high missing rate, the users are simply deleted. For data with a low missing rate, a mean interpolation method is adopted for filling, for example, some users lack the natural gas consumption in 2018 and 12 months, and the average value of the natural gas consumption in 16 years and 17 years and 12 months is taken as a substitute value of a missing value.
Randomly selecting K sample points from the natural gas load data set as a centroid, calculating the distance between the residual samples and the centroid to divide the attribution degree of the samples to the clusters, calculating the distance between each sample and the centroid based on the clustering result, and judging the sample to be an abnormal value if the distance is greater than a threshold value. When the model is used for judging the abnormal value of the natural gas load data set, the normal data and the abnormal data need to be ensured to be as complete as possible, otherwise, the clustering effect is influenced, and the abnormal value is influenced. The calculation method of the distance adopts an Euclidean distance method, the category number K of the clusters can be judged through an inflection point method, namely, the data agglomeration degree (SSE variance) is judged, when the agglomeration degree is sharply reduced along with the K value, an inflection point appears, and if the K value is continuously increased, the accuracy of natural gas load data classification is reduced.
And 2, dividing the natural gas data set obtained in the step 1 into a plurality of subdata sets according to a preset rule.
Specifically, in this embodiment, the natural gas data sets are divided according to the year, and the natural gas data sets are divided according to the year to obtain a plurality of subdata sets, so as to form comparative analysis on the time span.
And 3, respectively carrying out stability verification on the natural gas load sequence on the plurality of subdata sets obtained in the step 2.
Specifically, an ADF (automatic force analysis) unit root check method is used for checking whether a natural gas load sequence is stable or not so as to avoid a pseudo regression phenomenon in a regression process, and the ADF check method is realized by adding a dependent variable y into a regression equationtThe hysteresis differential term controls the correlation of a high-order sequence, the probability value of the natural gas load sequence is obtained according to a regression equation, the probability value is compared with a set probability value, the stationarity of the natural gas load sequence is determined, and the basic equation expression of an ADF unit root test method is as follows:
Figure BDA0003619975320000051
Figure BDA0003619975320000052
the original assumption is that: the natural gas load sequence with the original assumption of the sub data set has at least one unit root, and the alternative assumption is that the sequence has no unit root. The specific method comprises the following steps: a probability value P (typically 1%, 5% or 10%) is set before the test, and if the resulting test value is less than this probability value, the probability that the time series rejects the original hypothesis will be 1-P. The P value is set to 1% here, that is, if the unit root test value of the natural gas load sequence is less than 0.01, the probability that the sequence will be stable reaches 99%, and the sequence can be considered to be stable.
And 4, performing ARCH effect verification on the subdata sets which pass the stationarity verification in the step 3 respectively.
Specifically, the ARCH effect check is used for judging whether the data set has ARCH effect, and the fluctuation symmetry analysis based on the GARCH model can be carried out only through the ARCH effect judgment.
The detection of the ARCH effect generally includes residual square correlation diagram, ARCH-LM method (lagrange multiplier method) and autocorrelation function detection method, and the ARCH-LM method is most commonly used in the empirical model, so in the present embodiment, the ARCH-LM method is used to check the data set.
The ARCH-LM assay is described in detail below:
s4.1, performing preliminary estimation on the natural gas sequence of the sub-data set by applying an ordinary least square method (OLS), wherein the preliminary estimation expression is as follows:
yt＝x′tβ+ut
s4.2, obtaining the preliminary estimated value u according to the step S4.1tBuilding ut 2Further establishing an estimation equation, and obtaining a regression coefficient corresponding to the natural gas sequence according to the estimation equation, wherein the expression of the estimation equation is as follows:
Figure BDA0003619975320000061
examination of the effects of the ARCH in the natural gas sequence, i.e. in the examination formulaAll regression coefficients α0、α1…αqIf the value of (a) is 0 at the same time, and is 0 at the same time, the subdata set passes the ARCH effect check, otherwise, the subdata set does not pass the ARCH effect check.
And 5, performing volatility symmetry analysis on the natural gas load data in the sub data set verified by the ARCH effect by adopting an autoregressive condition variance model, and predicting the natural gas load according to an analysis result.
In contrast to the ARCH model, the GARCH model adds an autoregressive portion of σ t2, i.e., allows the conditional variance to rely on its own hysteresis, so that σ t2 is also a function of { σ t-12.. σ t-p2} while σ t2 is still the conditional variance, which is an estimate of the variance taken one step forward based on past information. Therefore, the GARCH model not only has the capability of describing the fluctuation, but also has longer hysteresis so that the structure is more complete, and the basic equation of the GARCH (1,1) model is described as follows:
Figure BDA0003619975320000071
in the formula, σtFor the current fluctuation rate, epsilontWhite noise, μ, independently identically distributed with zero mean unit variancetIs the current residual.
Through analysis based on the GARCH (1,1) model, fluctuation aggregation effect of the data set and fluctuation symmetry characteristics such as durability of fluctuation impacted by external factors can be well judged, and if the analysis result shows that the fluctuation symmetry characteristics of the data set are obvious, the sigma in the formula is obtained through the GARCH (1,1) model by a one-step rolling time window prediction methodtAnd mutAnd the prediction model is placed in other natural gas load prediction models by taking the parameter as a parameter, so that the prediction precision of the natural gas load is improved.
According to the method for analyzing the fluctuation symmetry of the natural gas load based on the GARCH model, before the fluctuation analysis of the GARCH model is carried out, the acquired natural gas load data is preprocessed, for example, the missing value is filled by a mean filling method, and the possible abnormal value is removed by a K-nearest neighbor method, so that the analysis is more accurate; and secondly, combining the fluctuation modeling analysis with the natural gas load, analyzing the fluctuation symmetry rule, and predicting the natural gas load according to the analysis result.
Example 1
A method for analyzing fluctuation symmetry of natural gas load based on a GARCH model comprises the following steps:
1) preprocessing the obtained natural gas data set: filling missing values by using a mean interpolation method, and detecting and removing abnormal values by using a K-nearest neighbor method;
2) dividing the natural gas data set according to the year: the data set can be segmented in a partitioning manner of at least every other year according to the size of the data set to form a comparative analysis on a time span;
3) and (3) stability checking: using ADF unit root test method to test whether the natural gas load sequence is stable or not so as to avoid the phenomenon of pseudo regression in the regression process, wherein the ADF test method adds dependent variable y to the right side of regression equationtThe hysteresis differential term of (a) controls the high order sequence correlation, the expression of the basic equation is as follows:
Figure BDA0003619975320000081
Figure BDA0003619975320000082
the verification results are shown in table 1:
TABLE 1 ADF Unit root check results
Figure BDA0003619975320000083
The P values are all less than 0.01, which indicates that the original hypothesis is rejected under the verification level of 1%, so that the natural gas sequence passes the stability verification.
4) Checking the ARCH effect: the ARCH effect check is used for judging whether the data set has the ARCH effect, and the fluctuation symmetry analysis based on the GARCH model can be carried out only through the ARCH effect judgment. The detection of the ARCH effect generally comprises a residual square correlation diagram, an ARCH-LM method (Lagrange multiplier method) and an autocorrelation function detection method, and the ARCH-LM method is most commonly used in an empirical model, so the ARCH-LM method is adopted for verifying the data set.
The LM test method and steps are as follows:
the method comprises the following steps: the sub-dataset is initially estimated by applying the Ordinary Least Squares (OLS), and the expression of the corresponding initial estimation of the sub-dataset by the OLS is as follows:
yt＝x′tβ+ut
step two, using the initial estimation value u estimated in the equationtBuilding ut 2And further establishing a corresponding estimation equation:
Figure BDA0003619975320000091
examination of the Effect of sequence ARCH, i.e. examination of all regression coefficients α in the formula0、α1…αqWhether or not the values of (b) are both 0.
The verification results are shown in table 2:
TABLE 2 ARCH Effect check-up results
Figure BDA0003619975320000092
P values were all less than 0.01, indicating that the original hypothesis was rejected at the 1% check level, and therefore the natural gas usage train passed the ARCH effect check.
5) Analysis based on the GARCH (1,1) model: the mobility analysis was performed on the natural gas usage load data set using the GARCH (1,1) model.
In contrast to the ARCH model, the GARCH model adds an autoregressive portion of σ t2, i.e., allows the conditional variance to rely on its own hysteresis, so that σ t2 is also a function of { σ t-12.. σ t-p2} while σ t2 is still the conditional variance, which is an estimate of the variance taken one step forward based on past information. Therefore, the GARCH model not only has the capability of describing the fluctuation, but also has longer hysteresis so that the structure is more complete, and the basic equation of the GARCH (1,1) model is described as follows:
Figure BDA0003619975320000093
the analytical results are shown in table 3:
TABLE 3 GARCH (1,1) model analysis results
Figure BDA0003619975320000094
Figure BDA0003619975320000101
From the results of the analysis of the GARCH (1,1) model, it can be concluded that: (1) the ARCH term and the GARCH term of the natural gas utilization sequence both show high statistical significance, namely obvious fluctuation aggregation characteristics; (2) the gas load of the station is impacted by high continuity in each time section, and the accuracy of a predicted value can be greatly improved by predicting the natural gas load according to the analysis result.
The invention also provides a system of the natural gas load fluctuation symmetry analysis method based on the GARCH model, which comprises,
the sub data set module is used for dividing the acquired natural gas load data set of the historical time period into a plurality of sub data sets according to a preset rule;
the stability checking module is used for performing stability checking on the natural gas load sequence on the sub-data sets;
the effect checking module is used for carrying out ARCH effect checking on the subdata set checked by the stationarity checking module;
and the analysis module is used for performing volatility symmetry analysis on the subdata set verified by the effect verification module by adopting an autoregressive condition variance model and predicting the gas load for the natural gas according to an analysis result.
The division of the modules in the embodiments of the present invention is schematic, and only one logical function division is provided, and in actual implementation, there may be another division manner, and in addition, each functional module in each embodiment of the present invention may be integrated in one processor, or may exist alone physically, or two or more modules are integrated in one module. The integrated module can be realized in a hardware mode, and can also be realized in a software functional module mode.
In yet another embodiment of the present invention, a computer device is provided that includes a processor and a memory for storing a computer program comprising program instructions, the processor for executing the program instructions stored by the computer storage medium. The Processor may be a Central Processing Unit (CPU), or may be other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable gate array (FPGA) or other Programmable logic device, discrete gate or transistor logic device, discrete hardware component, etc., which is a computing core and a control core of the terminal, and is specifically adapted to load and execute one or more instructions in a computer storage medium to implement a corresponding method flow or a corresponding function; the processor of the embodiment of the invention can be used for the operation of the natural gas load fluctuation symmetry analysis method based on the GARCH model.
In yet another embodiment of the present invention, the present invention further provides a storage medium, specifically a computer-readable storage medium (Memory), which is a Memory device in a computer device and is used for storing programs and data. It is understood that the computer readable storage medium herein can include both built-in storage media in the computer device and, of course, extended storage media supported by the computer device. The computer-readable storage medium provides a storage space storing an operating system of the terminal. Also, one or more instructions, which may be one or more computer programs (including program code), are stored in the memory space and are adapted to be loaded and executed by the processor. It should be noted that the computer readable storage medium may be a high-speed RAM memory, or a non-volatile memory (non-volatile memory), such as at least one disk memory. One or more instructions stored in the computer-readable storage medium may be loaded and executed by a processor to implement the corresponding steps of the above-described embodiments with respect to the method for analyzing natural gas load fluctuation symmetry based on the GARCH model.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, 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 is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations 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 apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, 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 apparatus 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 apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Finally, it should be noted that: although the present invention has been described in detail with reference to the above embodiments, it should be understood by those skilled in the art that: modifications and equivalents may be made to the embodiments of the invention without departing from the spirit and scope of the invention, which is to be covered by the claims.
The above-mentioned contents are only for illustrating the technical idea of the present invention, and the protection scope of the present invention is not limited thereby, and any modification made on the basis of the technical idea of the present invention falls within the protection scope of the claims of the present invention.

Claims (10)

1. A natural gas load fluctuation symmetry analysis method based on a GARCH model is characterized by comprising the following steps:
step 1, dividing an acquired natural gas consumption load data set of a historical time period into a plurality of subdata sets according to a preset rule;
step 2, respectively carrying out stability verification on the natural gas load sequence on the plurality of subdata sets obtained in the step 1;
step 3, performing ARCH effect verification on the subdata sets which pass through stationarity verification in the step 2 respectively;
and 4, performing mobility analysis on the sub data set verified by the ARCH effect by adopting an autoregressive conditional variance model, and predicting the gas load for natural gas according to the analysis result.
2. The method for analyzing the natural gas load fluctuation symmetry based on the GARCH model as claimed in claim 1, wherein in step 1, the data set of the natural gas load is divided into a plurality of sub data sets according to a set time period.
3. The method for analyzing the natural gas load fluctuation symmetry based on the GARCH model as claimed in claim 1, wherein in the step 2, an ADF unit root test method is used for testing the stationarity of the natural gas load sequence in the sub data set;
the ADF inspection method is characterized in that a hysteresis difference item of a dependent variable yt is added into a regression equation to control the correlation of a high-order sequence, the probability value of a natural gas load sequence of a sub data set is obtained according to the regression equation, and the probability value is compared with a set probability value to determine the stationarity of the natural gas load sequence.
4. The method for analyzing the symmetry of the natural gas load fluctuation based on the GARCH model of claim 3, wherein the equation expression of the ADF root-mean-square test method is as follows:
Figure FDA0003619975310000011
Figure FDA0003619975310000012
in the formula utIs the residual, betatAs a time trend term, ytFor disturbance at randomThe term, r, is a coefficient.
5. The method for analyzing the natural gas load fluctuation symmetry based on the GARCH model as claimed in claim 1, wherein the ARCH-LM method is adopted in the step 3 to perform ARCH effect check on the sub-data sets.
6. The natural gas load fluctuation symmetry analysis method based on the GARCH model as claimed in claim 5, wherein the ARCH effect verification method of the ARCH-LM method is as follows:
and performing preliminary estimation on the sub data sets by adopting a least square method, establishing an estimation equation according to the preliminary estimation value, obtaining regression coefficients corresponding to the natural gas sequences of the sub data sets according to the estimation equation, and determining the ARCH effect of the sub data sets according to the regression coefficients.
7. The method for analyzing the symmetry of the natural gas load fluctuation based on the GARCH model as claimed in claim 6, wherein the expression of the estimation equation is as follows:
Figure FDA0003619975310000021
in the formula, σtFor the current fluctuation rate, epsilontWhite noise, μ, independently identically distributed with zero mean unit variancetFor the current residual, ai、rjAre coefficients.
8. The method for analyzing the symmetry of the natural gas load fluctuation based on the GARCH model as claimed in claim 1, wherein the expression of the autoregressive conditional variance model in the step 4 is as follows:
Figure FDA0003619975310000022
in the formula, σtFor the current fluctuation rate, epsilontIs zeroIndependent equal distribution white noise, μ, of mean unit variancetIs the current residual.
9. The method for analyzing the natural gas load fluctuation symmetry based on the GARCH model as claimed in claim 1, wherein, before the step 1 is executed, missing values are filled in the acquired natural gas data set and abnormal values are deleted.
10. A system of the natural gas load fluctuation symmetry analysis method based on the GARCH model according to any one of claims 1 to 9, comprising,
the subdata set module is used for dividing the acquired natural gas consumption load data set of the historical time period into a plurality of subdata sets according to a preset rule;
the stability checking module is used for checking the stability of the natural gas load sequence on the sub data sets;
the effect checking module is used for checking the ARCH effect of the subdata set checked by the stationarity checking module;
and the analysis module is used for performing volatility symmetry analysis on the subdata set verified by the effect verification module by adopting an autoregressive condition variance model and predicting the gas load for the natural gas according to an analysis result.
CN202210480168.1A 2022-04-27 2022-04-27 Natural gas load fluctuation symmetry analysis method and system based on GARCH model Pending CN114723173A (en)

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Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20140156322A1 (en) * 2012-08-10 2014-06-05 Itron, Inc. Unified Framework for Electrical Load Forecasting
CN104123591A (en) * 2014-07-14 2014-10-29 上海师范大学 Smart grid classification and fuzzy neural network based natural gas load prediction method
CN109492813A (en) * 2018-11-13 2019-03-19 中石化石油工程技术服务有限公司 Short-term natural gas load combination forecasting method based on improved adaptive GA-IAGA
CN113222234A (en) * 2021-04-30 2021-08-06 中国石油天然气股份有限公司 Gas demand prediction method and system based on integrated modal decomposition

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20140156322A1 (en) * 2012-08-10 2014-06-05 Itron, Inc. Unified Framework for Electrical Load Forecasting
CN104123591A (en) * 2014-07-14 2014-10-29 上海师范大学 Smart grid classification and fuzzy neural network based natural gas load prediction method
CN109492813A (en) * 2018-11-13 2019-03-19 中石化石油工程技术服务有限公司 Short-term natural gas load combination forecasting method based on improved adaptive GA-IAGA
CN113222234A (en) * 2021-04-30 2021-08-06 中国石油天然气股份有限公司 Gas demand prediction method and system based on integrated modal decomposition

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
陈昊: ""非经典计量经济学在负荷预测中的应用研究"", 《中国优秀硕士论文全文数据库 工程科技Ⅱ辑》, 15 January 2007 (2007-01-15), pages 47 - 63 *

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