CN113919559A - Ultra-short-term prediction method and device for equipment parameters of comprehensive energy system - Google Patents

Ultra-short-term prediction method and device for equipment parameters of comprehensive energy system Download PDF

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CN113919559A
CN113919559A CN202111147126.8A CN202111147126A CN113919559A CN 113919559 A CN113919559 A CN 113919559A CN 202111147126 A CN202111147126 A CN 202111147126A CN 113919559 A CN113919559 A CN 113919559A
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parameters
equipment
sequence
prediction
parameter
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黄源烽
郝飞
陈根军
姜彬
解凯
顾全
庄怀东
蒲桂林
鲍永
林阳
唐迪
季炳伟
王钧
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NR Electric Co Ltd
NR Engineering Co Ltd
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NR Engineering Co Ltd
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Abstract

The invention discloses a method and a device for ultra-short-term prediction of equipment parameters of an integrated energy system, which complete mechanism analysis and correlation analysis by building a historical database and provide a theoretical basis for selecting characteristic parameters in the process of building a prediction model; the method comprises the steps of carrying out data division on a historical database according to different equipment working conditions and calculating corresponding characteristic identification quantities of the historical database, and providing a judgment standard for pre-judging, searching and acquiring input data of a prediction model under subsequent working conditions; finally, ultra-short-term prediction of the prediction parameters is realized through similarity analysis and gray scale modeling, and prediction results of the prediction parameters in 0-4 hours in the future and with the time resolution of 5 minutes are provided. The method effectively improves the prediction precision and provides an effective reference basis for the optimal scheduling and economic operation of the regional comprehensive energy system.

Description

Ultra-short-term prediction method and device for equipment parameters of comprehensive energy system
Technical Field
The invention relates to an ultra-short term prediction method and device for equipment parameters of an integrated energy system, and belongs to the technical field of integrated energy prediction.
Background
The comprehensive energy system is an important technical means for improving the energy utilization efficiency and reducing the energy utilization cost, and accurate supply and demand prediction is a key basis and an important precondition for economic planning, cooperative management and optimized scheduling of the comprehensive energy system. How to make accurate supply and demand prediction is the key point of ultra-short-term prediction of equipment parameters of the comprehensive energy system, and the ultra-short-term prediction of the equipment parameters needs to accurately predict the change condition of the equipment parameters within 0-4 hours in the future according to the internal change rule of the parameters and considering various influences of external factors on the parameter change.
At present, ultrashort-term prediction research aiming at equipment parameters of an integrated energy system is greatly developed in the aspect of basic theory, certain achievements are obtained in practical engineering application, the method can be divided into two types under the longitudinal view, one type is a prediction method based on a mechanism model, the method depends on mathematical description of equipment, the physical significance is clear, the functional relation is clear, the prediction process has stronger extrapolation performance, the difficulty of mathematical description is sharply increased along with the increase of the complexity of the mechanism of the equipment, and the feasibility of prediction implementation and the reliability of the prediction result are also sharply reduced. The other type is a prediction method based on a data model, and the method is characterized in that the incidence relation among variables is extracted on the basis of data samples, the development period is short, the calculation demand is small, the comprehensive cost of prediction solution is relatively low, but similarly, the method is difficult to explain the decision basis and the operation logic, and the uncertainty of the reliability of the prediction result and the hidden safety risk limit the popularization and the application of the prediction method in the industrial field.
Overall, the comprehensive energy system devices are of various types, and the operating rules of different devices have fluctuation in different time-space spans, and the uncertainty of the influence of various external factors makes the prediction algorithm based on a single model of a mechanism model or a data model poor in effect.
Based on the method, the invention provides the ultra-short term prediction method and the device of the equipment parameters of the comprehensive energy system, in order to improve the overall performance of the ultra-short term prediction method of the comprehensive energy system equipment and enhance the application effect of the method.
Disclosure of Invention
The purpose is as follows: in order to overcome the defects in the prior art, the invention provides the ultra-short-term prediction method and the device for the equipment parameters of the comprehensive energy system, which finish mechanism analysis and association degree analysis by building a historical database and provide a theoretical basis for selecting the characteristic parameters in the process of building a prediction model; the method comprises the steps of carrying out data division on a historical database according to different equipment working conditions and calculating corresponding characteristic identification quantities of the historical database, and providing a judgment standard for pre-judging, searching and acquiring input data of a prediction model under subsequent working conditions; finally, ultra-short-term prediction of the prediction parameters is realized through similarity analysis and gray scale modeling, and prediction results of the prediction parameters in 0-4 hours in the future and with the time resolution of 5 minutes are provided.
The technical scheme is as follows: in order to solve the technical problems, the technical scheme adopted by the invention is as follows:
the invention provides an ultra-short-term prediction method for equipment parameters of a comprehensive energy system, which comprises the following steps of:
and acquiring historical time sequence data of equipment parameters including prediction parameters of the comprehensive energy system equipment, and constructing a historical database.
And selecting characterization parameters related to the prediction parameters from the equipment parameters in the historical database according to the mechanism analysis of the equipment.
And performing correlation analysis on the characterization parameters, and extracting characteristic parameters from the characterization parameters.
And calculating the characteristic identification quantity under different operation condition types according to the characteristic parameters.
And constructing an online prediction model of the comprehensive energy system equipment.
Determining the type of the operating condition of the equipment to be predicted, selecting an extended sequence of the original basic sequence of the characteristic parameter which is most similar to the original basic sequence of the characteristic parameter at the current moment from the corresponding historical database as the input of an online prediction model of the equipment of the comprehensive energy system, and solving the online prediction model of the equipment of the comprehensive energy system to obtain the predicted value of the equipment parameter of the equipment to be predicted.
An ultra-short term prediction device for equipment parameters of an integrated energy system comprises the following modules:
and the data acquisition module is used for acquiring historical time sequence data of equipment parameters including the prediction parameters of the comprehensive energy system equipment and constructing a historical database.
And the mechanism analysis module is used for selecting the characterization parameters related to the prediction parameters from the equipment parameters of the historical database according to the mechanism analysis of the equipment.
And the correlation analysis module is used for performing correlation analysis on the characterization parameters and extracting the characteristic parameters from the characterization parameters.
And the working condition analysis module is used for calculating the characteristic identification quantity under different operation working condition types according to the characteristic parameters.
And the online prediction model building module is used for building an online prediction model of the comprehensive energy system equipment.
And the real-time prediction module is used for determining the type of the operation condition of the equipment to be predicted, selecting an extended sequence of the original basic sequence of the characteristic parameters which are most similar to the original basic sequence of the characteristic parameters at the current moment from the corresponding historical database as the input of the online prediction model of the comprehensive energy system equipment, and solving the online prediction model of the comprehensive energy system equipment to obtain the predicted value of the equipment parameters of the equipment to be predicted.
Preferably, the method further comprises the following steps: when a dead pixel exists in the historical database, the dead pixel is taken as a center, normal data before and after the dead pixel is selected according to the time sequence for curve fitting, the numerical value of the dead pixel is calculated according to the fitting function interpolation, and the numerical value is taken as the correction value of the dead pixel.
As a preferred scheme, the correlation analysis of the characterization parameters and the extraction of the characterization parameters from the characterization parameters include the following steps:
1) obtaining prediction parameter x from historical database0And a comparison sequence of p characterization parameters,
x0＝{x0(1),x0(2),…x0(i)…,x0(n)}
Figure BDA0003285588990000041
in the formula, x0As a reference sequence, x0(i) To predict the ith relevant parameter of the parameter,
Figure BDA0003285588990000042
for the comparison sequence of the ith characterizing parameter,
Figure BDA0003285588990000043
the j-th related parameter of the ith comparison sequence is shown, n is the number of elements contained in the sequence, and p is the number of the characterization parameters.
2) And performing dimensionless initialization processing on the reference sequence and the comparison sequence to obtain the dimensionless initialized reference sequence and comparison sequence.
x'0＝{1,x0(2)/x0(1),…,x0(n)/x0(1)}
Figure BDA0003285588990000044
3) Calculating a reference sequence x 'after dimension initialization'0And comparing the sequences
Figure BDA00032855889900000410
Grey correlation coefficient on the kth correlation parameter:
Figure BDA0003285588990000045
in the formula, eta is a resolution coefficient,
Figure BDA0003285588990000046
the minimum difference of the two levels is represented,
Figure BDA0003285588990000048
representing the two-step maximum difference.
4) Calculating gray correlation degree gamma of each characterization parameteri。
Figure BDA0003285588990000049
5) Setting a grey correlation threshold gamma0Extracting { gamma ] therefromi|γi＞γ0The characterization parameters of i ═ 1,2, …, p } form a new characteristic parameter sequence x1，x2，…，xqWherein q is the number of characteristic parameters, and q is less than or equal to p.
As a preferred scheme, the calculating the feature identification quantity under different operation condition categories according to the feature parameters includes the following steps:
and dividing the working condition type according to the operating working condition of the equipment, and dividing the historical data according to the working condition type.
And calculating the characteristic identification quantity corresponding to the working condition category according to the divided historical data. The method comprises the following steps:
1) establishing a characteristic parameter x1，x2，…，xqFor the prediction parameter x0Is a regression model
Figure BDA0003285588990000051
In the formula (I), the compound is shown in the specification,
Figure BDA0003285588990000052
in order to be the regression coefficient, the method,
Figure BDA0003285588990000053
is an object estimate calculated based on a regression model.
2) Each regression coefficient was obtained from the following equation
Figure BDA0003285588990000054
In the formula (I), the compound is shown in the specification,
Figure BDA0003285588990000055
are respectively x0，x1，x2，…，xqT, R are the dispersion matrix and regression matrix of the regression model, respectively; is provided with
Figure BDA0003285588990000056
Figure BDA0003285588990000057
In the formula, TijRepresenting the elements of the ith row and jth column of the matrix T, RiRepresenting the ith element in the matrix R.
3) Characterization of the prediction parameter x by λ0And a characteristic parameter x1，x2，…，xqBulk phase under this regimeThe degree of closeness is calculated by the formula:
Figure BDA0003285588990000061
and taking the lambda as the characteristic identification quantity corresponding to each group of working condition data.
As a preferred scheme, the building of the online prediction model of the integrated energy system device comprises the following steps:
1) when the prediction period is reached, the current moment is taken as a starting point, and the parameter x is simultaneously measured0，x1，x2，...，xqTracing m historical data as basic original sequences of online modeling:
Figure BDA0003285588990000062
to pair
Figure BDA0003285588990000063
Performing one-time accumulation to generate (1-AGO) to obtain a sequence
Figure BDA0003285588990000064
In the formula (I), the compound is shown in the specification,
Figure BDA0003285588990000065
2) by
Figure BDA0003285588990000066
Generating a sequence of close-proximity means
Figure BDA0003285588990000067
In the formula (I), the compound is shown in the specification,
Figure BDA0003285588990000068
in the formula, α is a background value.
3) The gray scale degree prediction model is established as
Figure BDA0003285588990000069
Wherein a is the coefficient of development, biAs a drive factor, h1(k-1) is a linearity correction amount; h is2The grey effect is indicated.
4) Gray scale prediction model can be converted to
Figure BDA00032855889900000610
In the formula (I), the compound is shown in the specification,
Figure BDA00032855889900000611
5) performing one-time subtraction reduction operation on the above formula to obtain an online prediction model of the prediction parameters:
Figure BDA0003285588990000071
preferably, the parameters a and bi、h1(k-1)、h2The specific calculation expression of (2) is:
U＝(BTB)-1BTY
in the formula, BTA transposed matrix representing the matrix B, (B)TB)-1Representation matrix BTInverse matrix of B
Figure BDA0003285588990000072
The method for determining the operation condition type of the equipment to be predicted comprises the following steps:
by means of the original base sequence, it is possible to,
Figure BDA0003285588990000073
calculating a prediction parameter x0And a characteristic parameter x1，x2，...，xqThe real-time identification number lambda at the current time.
And determining the type of the operating condition of the equipment to be predicted according to the real-time identification quantity lambda and the judgment condition of the current operating condition of the equipment. The determination condition is that
Figure BDA0003285588990000074
And taking the value of the next L, wherein L is the total working condition classification number.
The method comprises the following steps of selecting an expansion sequence of an original basic sequence of the characteristic parameter most similar to the current moment from a corresponding historical database as the input of an online prediction model of the comprehensive energy system equipment, solving the online prediction model of the comprehensive energy system equipment to obtain an equipment parameter prediction value of the equipment to be predicted, and comprises the following steps:
1) in the corresponding historical working condition data sequence, starting from the first historical data of the prediction parameters, selecting m data points to form a historical sequence
Figure BDA0003285588990000075
Passing and predicting measured sequences of parameters
Figure BDA0003285588990000076
Carrying out similarity judgment, and finding out an initial sequence number point s of a similar sequence meeting the following formula in the historical data sequence;
Figure BDA0003285588990000081
in the formula (I), the compound is shown in the specification,
Figure BDA0003285588990000082
data of the (s + k-1) th point in the prediction parameter history sequence from the(s) th point to the (s + m-1) th point;
2) obtaining corresponding characteristic parameter historical sequence
Figure BDA0003285588990000083
Figure BDA0003285588990000084
In the formula (I), the compound is shown in the specification,
Figure BDA0003285588990000085
the method comprises the steps that data of (s + m + k-1) th points in a characteristic parameter historical sequence from (s + m) th points to (s + m + f-1) th points are obtained, k is the number of points to be predicted, and f is the total number of predicted points;
will be provided with
Figure BDA0003285588990000086
Incorporating original base sequences of feature parameters
Figure BDA0003285588990000087
In the method, an extended sequence of the original basic sequence of the characteristic parameters is obtained
Figure BDA0003285588990000088
Figure BDA0003285588990000089
To pair
Figure BDA00032855889900000810
Performing one-time accumulation to generate (1-AGO) to obtain an input sequence of the prediction model
Figure BDA00032855889900000811
Figure BDA00032855889900000812
In the formula (I), the compound is shown in the specification,
Figure BDA00032855889900000813
3) will be provided with
Figure BDA00032855889900000814
Substituted in the formula
Figure BDA00032855889900000815
Computing
Figure BDA00032855889900000816
Figure BDA00032855889900000817
4) Will be provided with
Figure BDA00032855889900000818
Substituting the following formula to obtain the predicted value of the device prediction parameter at f points in the future as:
Figure BDA00032855889900000819
has the advantages that: compared with the prior art, the ultra-short-term prediction method and the ultra-short-term prediction device for the equipment parameters of the comprehensive energy system have the following beneficial effects that: the method integrates the theoretical accuracy of a mechanism model and the strong generalization deepening of a data driving model, and finally establishes an online gray scale degree ultra-short-term prediction model by means of correlation analysis, working condition data division and similarity principles, so that ultra-short-term prediction of specific parameters of the integrated energy system equipment under the multi-factor coupling condition is realized, the prediction precision is effectively improved, and an effective reference basis is provided for optimal scheduling and economic operation of the regional integrated energy system.
Drawings
FIG. 1 is a flow chart of the steps of the method of the present invention.
FIG. 2 is a schematic view of the structure of the apparatus of the present invention.
Detailed Description
The present invention will be further described with reference to the following examples.
As shown in fig. 1, in a first aspect, the present invention provides a method for ultra-short term prediction of equipment parameters of an integrated energy system, including the following steps:
and acquiring historical time sequence data of equipment parameters including prediction parameters of the comprehensive energy system equipment, and constructing a historical database.
And selecting characterization parameters related to the prediction parameters from the equipment parameters in the historical database according to the mechanism analysis of the equipment.
And performing correlation analysis on the characterization parameters, and extracting characteristic parameters from the characterization parameters.
And calculating the characteristic identification quantity under different operation condition types according to the characteristic parameters.
And constructing an online prediction model of the comprehensive energy system equipment.
Determining the type of the operating condition of the equipment to be predicted, selecting an extended sequence of the original basic sequence of the characteristic parameter which is most similar to the original basic sequence of the characteristic parameter at the current moment from the corresponding historical database as the input of an online prediction model of the equipment of the comprehensive energy system, and solving the online prediction model of the equipment of the comprehensive energy system to obtain the predicted value of the equipment parameter of the equipment to be predicted.
When the dead pixel in the historical database comprises data missing and data gross error, data correction is carried out by adopting a curve fitting method, specifically, the dead pixel is taken as a center, normal data before and after the dead pixel is selected according to the time sequence for curve fitting, the numerical value of the dead pixel is interpolated according to a fitting function and is taken as the correction value of the dead pixel.
The mechanism analysis of the equipment is to utilize a mechanism model with definite physical or practical significance to search the change rule of the prediction parameters along with the characterization parameters, taking a generator set as an example, and the mechanism model related in the operation process of the equipment comprises the following steps:
1) equation of energy balance
Figure BDA0003285588990000101
2) Equation of mass balance
Figure BDA0003285588990000102
3) Equation of working medium properties
f(h，p，T)＝0
4) Boundary constraint equation
f(λ1，λ2，λ3，…)＝0
In the formula: ρ represents density, c represents specific heat capacity, V represents volume, T represents temperature, τ represents time, q represents specific heat capacityinRepresenting input energy, qoutRepresenting output energy, DinDenotes the inlet flow, DoutDenotes the outlet flow, h denotes the enthalpy, P denotes the pressure, lambda1、λ2、λ3Representing the constraint.
Therefore, when the unit output energy is taken as a prediction parameter, the related characterization parameters can be selected to be temperature, pressure and flow. When the characteristic parameters are determined, characteristic parameters are extracted from the characteristic parameters through correlation analysis.
The correlation analysis of the characterization parameters and the extraction of the characteristic parameters from the characterization parameters comprise the following steps:
1) obtaining prediction parameter x from historical database0And a comparison sequence of p characterization parameters,
x0＝{x0(1)，x0(2)，…x0(i)…，x0(n)}
Figure BDA0003285588990000111
in the formula, x0As a reference sequence, x0(i) To predict the ith relevant parameter of the parameter,
Figure BDA0003285588990000112
for the comparison sequence of the ith characterizing parameter,
Figure BDA0003285588990000113
the j-th related parameter of the ith comparison sequence is shown, n is the number of elements contained in the sequence, and p is the number of the characterization parameters.
2) Further, performing dimensionless initialization processing on the reference sequence and the comparison sequence to obtain the dimensionless initialized reference sequence and comparison sequence.
x′0＝{1，x0(2)/x0(1)，…，x0(n)/x0(1)}
Figure BDA0003285588990000114
3) Calculating a reference sequence x 'after dimension initialization'0And comparing the sequences
Figure BDA0003285588990000119
Grey correlation coefficient on the kth correlation parameter:
Figure BDA0003285588990000115
wherein eta is a resolution coefficient, eta belongs to [0, 1], and when eta is less than or equal to 0.55, the resolution capability is optimal.
Figure BDA0003285588990000116
The minimum difference of the two levels is represented,
Figure BDA0003285588990000117
representing the two-step maximum difference.
4) Further, the gray correlation degree gamma of each characterization parameter is calculatedi。
Figure BDA0003285588990000118
5) Setting a grey correlation threshold gamma00.6, { γ ] extracted therefromi|γi＞γ0，i＝1,2, …, p } to form a new characteristic parameter sequence x1，x2，...，xqWherein q is the number of characteristic parameters, and q is less than or equal to p.
The method for calculating the characteristic identification quantity under different operation condition categories according to the characteristic parameters comprises the following steps:
eight working condition categories of a full state, a 90% state, a 70% state, a 50% state, a 30% state, a 10% state, an ascending state and a descending state are divided according to the running working conditions of the equipment, and historical data are divided according to the working condition categories.
And calculating the characteristic identification quantity corresponding to the working condition category according to the divided historical data. The method comprises the following steps:
1) establishing a characteristic parameter x1，x2，...，xqFor the prediction parameter x0Is a regression model
Figure BDA0003285588990000121
In the formula (I), the compound is shown in the specification,
Figure BDA0003285588990000122
in order to be the regression coefficient, the method,
Figure BDA0003285588990000123
is an object estimate calculated based on a regression model.
2) Further, each regression coefficient can be obtained by the following formula
Figure BDA0003285588990000124
In the formula (I), the compound is shown in the specification,
Figure BDA0003285588990000125
are respectively x0，x1，x2，...，xqT, R are the dispersion matrix and regression matrix of the regression model, respectively, having
Figure BDA0003285588990000126
Figure BDA0003285588990000127
In the formula, TijRepresenting the elements of the ith row and jth column of the matrix T, RiRepresenting the ith element in the matrix R.
3) Further, the prediction parameter x is characterized by λ0And a characteristic parameter x1，x2，...，xqThe overall correlation degree under the working condition is calculated by the formula:
Figure BDA0003285588990000128
therefore, lambda is used as a characteristic identification quantity corresponding to each group of working condition data, and when x is acquired0，x1，x2，...，xqWhen the data is actually measured, the real-time identification quantity of the data can be calculated and compared with the characteristic identification quantity, so that the current working condition state of the equipment is judged in advance, and a basis for predicting sample selection is further provided for implementation of real-time prediction.
The method for constructing the online prediction model of the comprehensive energy system equipment comprises the following steps:
when the prediction period is reached, the current moment is taken as a starting point, and the parameter x is simultaneously measured0，x1，x2，...，xqTracing m pieces of historical data as basic original sequences of online modeling, and establishing a prediction model:
1) basic original sequence
Figure BDA0003285588990000131
To pair
Figure BDA0003285588990000132
Performing one-time accumulation to generate (1-AGO) to obtain a sequence
Figure BDA0003285588990000133
In the formula (I), the compound is shown in the specification,
Figure BDA0003285588990000134
2) by
Figure BDA0003285588990000135
Generating a sequence of close-proximity means
Figure BDA0003285588990000136
In the formula (I), the compound is shown in the specification,
Figure BDA0003285588990000137
in the formula, α is a background value and is 0.5 as a default.
3) Further, a gray scale degree prediction model is established as
Figure BDA0003285588990000138
Wherein a is the coefficient of development, biAs a drive factor, h1(k-1) is a linearity correction amount; h is2The grey effect is indicated. The parameters a, bi、h1(k-1)、h2The specific calculation expression of (2) is:
U＝(BTB)-1BTY
in the formula, BTA transposed matrix representing the matrix B, (B)TB)-1Representation matrix BTInverse matrix of B
Figure BDA0003285588990000141
4) Further, the gray scale prediction model may be converted to
Figure BDA0003285588990000142
In the formula (I), the compound is shown in the specification,
Figure BDA0003285588990000143
5) performing one-time subtraction reduction operation on the above formula to obtain an online prediction model of the prediction parameters:
Figure BDA0003285588990000144
the method for determining the operation condition type of the equipment to be predicted comprises the following steps:
by means of the original base sequence, it is possible to,
Figure BDA0003285588990000145
calculating a prediction parameter x0And a characteristic parameter x1，x2，...，xqThe real-time identification number lambda at the current time.
And determining the type of the operating condition of the equipment to be predicted according to the real-time identification quantity lambda and the judgment condition of the current operating condition of the equipment. The determination condition is that
Figure BDA0003285588990000146
L is 1,2, …, and L is the value of L, wherein L is the total number of the working condition classifications.
The method comprises the following steps of selecting an expansion sequence of an original basic sequence of the characteristic parameter most similar to the current moment from a corresponding historical database as the input of an online prediction model of the comprehensive energy system equipment, solving the online prediction model of the comprehensive energy system equipment to obtain an equipment parameter prediction value of the equipment to be predicted, and comprises the following steps:
1) in the corresponding historical working condition data sequence, starting from the first historical data of the prediction parameters, selecting m data points to form a historical sequence
Figure BDA0003285588990000147
Passing and predicting measured sequences of parameters
Figure BDA0003285588990000148
Carrying out similarity judgment, and finding out an initial sequence number point s of a similar sequence meeting the following formula in the historical data sequence;
Figure BDA0003285588990000151
in the formula (I), the compound is shown in the specification,
Figure BDA0003285588990000152
data of the (s + k-1) th point in the prediction parameter history sequence from the(s) th point to the (s + m-1) th point;
2) obtaining corresponding characteristic parameter historical sequence
Figure BDA0003285588990000153
Figure BDA0003285588990000154
In the formula (I), the compound is shown in the specification,
Figure BDA0003285588990000155
the method comprises the steps that data of (s + m + k-1) th points in a characteristic parameter historical sequence from (s + m) th points to (s + m + f-1) th points are obtained, k is the number of points to be predicted, and f is the total number of predicted points;
will be provided with
Figure BDA0003285588990000156
Incorporating original base sequences of feature parameters
Figure BDA0003285588990000157
In the method, an extended sequence of the original basic sequence of the characteristic parameters is obtained
Figure BDA0003285588990000158
Figure BDA0003285588990000159
To pair
Figure BDA00032855889900001510
Performing one-time accumulation to generate (1-AGO) to obtain an input sequence of the prediction model
Figure BDA00032855889900001511
Figure BDA00032855889900001512
In the formula (I), the compound is shown in the specification,
Figure BDA00032855889900001513
3) will be provided with
Figure BDA00032855889900001514
Substituted in the formula
Figure BDA00032855889900001515
Computing
Figure BDA00032855889900001516
Figure BDA00032855889900001517
4) Will be provided with
Figure BDA00032855889900001518
Substituting the following formula to obtain the predicted value of the device prediction parameter at f points in the future as:
Figure BDA00032855889900001519
as shown in fig. 2, in a second aspect, the present invention provides an ultra-short term prediction apparatus for equipment parameters of an integrated energy system, including the following modules:
and the data acquisition module is used for acquiring historical time sequence data of equipment parameters including the prediction parameters of the comprehensive energy system equipment and constructing a historical database.
And the mechanism analysis module is used for selecting the characterization parameters related to the prediction parameters from the equipment parameters of the historical database according to the mechanism analysis of the equipment.
And the correlation analysis module is used for performing correlation analysis on the characterization parameters and extracting the characteristic parameters from the characterization parameters.
And the working condition analysis module is used for calculating the characteristic identification quantity under different operation working condition types according to the characteristic parameters.
And the online prediction model building module is used for building an online prediction model of the comprehensive energy system equipment.
And the real-time prediction module is used for determining the type of the operation condition of the equipment to be predicted, selecting an extended sequence of the original basic sequence of the characteristic parameters which are most similar to the original basic sequence of the characteristic parameters at the current moment from the corresponding historical database as the input of the online prediction model of the comprehensive energy system equipment, and solving the online prediction model of the comprehensive energy system equipment to obtain the predicted value of the equipment parameters of the equipment to be predicted.
Example 1:
taking a blast furnace in energy equipment in an integrated energy system as an example, the method for predicting the parameters of the gas generation amount of the blast furnace comprises the following specific steps:
step 1, collecting historical time sequence data of parameters of a blast furnace of the comprehensive energy system, and constructing a historical database.
Acquiring historical time sequence data of all equipment parameters including gas generation amount in blast furnace equipment of the comprehensive energy system, and correcting data of possible dead spots in the historical time sequence data by adopting a curve fitting method, wherein the dead spots comprise data loss or data gross error, and the specific process comprises the following steps: and taking the dead pixel as a center, selecting normal data before and after the dead pixel according to the time sequence to perform curve fitting, interpolating and calculating the numerical value of the dead pixel according to a fitting function, and taking the numerical value as a correction value of the dead pixel.
And 2, determining characterization parameters related to the blast furnace gas generation amount based on the mechanism analysis of the blast furnace.
Reasonable characterization parameters are selected according to the analysis result of the blast furnace gas generation mechanism, in the blast furnace smelting process, materials such as iron ore, coke, limestone and the like are loaded from the top of the blast furnace, and are subjected to reduction reaction with preheated air (mixed rich oxygen and coal powder) in the blast furnace, and finally blast furnace gas is discharged from the top of the blast furnace. Therefore, when the parameters of the gas generation amount need to be predicted, the related characteristic parameters can be selected from coke ratio, coal ratio, oxygen enrichment, air quantity, air temperature, air pressure and pressure difference in the furnace.
And 3, performing correlation analysis on the characteristic parameters, and extracting the characteristic parameters related to the blast furnace gas generation amount.
After the characteristic parameters are determined, extracting characteristic parameters from the characteristic parameters through correlation analysis to serve as a basis for calculating the characteristic identification quantity of the subsequent working conditions and establishing a prediction model, and the specific implementation steps are as follows:
1) and obtaining a reference sequence of gas generation amount, a comparison sequence of coke ratio, coal ratio, oxygen enrichment, air quantity, air temperature, air pressure and pressure difference in the furnace from a historical database.
x0＝{x0(1)，x0(2)，…x0(i)…，x0(n)}
Figure BDA0003285588990000171
In the formula, x0As a reference sequence, x0(i) Is the ith relevant parameter of the gas generation amount,
Figure BDA0003285588990000172
for the comparison sequence of the ith characterizing parameter,
Figure BDA0003285588990000173
is the j-th related parameter of the ith comparison sequence, n is the number of elements contained in the sequence, and p is a characterization parameterAnd (4) the number.
2) Further, performing dimensionless initialization processing on the reference sequence and the comparison sequence to obtain the dimensionless initialized reference sequence and comparison sequence.
x′0＝{1，x0(2)/x0(1)，…，x0(n)/x0(1)}
Figure BDA0003285588990000174
3) Calculating a reference sequence x 'after dimension initialization'0And comparing the sequences
Figure BDA0003285588990000176
Grey correlation coefficient on the kth correlation parameter:
Figure BDA0003285588990000175
wherein eta is a resolution coefficient, eta belongs to [0, 1], and when eta is less than or equal to 0.55, the resolution capability is optimal.
Figure BDA0003285588990000181
The minimum difference of the two levels is represented,
Figure BDA0003285588990000182
representing the two-step maximum difference.
4) Further, the gray correlation degree gamma of each characterization parameter is calculatedi。
Figure BDA0003285588990000183
5) Setting a grey correlation threshold gamma00.6, { γ ] extracted therefromi|γi＞γ0The characterization parameters of i ═ 1,2, …, p } form a new characteristic parameter sequence x1，x2，...，xqWherein, in the step (A),q is the number of characteristic parameters, and q is less than or equal to p.
In the gray correlation degree obtained by calculation, the air quantity is greater than the coke ratio, greater than the air pressure, greater than the coal ratio, greater than the air temperature, greater than 0.6, greater than the oxygen enrichment and greater than the pressure difference in the blast furnace, so that the air quantity, the coke ratio, the air pressure, the coal ratio and the air temperature are taken as characteristic parameters for predicting the blast furnace gas generation amount.
And 4, calculating the characteristic identification quantity of the blast furnace gas generation quantity under different working condition types.
After the characteristic parameters are determined, according to the operation conditions of the blast furnace equipment, dividing the operation conditions into eight operation condition categories of full load, 90% load, 70% load, 50% load, 30% load, 10% load, load increase and load decrease, dividing the historical data of the blast furnace gas generation amount and the characteristic parameters according to the operation condition categories, and calculating corresponding characteristic identification amounts, wherein the implementation steps comprise:
1) establishing the air quantity, coke ratio, air pressure, coal ratio and air temperature to the coal gas generation quantity x0Is a regression model
Figure BDA0003285588990000184
In the formula (I), the compound is shown in the specification,
Figure BDA0003285588990000185
in order to be the regression coefficient, the method,
Figure BDA0003285588990000186
for prediction parameter estimation based on regression model calculation, x1，x2，...，xqThe characteristic parameters of the air quantity, the coke ratio, the air pressure, the coal ratio and the air temperature are q-5.
2) Further, each regression coefficient can be obtained by the following formula
Figure BDA0003285588990000187
In the formula (I), the compound is shown in the specification,
Figure BDA0003285588990000191
are respectively x0，x1，x2，...，xqT, R are the dispersion matrix and regression matrix of the regression model, respectively, having
Figure BDA0003285588990000192
Figure BDA0003285588990000193
In the formula, TijRepresenting the elements of the ith row and jth column of the matrix T, RiRepresenting the ith element in the matrix R.
3) Further, lambda is used for representing the overall correlation degree of the gas generation quantity and the air quantity, the coke ratio, the air pressure, the coal ratio and the air temperature under the working condition, and the calculation formula is as follows:
Figure BDA0003285588990000194
therefore, the step is repeated to calculate the lambda value of each group of working condition data, the lambda value is used as the characteristic identification quantity corresponding to each working condition, when the actual measurement data of the gas generation quantity, the air quantity, the coke ratio, the air pressure, the coal ratio and the air temperature is obtained, the actual characteristic identification quantity can be calculated and compared with the characteristic identification quantity, so that the current working condition state of the blast furnace is judged, and the basis for selecting the prediction sample is further provided for the implementation of the actual prediction.
Step 5, establishing an online prediction model
When the prediction period is reached, the current moment is taken as a starting point, and m historical data of gas generation amount, air volume, coke ratio, air pressure, coal ratio and air temperature are traced back to serve as basic original sequences of online modeling, wherein the process of establishing the prediction model comprises the following steps:
1) obtaining basic original sequences of parameters of gas generation quantity, coke ratio, coal ratio, air quantity, air pressure and air temperature
Figure BDA0003285588990000195
To pair
Figure BDA0003285588990000196
Performing one-time accumulation to generate (1-AGO) to obtain a sequence
Figure BDA0003285588990000201
In the formula (I), the compound is shown in the specification,
Figure BDA0003285588990000202
2) for blast furnace gas generation, from
Figure BDA0003285588990000203
Generating a sequence of close-proximity means
Figure BDA0003285588990000204
In the formula (I), the compound is shown in the specification,
Figure BDA0003285588990000205
in the formula, α is a background value and is 0.5 as a default.
3) Further, a gray scale degree prediction model is established as
Figure BDA0003285588990000206
Wherein a is the coefficient of development, biAs a drive factor, h1(k-1) is a linearity correction amount; h is2The grey effect is indicated. The parameters a, bi、h1(k-1)、h2The specific calculation expression of (2) is:
U＝(BTB)-1BTY
in the formula, BTA transposed matrix representing the matrix B, (B)TB)-1Representing momentsArray BTInverse matrix of B
Figure BDA0003285588990000207
4) Further, the gray scale prediction model may be converted to
Figure BDA0003285588990000208
In the formula (I), the compound is shown in the specification,
Figure BDA0003285588990000209
5) performing a subtraction reduction operation on the above formula to obtain a gray predicted value of the blast furnace gas generation amount as follows:
Figure BDA00032855889900002010
step 6, input prediction and output prediction
With the original basic sequence in step 5,
Figure BDA0003285588990000211
calculating the real-time identification quantity lambda of the gas generation quantity, the air quantity, the coke ratio, the air pressure, the coal ratio and the air temperature at the current moment based on the step 4, and further judging the current working condition of the equipment to meet the requirement
Figure BDA0003285588990000212
And taking the value of the next L, wherein L is the total working condition classification number.
After the current working condition state of the equipment is determined, an expansion sequence of the original basic sequence of the characteristic parameters most similar to the current moment is required to be searched from the corresponding historical working condition data sequence to be used as the input of a prediction model, and the specific implementation process is as follows:
1) in the corresponding historical working condition data sequence, starting from the first historical data of the prediction parameters, selecting m data point groupsInto a historical sequence
Figure BDA0003285588990000213
Passing and predicting measured sequences of parameters
Figure BDA0003285588990000214
Carrying out similarity judgment, and finding out an initial sequence number point s of a similar sequence meeting the following formula in the historical data sequence;
Figure BDA0003285588990000215
in the formula (I), the compound is shown in the specification,
Figure BDA0003285588990000216
data of the (s + k-1) th point in the prediction parameter history sequence from the(s) th point to the (s + m-1) th point;
2) further, a corresponding characteristic parameter historical sequence is obtained
Figure BDA0003285588990000217
Figure BDA0003285588990000218
In the formula (I), the compound is shown in the specification,
Figure BDA0003285588990000219
the method comprises the steps that data of (s + m + k-1) th points in a characteristic parameter historical sequence from (s + m) th points to (s + m + f-1) th points are obtained, k is the number of points to be predicted, and f is the total number of predicted points;
will be provided with
Figure BDA00032855889900002110
Incorporating original base sequences of feature parameters
Figure BDA00032855889900002111
In the method, an extended sequence of the original basic sequence of the characteristic parameters is obtained
Figure BDA00032855889900002112
Figure BDA0003285588990000221
To pair
Figure BDA0003285588990000222
Performing one-time accumulation to generate (1-AGO) to obtain an input sequence of the prediction model
Figure BDA0003285588990000223
Figure BDA0003285588990000224
In the formula (I), the compound is shown in the specification,
Figure BDA0003285588990000225
3) will be provided with
Figure BDA0003285588990000226
Substituted in the formula
Figure BDA0003285588990000227
Computing
Figure BDA0003285588990000228
Figure BDA0003285588990000229
4) Will be provided with
Figure BDA00032855889900002210
Substituting the following formula to finally obtain the predicted value of the total f points of the predicted parameters at 0-4 hours in the future and with the time resolution of 5 minutes as follows:
Figure BDA00032855889900002211
example 2:
the invention provides an implementation method and system for ultra-short-term prediction of comprehensive energy system equipment, wherein a historical database is constructed, characterization parameters are obtained based on mechanism analysis for correlation analysis and characteristic parameters are extracted, working condition data are divided by combining with the operating conditions of the energy equipment, a sample space is built, then an online gray scale ultra-short-term prediction model is built by using the actual measurement data of the prediction parameters and the characteristic parameters, the input of the online prediction model is obtained by pre-judging the real-time working conditions, and finally the ultra-short-term prediction of the prediction parameters is realized. The model gives consideration to the theoretical accuracy of the mechanism model and the strong generalization and deepening of the data driving model, can realize the ultra-short-term prediction of the comprehensive energy equipment under the multi-factor coupling condition, effectively improves the prediction precision, and provides an effective reference basis for the optimal scheduling and the economic operation of the regional comprehensive energy system.
The above description is only of the preferred embodiments of the present invention, and it should be noted that: it will be apparent to those skilled in the art that various modifications and adaptations can be made without departing from the principles of the invention and these are intended to be within the scope of the invention.

Claims (9)

1. An ultra-short-term prediction method for equipment parameters of an integrated energy system is characterized by comprising the following steps: the method comprises the following steps:
acquiring historical time sequence data of equipment parameters including prediction parameters of the comprehensive energy system equipment, and constructing a historical database;
selecting a characterization parameter related to a prediction parameter from equipment parameters of a historical database according to mechanism analysis on the equipment;
performing correlation analysis on the characterization parameters, and extracting characteristic parameters from the characterization parameters;
calculating characteristic identification quantities under different operation condition categories according to the characteristic parameters;
constructing an online prediction model of the comprehensive energy system equipment;
determining the type of the operating condition of the equipment to be predicted, selecting an extended sequence of the original basic sequence of the characteristic parameter which is most similar to the original basic sequence of the characteristic parameter at the current moment from the corresponding historical database as the input of an online prediction model of the equipment of the comprehensive energy system, and solving the online prediction model of the equipment of the comprehensive energy system to obtain the predicted value of the equipment parameter of the equipment to be predicted.
2. The ultra-short term prediction method for the equipment parameters of the integrated energy system according to claim 1, characterized in that: further comprising: when a dead pixel exists in the historical database, the dead pixel is taken as a center, normal data before and after the dead pixel is selected according to the time sequence for curve fitting, the numerical value of the dead pixel is calculated according to the fitting function interpolation, and the numerical value is taken as the correction value of the dead pixel.
3. The ultra-short term prediction method for the equipment parameters of the integrated energy system according to claim 1, characterized in that: the correlation analysis of the characterization parameters and the extraction of the characteristic parameters from the characterization parameters comprise the following steps:
1) obtaining prediction parameter x from historical database0And a comparison sequence of p characterization parameters,
x0＝{x0(1),x0(2),…x0(i)…,x0(n)}
Figure FDA0003285588980000011
in the formula, x0As a reference sequence, x0(i) To predict the ith relevant parameter of the parameter,
Figure FDA0003285588980000021
for the comparison sequence of the ith characterizing parameter,
Figure FDA0003285588980000022
for the jth correlation parameter of the ith comparison sequence,n is the number of elements contained in the sequence, and p is the number of characterization parameters;
2) carrying out dimensionless initialization processing on the reference sequence and the comparison sequence to obtain a dimensionless initialized reference sequence and a comparison sequence;
x'0＝{1,x0(2)/x0(1),…,x0(n)/x0(1)}
Figure FDA0003285588980000023
3) calculating a reference sequence x 'after dimension initialization'0And comparing the sequences
Figure FDA0003285588980000024
Grey correlation coefficient on the kth correlation parameter:
Figure FDA0003285588980000025
in the formula, eta is a resolution coefficient,
Figure FDA0003285588980000026
the minimum difference of the two levels is represented,
Figure FDA0003285588980000027
represents the two-step maximum difference;
4) calculating gray correlation degree gamma of each characterization parameteri；
Figure FDA0003285588980000028
5) Setting a grey correlation threshold gamma0Extracting { gamma ] therefromi|γi＞γ0The characterization parameters of i ═ 1,2, …, p } form a new characteristic parameter sequence x1，x2，…，xqWherein q is the number of characteristic parameters，q≤p。
4. The ultra-short term prediction method for the equipment parameters of the integrated energy system according to claim 3, characterized in that: the method for calculating the characteristic identification quantity under different operation condition categories according to the characteristic parameters comprises the following steps:
dividing working condition types according to the operating working conditions of the equipment, and dividing historical data according to the working condition types;
calculating the characteristic identification quantity corresponding to the working condition category according to the divided historical data; the method comprises the following steps:
1) establishing a characteristic parameter x1，x2，…，xqFor the prediction parameter x0Is a regression model
Figure FDA0003285588980000031
In the formula (I), the compound is shown in the specification,
Figure FDA0003285588980000032
in order to be the regression coefficient, the method,
Figure FDA0003285588980000033
is an object estimation value calculated based on a regression model;
2) each regression coefficient was obtained from the following equation
Figure FDA0003285588980000034
In the formula (I), the compound is shown in the specification,
Figure FDA0003285588980000035
are respectively x0，x1，x2，…，xqT, R are the dispersion matrix and regression matrix of the regression model, respectively; is provided with
Figure FDA0003285588980000036
Figure FDA0003285588980000037
In the formula, TijRepresenting the elements of the ith row and jth column of the matrix T, RiRepresents the ith element in the matrix R;
3) characterization of the prediction parameter x by λ0And a characteristic parameter x1，x2，…，xqThe overall correlation degree under the working condition is calculated by the formula:
Figure FDA0003285588980000038
and taking the lambda as the characteristic identification quantity corresponding to each group of working condition data.
5. The ultra-short term prediction method for the equipment parameters of the integrated energy system according to claim 1, characterized in that: the method for constructing the online prediction model of the comprehensive energy system equipment comprises the following steps:
1) when the prediction period is reached, the current moment is taken as a starting point, and the parameter x is simultaneously measured0，x1，x2，…，xqTracing m historical data as basic original sequences of online modeling:
Figure FDA0003285588980000041
to pair
Figure FDA0003285588980000042
Performing one-time accumulation to generate (1-AGO) to obtain a sequence
Figure FDA0003285588980000043
In the formula (I), the compound is shown in the specification,
Figure FDA0003285588980000044
2) by
Figure FDA0003285588980000045
Generating a sequence of close-proximity means
Figure FDA0003285588980000046
In the formula (I), the compound is shown in the specification,
Figure FDA0003285588980000047
wherein, alpha is a background value;
3) the gray scale degree prediction model is established as
Figure FDA0003285588980000048
Wherein a is the coefficient of development, biAs a drive factor, h1(k-1) is a linearity correction amount; h is2The amount of gray effect;
4) gray scale prediction model can be converted to
Figure FDA0003285588980000049
In the formula (I), the compound is shown in the specification,
Figure FDA00032855889800000410
Figure FDA00032855889800000411
5) performing one-time subtraction reduction operation on the above formula to obtain an online prediction model of the prediction parameters:
Figure FDA00032855889800000412
6. the ultra-short term prediction method for the equipment parameters of the integrated energy system according to claim 5, characterized in that: the parameters a, bi、h1(k-1)、h2The specific calculation expression of (2) is:
U＝(BTB)-1BTY
in the formula, BTA transposed matrix representing the matrix B, (B)TB)-1Representation matrix BTInverse matrix of B
Figure FDA0003285588980000051
7. The ultra-short term prediction method for the equipment parameters of the integrated energy system according to claim 4, characterized in that: the method for determining the operation condition type of the equipment to be predicted comprises the following steps:
by means of the original base sequence, it is possible to,
Figure FDA0003285588980000052
calculating a prediction parameter x0And a characteristic parameter x1，x2，…，xqReal-time identification quantity lambda at the current moment;
determining the type of the operating condition of the equipment to be predicted according to the real-time identification quantity lambda and the judgment condition of the current operating condition of the equipment; the determination condition is that
Figure FDA0003285588980000053
And taking the value of the next L, wherein L is the total working condition classification number.
8. The ultra-short term prediction method for the equipment parameters of the integrated energy system according to claim 5, characterized in that: the method comprises the following steps of selecting an expansion sequence of an original basic sequence of the characteristic parameter most similar to the current moment from a corresponding historical database as the input of an online prediction model of the comprehensive energy system equipment, solving the online prediction model of the comprehensive energy system equipment to obtain an equipment parameter prediction value of the equipment to be predicted, and comprises the following steps:
1) in the corresponding historical working condition data sequence, starting from the first historical data of the prediction parameters, selecting m data points to form a historical sequence
Figure FDA0003285588980000054
Passing and predicting measured sequences of parameters
Figure FDA0003285588980000055
Carrying out similarity judgment, and finding out an initial sequence number point s of a similar sequence meeting the following formula in the historical data sequence;
Figure FDA0003285588980000061
in the formula (I), the compound is shown in the specification,
Figure FDA0003285588980000062
data of the (s + k-1) th point in the prediction parameter history sequence from the(s) th point to the (s + m-1) th point;
2) obtaining corresponding characteristic parameter historical sequence
Figure FDA0003285588980000063
Figure FDA0003285588980000064
In the formula (I), the compound is shown in the specification,
Figure FDA0003285588980000065
starting from the s + m th point to the s + m th pointData of an s + m + k-1 point in the characteristic parameter historical sequence ending at the s + m + f-1 point, wherein k is the number of points to be predicted, and f is the total number of predicted points;
will be provided with
Figure FDA0003285588980000066
Incorporating original base sequences of feature parameters
Figure FDA0003285588980000067
In the method, an extended sequence of the original basic sequence of the characteristic parameters is obtained
Figure FDA0003285588980000068
Figure FDA0003285588980000069
To pair
Figure FDA00032855889800000610
Performing one-time accumulation to generate (1-AGO) to obtain an input sequence of the prediction model
Figure FDA00032855889800000611
Figure FDA00032855889800000612
In the formula (I), the compound is shown in the specification,
Figure FDA00032855889800000613
3) will be provided with
Figure FDA00032855889800000614
Substituted in the formula
Figure FDA00032855889800000615
Computing
Figure FDA00032855889800000616
Figure FDA00032855889800000617
4) Will be provided with
Figure FDA00032855889800000618
Substituting the following formula to obtain the predicted value of the device prediction parameter at f points in the future as:
Figure FDA00032855889800000619
9. an ultra-short term prediction device for equipment parameters of an integrated energy system is characterized in that: the system comprises the following modules:
the data acquisition module is used for acquiring historical time sequence data of equipment parameters including prediction parameters of the comprehensive energy system equipment and constructing a historical database;
the mechanism analysis module is used for selecting a characterization parameter related to the prediction parameter from the equipment parameters of the historical database according to the mechanism analysis of the equipment;
the correlation analysis module is used for performing correlation analysis on the characterization parameters and extracting characteristic parameters from the characterization parameters;
the working condition analysis module is used for calculating characteristic identification quantities under different operation working condition types according to the characteristic parameters;
the online prediction model building module is used for building an online prediction model of the comprehensive energy system equipment;
and the real-time prediction module is used for determining the type of the operation condition of the equipment to be predicted, selecting an extended sequence of the original basic sequence of the characteristic parameters which are most similar to the original basic sequence of the characteristic parameters at the current moment from the corresponding historical database as the input of the online prediction model of the comprehensive energy system equipment, and solving the online prediction model of the comprehensive energy system equipment to obtain the predicted value of the equipment parameters of the equipment to be predicted.
CN202111147126.8A 2021-09-28 2021-09-28 Ultra-short-term prediction method and device for equipment parameters of comprehensive energy system Pending CN113919559A (en)

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CN115374572A (en) * 2022-10-21 2022-11-22 南京安全无忧网络科技有限公司 Process stability analysis system and method
CN116737510A (en) * 2023-08-08 2023-09-12 深圳阿比特科技有限公司 Data analysis-based intelligent keyboard monitoring method and system
CN116957367A (en) * 2023-09-21 2023-10-27 南昌大学 Parameter multi-time scale prediction method and system for comprehensive energy system operation strategy

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Publication number Priority date Publication date Assignee Title
CN115374572A (en) * 2022-10-21 2022-11-22 南京安全无忧网络科技有限公司 Process stability analysis system and method
CN116737510A (en) * 2023-08-08 2023-09-12 深圳阿比特科技有限公司 Data analysis-based intelligent keyboard monitoring method and system
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