CN114413448A - Real-time parameter identification method for first-order model of building to which air conditioner belongs - Google Patents

Real-time parameter identification method for first-order model of building to which air conditioner belongs Download PDF

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CN114413448A
CN114413448A CN202210083358.XA CN202210083358A CN114413448A CN 114413448 A CN114413448 A CN 114413448A CN 202210083358 A CN202210083358 A CN 202210083358A CN 114413448 A CN114413448 A CN 114413448A
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air conditioner
building
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order model
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CN114413448B (en
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马亮
周伟光
郭旭晨
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Nanjing Tianlang Defense Technology Co ltd
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    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F24—HEATING; RANGES; VENTILATING
    • F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
    • F24F11/00—Control or safety arrangements
    • F24F11/62—Control or safety arrangements characterised by the type of control or by internal processing, e.g. using fuzzy logic, adaptive control or estimation of values
    • F24F11/63—Electronic processing
    • F24F11/64—Electronic processing using pre-stored data
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02B—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO BUILDINGS, e.g. HOUSING, HOUSE APPLIANCES OR RELATED END-USER APPLICATIONS
    • Y02B30/00—Energy efficient heating, ventilation or air conditioning [HVAC]
    • Y02B30/70—Efficient control or regulation technologies, e.g. for control of refrigerant flow, motor or heating

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Abstract

The invention provides a real-time parameter identification method for a first-order model of a building to which an air conditioner belongs, which comprises the steps of constructing the first-order model of the building to which the air conditioner belongs; acquiring air conditioner load characteristic data and parameters to be identified in a preset time period within a target range and preprocessing the data and the parameters; performing first identification on a parameter to be identified by using a standard least square method; acquiring and preprocessing air conditioner load characteristic data at the next moment of the preset time period; based on the first identification result, performing second identification on the parameter to be identified by adopting a recursion auxiliary variable method; and updating the first-order model of the building to which the air conditioner belongs in real time based on the second identification result. The ETP model of the air-conditioning load is equivalent to a standard difference equation, an input/output sequence and a sequence to be identified are determined, and the air-conditioning load model in a target range is established in real time based on a recursion auxiliary variable method and is used for controlling and scheduling the air-conditioning load in real time.

Description

Real-time parameter identification method for first-order model of building to which air conditioner belongs
Technical Field
The invention belongs to the field of electric power system scheduling, and particularly relates to a real-time parameter identification method for a first-order model of a building to which an air conditioner belongs.
Background
The load aggregation business can acquire the on-off state of the user air conditioner and the real-time change condition of the indoor and outdoor temperature through the advanced measurement terminal and the bidirectional communication network of the smart grid. Among various parameter identification methods, the principle of the least square method is simple, the calculated amount is small, and the method is widely applied to online parameter identification in various fields. In order to reduce the calculated amount in the real-time parameter identification process, a recursion least square method is provided, and in order to enhance the anti-interference capability, an auxiliary variable is introduced, and a recursion auxiliary variable method is provided. By means of hardware support of a smart power grid and related online identification technology, online identification of air conditioner loads of load aggregators plays a crucial role in participation of the air conditioner loads in demand response.
The air conditioning load is an important demand response resource, the ETP model of the building is widely applied to various fields of air conditioning load control, but some parameters in the model are closely related to factors such as the thickness of a wall body of the building, the window area and the volume size and cannot be obtained through measurement, so that the parameters of the air conditioning load model need to be identified through a certain parameter identification means, the existing parameter identification of the air conditioning load model is weak, and the real-time online modeling of the air conditioning load is ignored.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides a real-time parameter identification method for a first-order model of a building to which an air conditioner belongs, which can realize real-time control and scheduling of air conditioner load.
The technical scheme provided by the invention is as follows:
the invention discloses a real-time parameter identification method for a first-order model of a building to which an air conditioner belongs, which comprises the following steps:
constructing a first-order model of a building to which the air conditioner belongs;
acquiring air conditioner load characteristic data and parameters to be identified in a preset time period within a target range and preprocessing the data and the parameters;
performing first identification on the parameter to be identified by adopting a standard least square method;
acquiring and preprocessing air conditioner load characteristic data at the next moment of the preset time period;
based on the first identification result, performing second identification on the parameter to be identified by adopting a recursion auxiliary variable method;
and updating the first-order model of the building to which the air conditioner belongs in real time based on the second identification result.
Further, the specific process of constructing the first-order model of the building to which the air conditioner belongs is as follows:
the first-order model of the building to which the air conditioner belongs is equivalent to a single input/output linear system;
the single input/output linear system is described using a standard form of difference equations.
Further, the first-order model of the building to which the air conditioner belongs specifically is as follows:
Figure 553282DEST_PATH_IMAGE001
wherein ,
Figure 593919DEST_PATH_IMAGE002
is composed of
Figure 518013DEST_PATH_IMAGE003
The time of day is input into the sequence,
Figure 152257DEST_PATH_IMAGE004
is composed of
Figure 718367DEST_PATH_IMAGE003
The time of day output sequence is output,
Figure 754456DEST_PATH_IMAGE005
is composed of
Figure 974085DEST_PATH_IMAGE003
A sequence of time-of-day random variables,
Figure 95625DEST_PATH_IMAGE006
and
Figure 199847DEST_PATH_IMAGE007
as the parameter to be identified, the identification information is obtained,
Figure 824863DEST_PATH_IMAGE008
。
further, the random variable sequence:
Figure 480973DEST_PATH_IMAGE009
wherein ,
Figure 824229DEST_PATH_IMAGE010
is composed of
Figure 732142DEST_PATH_IMAGE011
Uncorrelated random noise sequences with zero mean at time.
Further, the air conditioning load characteristic data includes indoor/outdoor temperature, and on/off state quantity of the air conditioning load.
Further, the first identification of the parameter to be identified by adopting the standard least square method specifically comprises the following steps:
Figure 477245DEST_PATH_IMAGE012
wherein ,
Figure 304255DEST_PATH_IMAGE013
to the total number of time of acquisition
Figure 134808DEST_PATH_IMAGE014
Is the result of parameter identification of the standard least square method,
Figure 580833DEST_PATH_IMAGE015
is a matrix of input sequences and output sequences,
Figure 446021DEST_PATH_IMAGE016
is an output matrix.
Further, the recursion auxiliary variable method specifically comprises the following steps:
Figure 319299DEST_PATH_IMAGE017
wherein ,
Figure 761781DEST_PATH_IMAGE018
in order to correct the coefficients of the coefficients,
Figure 745918DEST_PATH_IMAGE019
is the intermediate variable(s) of the variable,
Figure 465612DEST_PATH_IMAGE020
are auxiliary variables.
Further, the method for selecting the auxiliary variable specifically comprises:
Figure 509791DEST_PATH_IMAGE021
wherein ,
Figure 439570DEST_PATH_IMAGE022
，
Figure 227398DEST_PATH_IMAGE023
is the parameter identification result of the standard least square method.
Further, based on the first identification result, performing second identification on the to-be-identified parameter by using a recursion auxiliary variable method specifically comprises:
Figure 67178DEST_PATH_IMAGE024
wherein ,
Figure 282258DEST_PATH_IMAGE025
the method is a parameter identification result of a recursion auxiliary variable method.
According to the existing technology for researching air conditioning load, the adopted building model to which the air conditioner belongs generally cannot identify the relationship between model parameters and factors such as wall thickness, window area, volume size and the like of a building, the method for identifying the real-time parameters of the first-order model of the building to which the air conditioner belongs is characterized in that an ETP (extract-transform-point) model of the air conditioning load is equivalent to a standard difference equation, an input/output sequence and a sequence to be identified are determined, an air conditioning load model in a target range is established in real time based on a recursion auxiliary variable method and is used for controlling and scheduling the air conditioning load in real time, the anti-jamming capability of the method is remarkably improved compared with the prior art, and the error of a parameter identification result is reduced by 40 percent compared with a recursion least square method.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings which are required to be used in the technical solution description will be briefly introduced below, it is obvious that the exemplary embodiments of the present invention and the description thereof are only used for explaining the present invention and do not constitute an unnecessary limitation of the present invention, and for those skilled in the art, other drawings can be obtained according to the provided drawings without inventive labor. In the drawings:
FIG. 1 is a schematic flow chart illustrating a real-time parameter identification method according to the present invention;
FIG. 2 is a schematic diagram showing the comparison between the predicted result and the actual parameter of the RIV algorithm at a noise-to-signal ratio of 16.06% in example 1 of the present invention;
FIG. 3 is a graph showing the variation of the error of the RIV algorithm and the error of the RLS algorithm with the number of iterations when the noise-to-signal ratio is 16.06% in example 1 of the present invention;
FIG. 4 is a comparison graph of the predicted results and actual parameters of the RIV algorithm at a signal-to-noise ratio of 25.94% in example 1 of the present invention;
fig. 5 is a graph showing the error of the RIV algorithm and the error of the RLS algorithm as a function of the number of iterations at a noise-to-signal ratio of 25.94% in example 1 of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1, the present embodiment provides a method for identifying real-time parameters of a first-order model of a building to which an air conditioner belongs, including the following steps:
s1: constructing a first-order model of a building to which the air conditioner belongs;
specifically, a first-order model of a building to which the air conditioner belongs is equivalent to a single-input/output linear system, and is described by using a standard form of a differential equation to determine
Figure 309120DEST_PATH_IMAGE026
The input and output sequence of the time is
Figure 25272DEST_PATH_IMAGE027
，
Figure 453980DEST_PATH_IMAGE028
The random variable sequence of the time is
Figure 839962DEST_PATH_IMAGE029
The number of the parameters to be identified is
Figure 619699DEST_PATH_IMAGE030
The parameter to be identified is
Figure 873963DEST_PATH_IMAGE031
And
Figure 157176DEST_PATH_IMAGE032
then, the first-order model of the building to which the air conditioners in the target range belong is:
Figure 979639DEST_PATH_IMAGE033
(1)
in the formula ：
Figure 981093DEST_PATH_IMAGE034
is the order of the difference equation,
Figure 179993DEST_PATH_IMAGE035
，
Figure 442347DEST_PATH_IMAGE036
the zero mean uncorrelated noise sequences at time instants are
Figure 170132DEST_PATH_IMAGE037
；
S2: acquiring air conditioner load characteristic data and parameters to be identified in a preset time period within a target range and preprocessing the data and the parameters;
specifically, referring to fig. 2, in this embodiment, the advanced metering terminal of the smart grid acquires input and output data and performs preprocessing on the input and output data to be acquired
Figure 924461DEST_PATH_IMAGE038
Of time of day
Figure 395894DEST_PATH_IMAGE039
An input/output sequence, listed according to equation (1)
Figure 778334DEST_PATH_IMAGE013
The difference equation is:
Figure 677019DEST_PATH_IMAGE040
Figure 653066DEST_PATH_IMAGE041
Figure 928189DEST_PATH_IMAGE042
Figure 430715DEST_PATH_IMAGE043
(2)
writing equation (2) in matrix form:
Figure 500302DEST_PATH_IMAGE044
(3)
in the formula ,
Figure 963644DEST_PATH_IMAGE045
(4)
Figure 776880DEST_PATH_IMAGE046
(5)
Figure 133912DEST_PATH_IMAGE047
(6)
Figure 374400DEST_PATH_IMAGE048
(7)
Figure 325038DEST_PATH_IMAGE049
(8)
in the formula ,
Figure 941965DEST_PATH_IMAGE050
to be the output matrix, the output matrix is,
Figure 294449DEST_PATH_IMAGE051
is a matrix composed of input and output sequences,
Figure 96051DEST_PATH_IMAGE052
is a parameter matrix to be identified,
Figure 268407DEST_PATH_IMAGE053
is an error matrix;
s3: performing first identification on the parameter to be identified by adopting a standard least square method;
specifically, the data collected in S2 is subjected to parameter estimation by a standard least square method, and an initial value of a recursion auxiliary variable method recursion process is obtained;
Figure 423445DEST_PATH_IMAGE054
(9)
Figure 630435DEST_PATH_IMAGE055
(10)
in the formula ：
Figure 478305DEST_PATH_IMAGE019
is the intermediate variable(s) of the variable,
Figure 262590DEST_PATH_IMAGE014
the estimated value of the parameter to be identified is obtained by a standard least square method;
s4: acquiring and preprocessing air conditioner load characteristic data at the next moment of the preset time period;
specifically, collecting
Figure 221319DEST_PATH_IMAGE056
The input and output data of the moment are obtained by preprocessing the input and output data
Figure 282816DEST_PATH_IMAGE057
The input and output sequence of the time is
Figure 301588DEST_PATH_IMAGE058
；
S5: based on the first identification result, performing second identification on the parameter to be identified by adopting a recursion auxiliary variable method;
in particular, by
Figure 573169DEST_PATH_IMAGE059
Input-output sequence of time instants
Figure 70009DEST_PATH_IMAGE060
And
Figure 986013DEST_PATH_IMAGE019
and calculating a correction coefficient:
Figure 175686DEST_PATH_IMAGE061
(11)
by auxiliary variables
Figure 75509DEST_PATH_IMAGE020
In the alternative formula (11)
Figure 500674DEST_PATH_IMAGE062
And obtaining a recursion auxiliary variable method:
Figure 271184DEST_PATH_IMAGE063
(12)
wherein the auxiliary variable
Figure 631758DEST_PATH_IMAGE020
The selection method is as follows:
Figure 753298DEST_PATH_IMAGE064
(13)
in the formula ,
Figure 716574DEST_PATH_IMAGE065
(14)
calculating the estimated value of the parameter to be identified according to the formula (13)
Figure 607170DEST_PATH_IMAGE025
：
Figure 138646DEST_PATH_IMAGE066
(15)
S6: updating the first-order model of the building to which the air conditioner belongs in real time based on the second identification result;
in particular, according to
Figure 747481DEST_PATH_IMAGE025
The updating method comprises the following steps of (1) updating a first-order model of a building to which the air conditioner belongs in real time;
s7: updating the intermediate variable and resetting the cycle parameter;
specifically, calculating
Figure 514449DEST_PATH_IMAGE067
Of time of day
Figure 525130DEST_PATH_IMAGE068
：
Figure 227507DEST_PATH_IMAGE069
(16)
Then order
Figure 58060DEST_PATH_IMAGE070
Returning to step S4.
It should be noted that the preprocessing in steps S2 and S4 is specifically as follows:
a: pre-treatment for indoor/outdoor temperature:
a 1: the average value of the indoor/outdoor temperature was calculated according to the following formula
Figure 238506DEST_PATH_IMAGE071
And standard deviation of
Figure 228327DEST_PATH_IMAGE072
：
Figure 101605DEST_PATH_IMAGE073
(17)
Figure 685033DEST_PATH_IMAGE074
(18)
in the formula ：
Figure 669170DEST_PATH_IMAGE075
represents the first to the successive acquisitions
Figure 388864DEST_PATH_IMAGE076
Individual indoor/outdoor temperatures;
a 2: judgment of
Figure 557677DEST_PATH_IMAGE077
Whether or not: if not, explain
Figure 362822DEST_PATH_IMAGE078
Is normal data; otherwise it explains
Figure 150650DEST_PATH_IMAGE078
If the data is bad, correction is needed;
a 3: the bad data correction formula is as follows:
Figure 724851DEST_PATH_IMAGE079
(19)
in the formula ：
Figure 64565DEST_PATH_IMAGE080
indicating modified the second
Figure 91427DEST_PATH_IMAGE081
The temperature of the individual indoor/outdoor air,
Figure 682945DEST_PATH_IMAGE082
is a self-defined correction coefficient, and
Figure 111653DEST_PATH_IMAGE083
；
b: preprocessing the on-off state quantity of the air conditioner load:
in the process of indoor temperature rising or falling, the on-off state of the air conditioner is kept unchanged, and the air conditioner switch is changed only when the set temperature upper and lower limits are reached: let three switch state quantities acquired continuously be
Figure 763214DEST_PATH_IMAGE084
The three indoor temperatures correspondingly collected are as follows,
Figure 667585DEST_PATH_IMAGE085
the following judgment is made:
b 1: if it is
Figure 797215DEST_PATH_IMAGE086
Then represents
Figure 80429DEST_PATH_IMAGE087
Is normal data;
b 2: if it is
Figure 902891DEST_PATH_IMAGE088
And is
Figure 28979DEST_PATH_IMAGE086
Then represents
Figure 696721DEST_PATH_IMAGE087
Is bad data, needs to be corrected, and order
Figure 365599DEST_PATH_IMAGE089
；
b 3: if it is
Figure 358963DEST_PATH_IMAGE090
And then:
in that
Figure 582134DEST_PATH_IMAGE091
The method comprises the following steps: if it is
Figure 443780DEST_PATH_IMAGE092
Then represents
Figure 701586DEST_PATH_IMAGE087
Is normal data; if it is
Figure 600272DEST_PATH_IMAGE093
Then represents
Figure 841897DEST_PATH_IMAGE087
Is bad data, needs to be corrected, and order
Figure 117021DEST_PATH_IMAGE094
；
In that
Figure 353967DEST_PATH_IMAGE095
The method comprises the following steps: if it is
Figure 689133DEST_PATH_IMAGE096
Then represents
Figure 886896DEST_PATH_IMAGE087
Is normal data; if it is
Figure 965711DEST_PATH_IMAGE097
Then represents
Figure 322743DEST_PATH_IMAGE098
Is bad data, needs to be corrected, and order
Figure 563231DEST_PATH_IMAGE099
；
in the formula ：
Figure 779449DEST_PATH_IMAGE100
representing a logical and computation.
It should be noted that the method for selecting the auxiliary variable specifically includes the following steps:
Figure 396375DEST_PATH_IMAGE101
(20)
according to equation (20), equation (1) can be written as:
Figure 483280DEST_PATH_IMAGE102
(21)
from equation (21) we can obtain:
Figure 19303DEST_PATH_IMAGE103
(22)
wherein ,
Figure 191659DEST_PATH_IMAGE104
Figure 612276DEST_PATH_IMAGE105
。
the least squares algorithm then has the expression:
Figure 553687DEST_PATH_IMAGE106
(23)
introducing Frechet's theorem:
theorem 1: suppose that
Figure 526191DEST_PATH_IMAGE107
Is to converge to a constant with probability 1
Figure 451422DEST_PATH_IMAGE108
The random variables of (a) then have:
Figure 410151DEST_PATH_IMAGE109
(24)
namely:
Figure 471647DEST_PATH_IMAGE110
(25)
applying the above convergence theorem, it can be concluded that:
Figure 490419DEST_PATH_IMAGE111
(26)
if it is not
Figure 496421DEST_PATH_IMAGE029
Is white noise, then
Figure 258841DEST_PATH_IMAGE112
It can be deduced that:
Figure 174844DEST_PATH_IMAGE113
(27)
at this point, an unbiased estimate can be obtained;
if it is not
Figure 364517DEST_PATH_IMAGE029
Is not white noise, at this time
Figure 123395DEST_PATH_IMAGE114
. If an unbiased estimate of the parameters is desired, and the equation is still valid, then an auxiliary vector needs to be introduced, defining an auxiliary matrix as follows:
Figure 689505DEST_PATH_IMAGE115
(28)
wherein the auxiliary variables must satisfy the following two conditions:
Figure 460015DEST_PATH_IMAGE116
Figure 820589DEST_PATH_IMAGE117
is a non-singular matrix;
Figure 676550DEST_PATH_IMAGE118
Figure 229625DEST_PATH_IMAGE119
and
Figure 120220DEST_PATH_IMAGE029
independently, i.e.
Figure 651696DEST_PATH_IMAGE120
Is a non-singular matrix;
if the auxiliary variable satisfies the above two conditions, then there are:
Figure 260531DEST_PATH_IMAGE121
(29)
wherein ,
Figure 761920DEST_PATH_IMAGE122
is an auxiliary variable parameter estimation value. If the selected auxiliary variables can satisfy the above two conditions, unbiased consistent parameter estimates can be obtained.
Based on the above analysis, an auxiliary variable method can be obtained as follows:
Figure 507022DEST_PATH_IMAGE123
(30)
assuming the auxiliary variable is
Figure 209399DEST_PATH_IMAGE124
Defining an auxiliary matrix, wherein:
Figure 305531DEST_PATH_IMAGE125
(31)
regarding the selection of the auxiliary variables, there are three methods, specifically as follows:
the self-adaptive filtering method comprises the following steps:
Figure 751556DEST_PATH_IMAGE126
(32)
or
Figure 741377DEST_PATH_IMAGE127
(33)
wherein ,
Figure 614655DEST_PATH_IMAGE128
is that
Figure 666925DEST_PATH_IMAGE003
Parameter estimates of the time instant auxiliary variables. The method is to use the auxiliary model parameters
Figure 916641DEST_PATH_IMAGE129
Performing smoothing processing according to the smoothing parameters
Figure 760969DEST_PATH_IMAGE130
And
Figure 805148DEST_PATH_IMAGE131
the specific situation is selected.
Input hysteresis method:
Figure 344714DEST_PATH_IMAGE132
(34)
this time is:
Figure 398121DEST_PATH_IMAGE133
。
output hysteresis method:
will make noise
Figure 831376DEST_PATH_IMAGE029
Consider the following model:
Figure 46457DEST_PATH_IMAGE134
(35)
wherein ,
Figure 338898DEST_PATH_IMAGE010
is uncorrelated random noise with zero mean, and:
Figure 664837DEST_PATH_IMAGE135
(36)
the auxiliary variable may be selected as follows:
Figure 624703DEST_PATH_IMAGE136
(37)
this time is:
Figure 869739DEST_PATH_IMAGE137
(38)
it should be noted that, the derivation process of the recursion auxiliary variable method is as follows:
the basic idea of the recursion algorithm can be summarized as follows: new estimated value
Figure 383897DEST_PATH_IMAGE138
= old estimate
Figure 513527DEST_PATH_IMAGE139
+ a correction term;
defining:
Figure 327899DEST_PATH_IMAGE140
(39)
this can be deduced from equation (39):
Figure 9416DEST_PATH_IMAGE141
(40)
Figure 10871DEST_PATH_IMAGE142
(41)
the drive type (30) of the formulae (40) and (41) can be derived:
Figure 944191DEST_PATH_IMAGE143
(42)
inverting the matrix
Figure 613070DEST_PATH_IMAGE144
A drive-in (40) from which can be pushed:
Figure 340855DEST_PATH_IMAGE145
(43)
the following can be derived by bringing formula (43) into formula (39):
Figure 954239DEST_PATH_IMAGE146
(44)
the formula of the recursion auxiliary variable method is as follows:
Figure 691251DEST_PATH_IMAGE147
(45)
Figure 949057DEST_PATH_IMAGE148
(46)
Figure 847742DEST_PATH_IMAGE149
(47)
wherein ,
Figure 948422DEST_PATH_IMAGE150
in order to be an auxiliary vector,
Figure 223546DEST_PATH_IMAGE151
is a recognition parameter
Figure 335859DEST_PATH_IMAGE152
At the moment of time
Figure 671025DEST_PATH_IMAGE003
In a recursive algorithm, the initial value is usually selected
Figure 134367DEST_PATH_IMAGE153
Is a very large positive definite matrix,
Figure 337816DEST_PATH_IMAGE154
，
Figure 39055DEST_PATH_IMAGE155
。
it should be noted that the mean square convergence of the recursive auxiliary variable method proves as follows:
firstly, proposing a characteristic value displacement and singular value displacement theorem:
lemma 1[ eigenvalue displacement lemma]: setting matrix
Figure 545123DEST_PATH_IMAGE156
Is/are as follows
Figure 495761DEST_PATH_IMAGE157
A characteristic value of
Figure 237321DEST_PATH_IMAGE158
Matrix of rules
Figure 324226DEST_PATH_IMAGE159
Has a characteristic value of
Figure 1195DEST_PATH_IMAGE160
, wherein ,
Figure 173550DEST_PATH_IMAGE161
is a constant.
Lemma 2 singular value displacement lemma]: setting matrix
Figure 718801DEST_PATH_IMAGE162
Is/are as follows
Figure 660212DEST_PATH_IMAGE157
A characteristic value of
Figure 508083DEST_PATH_IMAGE163
Memory for recording
Figure 167734DEST_PATH_IMAGE164
Then there is
Figure 251097DEST_PATH_IMAGE165
，
Figure 312594DEST_PATH_IMAGE166
Is formed in which
Figure 331365DEST_PATH_IMAGE167
。
Theorem 3: is provided with
Figure 478313DEST_PATH_IMAGE168
A sequence of random noise vectors with zero mean and bounded variance, i.e.
Figure 240732DEST_PATH_IMAGE169
Input signal
Figure 281370DEST_PATH_IMAGE170
And an auxiliary vector
Figure 471043DEST_PATH_IMAGE171
And
Figure 839707DEST_PATH_IMAGE172
uncorrelated, and the system satisfies weak persistent excitation to guarantee
Figure 140238DEST_PATH_IMAGE173
Are non-singular, i.e.:
Figure 300961DEST_PATH_IMAGE174
(48)
Figure 661535DEST_PATH_IMAGE175
(49)
definition of
Figure 517496DEST_PATH_IMAGE176
Is provided with
Figure 621718DEST_PATH_IMAGE177
And is and
Figure 636948DEST_PATH_IMAGE178
and
Figure 168423DEST_PATH_IMAGE172
uncorrelated, then the parameters of the RIV algorithm are estimated
Figure 511680DEST_PATH_IMAGE179
Is consistently converged on the true parameters
Figure 154014DEST_PATH_IMAGE180
Namely:
Figure 23750DEST_PATH_IMAGE181
or
Figure 726126DEST_PATH_IMAGE182
wherein ,
Figure 556679DEST_PATH_IMAGE157
is a matrix
Figure 2704DEST_PATH_IMAGE183
The rank of (c) is determined,
Figure 726946DEST_PATH_IMAGE184
is in a matrix
Figure 600224DEST_PATH_IMAGE185
Maximum eigenvalue, e.g.
Figure 918073DEST_PATH_IMAGE186
For example, say that
Figure 167789DEST_PATH_IMAGE187
。
The following was demonstrated:
defining a parameter estimation error vector:
Figure 746538DEST_PATH_IMAGE188
(50)
subtracting the two sides of the formula (45) at the same time
Figure 56296DEST_PATH_IMAGE189
The following can be obtained:
Figure 861441DEST_PATH_IMAGE190
(51)
by bringing formulae (21) and (39) into formula (30), it is possible to obtain:
Figure 383690DEST_PATH_IMAGE191
(52)
left-multiplying both sides of equation (40) simultaneously
Figure 82524DEST_PATH_IMAGE192
The following can be obtained:
Figure 32026DEST_PATH_IMAGE193
(53)
when formula (53) is taken into formula (52), it is possible to obtain:
Figure 590046DEST_PATH_IMAGE194
(54)
wherein ,
Figure 775040DEST_PATH_IMAGE195
，
Figure 203747DEST_PATH_IMAGE196
，
Figure 855308DEST_PATH_IMAGE197
，
Figure 369466DEST_PATH_IMAGE198
。
using theory 1 and theory 2, for arbitrary
Figure 623730DEST_PATH_IMAGE199
Comprises the following steps:
Figure 172523DEST_PATH_IMAGE200
(55)
defining:
Figure 729406DEST_PATH_IMAGE201
(56)
it can be deduced that:
Figure 730860DEST_PATH_IMAGE202
(57)
and because:
Figure 788815DEST_PATH_IMAGE203
(58)
it can be deduced that:
Figure 192115DEST_PATH_IMAGE204
(59)
further pushing out:
Figure 185478DEST_PATH_IMAGE205
(60)
Figure 674228DEST_PATH_IMAGE206
(61)
the following can be derived by bringing formula (61) and formula (60) into formula (54):
Figure 145661DEST_PATH_IMAGE207
(62)
Figure 528101DEST_PATH_IMAGE208
(63)
from the above, although
Figure 692366DEST_PATH_IMAGE029
Colored noise, as long as the system is continuously excited,
Figure 668412DEST_PATH_IMAGE029
zero mean and bounded variance, and
Figure 677957DEST_PATH_IMAGE209
is not related, the condition can be satisfied
Figure 180482DEST_PATH_IMAGE210
And
Figure 250069DEST_PATH_IMAGE211
then the RIV algorithm has mean square convergence, i.e. parameter estimation error
Figure 713412DEST_PATH_IMAGE212
To be provided with
Figure 526647DEST_PATH_IMAGE179
Is consistently converged on the true parameters
Figure 24624DEST_PATH_IMAGE180
。
Example 1
The equivalent standard difference equation of the first-order model of the building to which the air conditioner belongs constructed in the embodiment is as follows:
Figure 655326DEST_PATH_IMAGE213
the required identification parameter is
Figure 340385DEST_PATH_IMAGE214
, wherein ,
Figure 957311DEST_PATH_IMAGE215
the term is colored noise.
According to the method of the invention, the real-time parameter discrimination results are as follows:
table 1 shows the prediction results and the accuracy of the method (RIV) of the invention as a function of the number of iterations for a noise to signal ratio of 16.06%.
TABLE 1 noise-to-signal ratio of 16.6%, RIV algorithm parameters and error variation with iteration number
Figure DEST_PATH_IMAGE216
Referring to fig. 2, fig. 2 is a comparison graph of the predicted result and the actual parameter of the RIV algorithm parameter when the noise-to-signal ratio is 16.06%;
referring to fig. 3, fig. 3 is a graph of the error of the RIV algorithm and the error of the recursive least squares algorithm (RLS) as a function of the number of iterations for a noise to signal ratio of 16.06%;
it can be seen that in the initial stage, the errors of the RIV algorithm and the RLS algorithm are both large, convergence is performed quickly as the number of iterations increases, and the error of the RIV algorithm is always smaller than that of the RLS, which reduces the error by nearly 40%.
Table 2 shows that the identification result and precision of each parameter of the RIV algorithm change along with the iteration number under the condition that the noise-to-signal ratio is 25.94%.
TABLE 2 noise-to-signal ratio of 25.94%, RIV algorithm parameters and error variation with iteration number
Figure 903270DEST_PATH_IMAGE217
Referring to fig. 4, fig. 4 is a comparison graph of the predicted result of the RIV algorithm parameter and the actual parameter when the noise-to-signal ratio is 25.94%, and it can be seen that the predicted value of the RIV algorithm is substantially consistent with the actual value curve, but the predicted deviation at the corner of the curve is lower and the noise-to-signal ratio is slightly larger due to the slightly improved noise-to-signal ratio.
Referring to fig. 5, fig. 5 is a graph of the variation of the error of the RIV algorithm and the error of the RLS algorithm with the number of iterations when the noise-to-signal ratio is 25.94%, and it can be seen that in the initial stage, the errors of the RIV and the RLS algorithms are both large, and converge quickly as the number of iterations increases, and the error of the RIV algorithm is always smaller than that of the RLS, and the error is reduced by nearly 40%.
In addition, an embodiment of the present invention further provides a computer-readable storage medium, where the computer-readable storage medium may store a program, and when the program is executed, the program includes some or all of the steps of the method for identifying the first-order model of the building to which the air conditioner belongs in the above method embodiments.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable memory. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a memory and includes several instructions for causing a computer device (which may be a personal computer, a server, a network device, or the like) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned memory comprises: u disk, Read-Only Memory (ROM), Random Access Memory (RAM)
Memory), a removable hard disk, a magnetic or optical disk, or the like.
Those skilled in the art will appreciate that all or part of the steps in the methods of the above embodiments may be implemented by associated hardware instructed by a program, which may be stored in a computer-readable memory, which may include: flash memory disks, read-only memory, random access memory, magnetic or optical disks, and the like.
The above-mentioned embodiments, objects, technical solutions and advantages of the present application are further described in detail, it should be understood that the above-mentioned embodiments are only examples of the present application, and are not intended to limit the scope of the present application, and any modifications, equivalent substitutions, improvements and the like made on the basis of the technical solutions of the present application should be included in the scope of the present application.

Claims (10)

1. A real-time parameter identification method for a first-order model of a building to which an air conditioner belongs is characterized by comprising the following steps:
constructing a first-order model of a building to which the air conditioner belongs;
acquiring air conditioner load characteristic data and parameters to be identified in a preset time period within a target range and preprocessing the data and the parameters;
performing first identification on the parameter to be identified by adopting a standard least square method;
acquiring and preprocessing air conditioner load characteristic data at the next moment of the preset time period;
based on the first identification result, performing second identification on the parameter to be identified by adopting a recursion auxiliary variable method;
and updating the first-order model of the building to which the air conditioner belongs in real time based on the second identification result.
2. The method for identifying the real-time parameters of the first-order model of the building to which the air conditioner belongs according to claim 1, wherein the concrete process for constructing the first-order model of the building to which the air conditioner belongs is as follows:
the first-order model of the building to which the air conditioner belongs is equivalent to a single input/output linear system;
the single input/output linear system is described using a standard form of difference equations.
3. The method for identifying the real-time parameters of the first-order building model to which the air conditioner belongs according to claim 2, wherein the first-order building model to which the air conditioner belongs specifically comprises:
Figure 328574DEST_PATH_IMAGE001
wherein ,
Figure 915413DEST_PATH_IMAGE002
is composed of
Figure 404163DEST_PATH_IMAGE003
The time of day is input into the sequence,
Figure 875596DEST_PATH_IMAGE004
is composed of
Figure 133402DEST_PATH_IMAGE003
The time of day output sequence is output,
Figure 422301DEST_PATH_IMAGE005
is composed of
Figure 663926DEST_PATH_IMAGE003
A sequence of time-of-day random variables,
Figure 673470DEST_PATH_IMAGE006
and
Figure 785783DEST_PATH_IMAGE007
as the parameter to be identified, the identification information is obtained,
Figure 120949DEST_PATH_IMAGE008
。
4. the method for identifying the real-time parameters of the first-order model of the building to which the air conditioner belongs according to claim 3, wherein the random variable sequence is as follows:
Figure 708926DEST_PATH_IMAGE009
wherein ,
Figure 522161DEST_PATH_IMAGE010
is composed of
Figure 754559DEST_PATH_IMAGE011
Uncorrelated random noise sequences with zero mean at time.
5. The method as claimed in claim 1, wherein the air-conditioning load characteristic data includes indoor/outdoor temperature, and on-off state quantity of air-conditioning load.
6. The method for identifying the real-time parameters of the first-order model of the building to which the air conditioner belongs according to claim 1, wherein the first identification of the parameters to be identified by adopting a standard least square method specifically comprises the following steps:
Figure 260627DEST_PATH_IMAGE012
wherein ,
Figure 70320DEST_PATH_IMAGE013
to the total number of time of acquisition
Figure 687246DEST_PATH_IMAGE014
Is the result of parameter identification of the standard least square method,
Figure 774151DEST_PATH_IMAGE015
is a matrix of input sequences and output sequences,
Figure 716699DEST_PATH_IMAGE016
is an output matrix.
7. The method for identifying the real-time parameters of the first-order model of the building to which the air conditioner belongs according to claim 1, wherein the recursion auxiliary variable method specifically comprises the following steps:
Figure 889054DEST_PATH_IMAGE017
wherein ,
Figure 168726DEST_PATH_IMAGE018
in order to correct the coefficients of the coefficients,
Figure 375716DEST_PATH_IMAGE019
is the intermediate variable(s) of the variable,
Figure 489166DEST_PATH_IMAGE020
are auxiliary variables.
8. The method for identifying the real-time parameters of the first-order model of the building to which the air conditioner belongs according to claim 7, wherein the method for selecting the auxiliary variables specifically comprises the following steps:
Figure 883238DEST_PATH_IMAGE021
wherein ,
Figure 966600DEST_PATH_IMAGE022
，
Figure 762518DEST_PATH_IMAGE023
is the parameter identification result of the standard least square method.
9. The method for identifying the real-time parameters of the first-order model of the building to which the air conditioner belongs according to claim 1, wherein the second identification of the parameters to be identified by using a recursion auxiliary variable method based on the first identification result specifically comprises the following steps:
Figure 515710DEST_PATH_IMAGE024
wherein ,
Figure 928237DEST_PATH_IMAGE025
the method is a parameter identification result of a recursion auxiliary variable method.
10. A computer-readable storage medium, wherein the storage medium contains the method for identifying the real-time parameters of the first-order model of the building to which the air conditioner belongs according to any one of claims 1 to 9.
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