CN113435031B - Parameter identification method and system of first-order ETP model of house to which air conditioner belongs - Google Patents
Parameter identification method and system of first-order ETP model of house to which air conditioner belongs Download PDFInfo
- Publication number
- CN113435031B CN113435031B CN202110701938.6A CN202110701938A CN113435031B CN 113435031 B CN113435031 B CN 113435031B CN 202110701938 A CN202110701938 A CN 202110701938A CN 113435031 B CN113435031 B CN 113435031B
- Authority
- CN
- China
- Prior art keywords
- air conditioner
- house
- model
- power
- period
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 34
- 238000005457 optimization Methods 0.000 claims abstract description 14
- 230000001419 dependent effect Effects 0.000 claims abstract description 3
- 238000012935 Averaging Methods 0.000 claims description 6
- 238000004378 air conditioning Methods 0.000 description 18
- 230000004044 response Effects 0.000 description 13
- 238000005070 sampling Methods 0.000 description 12
- 238000004364 calculation method Methods 0.000 description 11
- 238000004088 simulation Methods 0.000 description 10
- 230000008859 change Effects 0.000 description 7
- 230000009286 beneficial effect Effects 0.000 description 6
- 230000002354 daily effect Effects 0.000 description 4
- 239000011159 matrix material Substances 0.000 description 4
- 238000005516 engineering process Methods 0.000 description 3
- NAWXUBYGYWOOIX-SFHVURJKSA-N (2s)-2-[[4-[2-(2,4-diaminoquinazolin-6-yl)ethyl]benzoyl]amino]-4-methylidenepentanedioic acid Chemical compound C1=CC2=NC(N)=NC(N)=C2C=C1CCC1=CC=C(C(=O)N[C@@H](CC(=C)C(O)=O)C(O)=O)C=C1 NAWXUBYGYWOOIX-SFHVURJKSA-N 0.000 description 2
- 230000002776 aggregation Effects 0.000 description 2
- 238000004220 aggregation Methods 0.000 description 2
- 230000006399 behavior Effects 0.000 description 2
- 238000004422 calculation algorithm Methods 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 230000003203 everyday effect Effects 0.000 description 2
- 238000009472 formulation Methods 0.000 description 2
- 238000010438 heat treatment Methods 0.000 description 2
- 230000006872 improvement Effects 0.000 description 2
- 239000000203 mixture Substances 0.000 description 2
- 239000002245 particle Substances 0.000 description 2
- 230000008569 process Effects 0.000 description 2
- 238000004458 analytical method Methods 0.000 description 1
- 238000007405 data analysis Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000005259 measurement Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 230000009466 transformation Effects 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2111/00—Details relating to CAD techniques
- G06F2111/10—Numerical modelling
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
- G06F2119/06—Power analysis or power optimisation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
- G06F2119/08—Thermal analysis or thermal optimisation
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Computer Hardware Design (AREA)
- Evolutionary Computation (AREA)
- Geometry (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Air Conditioning Control Device (AREA)
Abstract
The invention discloses a parameter identification method and a system of a first-order ETP model of a house to which an air conditioner belongs, and belongs to the field of parameter identification. Comprising the following steps: acquiring load data in each historical period of a resident air conditioner to be identified and outdoor temperature data in a corresponding period; calculating the average load of the air conditioner in each period, and linearly fitting the data of all the periods by taking the outdoor temperature as an independent variable and the average load as a dependent variable, wherein the fitted slope reciprocal is the house equivalent thermal resistance; constructing an optimization model based on the idea of least square, and solving the optimization model to obtain indoor temperature and indoor heat source power; calculating the working power of the air conditioner according to the historical load data of the resident air conditioner to be identified; and combining the outdoor temperature and identifying the equivalent thermal resistance, the indoor temperature, the indoor heat source power and the air conditioner working power of the house, and calculating the equivalent heat capacity of the house. The method divides the parameters to be identified into steady state and dynamic state, solves the steady state parameters first, calculates the dynamic parameters, and improves the accuracy and the robustness of the constructed air conditioner model.
Description
Technical Field
The invention belongs to the field of parameter identification of traditional physical models of air conditioner loads, and particularly relates to a parameter identification method and system of a first-order ETP (equivalent thermal parameter) model of a house to which an air conditioner belongs.
Background
With the gradual popularization of advanced measurement systems typified by smart meters among residential users, historical load data of residential users can be obtained in large quantities. Based on the traditional physical model of the air conditioner load, the parameters of the model can be identified by combining a data driving technology, so that the air conditioner load model can be established more accurately. Therefore, how to extract the key characteristic quantity of the air conditioner historical load curve and further accurately identify the relevant parameters of the air conditioner load physical model is an urgent problem to be solved.
Patent CN109827310a proposes a method for building a resident air conditioning load group model, the main idea of which is: and acquiring historical operation data and outdoor temperature of residential air conditioner loads, establishing a residential air conditioner load group aggregation model, and acquiring air conditioner load aggregation model parameters by using a parameter identification method. However, it has the following drawbacks: 1) The object of the constructed and identified model is an aggregate air conditioner, the difference of the running performances of different air conditioners is ignored, the value of the air conditioner load data of each household is not fully mined, and the problem of lower precision of the calculation result of the model exists; 2) The difference between the indoor temperature calculation result and the actual value of the user is large, and the comfort level of the user cannot be guaranteed.
Patent CN105204334a proposes a method for identifying real-time parameters of a first-order model of a building to which an air conditioner belongs based on a recursive least square method, which mainly comprises the following steps: and (3) equivalent the ETP model of the building to which the air conditioner belongs to a standard differential equation, simultaneously determining an input and output sequence and a parameter sequence to be identified, and identifying real-time parameters of the air conditioner load model by using a recursive least square method. However, it has the following drawbacks: 1) And carrying out deformation treatment on the ETP model, wherein the recognized coefficient cannot directly represent the physical meaning of the parameter in the ETP model. ; 2) The indoor temperature parameter is assumed to be acquired and is not recognized online.
Patent CN105159085A proposes a real-time parameter identification method of a second-order equivalent thermal parameter model of a building to which an air conditioner belongs, and the main idea is as follows: and establishing a second differential equation set of a building to which the air conditioner load belongs, determining input output quantity and parameters to be identified, and carrying out real-time parameter identification by utilizing a cooperative particle swarm algorithm. However, it has the following drawbacks: 1) Only the equivalent thermal resistance in the equivalent thermal parameter model is identified on line, and the obtained model can not effectively simulate the actual running condition of the air conditioner; 2) The parameter online identification needs a faster calculation speed, and the traditional particle swarm optimization solving algorithm cannot meet the calculation requirement of real-time performance.
Disclosure of Invention
Aiming at the defects and improvement demands of the prior art, the invention provides a parameter identification method and a system of a first-order ETP model of a house to which an air conditioner belongs, which aim to accurately identify key parameters of an air conditioner load physical model based on historical data of air conditioner loads of residential users and combine a data driving technology to establish the air conditioner load physical model with high precision and strong robustness, and provide a basis for simulating and calculating response potential of the air conditioner load demands.
In order to achieve the above object, according to a first aspect of the present invention, there is provided a method for identifying parameters of a first-order ETP model of a house to which an air conditioner belongs, the method comprising:
s1, acquiring load data in each history period of a resident air conditioner to be identified and outdoor temperature data in a corresponding period;
S2, calculating the average load of the air conditioner in each period, and linearly fitting the data of all the periods by taking the outdoor temperature as an independent variable and the average load as a dependent variable, wherein the fitted slope reciprocal is the house equivalent thermal resistance in the first-order ETP model;
S3, constructing an optimization model based on the idea of least square, and solving the optimization model to obtain the indoor temperature and the indoor heat source power in the first-order ETP model;
S4, calculating the working power of the air conditioner in the first-order ETP model according to the historical load data of the resident air conditioner to be identified;
S5, combining the outdoor temperature with the identified house equivalent thermal resistance, the indoor temperature, the indoor heat source power and the air conditioner working power, and calculating the house equivalent heat capacity in the first-order ETP model.
Preferably, the first order ETP model is as follows:
Wherein, T i (T) represents the indoor temperature at the moment T; t o (T) represents the outdoor temperature at time T; r a represents house thermal resistance; c a represents house heat capacity; p AC (t) represents the working power of the air conditioner at the moment t; p other (t) represents the indoor heat source power at the moment t; η represents an energy efficiency coefficient of the air conditioner.
The beneficial effects are that: the invention improves the original first-order ETP model, introduces the indoor heat source power, and can further consider the influence of heat sources such as electric appliance work, personnel flow and the like in the house on the indoor temperature and the air conditioner working state, thereby improving the modeling precision of the house, and the ETP model obtained by identification has higher applicability and robustness.
Preferably, in step S3, the constructed optimization model is as follows:
Wherein, The working power average value of the air conditioner in the m period is represented, R a represents house thermal resistance, eta represents the energy efficiency coefficient of the air conditioner, and IRepresents the outdoor temperature average value in m time period,/>Represents the indoor temperature average value in m time period,/>Represents the average value of indoor heat source power in m time periods, and T set,min and T set,max respectively represent the lower limit and the upper limit of indoor temperature set values,/>AndRespectively representing the lower limit and the upper limit of the indoor heat source power.
The beneficial effects are that: the invention builds an optimization solving model of the indoor temperature and the indoor heat source power based on the idea of least square, establishes an optimization objective function with the minimum error of an actual value and a simulation value, and simultaneously carries out linear constraint on two variables to be optimized of the indoor temperature and the indoor heat source power by combining the actual situation.
Preferably, step S4 is specifically as follows:
setting a power threshold based on the air conditioner historical load data;
If the real-time power of the air conditioner is larger than the power threshold value, the air conditioner is in a working state at the moment, otherwise, the air conditioner is in a standby state or a closing state at the moment;
and extracting the working power of the air conditioner at all times in the running state period, and averaging to obtain the working power of the air conditioner.
The beneficial effects are that: according to the invention, the comparison is performed by setting the power threshold value, and because the state of the air conditioner is frequently changed, all the working moments of the air conditioner in the sampling period are effectively extracted, and the working power of the air conditioner is calculated in an average manner, so that the influence of the state change of the air conditioner on the working power calculation result of the air conditioner is eliminated, and the identification accuracy is improved.
Preferably, step S5 comprises the sub-steps of:
S51, recognizing an air conditioner starting time t 0 and a time t 1 when the air conditioner is first changed from work to standby from load data in each period of air conditioner history;
s52. calculating a time difference Δt=t 1-t0 between two moments;
S53, substituting the time difference deltat, the outdoor temperature, the identified house equivalent thermal resistance, the indoor temperature, the indoor heat source power and the air conditioner working power into the following formula, and calculating the house equivalent heat capacity in the first-order ETP model:
Wherein, R a represents house heat resistance, C a represents indoor heat capacity, eta represents energy efficiency coefficient of an air conditioner, P AC represents air conditioner working power, P other represents indoor heat source power, T o represents outdoor temperature, T i represents indoor temperature, and DeltaT represents indoor temperature variation.
The beneficial effects are that: according to the invention, the characteristic value in the air conditioner load data is identified, so that the indoor temperature change period after the air conditioner is started is effectively extracted, and the equivalent heat capacity of the house is directly and effectively calculated based on the temperature and time data of the indoor temperature change period due to the direct correlation of the equivalent heat capacity of the house and the speed of the change of the indoor temperature.
Preferably, the search identifies a plurality of t 0 over a period of time, calculates the house equivalent heat capacity, respectively, and averages the house equivalent heat capacity as the final house equivalent heat capacity for the user's house.
The beneficial effects are that: the invention reduces random error by a method of averaging multiple groups of data, and has larger randomness due to the influence of resident behaviors and acquisition errors of the house equivalent heat capacity result calculated at a time, so that the randomness of the calculation result can be further eliminated by averaging multiple groups of data, the calculation error is reduced, and the identification accuracy is improved.
In order to achieve the above object, according to a second aspect of the present invention, there is provided a parameter identification system of a first-order ETP model of a house to which an air conditioner belongs, including: a computer readable storage medium and a processor;
The computer-readable storage medium is for storing executable instructions;
The processor is configured to read the executable instructions stored in the computer readable storage medium, and execute the parameter identification method of the first-order ETP model of the house to which the air conditioner belongs according to the first aspect.
In general, through the above technical solutions conceived by the present invention, the following beneficial effects can be obtained:
According to the invention, a single user is taken as a research object, parameters to be identified of the first-order ETP model are divided into steady-state parameters (house equivalent thermal resistance, indoor temperature and indoor heat source power) and dynamic parameters (air conditioner working power and house equivalent heat capacity), the steady-state parameters are identified in a fitting and optimizing solving mode, and then the dynamic parameters are calculated, so that all parameters of the first-order ETP model are directly identified. Based on an equivalent thermal parameter model of a resident user house, a physical model of a building to which the air conditioning load belongs is established, and key parameters in the air conditioning physical model are accurately identified by combining a large amount of historical data and outdoor temperature data of the air conditioning load by adopting a big data analysis technology, so that the accuracy and the robustness of the established air conditioning model are further improved. Based on the method, the obtained air conditioner model can be used for the simulation calculation of the power curve under different demand response strategies, and the simulation result can accurately represent the demand response potential of the actual residential user air conditioner in the region. The method provides reference for analysis and prediction of the next-day demand response potential of the air conditioner load, and provides guidance for formulation of a next-day demand response scheme and flexible scheduling of an electric power system, so that the method has important practical significance and good application prospect.
Drawings
FIG. 1 is a first order thermodynamic equivalent thermal parameter model of a building to which a residential user air conditioner belongs;
FIG. 2 is a schematic diagram of a 24 hour time division of a user's air conditioning load;
FIG. 3 is a graph of the fit of average operating power and outdoor temperature data and temperature response slope for a user air conditioner;
FIG. 4 is a graph showing the result of identifying the indoor heat source power and the indoor temperature in 61 days of a certain user;
FIG. 5 is a graph of power change after an initial start of a user's air conditioning load;
FIG. 6 is a graph comparing actual and simulated values of a user's air conditioning load curve at 8 months 9 days and 9 months 9 days;
FIG. 7 is a graph showing the comparison of actual and simulated values of an aggregate load curve of an air conditioner for 80 residents on 8 months and 9 days and 9 months and 9 days.
Detailed Description
The present invention will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present invention more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention. In addition, the technical features of the embodiments of the present invention described below may be combined with each other as long as they do not collide with each other.
In order to accurately establish a physical model of the residential user air conditioning load in the actual area, further estimate the daily demand response potential of the residential user air conditioning load, and provide guidance for the formulation of the next daily demand response scheme and the flexible scheduling of the power system. The invention provides a data-driven resident user air conditioner load physical model parameter identification method, which comprises the following specific steps:
Step 1: and establishing a thermodynamic model of a building to which the resident air conditioner belongs, namely an air conditioner load model, and dividing parameters to be identified in the model into steady-state parameters and dynamic parameters according to the running state of the air conditioner.
Step 1.1: and establishing a thermodynamic model of a building to which the residential air conditioner belongs.
In the present embodiment, the formula (1) is in the form of differential equation of the equivalent thermodynamic model of the established air conditioner. Fig. 1 is a first order thermodynamic equivalent thermal parameter model of a building to which a residential air conditioner belongs.
Wherein, T i (T) is the temperature (unit:. Degree. C.) in the room at time T; t o (T) is the external temperature (unit is DEG C) at the moment T; r a is the indoor thermal resistance, i.e. the reciprocal of the coefficient of heat loss of air (in units of DEG C/W); c a is the indoor heat capacity (unit: J/. Degree.C.); p AC (t) is the refrigerating or heating power (unit: W) of the air conditioner at time t, and the refrigerating or heating power comprises the running power P AC,on and the standby power P AC,off;Pother (t) which are the indoor heat source power at time t (unit: W); η is the energy efficiency coefficient of the air conditioner. Assuming that the outdoor temperature is kept to be T o(tk) in the [ T k,tk+1 ] period, solving an equation (discretization of a differential equation) by taking T i(tk) as an initial value, and obtaining the indoor temperature at the time T k+1.
Step 1.2: and dividing the parameters to be identified in the model into steady-state parameters and dynamic parameters according to the running state of the air conditioner.
And (3) performing equivalent transformation on the formula (1) to obtain a formula (2), and accumulating the real-time running power of the air conditioner at all moments in a period of time (t mk,tmk+k). Taking the average of the variables over this period of time yields equation (4), where the derivative term is equivalent to 0 during the averaging process.
Wherein,The average running power, the indoor average temperature, the average power of other indoor heat sources and the outdoor average temperature of the air conditioner in m time periods are respectively.
In this embodiment, the load data and the outdoor temperature data of the air conditioner of the residential user can be directly obtained, so that the steady-state parameters to be identified in the air conditioner model are the house equivalent thermal resistance R a eta and the indoor temperature based on the formula (4) under the steady operation state of the air conditionerIndoor heat source power/>And (3) combining a formula (1) under the dynamic running state of the air conditioner, wherein the dynamic parameters to be identified in the air conditioner model are house equivalent heat capacity C a/eta, air conditioner running power P AC,on and air conditioner standby power P AC,off.
Step 2: and collecting historical load data of each resident air conditioner and outdoor temperature historical data of corresponding dates.
The air conditioning load data of each household of 80 resident users in a certain region from 1 month 1 day 2018 to 31 days 12 months 2018 are known, the load sampling frequency is 1min, that is 1440 load data sampling points are arranged every day, and besides meteorological factor data including daily average temperature in the region 2018 every day are also known. Wherein data of 6 months and 7 months are used for identifying air conditioner model parameters, and data of 8 months and 9 months are used for verifying model identification results.
Step 2.1: and collecting historical load information of air conditioners of residents of each household, and constructing a load sequence matrix.
Sampling the air conditioner N-day historical load data at the sampling rate of M sampling points per day to obtain an air conditioner N-day historical load sequence matrix L:
In the present embodiment, the sampling frequency is 1 minute, i.e., m=1440; the air conditioning history load was selected from data for 61 days, i.e., n=61, for 6 months and 7 months.
Step 2.2: and acquiring outdoor temperature data of the region and constructing an outdoor temperature sequence matrix.
Sampling is carried out at the sampling rate of G sampling points per day, and outdoor temperature history data sequences W of each of the N days are respectively obtained.
In this embodiment, the sampling frequency is 60 minutes, i.e., g=24, due to the slow change in the outdoor temperature. Acquiring an outdoor temperature data sequence with a longitudinal dimension of N=61 days to obtain an outdoor temperature sequence matrix W:
Step 3: based on the historical load data and the temperature data, a fitting method and a least square method are adopted to identify steady-state parameters in the air conditioner model.
Step 3.1: based on the historical load data and the outdoor temperature data, the house thermal resistance parameters in the model are identified by adopting a fitting method.
The average running power of the air conditioner and the outdoor temperature have a linear relation, and the slope is 1/R a eta, so that the data at all sampling moments of 1 day are firstly divided into K time periods on the basis of the historical load data and the outdoor temperature data of the air conditioner, and the time periods when the air conditioner is not started are removed. Then calculating the average running power of the air conditioner in each opening period, and identifying the temperature response slope 1/R a eta of the air conditioner by a fitting method, so as to calculate the equivalent thermal resistance R a eta of the house.
In this embodiment, the air conditioning load data for 24 hours per 1 day is divided into 24 time periods on average every 1 hour, that is, k=24, and fig. 2 is a schematic diagram of time period division. And calculating the average running power and the corresponding outdoor temperature of the air conditioner in all the time periods in 61 days of a certain user to obtain a fitting result graph of the average running power and the outdoor temperature data of the air conditioner of the user and the temperature response slope, as shown in fig. 3. The temperature response slope of this user was 0.09 kW/. Degree.C.
Step 3.2: and constructing an optimization solving model by adopting a least square method, and identifying two parameters of indoor temperature and indoor heat source power in the air conditioner model.
Based on the formula (4), an optimization model is built with minimum errors of actual values and simulation values of the air conditioner operating power in all periods of the day. Since the calculation result per day is a random fluctuation, it is necessary to solve the result for a plurality of days, and take the average value as the final value.
Wherein, formula (7) is an objective function, formula (8) represents upper and lower limits of the indoor temperature set point, and formula (9) represents upper and lower limits of the indoor heat source power.
In this embodiment, for a certain user air conditioning load, data of all time periods of a day is used to construct an optimization model, and then an optimal value of indoor heat source power and indoor temperature is calculated. Meanwhile, considering the influence of the uncertainty of the user behavior on the calculation result, calculating 61-day values respectively, and taking an average as a final result. As shown in fig. 4, the recognition results of the indoor heat source power and the indoor temperature in 61 days of a certain user can find that the daily optimized result randomly fluctuates around the average value, so that it is feasible to take the average value as the final result, while reducing the influence of uncertainty to some extent.
Step 4: and extracting a load curve characteristic value based on the historical load data, and identifying dynamic parameters in the air conditioner model.
Step 4.1: and extracting a load curve characteristic value based on the historical load data, and identifying two parameters of the air conditioner running power and the air conditioner standby power in the air conditioner model.
And setting a power threshold based on the historical load curve of the air conditioner, and dividing the working state of the air conditioner into an operation state and a standby state. Extracting the running power of the air conditioner at all times in the running state period and the standby power of the air conditioner at all times in the standby state period, and respectively averaging to obtain the actual running power and the standby power of the user air conditioner.
In this embodiment, for a certain air conditioner, historical load data within 61 days is extracted, the running power and standby power of the air conditioner at all times are identified, and the running power and standby power are averaged to obtain final values.
Step 4.2: and identifying house heat capacity parameters in the air conditioner model based on the historical load data.
The identification of the equivalent heat capacity C a/eta of the user house relates to the dynamic change process of the indoor temperature, so that the time point t 0 when the air conditioner just starts to operate is found through the characteristic identification of the load curve. It is assumed that before this point the indoor temperature and the outdoor temperature of the user are equal. After the air conditioner is started, the indoor temperature of the user starts to drop, the air conditioner continuously operates until the indoor temperature is reduced to a set temperature value, and the air conditioner stops operating. The total time of operation of the air conditioner during the period of time is identified. The equivalent heat capacity of the user can be calculated based on the formula (10) by combining the equivalent thermal resistance and the indoor temperature set value. The search identifies a number of t 0 over a period of time, calculates C a/η, respectively, and averages as the final C a/η for the user's premises.
In this embodiment, as shown in fig. 5, after the user turns on the air conditioner at time t 0, the indoor temperature of the house is reduced to the set value after Δt time, and the air conditioner is turned into the standby state. And (5) calculating the equivalent heat capacity parameter of the user house based on the formula (10).
In the present embodiment, the identification results of the dynamic and steady state parameters of the 10-user resident air conditioning model are randomly selected as shown in table 1. It can be found that the difference between the house and the air conditioning parameters of different residents is large, so that the model obtained by assuming the parameters is also explained again and cannot represent the actual condition of the resident air conditioning load operation.
Table 1 identification results of dynamic and steady state parameters of randomly selected 10 family resident air conditioner model
In order to further verify the accuracy of the identification method, the power curve of the air conditioner is calculated in a simulation mode based on the identification result of the air conditioner load model parameters, and the power curve is compared with the load curve of an actual air conditioner. Fig. 6 is a comparison graph of actual values and simulation values of an air conditioning load curve of a certain user on 8 months and 9 days, and it can be found that the power curve obtained by simulation after parameter identification is almost consistent with the actual values, so that the validity of the identification result of the resident parameters of each user is further verified. Fig. 7 is a graph comparing actual values and simulation values of an aggregate load curve of an 80-family resident air conditioner on 8 months and 9 days, and from the perspective of aggregate power, the proposed method for parameter identification can prove that the total power of resident air conditioners in a certain area can be effectively simulated, and further can be used for the simulation calculation of power curves under different demand response strategies, and the simulation result can accurately represent the demand response potential of the resident air conditioners in the actual area. Therefore, the air conditioner model parameter identification method has important practical significance and good application prospect.
It will be readily appreciated by those skilled in the art that the foregoing description is merely a preferred embodiment of the invention and is not intended to limit the invention, but any modifications, equivalents, improvements or alternatives falling within the spirit and principles of the invention are intended to be included within the scope of the invention.
Claims (4)
1. A parameter identification method of a first-order ETP model of a house to which an air conditioner belongs is characterized by comprising the following steps:
s1, acquiring load data in each history period of a resident air conditioner to be identified and outdoor temperature data in a corresponding period;
S2, calculating the average load of the air conditioner in each period, and linearly fitting the data of all the periods by taking the outdoor temperature as an independent variable and the average load as a dependent variable, wherein the fitted slope reciprocal is the house equivalent thermal resistance in the first-order ETP model;
S3, constructing an optimization model based on the idea of least square, and solving the optimization model to obtain the indoor temperature and the indoor heat source power in the first-order ETP model;
S4, calculating the working power of the air conditioner in the first-order ETP model according to the historical load data of the resident air conditioner to be identified;
s5, combining the outdoor temperature with the identified house equivalent thermal resistance, the indoor temperature, the indoor heat source power and the air conditioner working power to calculate the house equivalent heat capacity in the first-order ETP model;
the first order ETP model is as follows:
Wherein, T i (T) represents the indoor temperature at the moment T; t o (T) represents the outdoor temperature at time T; r a represents house thermal resistance; c a represents house heat capacity; p AC (t) represents the working power of the air conditioner at the moment t; p other (t) represents the indoor heat source power at the moment t; η represents an energy efficiency coefficient of the air conditioner;
in step S3, the constructed optimization model is as follows:
Wherein, The working power average value of the air conditioner in the m period is represented, R a represents house thermal resistance, eta represents the energy efficiency coefficient of the air conditioner, and IRepresents the outdoor temperature average value in m time period,/>Represents the indoor temperature average value in m time period,/>Represents the average value of indoor heat source power in m time periods, and T set,min and T set,max respectively represent the lower limit and the upper limit of indoor temperature set values,/>And/>Respectively representing the lower limit and the upper limit of the indoor heat source power;
Step S5 comprises the following sub-steps:
S51, recognizing an air conditioner starting time t 0 and a time t 1 when the air conditioner is first changed from work to standby from load data in each period of air conditioner history;
s52. calculating a time difference Δt=t 1-t0 between two moments;
S53, substituting the time difference deltat, the outdoor temperature, the identified house equivalent thermal resistance, the indoor temperature, the indoor heat source power and the air conditioner working power into the following formula, and calculating the house equivalent heat capacity in the first-order ETP model:
Wherein, R a represents house heat resistance, C a represents indoor heat capacity, eta represents energy efficiency coefficient of an air conditioner, P AC represents air conditioner working power, P other represents indoor heat source power, T o represents outdoor temperature, T i represents indoor temperature, and DeltaT represents indoor temperature variation.
2. The method according to claim 1, wherein step S4 is specifically as follows:
setting a power threshold based on the air conditioner historical load data;
If the real-time power of the air conditioner is larger than the power threshold value, the air conditioner is in a working state at the moment, otherwise, the air conditioner is in a standby state or a closing state at the moment;
and extracting the working power of the air conditioner at all times in the running state period, and averaging to obtain the working power of the air conditioner.
3. The method of claim 1, wherein the searching identifies a plurality of t 0 over a period of time, calculates house equivalent heat capacity, respectively, and averages as final house equivalent heat capacity of the user's house.
4. A parameter identification system of a first-order ETP model of a house to which an air conditioner belongs is characterized by comprising: a computer readable storage medium and a processor;
The computer-readable storage medium is for storing executable instructions;
The processor is configured to read the executable instructions stored in the computer-readable storage medium, and execute the parameter identification method of the first-order ETP model of the house to which the air conditioner belongs according to any one of claims 1 to 3.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110701938.6A CN113435031B (en) | 2021-06-24 | 2021-06-24 | Parameter identification method and system of first-order ETP model of house to which air conditioner belongs |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110701938.6A CN113435031B (en) | 2021-06-24 | 2021-06-24 | Parameter identification method and system of first-order ETP model of house to which air conditioner belongs |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN113435031A CN113435031A (en) | 2021-09-24 |
| CN113435031B true CN113435031B (en) | 2024-05-14 |
Family
ID=77753766
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110701938.6A Active CN113435031B (en) | 2021-06-24 | 2021-06-24 | Parameter identification method and system of first-order ETP model of house to which air conditioner belongs |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113435031B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114440428A (en) * | 2021-12-30 | 2022-05-06 | 深圳供电局有限公司 | Method, device, equipment and medium for identifying equivalent thermal parameters of variable frequency air conditioner on line |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104701839A (en) * | 2014-09-03 | 2015-06-10 | 国家电网公司 | Air conditioner load modeling method based on least squares parameter identification |
| CN105204334A (en) * | 2015-09-09 | 2015-12-30 | 东南大学 | Method for recognizing first-order model real-time parameters of building with air conditioner based on recursive least-squares method |
| CN109827310A (en) * | 2019-01-31 | 2019-05-31 | 河海大学 | A kind of residual air-conditioning load group polymerization model method for building up |
| CN110673489A (en) * | 2019-10-25 | 2020-01-10 | 国网山东省电力公司电力科学研究院 | Heat load identification method for commercial building room |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106091239B (en) * | 2016-06-06 | 2018-10-19 | 清华大学 | A kind of primary frequency regulation of power network method based on heavy construction air conditioner load cluster |
-
2021
- 2021-06-24 CN CN202110701938.6A patent/CN113435031B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104701839A (en) * | 2014-09-03 | 2015-06-10 | 国家电网公司 | Air conditioner load modeling method based on least squares parameter identification |
| CN105204334A (en) * | 2015-09-09 | 2015-12-30 | 东南大学 | Method for recognizing first-order model real-time parameters of building with air conditioner based on recursive least-squares method |
| CN109827310A (en) * | 2019-01-31 | 2019-05-31 | 河海大学 | A kind of residual air-conditioning load group polymerization model method for building up |
| CN110673489A (en) * | 2019-10-25 | 2020-01-10 | 国网山东省电力公司电力科学研究院 | Heat load identification method for commercial building room |
Non-Patent Citations (2)
| Title |
|---|
| Day Ahead Bidding of a Load Aggregator Considering Residential Consumers Demand Response Uncertainty Modeling;Zhaofang Song 等;applied sciences;20201019;全文 * |
| 基于空调自适应修正模型的户用微电网能量优化;窦晓波 等;电力系统自动化;20170810(第15期);全文 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN113435031A (en) | 2021-09-24 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Zhang et al. | An optimal and learning-based demand response and home energy management system | |
| Shao et al. | Development of physical-based demand response-enabled residential load models | |
| Huang et al. | Simulation-based performance evaluation of model predictive control for building energy systems | |
| Kara et al. | Quantifying flexibility of residential thermostatically controlled loads for demand response: a data-driven approach | |
| CN108036468B (en) | Aggregation control method for air conditioning system | |
| Li et al. | Developing smart and real-time demand response mechanism for residential energy consumers | |
| Qian et al. | Power consumption and energy efficiency of VRF system based on large scale monitoring virtual sensors | |
| Xiao et al. | Status quo and opportunities for building energy prediction in limited data Context—Overview from a competition | |
| CN113435031B (en) | Parameter identification method and system of first-order ETP model of house to which air conditioner belongs | |
| CN114237057A (en) | Dynamic modeling method, system, equipment and medium for intelligent building electricity analysis | |
| Abdeen et al. | Comparative review of occupant-related energy aspects of the National Building Code of Canada | |
| CN115081902A (en) | Comprehensive planning method, device, equipment and medium based on source network load-storage cooperation | |
| CN112560160B (en) | Model and data driven heating ventilation air conditioner optimal set temperature acquisition method and equipment | |
| CN105894137A (en) | Residential electricity demand forecasting method and system | |
| CN105570867A (en) | Regulating method and system for direct heat supply flue gas boiler load parameters | |
| CN116862129B (en) | Zero-energy-consumption building data analysis method and system | |
| CN116776283A (en) | Air conditioner monitoring and parameter identification method of self-coding air conditioner collaborative identification model | |
| CN116663997A (en) | Energy-saving effect evaluation method, system, equipment and medium for split air conditioner | |
| CN115271168A (en) | Method and device for predicting response potential of electrical load and storage medium | |
| Gong et al. | An aggregated and equivalent home model for power system studies with examples of building insulation and HVAC control improvements | |
| Yu | Mining hidden knowledge from measured data for improving building energy performance | |
| CN113341792A (en) | Building data acquisition method and system based on BIM | |
| CN113449909A (en) | Cold load prediction method of cold accumulation air conditioner based on improved long-term and short-term memory network | |
| Fang et al. | Clustering and analysis of household power load based on HMM and multi-factors | |
| Yamamoto et al. | Advanced thermal environment and energy consumption calculation over time using combined energy simulation, computational fluid dynamics, and heat source model |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |