CN109165418B - Room temperature measuring method based on household calorimeter data - Google Patents
Room temperature measuring method based on household calorimeter data Download PDFInfo
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- CN109165418B CN109165418B CN201810863070.8A CN201810863070A CN109165418B CN 109165418 B CN109165418 B CN 109165418B CN 201810863070 A CN201810863070 A CN 201810863070A CN 109165418 B CN109165418 B CN 109165418B
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- 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
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24D—DOMESTIC- OR SPACE-HEATING SYSTEMS, e.g. CENTRAL HEATING SYSTEMS; DOMESTIC HOT-WATER SUPPLY SYSTEMS; ELEMENTS OR COMPONENTS THEREFOR
- F24D19/00—Details
- F24D19/10—Arrangement or mounting of control or safety devices
- F24D19/1006—Arrangement or mounting of control or safety devices for water heating systems
- F24D19/1009—Arrangement or mounting of control or safety devices for water heating systems for central heating
Abstract
The invention discloses a room temperature measurement method based on household calorimeter data, and belongs to the technical field of heating. According to the room temperature measuring method based on the household calorimeter data, the calorimeter is installed at the household heat supply inlet, and the indoor temperature is calculated by utilizing the heat supply data measured by the calorimeter. The room temperature measuring method based on the household calorimeter data is convenient to maintain, low in cost, free from entering the home of a user, and good in popularization and application value.
Description
Technical Field
The invention relates to the technical field of heating and heat supply, and particularly provides a room temperature measurement method based on household calorimeter data.
Background
With the rapid development of the economic society of China, the energy consumption of the society of China keeps the potential of rapid growth. The huge energy consumption not only brings huge pressure to the energy supply and economic stable development of China, but also creates serious social problems such as aggravation of environmental pollution.
Wherein building energy consumption is an important component of social energy consumption. The statistics shows that the total energy consumption of the building in China in 2014 is about 8.19 hundred million tons of standard coal, and the total energy consumption of the building in China accounts for about 20% of the total energy consumption of the society in China. Central heating is a main mode of heating in winter in northern areas of China, and according to statistics of related data, the energy consumption of heating in northern areas of China accounts for more than 65% of the total energy consumption of buildings, and some areas are even up to 90%. Therefore, reducing the energy consumption of heating and heating has great significance for national energy conservation and emission reduction. An important means of reducing the energy consumption of heating is to prevent excessive heating. When the central heating is designed according to the specification of the national standard GB 50019-2003 'design Specification for heating ventilation and air conditioning', the indoor calculated temperature of the main room of the civil building is preferably between 16 ℃ and 24 ℃, and the central heating has a certain fluctuation. The national current standard of indoor air quality standard (GB/T18883) requires that the indoor temperature of the main room of civil building is set above 16 ℃ as the qualified temperature and 18 ℃ as the standard room temperature. However, in the central heating process, the heating company cannot know the real indoor temperature, so that the indoor temperature of residents is ensured to reach the standard, and the situation of excessive heating is often caused. Far exceeding 22 c and exceeding 25 c, this results in transitional heating. The indoor temperature is too high, and on the one hand, the indoor temperature is unfavorable for the health. Scientific researches show that when the heating temperature exceeds 22 ℃, indoor air is dry, and thus the body temperature regulating function of a human body is affected, so that the body temperature is increased, the blood vessel is dilated, the heart rate is accelerated, endocrine disturbance and the like are caused. On the other hand, too high a temperature causes a great amount of waste of energy consumption, and the energy consumption is statistically increased by more than 10% when the room temperature is increased by more than 18 ℃. Therefore, the acquisition of the indoor temperature of the residents and the accurate heat supply load adjustment according to the indoor temperature of the residents are important means for realizing heat supply and energy conservation.
There are thermal companies that have attempted to acquire resident room temperature data by installing temperature sensors into the resident's room, but most end up with failure. On the one hand, because of the excessive cost and pressure of installing the temperature sensor per household, on the other hand, residents are very exclusive of installing temperature measuring equipment from thermal power companies in the home all the year round.
Disclosure of Invention
The technical task of the invention is to provide a room temperature measuring method based on user heat meter data, which is convenient to maintain and low in cost and does not need to enter the home of a user aiming at the existing problems.
In order to achieve the above purpose, the present invention provides the following technical solutions:
the room temperature measuring method based on the heat meter data for the household comprises the steps of installing a heat meter at a heat supply inlet of a household, and calculating the indoor temperature by utilizing the heat supply data measured by the heat meter.
The indoor temperature is calculated according to the heat supply data measured by the heat meter, so that a thermal company can adjust heating according to the indoor real-time temperature, and the indoor temperature can be effectively guaranteed not to be too low, and the heating can not be transited.
Preferably, the room temperature measurement method specifically comprises the following steps:
s1: a heat meter is installed at a household heat supply inlet, and the heat meter measures heat supply data: water supply flow q and water supply temperature T in And backwater temperature T out ；
S2: an indoor temperature sensor is installed in an indoor activity area of a resident, and indoor real-time temperature T is measured;
s3: an outdoor temperature sensor is arranged at the outdoor back of the user, and the outdoor real-time temperature T is measured 0 ；
S4: for the measured water supply flow q and water supply temperature T in Temperature T of backwater out Indoor temperature T, outdoor temperature T 0 And (3) carrying out data analysis to obtain the relationship among heat supply data, outdoor temperature and indoor temperature as shown in the following formula (1): t=f (q, T in ,T out ,T 0 ) (1)；
S5: dismantling the indoor temperature sensor;
s6: the heat meter continuously collects heat supply data, the outdoor temperature sensor continuously collects outdoor real-time temperature, and the obtained relationship is utilized to predict indoor temperature.
Firstly, an indoor temperature sensor is installed in an indoor activity area of a resident, an indoor real-time temperature T is collected, after the relation between the indoor temperature, heat supply data and the outdoor temperature is obtained, the indoor temperature sensor is removed, and the indoor real-time temperature is calculated through the heat supply data collected by a calorimeter and the outdoor temperature. On one hand, the resident can be prevented from rejecting temperature measuring equipment from a thermal company to be installed in home all the year round, and on the other hand, the real-time indoor temperature can be obtained according to heat supply data and outdoor temperature.
Preferably, in step S4, data analysis is performed by using mechanism modeling, a mechanism model is built, and an energy conservation process is built for T:
wherein T is the indoor temperature, T in For supplying water at temperature T out For backwaterTemperature, T 0 C is the outdoor temperature p1 Specific heat of indoor air, V is indoor heating area, c p2 For the specific heat of water supply, q is the water supply flow, h 1 And A 1 The heat exchange coefficient and the heat exchange area are respectively, the formula (2) is simplified to be
The parameters a and b are optimized using the least squares method.
Identifying the mechanism model parameters a and b by adopting identification data, wherein the method is to optimize nonlinear model parameters by adopting a least square method, and an optimization equation is as follows:
U＝[u 1 u 2 u 3 u 4 ] T ＝[T in T out T 0 q] T
wherein y is room temperature T, y m Is a model value of room temperature, T s For sampling time, here T s 30min. θ represents the parameter sought.
Adopting unconstrained nonlinear optimization for the minimization problem, and obtaining optimal parameters by using an optimization solver fminunc in matlab, wherein the optimal parameters are as follows: θ= [0.0040.004]. This value is the result of taking a sampling time of 30min, if the parameter value is divided by 30 in units of 1 min.
The identification data is sampled once for 30min, 331 groups of identification data are collected altogether, and theta is calculated by utilizing the collected identification data, so that the optimal parameters are obtained.
And acquiring 1395 groups of verification data by using the obtained optimal parameters, and verifying the parameters.
The identification data are initial sampling data, the verification data are data acquired by using the obtained parameters and are used for verifying the correctness of the parameters, and the sampling time of the verification data is 30min as the sampling time of the obtained parameters.
Writing the formula (2) into a discrete form according to the obtained optimal parameters:
y m (k+1)＝0.004q(k)(T in (k)-T out (k))-0.004(y(k)-T 0 (k))+y(k)
all the above values are established once at a sampling time of 30min, and if the sampling time is changed, the parameters need to be modified accordingly. For example, the sampling time in the invention is 30min once, if the sampling time is changed to 1min once, the parameter value is divided by 30; the sampling time is changed to 1h once, and the parameter value is multiplied by 2.
Preferably, the data analysis further comprises the analysis of T out Establishing an energy conservation process
Wherein T is the indoor temperature, T in For supplying water at temperature T out T is the return water temperature 0 C is the outdoor temperature p2 Specific heat of water supply, V 2 Q is the water supply flow, h is the volume of hot water in the heat exchanger 2 And A 2 The heat exchange coefficient of the heat exchanger and the indoor convection heat exchange are respectively the heat exchange coefficient and the heat exchange area, and the heat exchange coefficient is simplified with the formula (3)
The parameters c and d are optimized using the least squares method.
In performing virtual simulation, T is required for simulation closed-loop control out As interference, but T out And T is 0 It varies with the heating temperature and the room temperature and the pressure difference. Therefore, to better simulate the heating process, T is measured out Modeling is carried out, and the original model is expanded.
In equation (3), it is assumed that the hot water temperature in the heat exchanger is equal to T out I.e. water is supplied from T in Instantly drop to T out And then exchanges heat with the indoor temperature T due to the difference value. The parameters c and d in the formula (3) are calculated in the same manner as in the identification method of the formula (2), and c=0.968 and d= 0.0852 are given. Writing formula (3) in discrete form is:
T out (k+1)＝0.0968q(k)(T in (k)-T out (k))-0.0852(y(k)-T 0 (k))+T out (k)。
preferably, in step S4, data analysis is performed using a linear model ARX model.
Preferably, in the ARX model, T is 0 、T in 、T out And q four values as inputs, y=t as output, resulting in
The input and output data are identified to obtain a parameter a 1 、a 2 、b 11 、b 12 、b 21 、b 22 、b 31 、b 32 、b 41 And b 42 。
Using the identification data to calculate a 1 ＝-1.4684，a 2 ＝0.5279，b 11 ＝0.0259，b 12 ＝-0.0423，b 21 ＝0.0745，b 22 ＝-0.0360，b 31 ＝0.0497，b 32 ＝-0.0438，b 41 ＝0.2536，b 42 ＝0.1116。
The formula (4) is: y (k) = 1.4684y (k-1) -0.5279y (k-2) +0.0259T in (k-1)-0.0423T in (k-2)+0.0745T out (k-1)-0.0360T out (k-2)+0.0497T 0 (k-1)-0.0438T 0 (k-2)+0.2536q(k-1)+0.1116q(k-2)。
And verifying the formula by adopting verification data.
The identification data is initial data, the identification data is used for obtaining parameters, then identification is carried out to obtain verification data, and the verification data is compared with the initial identification data to verify the correctness of the parameters.
The step response model can intuitively display the influence of each input quantity on the room temperature, the matlab simulation is used for obtaining the step response model, the ARX model comprises input and output, the influence of the input quantity on the output corresponds to the step response model, and when the ARX model has different numbers of input values, the ARX model corresponds to different numbers of step response models. In the ARX model by adopting the step response model, T is calculated 0 、T in 、T out And q four values as inputs, y=t as output, the effect of each input amount on room temperature is shown.
Preferably, in the ARX model, T in And T 0 Two values as input, y=t as output, resulting in
The input and output data are identified to obtain a parameter a 1 、a 2 、b 11 、b 12 、b 21 And b 22 。
Using the identification data to calculate a 1 ＝-1.5156，a 2 ＝0.5417，b 11 ＝0.0477，b 12 ＝-0.0384，b 21 ＝0.0492，b 22 ＝-0.0416。
The formula (6) is: y (k) = 1.5156y (k-1) -0.5417y (k-2) +0.0477T in (k-1)-0.0.0384T in (k-2)+0.0492T out (k-1)-0.0416T out (k-2)。
And verifying the formula by adopting verification data.
The identification data is initial data, the identification data is used for obtaining parameters, then identification is carried out to obtain verification data, and the verification data is compared with the initial identification data to verify the correctness of the parameters.
The step response model can intuitively display the influence of each input quantity on the room temperature, the matlab simulation is used for obtaining the step response model, the ARX model comprises input and output, and the influence of the input quantity on the output and the influence of the step response model on the outputIf the ARX model has different numbers of input values, the ARX model corresponds to different numbers of step response models. In the ARX model by adopting the step response model, T is calculated 0 、T in 、T out And q four values as inputs, y=t as output, the effect of each input amount on room temperature is shown.
Compared with the prior art, the room temperature measuring method based on the user calorimeter data has the following outstanding beneficial effects: according to the room temperature measuring method based on the household calorimeter data, the temperature data in the resident room can be obtained without installing a temperature sensor in the resident room, so that a thermal company can adjust the heating quantity according to the temperature in the resident room, the temperature in the resident room can be prevented from being too low, normal heating of the resident is prevented from being influenced, the temperature in the resident room is prevented from being too high, energy waste is caused, and the method has good popularization and application values.
Drawings
FIG. 1 is a schematic diagram of an acquisition process of identification data of a process of establishing energy conservation for T in a first embodiment of the invention;
FIG. 2 is a schematic diagram of a process for collecting verification data of a process for establishing energy conservation for T in accordance with a first embodiment of the present invention;
FIG. 3 shows a pair T in accordance with one embodiment of the present invention out Establishing an acquisition process schematic diagram of identification data of an energy conservation process;
FIG. 4 shows a pair T in accordance with one embodiment of the present invention out Establishing a schematic diagram of an acquisition process of verification data of an energy conservation process;
FIG. 5 is a schematic diagram of a step response model corresponding to an ARX model with four inputs, y=T in a second embodiment of the present invention;
FIG. 6 is a schematic diagram of a step response model corresponding to the ARX model with two inputs, y=T in the second embodiment of the present invention;
fig. 7 is a schematic diagram of an open-loop analysis corresponding to a step response model with two inputs, y=t output, of the ARX model in the second embodiment of the present invention.
Detailed Description
The room temperature measuring method based on the user heat meter data according to the present invention will be described in further detail with reference to the accompanying drawings and examples.
According to the room temperature measuring method based on the household calorimeter data, the calorimeter is installed at the household heat supply inlet, and the indoor temperature is calculated by collecting the heat supply data through the calorimeter. The method specifically comprises the following steps:
s1: a heat meter is installed at a household heat supply inlet, and the heat meter measures heat supply data: water supply flow q and water supply temperature T in And backwater temperature T out 。
S2: an indoor temperature sensor is installed in an indoor activity area of a resident, and indoor real-time temperature T is measured.
S3: an outdoor temperature sensor is arranged at the outdoor back of the user, and the outdoor real-time temperature T is measured 0 。
S4: for the measured water supply flow q and water supply temperature T in Temperature T of backwater out Indoor temperature T, outdoor temperature T 0 And (3) carrying out data analysis to obtain the relationship among heat supply data, outdoor temperature and indoor temperature as shown in the following formula (1): t=f (q, T in ,T out ,T 0 ) (1)。
S5: and removing the indoor temperature sensor.
S6: the heat meter continuously collects heat supply data, the outdoor temperature sensor continuously collects outdoor real-time temperature, and the obtained relationship is utilized to predict indoor temperature.
In the invention, two methods are adopted to carry out data analysis on the step S4 to obtain the indoor temperature T and the outdoor temperature T 0 Water supply flow q and water supply temperature T in And backwater temperature T out Specific relation of (3).
Example 1
And (5) carrying out data analysis by using mechanism modeling, and establishing a mechanism model. The data analysis process includes the steps of comparing T with T out The energy conservation processes are respectively established.
1. Establishing an energy conservation process for T:
wherein T is the indoor temperature, T in For supplying water at temperature T out T is the return water temperature 0 C is the outdoor temperature p1 Specific heat of indoor air, V is indoor heating area, c p2 For the specific heat of water supply, q is the water supply flow, h 1 And A 1 The heat exchange coefficient and the heat exchange area are respectively, the formula (2) is simplified to be
And identifying the mechanism model parameters by adopting identification data, wherein the method is to optimize nonlinear model parameters a and b by adopting a least square method. The optimization equation is:
U＝[u 1 u 2 u 3 u 4 ] T ＝[T in T out T 0 q] T
wherein y is room temperature T, y m Is a model value of room temperature, T s For sampling time, here T s For 30min, θ represents the parameter sought.
Adopting unconstrained nonlinear optimization for the minimization problem, and obtaining optimal parameters by using an optimization solver fminunc in matlab, wherein the optimal parameters are as follows: θ= [0.0040.004]. The identification data is sampled once for 30min, the identification data of the group is collected 331 together, and as shown in fig. 1, which is a schematic diagram of the collection process, the data represents a sampling value of room temperature, and the model represents a model value of room temperature. And calculating the parameter theta by using the identification data. If the sampling time of the identification data is 1min, the obtained parameter value needs to be divided by 30.
By using the obtained optimal parameters, 1395 sets of verification data are collected, and as shown in fig. 2, a schematic diagram of a collection process is shown, so as to obtain a verification result. In fig. 2, the data represents a room temperature sampling value obtained when the optimum parameters are verified, and the model represents a model value obtained when the optimum parameters are verified.
As shown in table 1, the statistics of the identification data and the verification data are shown.
TABLE 1
| Statistical results | Mean value of | Mean square error |
| Identification data 331 set | -0.0016 | 0.1 |
| Verification data 1395 set | 0.0042 | 0.1108 |
Writing the formula (2) into a discrete form according to the obtained optimal parameters:
y m (k+1)＝0.004q(k)(T in (k)-T out (k))-0.004(y(k)-T 0 (k))+y(k)
2. in performing virtual simulation, T is required for simulation closed-loop control out As interference, but T out And T is 0 It varies with the heating temperature and the room temperature and the pressure difference. Therefore, to better simulate the heating process, T is measured out Modeling is carried out, and the original model is expanded.
For T out Establishing an energy conservation process:
wherein T is the indoor temperature, T in For supplying water at temperature T out T is the return water temperature 0 C is the outdoor temperature p2 Specific heat of water supply, V 2 Q is the water supply flow, h is the volume of hot water in the heat exchanger 2 And A 2 The heat exchange coefficient and the heat exchange area of the heat exchanger and the indoor convection heat exchange are respectively. In the formula (3), the temperature of hot water in the heat exchanger is equal to T out I.e. water is supplied from T in Instantly drop to T out And then exchanges heat with the indoor temperature T due to the difference value. Simplifying (3) to have
According to the same identification method as that for T, the parameters c and d in the equation are calculated, as shown in FIG. 3, which is a schematic diagram of the acquisition process of identification data, wherein the data represents the sampling value of room temperature, and the model represents the model value of room temperature. According to the acquired identification data, the parameters c=0.0968 and d= 0.0852 are obtained through calculation. Writing the formula (3) into a discrete form according to the obtained optimal parameters:
T out (k+1)＝0.0968q(k)(T in (k)-T out (k))-0.0852(y(k)-T 0 (k))+T out (k)。
the verification data is taken for model verification, and fig. 4 is a schematic diagram of a collection process of the verification data. In fig. 4, the data represents a room temperature sampling value obtained when the optimum parameters are verified, and the model represents a model value obtained when the optimum parameters are verified.
Example 2
Data analysis using linear model ARX model
1. In ARX model, T will be 0 、T in 、To ut And q four values as inputs, y=t as output, resulting in
The input and output data are identified to obtain a parameter a 1 、 a2 、b 11 、b 12 、b 21 、b 22 、b 31 、b 32 、b 41 And b 42 . As shown in Table 2, a is calculated by using the identification data 1 、a 2 、b 11 、b 12 、b 21 、b 22 、b 31 、b 32 、b 41 And b 42 And (5) counting the value, the identification data and the verification data.
TABLE 2
The formula (4) is thus: y (k) = 1.4684y (k-1) -0.5279y (k-2) +0.0259T in (k-1)-0.0423T in (k-2)+0.0745T out (k-1)-0.0360T out (k-2)+0.0497T 0 (k-1)-0.0438T 0 (k-2)+0.2536q(k-1)+0.1116q(k-2)。
As shown in FIG. 5, T is 0 、T in 、T out And q four values are used as inputs, y=T is used as a step response model corresponding to the ARX model when the output is performed, in the graph, u1 is a heat supply temperature, u2 is an output temperature, u3 is an outdoor temperature, u4 is a flow, and in the step response model, u1 is a heat supply temperature rise and an indoor temperature fall, which are not in accordance with reality. In addition, the static gain of the heating temperature and the backwater temperature to the indoor temperature is too small, which is only 0.3 and 0.1.
2. In ARX model, T in And T 0 For two inputs, y=t as output, resulting in
To input and output dataIdentifying to obtain parameter a 1 、a 2 、b 11 、b 12 、b 21 And b 22 . As shown in Table 4, a is calculated by using the identification data 1 、a 2 、b 11 、b 12 、b 21 、b 22 、b 31 And b 32 And (5) counting the value, the identification data and the verification data.
| a1 | a2 | b11 | b12 | b21 | b22 |
| -1.5156 | 0.5417 | 0.0477 | -0.0384 | 0.0492 | -0.0416 |
| Identification data | Validating data | ||||
| Deviation mean value | -0.0255 | 1.5740 | |||
| Root mean square deviation | 0.3996 | 1.3087 |
As shown in fig. 6, T in And T 0 For two inputs, y=t is taken as the corresponding step response model of the ARX model when outputting, u1 is the heating temperature, u2 is the output temperature, the gains of the heating temperature and the outdoor temperature in the step response model to the indoor temperature are too small, namely 0.35 and 0.3, and the response time is only 0.05 and 0.15.
4. Further open loop analysis of the y=t output for two inputs in the ARX model
Discrete model form:
y(k)+a 1 y(k-1)+a 2 y(k-2)＝b 11 u 1 (k-1)+b 12 u 1 (k-2)+b 21 u 2 (k-1)+b 22 u 2 (k-2)
the sampling time is half an hour, the corresponding value is 0.5, and the continuous transfer function is obtained:
Zero/pole/gain from input 2 to output:
as shown in fig. 7, the abscissa in the step response model is in minutes, consistent with the step response obtained by the program of fig. 6. Description of two transfer functions
And->Are all approximate first-order inertial links.
As can be seen from the step response model shown in fig. 7:
● The heating temperature rises to 1 ℃, the indoor temperature rises to 0.35 ℃ to be stable after 30 hours, and the heating temperature can rise to approximately 0.3 ℃ after 10 hours.
● The outdoor temperature rises to 1 ℃, the indoor temperature rises to 0.3 ℃ after 30 hours and is stable, and the indoor temperature can rise to 0.25 ℃ after 10 hours.
The MPC (Model Predictive Control model predictive control) parameters can be judged by the step response model: the predicted time domain should approximately reach steady state time, so the predicted time domain P should take between 20 and 100. The predictive model may reach steady state 60 a preliminary time, i.e., 30 hours later.
The above embodiments are only preferred embodiments of the present invention, and it is intended that the common variations and substitutions made by those skilled in the art within the scope of the technical solution of the present invention are included in the scope of the present invention.
Claims (5)
1. The room temperature measurement method based on the user calorimeter data is characterized by comprising the following steps of: the heat meter is installed at the household heat supply inlet, and the indoor temperature is calculated by utilizing heat supply data measured by the heat meter, and the method comprises the following steps:
s1, installing a heat meter at a household heat supply inlet, and measuring heat supply data by the heat meter: water supply flow q and water supply temperature T in And backwater temperature T out ；
S2, installing an indoor temperature sensor in an indoor activity area of a resident, and measuring indoor real-time temperature T;
s3, installing an outdoor temperature sensor at an outdoor shadow part to measure outdoor real-time temperature T 0 ；
S4, measuring the water supply flow q and the water supply temperature T in Temperature T of backwater out Indoor temperature T, outdoor temperature T 0 And (3) carrying out data analysis to obtain the relationship among heat supply data, outdoor temperature and indoor temperature as shown in the following formula (1):
（1）；
s5, detaching the indoor temperature sensor;
s6, continuously acquiring heat supply data by the heat meter, continuously acquiring outdoor real-time temperature by the outdoor temperature sensor, and predicting the indoor temperature by utilizing the acquired relationship.
2. The room temperature measurement method based on user calorimeter data of claim 1, wherein: in the step S4, data analysis is carried out by utilizing mechanism modeling, a mechanism model is established, and an energy conservation process is established for T:
（2）
wherein T is the indoor temperature, T in For supplying water at temperature T out T is the return water temperature 0 C is the outdoor temperature p1 Specific heat of indoor air, V 1 C, for heating the room p2 For the specific heat of water supply, q is the water supply flow, h 1 And A 1 The heat exchange coefficient and the heat exchange area are respectively, the formula (2) is simplified to be
Optimizing parameters a and b by adopting a least square method;
also include a pair T out Establishing an energy conservation process:
（3）
wherein T is the indoor temperature, T in For supplying water at temperature T out T is the return water temperature 0 C is the outdoor temperature p2 Specific heat of water supply, V 2 Q is the water supply flow, h is the volume of hot water in the heat exchanger 2 And A 2 The heat exchange coefficient and the heat exchange area of the heat exchanger and the indoor convection heat exchange are respectively, and the formula (3) is simplified
And optimizing the parameters c and d by adopting a least square method.
3. The room temperature measurement method based on user calorimeter data of claim 1, wherein: in step S4, data analysis is performed using the linear model ARX model.
4. A room temperature measurement method based on user calorimeter data as claimed in claim 3, wherein: in the ARX model, T is taken as 0 、T in 、T out And q four values as inputs, y=t as output, resulting in
（4）
The parameter a is obtained by mass identification 1 、a 2 、b 11 、b 12 、b 21 、b 22 、b 31 、b 32 、b 41 And b 42 。
5. A room temperature measurement method based on user calorimeter data as claimed in claim 3, wherein: in the ARX model, T in And T 0 Two values as input, y=t as output, resulting in
（6）
The parameter a is obtained by mass identification 1 、a 2 、b 11 、b 12 、b 21 And b 22 。
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| CN110878959B (en) * | 2019-11-28 | 2021-04-27 | 济南大学 | Building temperature control method and system based on model predictive control |
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