CN110878959B - Building temperature control method and system based on model predictive control - Google Patents

Building temperature control method and system based on model predictive control Download PDF

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CN110878959B
CN110878959B CN201911189765.3A CN201911189765A CN110878959B CN 110878959 B CN110878959 B CN 110878959B CN 201911189765 A CN201911189765 A CN 201911189765A CN 110878959 B CN110878959 B CN 110878959B
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temperature
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control
water supply
building
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CN110878959A (en
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蒋萍
鞠晨曦
李实�
王孝红
于宏亮
黄冰
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University of Jinan
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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

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Abstract

The invention discloses a building temperature control method and a building temperature control system based on model predictive control. The system comprises: the MPC controller maintains the room temperature of the building at the determined most comfortable temperature value and calculates the optimal heat supply temperature value; the PID controller controls the actuating mechanism to respond according to the optimal heat supply temperature value and the secondary side water supply and return temperature; the actuating mechanism is used for adjusting the flow of the hot water at the primary side of the heat exchanger and controlling the temperature of the hot water at the secondary side of the heat exchanger; the first temperature sensor is used for collecting the temperature of hot water on the secondary side of the heat exchanger and sending the temperature to the PID controller; and the second temperature sensor is used for acquiring the indoor real-time temperature of the building and sending the indoor real-time temperature to the MPC controller. The invention makes the indoor temperature more stable, improves the comfort level of users and obviously reduces the energy consumption of heat supply.

Description

Building temperature control method and system based on model predictive control
Technical Field
The invention relates to a building temperature control method and system based on model predictive control, and belongs to the technical field of heat supply control.
Background
Many countries have heating requirements in winter, including heating designs for residential and non-residential buildings. In northern areas of China, cities and partial rural areas are mainly used for central heating. Steam and hot water generated by a centralized heat source reach a secondary heat exchange station through a primary heat exchange station and a primary pipe network, heat exchange is carried out in the secondary heat exchange station, heat is transferred to a secondary pipe network, and the heat is transferred to a heat user of a base layer through the secondary pipe network for production and life. As shown in fig. 1, the secondary heat exchange station is responsible for heat concentration and exchange, and the heat supply temperature directly determines the thermal comfort of the heat user, and is a key component of the heating system.
At present, the heating temperature of a heat exchange station is mostly adjusted by an operator according to experience and the current outdoor temperature, the water supply temperature is increased when the outdoor temperature is reduced, the water supply temperature is adjusted when the outdoor temperature is increased, the heating effect depends on the professional experience of the operator, the automation and intelligence degree is low, and the method is an open-loop control method. In addition, since the heating system has a large time constant and the operator does not consider the prediction of the future weather when the heating temperature is set, it may take a long time for the indoor temperature to be stabilized within the comfort range after the supply water temperature is adjusted, and it is likely that insufficient or excessive heating may be caused, resulting in a reduction in the thermal comfort of the user or unnecessary waste of energy.
Therefore, there is a need to develop a temperature control measure for a building that effectively improves the heating effect of the heat exchange station.
Disclosure of Invention
Aiming at the defects of the method, the invention provides a building temperature control method and system based on model predictive control, which can effectively improve the heat supply effect of a heat exchange station, make the indoor temperature more stable, improve the comfort level of users and obviously reduce the heat supply energy consumption.
The technical scheme adopted for solving the technical problems is as follows:
on one hand, the building temperature control method based on model Predictive control provided by the embodiment of the invention adjusts the heating temperature based on Model Predictive Control (MPC) (model Predictive control), and adds the prediction of interference in the Predictive control and compensates the upcoming change of the environmental temperature.
As a possible implementation manner of this embodiment, the method includes the following steps:
s1: required data preparation: weather forecast data, an indoor temperature ideal value set by a user, building indoor temperature data, and temperature and flow of supply and return water at the secondary side of the heat exchanger;
s2: the MPC calculates to obtain the current optimal water supply temperature set value u (k) according to the required data;
s3: transmitting the calculated water supply temperature set value u (k) to a bottom layer control loop, and controlling an actuating mechanism to respond by the bottom layer control loop;
s4: the execution mechanism adjusts the flow of the hot water at the primary side of the heat exchanger, and then controls the temperature of the hot water at the secondary side through the heat exchanger;
s5: the indoor temperature changes along with the changes of the water supply temperature and the water supply flow, and meanwhile, the indoor real-time temperature, the secondary side water supply and return temperature and the flow are measured by the sensor and fed back to the MPC for the next-time optimization.
As a possible implementation of this embodiment, in step S2, the MPC maintains the room temperature y (k) at the most comfortable value r (k) determined by the user or station operator; acquiring environmental temperature data D (k) of the future 24 hours from a weather forecast center through the Internet, wherein the variable is regarded as a prediction interference variable in an MPC algorithm, and a temperature sensor located outdoors gives the current environmental temperature T0(k) to compensate errors; the MPC calculates a set value u (k) of the water supply temperature at the current time, and the value is used as a set point of the lower-layer PID control; the pressure difference Δ p (k) between the supply and return water is measured and regarded as a measured disturbance.
As a possible implementation manner of this embodiment, the process of model predictive control includes the following steps:
establishing a mechanism model aiming at the heat exchange process of the building;
using the model to predict a model output in the future within a limited range;
the error between the model output and the measured value is minimized by optimization.
As a possible implementation manner of this embodiment, the mechanism model is:
Figure GDA0002968266280000031
Figure GDA0002968266280000032
wherein T is the indoor temperature, TinFor temperature of the water supply, ToutIs the return water temperature, T0Is the outdoor temperature, cp1Specific heat of indoor air, V indoor heating area, cp2Specific heat for water supply, q water supply flow, h1A1And h2A2The heat transfer coefficient is multiplied by the heat transfer area, and the outdoor heat transfer and the indoor heat transfer are assumed to be convective.
As a possible implementation of this embodiment, if no flow sensor is installed on site, the pressure difference Δ p between the hot water supply pipe and the water return pipe is used, and their squared relationship calculates the flow rate from the pressure difference:
Figure GDA0002968266280000033
the mechanism model is converted into:
Figure GDA0002968266280000034
Figure GDA0002968266280000035
wherein θ ═ θ1 θ2 θ3 θ4]TIs a model parameter vector, and the prediction of the outdoor temperature is included in the parameter θ 2.
As a possible implementation manner of this embodiment, discretizing the mechanism model to obtain a discretization state space model is:
x(k+1)＝A(k)x(k)+B(k)u(k)+Bdd(k)
y(k+1)＝Cx(k)
wherein:
x＝[T Tout],u＝Tin,d＝T0,y＝T
Figure GDA0002968266280000036
Figure GDA0002968266280000037
wherein d (k) represents future disturbance data, i.e. weather forecast data; x (k) represents a state vector; u (k) represents the manipulated variable, i.e. the supply water temperature.
As a possible implementation manner of this embodiment, the model output is:
Figure GDA0002968266280000041
wherein, the left side of the equation
Figure GDA0002968266280000042
For output prediction, x (k) is the current time state vector, u (k-1) is the manipulated variable at the previous time, Δ U (k) is the manipulated variable in the control time domain, D (k) is the prediction for future interference, Ey is error feedback, ΦA、ФBG and GdIs a coefficient matrix; the specific description is as follows:
ΔU＝[Δu(k) … Δu(k+Nu-1)]T
Figure GDA0002968266280000043
R＝[r(k+1) … r(k+Np)]T
D＝[d(k) … d(k+Np-1)]T
Figure GDA0002968266280000044
Figure GDA0002968266280000045
Figure GDA0002968266280000046
Figure GDA0002968266280000047
output feedback is applied to correct the error between the model output and the measured value:
Figure GDA0002968266280000048
Figure GDA0002968266280000049
wherein ekThe error is expressed as the difference between the actual output and the predicted output.
As a possible implementation manner of this embodiment, the process of minimizing the error between the model output and the measured value by optimization is as follows:
the objective function is:
Figure GDA00029682662800000410
the following optimization problems are solved:
Figure GDA0002968266280000051
Figure GDA0002968266280000052
Δumin≤Δu(k+j)≤Δumax
umin≤u(k+j)≤umax
Figure GDA0002968266280000053
where J is the objective function of the optimization, R is the setpoint vector, R is the setpoint, w is the weight of the steering input changes, Np is the prediction horizon, Nc is the control horizon, u is the control horizonminAnd umaxRespectively minimum and maximum constraints of the steering input, DeltauminAnd Δ umaxIs a constraint on the rate of change of the steering input,yminand ymaxIs a constraint on the prediction output;
minimizing the objective function, solving the optimization problem to obtain the optimal control vector:
ΔU*(k)＝[GTG+W]-1GTE(k)＝KE(k)
wherein:
E(k)＝R(k)-ΦAx(k)-ΦBu(k-1)-GdD(k)-Ey(k)
and (3) acting the first element of the control vector on the system, and obtaining a feedback value to be used as data required by optimization at the next moment.
In another aspect, a building temperature control system based on model predictive control provided in an embodiment of the present invention includes:
the MPC controller is used for maintaining the room temperature of the building at the most comfortable temperature value determined by a user or a station operator and calculating the optimal heating temperature value at the current time;
the PID controller is used for controlling the actuating mechanism to respond according to the optimal heat supply temperature value and the secondary side water supply and return temperature;
the actuating mechanism is used for adjusting the flow of the hot water at the primary side of the heat exchanger and controlling the temperature of the hot water at the secondary side of the heat exchanger;
the first temperature sensor is used for collecting the temperature of hot water on the secondary side of the heat exchanger and sending the temperature to the PID controller;
and the second temperature sensor is used for acquiring the indoor real-time temperature of the building and sending the indoor real-time temperature to the MPC controller.
The technical scheme of the embodiment of the invention has the following beneficial effects:
the invention provides a heat supply temperature adjusting scheme based on model predictive control, and the prediction of future interference is added in the predictive control to compensate the upcoming environmental temperature change, thereby effectively improving the heat supply effect of a heat exchange station, enabling the indoor temperature to be more stable, improving the comfort level of users and obviously reducing the heat supply energy consumption.
The invention analyzes the defects of the existing central heating system, improves the existing central heating system by pertinently adopting a model predictive control method containing measurable interference, adds an optimization supervision layer on the upper layer of the existing PID control loop, sets an optimized PID set value in real time, replaces the traditional rule control set value setting, calculates the optimal control variable at the current moment at each sampling moment and acts on the system, and the optimization algorithm contains the prediction and feedback correction of the future interference.
Description of the drawings:
FIG. 1 is a schematic diagram of the operation of a secondary heat exchange station;
FIG. 2 is a flow chart illustrating a method for building temperature control based on model predictive control in accordance with an exemplary embodiment;
FIG. 3 is a schematic diagram of a model predictive control-based building temperature control system according to an exemplary embodiment;
FIG. 4 is a schematic diagram of an MPC configuration for a heating process;
FIG. 5 is a schematic view of an indoor heat exchange process for a building;
FIG. 6 is a schematic comparison of MPC simulation results with RBC.
Detailed Description
The invention is further illustrated by the following examples in conjunction with the accompanying drawings:
in order to clearly explain the technical features of the present invention, the following detailed description of the present invention is provided with reference to the accompanying drawings. The following disclosure provides many different embodiments, or examples, for implementing different features of the invention. To simplify the disclosure of the present invention, the components and arrangements of specific examples are described below. Furthermore, the present invention may repeat reference numerals and/or letters in the various examples. This repetition is for the purpose of simplicity and clarity and does not in itself dictate a relationship between the various embodiments and/or configurations discussed. It should be noted that the components illustrated in the figures are not necessarily drawn to scale. Descriptions of well-known components and processing techniques and procedures are omitted so as to not unnecessarily limit the invention.
FIG. 3 is a schematic diagram illustrating a model predictive control-based building temperature control system in accordance with an exemplary embodiment. As shown in fig. 3, an embodiment of the present invention provides a building temperature control system based on model predictive control, including:
the MPC controller is used for maintaining the room temperature of the building at the most comfortable temperature value determined by a user or a station operator and calculating the optimal heating temperature value at the current time;
the PID controller is used for controlling the actuating mechanism to respond according to the optimal heat supply temperature value and the secondary side water supply and return temperature;
the actuating mechanism is used for adjusting the flow of the hot water at the primary side of the heat exchanger and controlling the temperature of the hot water at the secondary side of the heat exchanger;
the first temperature sensor is used for collecting the temperature of hot water on the secondary side of the heat exchanger and sending the temperature to the PID controller;
and the second temperature sensor is used for acquiring the indoor real-time temperature of the building and sending the indoor real-time temperature to the MPC controller.
According to the building temperature control method based on model predictive control provided by the embodiment of the invention, the MPC is controlled based on model predictive control to regulate the heating temperature, the prediction of interference is added in the predictive control, and the upcoming change of the environmental temperature is compensated.
FIG. 2 is a flow chart illustrating a method for building temperature control based on model predictive control in accordance with an exemplary embodiment. As shown in fig. 2, a building temperature control method based on model predictive control according to an embodiment of the present invention includes the following steps:
s1: required data preparation: weather forecast data, an indoor temperature ideal value set by a user, building indoor temperature data, and temperature and flow of supply and return water at the secondary side of the heat exchanger;
s2: the MPC calculates to obtain the current optimal water supply temperature set value u (k) according to the required data;
s3: transmitting the calculated water supply temperature set value u (k) to a bottom layer control loop, and controlling an actuating mechanism to respond by the bottom layer control loop;
s4: the execution mechanism adjusts the flow of the hot water at the primary side of the heat exchanger, and then controls the temperature of the hot water at the secondary side through the heat exchanger;
s5: the indoor temperature changes along with the changes of the water supply temperature and the water supply flow, and meanwhile, the indoor real-time temperature, the secondary side water supply and return temperature and the flow are measured by the sensor and fed back to the MPC for the next-time optimization.
The technical scheme adopted by the invention is as follows: model Predictive Control (MPC) containing future measurable disturbances is added as a superior to the underlying PID control to provide set points for PID control. The water supply temperature of the conventional secondary heat exchange station is mostly adjusted by an operator according to experience and the current weather condition, and in the invention, the water supply temperature is not manually adjusted any more but is obtained by optimizing and calculating the MPC. In actual operation, a supervisory layer MPC controller is added on an upper layer controlled by a secondary heat exchange station PID, an optimized water supply temperature value u (k) is calculated by an MPC optimization algorithm by utilizing a known weather forecast and past water supply temperature information and combining a temperature set value given by a user side or an operator side, and is transmitted to a lower layer PID to replace a water supply temperature value given by the operator according to experience and current weather, so that an execution mechanism (such as a valve and a pump in figure 1) is adjusted, and the control of indoor temperature is improved.
MPC is widely used in industry as an advanced control method. MPC has the characteristics of rolling time domain and feedback correction, the optimization problem comprises the prediction of future interference, the optimal control vector of the current moment is obtained by calculating the optimization problem at each moment, and the first item is acted on the control system. And solving the optimization problem again at the next moment, repeating the steps and rolling forward. Aiming at the defects of the existing heating control scheme, the main advantages of the MPC applied to the heating system are as follows:
1) handling conflicting optimization objectives: maximizing user comfort and minimizing energy consumption.
2) Including predictions of future interference (ambient temperature, occupancy, solar radiation).
3) There are many constraints on the heating process and MPCs can handle the constraint problem.
4) The problem of multi-input multi-output coupling can be solved, and the adverse effect of the large time constant of the building on the control can be eliminated.
Figure 4 shows the MPC configuration for a heating process. The purpose of the MPC is to maintain the room temperature y (k) at the most comfortable value r (k) determined by the user or site operator. The room temperature is a value calculated from an average value of temperature data obtained by a plurality of wireless sensors. Ambient temperature data d (k) for the next 24 hours is obtained from the weather forecasting centre via the internet, this variable is considered as a predictive disturbance variable in the MPC algorithm, and the temperature sensor located outdoors gives the current ambient temperature T0(k) to compensate for the error. The pressure difference Δ p (k) between the supply and return water can be measured and regarded as a measured disturbance. The MPC calculates the optimal heating temperature value for the current time u (k), which is used as the set point for the underlying PID control.
In order to realize model predictive control, a mechanism model for the building heat exchange process needs to be established, and the model needs to be capable of correctly reflecting the system dynamics, and the building heat exchange process is shown in fig. 5, wherein Tin is the water supply temperature, Tout is the return water temperature, T is the indoor temperature, T0 is the outdoor temperature, and q is the water supply flow.
The mechanism model can be written as:
Figure GDA0002968266280000091
Figure GDA0002968266280000092
wherein T is the indoor temperature, TinFor temperature of the water supply, ToutIs the return water temperature, T0Is the outdoor temperature, cp1Specific heat of indoor air, V indoor heating area, cp2Specific heat for water supply, q water supply flow, h1A1And h2A2The heat transfer coefficient is multiplied by the heat transfer area, and the outdoor heat transfer and the indoor heat transfer are assumed to be convective.
The flow rate can be calculated from the pressure difference Δ p between the hot water supply pipe and the return pipe by not installing a flow sensor on site, and thus taking into account their squared relationship:
Figure GDA0002968266280000093
the model was converted to:
Figure GDA0002968266280000094
Figure GDA0002968266280000095
wherein θ ═ θ1 θ2 θ3 θ4]TIs a model parameter vector, and the prediction of the outdoor temperature is contained in the parameter theta2In (1).
Further discretizing the model to obtain a discretized state space model:
x(k+1)＝A(k)x(k)+B(k)u(k)+Bdd(k)
y(k+1)＝Cx(k)
wherein:
x＝[T Tout],u＝Tin,d＝T0,y＝T
Figure GDA0002968266280000101
Figure GDA0002968266280000102
wherein d (k) represents future disturbance data, i.e. weather forecast data; x (k) represents a state vector; u (k) represents the manipulated variable, i.e. the supply water temperature.
After the model is established, the model predictive control uses the model to predict the model output in a limited range in the future, and the error between the model output and the expected value can be minimized through an optimization method.
The model prediction output is as follows:
Figure GDA0002968266280000103
wherein, the left side of the equation
Figure GDA0002968266280000104
For output prediction, x (k) is the current time state vector, u (k-1) is the manipulated variable at the previous time, Δ U (k) is the manipulated variable in the control time domain, D (k) is the prediction for future interference, Ey is error feedback, ΦA、ФBG and GdIs a coefficient matrix; the specific description is as follows:
ΔU＝[Δu(k) … Δu(k+Nu-1)]T
Figure GDA0002968266280000105
R＝[r(k+1) … r(k+Np)]T
D＝[d(k) … d(k+Np-1)]T
Figure GDA0002968266280000106
Figure GDA0002968266280000107
Figure GDA0002968266280000111
Figure GDA0002968266280000112
output feedback is applied to correct the error between the model output and the measured value:
Figure GDA0002968266280000113
Figure GDA0002968266280000114
wherein ekThe error is expressed as the difference between the actual output and the predicted output.
The optimization objective function is written as:
Figure GDA0002968266280000115
in general, predictive control solves the following optimization problem:
Figure GDA0002968266280000116
Figure GDA0002968266280000117
Δumin≤Δu(k+j)≤Δumax
umin≤u(k+j)≤umax
Figure GDA0002968266280000118
where J is the objective function of the optimization, R is the setpoint vector, R is the setpoint, w is the weight of the steering input changes, Np is the prediction horizon, Nc is the control horizon, u is the control horizonminAnd umaxRespectively minimum and maximum constraints of the steering input, DeltauminAnd Δ umaxIs a constraint on the rate of change of the steering input, yminAnd ymaxIs a constraint on the prediction output.
Minimizing the objective function, solving the optimization problem to obtain the optimal control vector:
ΔU*(k)＝[GTG+W]-1GTE(k)＝KE(k)
wherein:
E(k)＝R(k)-ΦAx(k)-ΦBu(k-1)-GdD(k)-Ey(k)
according to the MPC 'rolling time domain' principle, the first element of the control vector is acted on the system, and the feedback value is obtained and then used as data required by the optimization at the next moment. And repeating the steps at the next moment to obtain the control vector, and repeating the steps.
In the case of rule-based control, field data is used to compare MPC performance. The simulation results are shown in fig. 6. The first graph in fig. 6 shows the ambient temperature change over a 25 day period. The ambient temperature varies between-3 ℃ and 20 ℃. The second graph in fig. 6 shows the pressure difference change, which remains almost constant over a long time. There was a manual control for 13 days. After a period of 13 days, the heating temperature and the pressure difference were controlled to a low value and kept constant, since the ambient temperature was relatively high, and thus manual control was performed.
The third graph in fig. 6 shows the heating temperature, and the fourth graph in fig. 6 shows the room temperature. Conventional RBCs derive a heating temperature from a currently measured ambient temperature. The control rules are pre-written into the expert rules table. This control law is open-loop and coarse, cannot accurately control the room temperature to a reference value, and cannot predict and compensate the ambient temperature in advance. The MPC may predict the ambient temperature and compensate in advance using weather forecasts. As can be seen from fig. 6, the room temperature of the heating system using MPC can be kept near the optimal level for a long time, while RBC will have larger variation, which results in reduced thermal comfort for users or waste of energy.
Compared with the traditional regular control, the prediction control method can obviously improve the control performance, compensate future outdoor temperature change in advance, ensure the heat utilization comfort of users and reduce energy consumption.
The foregoing is only a preferred embodiment of the present invention, and it will be apparent to those skilled in the art that various modifications and improvements can be made without departing from the principle of the present invention, and these modifications and improvements are also considered to be within the scope of the present invention.

Claims (2)

1. A building temperature control method based on model predictive control is characterized in that a MPC is controlled based on model predictive control to regulate the heating temperature, and the prediction of interference is added in the predictive control, and the upcoming change of the environmental temperature is compensated;
the method comprises the following steps:
s1: required data preparation: weather forecast data, an indoor temperature ideal value set by a user, building indoor temperature data, and temperature and flow of supply and return water at the secondary side of the heat exchanger;
s2: the MPC calculates to obtain the current optimal water supply temperature set value u (k) according to the required data;
s3: transmitting the calculated water supply temperature set value u (k) to a bottom layer control loop, and controlling an actuating mechanism to respond by the bottom layer control loop;
s4: the execution mechanism adjusts the flow of the hot water at the primary side of the heat exchanger, and then controls the temperature of the hot water at the secondary side through the heat exchanger;
s5: the indoor temperature changes along with the change of the water supply temperature and the flow, and meanwhile, the indoor real-time temperature, the secondary side water supply and return temperature and the flow are measured by the sensor and fed back to the MPC for the next-time optimization;
in step S2, the MPC maintains room temperature y (k) at the most comfortable value r (k) determined by the user or site operator; acquiring environment temperature data D (k) of 24 hours in the future from a weather forecast center through the Internet, wherein the environment temperature data D (k) is regarded as a prediction interference variable in an MPC algorithm, and a temperature sensor located outdoors gives a current environment temperature T0(k) to compensate errors; the MPC calculates a set value u (k) of the water supply temperature at the current time, and the value is used as a set point of the lower-layer PID control; measuring the pressure difference Δ p (k) between the supply and return water and regarding it as a measured disturbance;
the process of model predictive control includes the steps of:
establishing a mechanism model aiming at the heat exchange process of the building;
using the model to predict a model output in the future within a limited range;
minimizing the error between the model output and the measured value by optimization;
the mechanism model is as follows:
Figure FDA0002982214840000021
Figure FDA0002982214840000022
wherein T is the indoor temperature, TinFor temperature of the water supply, ToutIs the return water temperature, T0Is the outdoor temperature, cp1Is the specific heat of indoor air, V1And V2For heating areas in the room, cp2Specific heat for water supply, q water supply flow, h1A1And h2A2The heat exchange coefficient is multiplied by the heat exchange area, and the outdoor heat exchange and the indoor heat exchange are assumed to be convective;
if no flow sensor is installed on site, the pressure difference Δ p between the hot water supply pipe and the water return pipe is used, and the square relationship between the pressure difference Δ p and the pressure difference Δ p is used to calculate the flow rate by the pressure difference:
Figure FDA0002982214840000023
the mechanism model is converted into:
Figure FDA0002982214840000024
Figure FDA0002982214840000025
wherein θ ═ θ1 θ2 θ3 θ4]TIs a model parameter vector, and the prediction of the outdoor temperature is included in the parameter theta 2;
discretizing the mechanism model to obtain a discretization state space model:
x(k+1)＝A(k)x(k)+B(k)u(k)+Bdd(k)
y(k+1)＝Cx(k)
wherein:
x＝[T Tout],u＝Tin,d＝T0,y＝T，
Figure FDA0002982214840000026
Figure FDA0002982214840000027
C＝[1 0]
wherein d (k) represents future disturbance data, i.e. weather forecast data; x (k) represents a state vector; u (k) represents the manipulated variable, i.e. the supply water temperature;
the model output is:
Figure FDA0002982214840000031
wherein, the left side of the equation
Figure FDA0002982214840000032
For output prediction, x (k) is the current time state vector, u (k-1) is the manipulated variable at the previous time, Δ U (k) is the manipulated variable in the control time domain, D (k) is the prediction for future interference, Ey is error feedback, ΦA、ФBG and GdIs a coefficient matrix;
output feedback is applied to correct the error between the model output and the measured value:
Figure FDA0002982214840000033
Figure FDA0002982214840000034
wherein ekThe error is expressed as the difference between the actual output and the predicted output.
2. The model predictive control-based building temperature control method of claim 1, wherein the process of minimizing the error between the model output and the measured value by optimization is:
the objective function is:
Figure FDA0002982214840000035
the following optimization problems are solved:
Figure FDA0002982214840000036
Figure FDA0002982214840000037
Δumin≤Δu(k+j)≤Δumax
umin≤u(k+j)≤umax
Figure FDA0002982214840000038
where J is the objective function of the optimization, R is the set point vector, R is the set point, w is the weight of the manipulated input changes, Np is the prediction time domain, Nu is the control time domain, u is the control time domainminAnd umaxRespectively, a minimum and a maximum approximation of the manipulation inputBundle, Δ uminAnd Δ umaxIs a constraint on the rate of change of the steering input, yminAnd ymaxIs a constraint on the prediction output;
minimizing the objective function, solving the optimization problem to obtain the optimal control vector:
ΔU*(k)＝[GTG+W]-1GTE(k)＝KE(k)
wherein:
E(k)＝R(k)-ΦAx(k)-ΦBu(k-1)-GdD(k)-Ey(k)
and (3) acting the first element of the control vector on the system, and obtaining a feedback value to be used as data required by optimization at the next moment.
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