Thermal activation building control method, computer equipment and storage medium

Abstract

The invention relates to a thermally activated building control method, computer equipment and a storage medium. According to the method, the thermal activation building system control model can be constructed through the corresponding temperature adjustment building, and the thermal influence parameters in the model are classified into the state vector matrix and the input vector matrix; and a first coefficient matrix corresponding to the state vector matrix and a second coefficient matrix corresponding to the input vector matrix are obtained by using a thermal resistance and thermal capacity energy balance relationship between parameters, so that an accurate temperature-regulating building temperature control equation can be formed. And control signals for controlling the water supply temperature and the air supply temperature can be generated based on the parameter numerical values after the period time length is predicted, so that the temperature of the temperature adjusting building after the period time length is predicted is accurately controlled, and the problems of low response speed and low precision of heat activation building control are avoided.

Description

Technical Field

[0001] This application relates to the technical field of servers, and particularly to a method for thermally activating building control, a computer device, and a storage medium. Background Art

[0002] A thermally activated building system (TABS) buries water pipes deep in the middle of a concrete floor slab and uses the heat storage characteristics of the floor slab to slowly release energy. Its essence is a system that uses the thermal inertia of the building structure itself to adjust the indoor temperature. TABS has a slow thermal response speed, can stably adjust the indoor temperature, improve indoor thermal comfort, and at the same time avoid various problems caused by the frequent start and stop of traditional building air conditioning systems.

[0003] Although the existing control methods for TABS systems can ensure the stable operation of buildings and indoor thermal comfort, there are still some obvious disadvantages in practical applications, which are specifically as follows:

[0004] (1) Control response lag. The TABS system has a large thermal inertia and a long thermal response time. When the building's cooling and heating loads change rapidly, the TABS system cannot adjust the temperature immediately, which may cause indoor temperature fluctuations or fail to meet the indoor thermal comfort requirements. Therefore, traditional temperature feedback-based control strategies cannot respond quickly in the face of complex environmental changes. Especially in the case of rapid climate changes such as changes in solar radiation, the control system cannot adjust in time, thus affecting the indoor thermal comfort and system energy efficiency of the building.

[0005] (2) Insufficient accuracy of control strategies. Existing control methods are mostly control strategies based on simple feedback parameters such as ambient temperature, indoor temperature, and humidity, lacking a fine model of the heat conduction process inside TABS and between TABS and the room. In addition, in practical applications, there are significant differences in the distribution of building cooling and heating loads. Especially in large-area buildings or multi-functional spaces, existing control methods do not consider the dynamic changes in cooling and heating loads between different rooms or areas. The simplified control methods are difficult to achieve precise adjustment, resulting in too high or too low temperatures in local areas and affecting the thermal comfort of the building.

[0006] (3) Poor adaptability. Existing TABS control methods are mostly based on specific building structures and heat load distributions, lacking the ability to adaptively adjust to changes in the building environment and usage patterns. In the face of fluctuations in building cooling and heating loads, changes in thermal comfort requirements, or sudden changes in external weather conditions, existing control methods cannot be flexibly adjusted, resulting in excessive or insufficient system energy supply, and thus causing unnecessary energy waste. Especially in buildings with variable climate conditions and different functional requirements, the generality and adaptability of existing control methods are insufficient.

[0007] (4) Lack of system integration. The control system of the existing TABS is independent of the control of the building's main air-conditioning systems such as the ventilation system and the dehumidification system, lacking effective integration and coordination. Most of the existing control methods set different control systems as independent modules, lacking a unified integration platform, resulting in conflicts between the TABS and the conventional air-conditioning system during the actual operation process, leading to increased energy consumption.

[0008] Generally speaking, the existing TABS control methods have certain limitations in terms of response speed, accuracy, adaptability, real-time performance, and system integration. It is necessary to combine artificial intelligence algorithms and control theories to improve its comprehensive performance and application scope. Summary of the Invention

[0009] Based on this, a thermal activation building control method, a computer device, and a storage medium are provided to solve the technical problems that the existing TABS control methods have certain limitations in terms of response speed, accuracy, adaptability, real-time performance, and system integration, resulting in slow response speed and low accuracy of thermal activation building control.

[0010] On the one hand, a thermal activation building control method is provided, and the method includes:

[0011] Construct a thermal activation building system control model for the temperature-adjusting building, set thermal influence parameters in the thermal activation building system control model, and classify the thermal influence parameters into a state vector matrix and an input vector matrix;

[0012] Obtain a first coefficient matrix corresponding to the state vector matrix and a second coefficient matrix corresponding to the input vector matrix according to the heat resistance, heat capacity energy balance relationship between the parameters in the state vector matrix and the parameters in the input vector matrix;

[0013] Multiply the state vector matrix by the first coefficient matrix and then sum it with the product of the input vector matrix multiplied by the second coefficient matrix to form a temperature control equation for the temperature-adjusting building;

[0014] Input the parameter values after the prediction cycle time length into the temperature control equation for the temperature-adjusting building, and generate a control signal for controlling the supply water temperature and the supply air temperature with the goal of minimizing the supply water temperature in the pipes of the temperature-adjusting building floor and minimizing the energy efficiency cost, demand deviation cost, and operation cost.

[0015] In one embodiment, the constructing a thermal activation building system control model for the temperature-adjusting building includes:

[0016] Construct a building model, a meteorological model, a water supply model, a air supply model, a power statistics model, and an observer in the thermal activation building system control model corresponding to the temperature-adjusting building;

[0017] Set a meteorological parameter mode, a sky temperature module, and a solar radiation module in the meteorological model, and associate the meteorological model with the outdoor environmental heat impact parameters of the building model;

[0018] Set an internal heat gain module of the building, an air source heat pump, a variable frequency water pump, a water supply parameter value module, and a parameter conversion module in the water supply model, and associate the water supply model with the heat impact parameters of the pipes in the floor slab of the building model;

[0019] Set a ventilation parameter value module and a variable frequency fan in the air supply model, and associate the air supply model with the heat impact parameters of the indoor air conditioning system of the building model;

[0020] Set a floor heating unit conversion module, a water side power module, and a total heating / cooling power module in the power statistics model, and associate the power statistics model with the heat impact parameters of the building model;

[0021] Set a temperature display module, an uncomfortable duration statistics module, a total power display module, and a building load display module in the observer, and associate the observer with the heat impact parameters of the building model. The observer is used to statistically calculate the control output data of the temperature-controlled building at the current moment and obtain the state vector matrix value at the current moment, the temperature control equation value of the temperature-controlled building at the current moment, and the temperature control equation value of the temperature-controlled building after the prediction cycle time length according to the control output data at the current moment.

[0022] In one embodiment, the control model of the heat-activated building system for the corresponding temperature-controlled building further includes:

[0023] Set a TABS module. The TABS module is connected to the building model and the parameter conversion module. The data transmitted from the building model to the TABS module includes the star node temperature, the surface temperatures of the floor and the ceiling, and the combined heat fluxes of the floor and the ceiling. The data transmitted from the TABS module to the building model includes the outlet temperature of the fluid, the mass flow rate, the average temperature of the floor slab, and the heat outputs of the floor and the ceiling. The TABS module also transmits the energy stored in the floor slab and the heat transfer coefficient at the end of each time step. The parameter conversion module is used to calculate the heat transfer amount of each unit by dividing each floor or ceiling into multiple units according to the combined heat fluxes of the floor and the ceiling output by the building model.

[0024] Set an MPC controller. The MPC controller is connected to the building model, the TABS module, the water supply model, the air supply model, and the observer. The MPC controller is used to generate control signals for controlling the water supply temperature and the air supply temperature and control the variable frequency water pump and the variable frequency fan according to the control signals.

[0025] In one embodiment, the MPC controller is configured to generate control signals for controlling the supply water temperature and the supply air temperature and control the variable-frequency water pump and the variable-frequency air blower according to the control signals, including:

[0026] Obtain the state vector matrix value at the current moment, the temperature control equation value of the temperature-adjusted building at the current moment, and the temperature control equation value of the temperature-adjusted building after the prediction period time length, and obtain the parameter values after the prediction period time length;

[0027] According to the state vector matrix value at the current moment, the temperature control equation value of the temperature-adjusted building at the current moment, the temperature control equation value of the temperature-adjusted building after the prediction period time length, and the parameter values after the prediction period time length, divide the prediction period time length into multiple time periods, generate the input vector matrix value corresponding to each time period, and generate the control signal corresponding to each time period from the input vector matrix value corresponding to each time period;

[0028] Send the control signals of each time period to the variable-frequency water pump and the variable-frequency air blower in sequence according to the time sequence.

[0029] In one embodiment, setting a heat influence parameter in the heat-activated building system control model and classifying the heat influence parameter into a state vector matrix and an input vector matrix includes:

[0030] Obtain the state vector matrix X of the temperature-adjusted building, and the parameters in the state vector matrix include the average temperature T of the floor of the temperature-adjusted building avg,concrete , the dry-bulb temperature T of the indoor air air,room , the temperature T of the inner wall of the temperature-adjusted building iw and the temperature T of the outer wall of the temperature-adjusted building ew , then

[0031] Obtain the input vector matrix U of the temperature-adjusted building, and the parameters in the input vector matrix include the supply water temperature T of the pipes in the floor of the temperature-adjusted building water,supply , the supply air temperature T of the indoor air-conditioning system air,supply , the dry-bulb temperature T of the outdoor environment amb , the heat dissipation Q of indoor personnel and equipment gain and the solar radiation intensity Q rad , then

[0032] In one embodiment, obtaining the first coefficient matrix corresponding to the state vector matrix and the second coefficient matrix corresponding to the input vector matrix according to the heat resistance and heat capacity energy balance relationship between the parameters in the state vector matrix and the parameters in the input vector matrix includes:

[0033] Set a thermal resistance and a heat capacity between the parameters in the state vector matrix and the parameters in the input vector matrix to form a fourth-order RC model;

[0034] Write a set of linear differential equations according to the energy balance in different states in the RC model as follows:

[0035]

[0036] where g wd is the total transmittance of the temperature-controlled building exterior window, and R1 is the thermal resistance between the average temperature T avg,concrete of the temperature-controlled building floor and the dry-bulb temperature T air,room of the indoor air. R2 is the thermal resistance between the supply air temperature T air,supply of the indoor air-conditioning system and the dry-bulb temperature T amb of the outdoor environment. R3 is the thermal resistance between the average temperature T avg,concrete of the temperature-controlled building floor and the water supply temperature T water,supply of the pipeline in the temperature-controlled building floor. R4 is the thermal resistance between the supply air temperature T air,supply of the indoor air-conditioning system and the dry-bulb temperature T air,room of the indoor air. R5 is the thermal resistance between the dry-bulb temperature T air,room of the indoor air and the temperature T ew of the temperature-controlled building exterior wall. R6 is the thermal resistance between the dry-bulb temperature T amb of the outdoor environment and the temperature T ew of the temperature-controlled building exterior wall. R7 is the thermal resistance between the dry-bulb temperature T air,room of the indoor air and the temperature T iw of the temperature-controlled building interior wall. C concrete is the heat capacity between the average temperature T avg,concrete of the temperature-controlled building floor and the temperature-controlled building floor. C ew is the heat capacity between the temperature T ew of the temperature-controlled building exterior wall and the temperature-controlled building floor. C iw is the heat capacity between the temperature T iw of the temperature-controlled building interior wall and the temperature-controlled building floor. C air,room is the heat capacity between the dry-bulb temperature T air,room of the indoor air and the temperature-controlled building floor;

[0037] Obtain the first coefficient matrix corresponding to the state vector matrix as:

[0038]

[0039] Obtain the second coefficient matrix corresponding to the input vector matrix as:

[0040]

[0041] In one embodiment, the step of multiplying the state vector matrix by the first coefficient matrix and summing the state vector matrix by the second coefficient matrix to form the temperature control equation of the temperature-adjusting building comprises:

[0042] The step of inputting the parameter value after the prediction cycle time length into the temperature control equation of the temperature control building to generate a control signal for controlling the water supply temperature and the air supply temperature according to the goal of minimizing the water supply temperature of the pipeline fluid in the floor of the temperature control building and minimizing the energy efficiency cost, demand deviation cost, and operation cost includes:

[0043] The objective function corresponding to the temperature control equation of the temperature control building is formed according to the goal of minimizing the water supply temperature of the pipe fluid in the floor of the temperature control building and minimizing the energy efficiency cost, demand deviation cost and operation cost. Among them J e (k) is the energy efficiency cost at the kth moment, J d (k) is the demand deviation cost at the kth moment, C op (k) is the operating cost at the kth moment, α is the weight coefficient of energy efficiency cost, β is the weight coefficient of demand deviation cost, γ is the weight coefficient of operating cost, Δt concrete H is the length of the prediction cycle. p The number of time periods after dividing the prediction period into multiple time periods;

[0044] The temperature control equation of the temperature-adjusting building is combined with the objective function to obtain After solving, control signals for controlling the water supply temperature and air supply temperature are generated.

[0045] In one embodiment, in the objective function,

[0046] The energy efficiency cost at the kth moment is Where Q heating is the heating power value of the unit at the kth moment, Q cooling is the cooling power value of the unit at the kth moment, COP is the heating coefficient of the unit, and EER is the cooling coefficient of the unit (the unit is an air source heat pump and a variable frequency fan);

[0047] The demand deviation cost at the kth moment is ε heating =T air,room -T comf,max ,ε heating ≥0,ε cooling =T comf,min -T air,room, ε cooling ≥0, where ε heating is the deviation value between the indoor temperature at the k-th moment and the upper limit of the thermal comfort temperature, and ε cooling is the deviation value between the indoor temperature at the k-th moment and the lower limit of the thermal comfort temperature;

[0048] The operating cost at the k-th moment is C op (k) = C energy (k) + C maint (k) + C equip (k), where C energy (k) is the cost of energy consumption at the k-th moment, and C maint (k) is the cost of equipment maintenance and upkeep at the k-th moment, and C equip (k) is the cost of system scheduling and management at the k-th moment.

[0049] On the other hand, a computer device is provided, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, the following steps are implemented:

[0050] Construct a thermal activation building system control model for a temperature-controlled building, set thermal influence parameters in the thermal activation building system control model, and classify the thermal influence parameters into a state vector matrix and an input vector matrix;

[0051] Obtain a first coefficient matrix corresponding to the state vector matrix and a second coefficient matrix corresponding to the input vector matrix according to the thermal resistance, heat capacity, and energy balance relationship between the parameters in the state vector matrix and the parameters in the input vector matrix;

[0052] Multiply the state vector matrix by the first coefficient matrix and then sum it with the product of the input vector matrix multiplied by the second coefficient matrix to form a temperature-controlled building temperature control equation;

[0053] Input the parameter values after the prediction cycle time length into the temperature-controlled building temperature control equation, and generate a control signal for controlling the water supply temperature and the air supply temperature with the goal of minimizing the lowest water supply temperature of the pipeline fluid in the temperature-controlled building floor and the energy efficiency cost, demand deviation cost, and operating cost.

[0054] On another hand, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the following steps are implemented:

[0055] Construct a thermal activation building system control model for a temperature-controlled building, set thermal influence parameters in the thermal activation building system control model, and classify the thermal influence parameters into a state vector matrix and an input vector matrix;

[0056] Obtain the first coefficient matrix corresponding to the state vector matrix and the second coefficient matrix corresponding to the input vector matrix according to the thermal resistance and heat capacity energy balance relationship between the parameters in the state vector matrix and the parameters in the input vector matrix;

[0057] Multiply the state vector matrix by the first coefficient matrix and sum it with the input vector matrix multiplied by the second coefficient matrix to form a temperature-controlled equation for the temperature-controlled building;

[0058] Input the parameter values after the prediction cycle time length into the temperature-controlled equation for the temperature-controlled building, and generate control signals for controlling the water supply temperature and the air supply temperature with the goal of minimizing the water supply temperature in the pipes of the floor slab of the temperature-controlled building and minimizing the energy efficiency cost, demand deviation cost, and operation cost.

[0059] The above thermal activation building control method, computer device, and storage medium construct a thermal activation building system control model for the corresponding temperature-controlled building, classify the thermal influence parameters in the model into a state vector matrix and an input vector matrix, and utilize the thermal resistance and heat capacity energy balance relationship between the parameters to obtain the first coefficient matrix corresponding to the state vector matrix and the second coefficient matrix corresponding to the input vector matrix, which can form an accurate temperature-controlled equation for the temperature-controlled building. Based on the parameter values after the prediction cycle time length, control signals for controlling the water supply temperature and the air supply temperature can be generated, realizing precise control of the temperature of the temperature-controlled building after the prediction cycle time length and avoiding the problems of slow response speed and low accuracy in thermal activation building control. Description of the Drawings

[0060] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0061] Figure 1 It is an application environment diagram of the thermal activation building control method in an embodiment of the present application;

[0062] Figure 2 It is a node diagram of a typical floor slab cross-section in an embodiment of the present application;

[0063] Figure 3 It is a schematic diagram of data transfer between the building model Type56 and the TABS module Type360 in an embodiment of the present application;

[0064] Figure 4 It is a flow schematic diagram of the thermal activation building control method in an embodiment of the present application;

[0065] Figure 5 It is the structural block diagram of the fourth-order RC model in an embodiment of the present application;

[0066] Figure 6 It is the control framework diagram of the TABS system based on model predictive control in an embodiment of the present application;

[0067] Figure 7 It is the structural diagram of the floor slab in the verification case in an embodiment of the present application, which is a structure with air blocks;

[0068] Figure 8 In an embodiment of the present application, Figure 7 It is the structural diagram of dividing the floor slab shown into a discretized node network;

[0069] Figure 9 It is the schematic diagram of the simulation results of the heat supply and energy consumption of the heat pump in an embodiment of the present application;

[0070] Figure 10 It is the internal structural diagram of the computer device in an embodiment of the present application. Detailed implementation manners

[0071] In order to make the objectives, technical solutions and advantages of the present application clearer, the present application will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0072] As described in the background art, due to the long heat response time of the TABS system, it is necessary to coordinate with systems such as ventilation and dehumidification. Therefore, the control strategy of TABS must be highly flexible, predictive and adaptable, and be able to adjust the operation mode of the system according to environmental changes, indoor activities and external climate conditions. Common control methods of the TABS system are:

[0073] On / Off control. On / Off control is the most basic TABS control method, which uses the indoor temperature or other environmental parameters as the control basis. When the indoor temperature exceeds the set temperature threshold, the TABS system starts to heat or cool, and stops working after reaching the target temperature. This control method is simple and easy to implement, but it will cause large fluctuations in the indoor temperature, resulting in high energy consumption and difficulty in ensuring indoor comfort.

[0074] Intermittent Operation Control. Intermittent operation control improves energy efficiency and reduces indoor temperature fluctuations by periodically starting and stopping the water supply flow of TABS. In the intermittent operation mode, when the water supply flow stops, heat still flows towards the area with a lower temperature, creating a larger temperature gradient between TABS and the indoor air. After the water supply flow resumes, it accelerates heat transfer, thus achieving higher energy efficiency. In practical applications, intermittent operation is carried out by setting a fixed time period or dynamically adjusting the time period as needed.

[0075] Pulse Width Modulation (PWM). PWM is a periodic control method. At the beginning of each cycle, the control system first performs a "cleaning" process, that is, letting the fluid circulate in TABS without heating or cooling. Subsequently, it decides whether to continue circulating or perform heating / cooling according to actual needs. The system adjusts the pulse duration according to the set temperature value. This control method improves the energy efficiency of the system, reduces temperature fluctuations in the room, and enhances the indoor thermal comfort.

[0076] Supply Temperature Control. Supply temperature control most directly affects the thermal efficiency and indoor comfort of the TABS system. By controlling the supply temperature, it ensures that the system can quickly respond to indoor temperature changes during heating or cooling. In theory, TABS can maintain indoor temperature stability through self-regulation effects. When the indoor temperature is higher or lower than the set temperature, the system automatically adjusts the supply temperature to provide heating or cooling. However, this self-regulation effect cannot meet large temperature changes, so additional supply temperature control is needed to optimize system performance.

[0077] To solve the above problems, in the embodiments of the present invention, a thermally activated building control method is creatively proposed.

[0078] The thermally activated building control method provided in this application can be simulated and applied to such as Figure 1In the TRNSYS environment shown. Among them, a control model of the thermally activated building system is constructed for the temperature-controlled building. The thermal performance simulation of the building room adopts the multi-zone building model Type56, which is used to calculate the thermal behavior of a single or multiple zones in the building. During the simulation process, first, the geometric information of the building, the zone division, the heat release of the internal personnel and equipment in the building, etc. are input by TRNBuild. External environmental parameters such as the outdoor ambient air temperature and solar radiation intensity are transmitted to the building model through the meteorological data component Type109. Based on these inputs, the Type56 module calculates the temperature and energy changes in each zone of the building by solving the heat transfer and heat balance equations. For the construction of thermal parameters of different building materials such as exterior windows, exterior walls, floors, and ceilings, Type56 can simulate complex physical processes such as heat conduction and radiation exchange, and output detailed parameters such as the dry-bulb temperature, wet-bulb temperature, relative humidity of the zone air, and the heating and cooling loads of the building. Through Type56, the indoor thermal comfort of the building can also be evaluated, and indicators such as radiant temperature, operative temperature, and thermal comfort PMV-PPD are output. According to these indicators, the input parameters can be dynamically adjusted to maximize the building energy efficiency and ensure the indoor thermal comfort.

[0079] The Type360 module in the model is used to simulate the floor heating in TABS. Its working principle is based on the finite difference method, and it is mainly used to calculate the heat transfer process of the heat medium through the pipes in the building. The Type360 module can simulate the storage and transfer of heat in structures such as walls, floors, or ceilings. The Type360 module uses the finite element method to calculate the heat transfer of the TABS floor, divides the representative cross-section into multiple finite element nodes, and calculates the heat storage and transfer of each node, as well as the temperature change of the fluid.

[0080] As Figure 2 shown, Figure 2 is a node diagram of a typical floor cross-section. Nodes 1 to 13 represent the concrete floor, and nodes 14 and 15 represent the pipe fluid. The input parameters of the Type360 module include the inlet temperature of the fluid, the mass flow rate, the air temperature adjacent to the floor and ceiling, and the geometric parameters of the panel, etc. The output parameters include the outlet temperature of the fluid, the mass flow rate, the average temperature of the floor, the surface temperatures of the floor and ceiling, the heat output power through the floor and ceiling, the heat stored in the floor, etc.

[0081] In the model, the MPC control function is implemented through the Type3157 module. First, relevant module parameters in TRNSYS, such as data on indoor air temperature, average temperature of the floor slab, etc., are packed into a dictionary TRNData through the CFFI interface and passed to the Python environment. These data serve as the input conditions for the MPC algorithm calculation. Then, the Python script reads the input data in the TRNData dictionary in real time and generates output values according to the preset control model. The output signals of the MPC algorithm, such as supply water temperature and flow rate, are written into the outputs field of the dictionary and fed back to TRNSYS through the CFFI interface. Finally, TRNSYS adjusts the supply water temperature and flow rate to the Type360 module according to the control signals fed back by Python. Through this data transfer and control signal feedback, TRNSYS and Python can achieve dynamic temperature regulation and energy optimization, ensuring that the temperature in the building remains within an acceptable range of thermal comfort while maximizing energy savings.

[0082] In addition to the building model Type56 and the TABS module Type360, the air source heat pump in the TRNSYS model uses Type922a to provide hot water or cold water, and the variable frequency water pump Type110 changes the supply water flow rate according to the control signal. Since the indoor load eliminated by the air conditioning system's air supply accounts for a relatively low proportion, to simplify the model, the air supply temperature and flow rate of the air conditioning system are directly provided with air supply parameters using the Equation module, and the more complex finned tube cooler module is no longer used. To display the simulation results, multiple display modules Type65a are used to display the simulation results and also save the data files of the relevant results.

[0083] Data is transferred between the building model Type56 and the TABS module Type360, such as Figure 3As shown in the figure. In the above TRNSYS environment, data needs to be transferred between the building model Type56 and the TABS module Type360 because they are responsible for different simulation tasks. Type56 mainly simulates the internal thermal environment and heat load of the building, while Type360 simulates the thermal performance of the floor slab. Therefore, they can only work together through data interaction. The data transmitted from Type56 to Type360 mainly includes star node temperature, surface temperatures of the floor and ceiling, and comprehensive heat fluxes of the floor and ceiling. The data transmitted from Type360 to Type56 mainly includes outlet temperature of the fluid, mass flow rate, average temperature of the floor slab, and heat outputs of the floor and ceiling. In addition, Type360 also transmits the energy stored in the floor slab and the heat transfer coefficient at the end of each time step. It should be particularly noted that the data transfer between Type360 and Type56 must be converted through a parameter conversion module because the heat flux output by Type56 represents the total heat transfer of the entire floor or ceiling, while Type360 divides the entire floor or ceiling into multiple units for calculation. Therefore, Type360 calculates the heat transfer of each unit. To ensure data consistency, the heat flux output by Type56 needs to be normalized according to the number of units divided for the floor or ceiling, that is, dividing the total heat flux by the number of divided units and then transmitting it to Type360.

[0084] In one embodiment, as Figure 4 shown, a method for controlling a thermally activated building is provided, including the following steps:

[0085] Step S1, constructing a control model for the thermally activated building system corresponding to the temperature-adjustable building, setting thermal influence parameters in the control model of the thermally activated building system, and classifying the thermal influence parameters into a state vector matrix and an input vector matrix;

[0086] Step S2, obtaining a first coefficient matrix corresponding to the state vector matrix and a second coefficient matrix corresponding to the input vector matrix according to the heat resistance, heat capacity, and energy balance relationship between the parameters in the state vector matrix and the parameters in the input vector matrix;

[0087] Step S3, multiplying the state vector matrix by the first coefficient matrix and summing it with the product of the input vector matrix multiplied by the second coefficient matrix to form a temperature control equation for the temperature-adjustable building;

[0088] Step S4, inputting the parameter values after the prediction cycle time length into the temperature control equation for the temperature-adjustable building, and generating a control signal for controlling the supply water temperature and the supply air temperature with the goal of minimizing the supply water temperature of the pipeline fluid in the floor slab of the temperature-adjustable building and minimizing the energy efficiency cost, demand deviation cost, and operation cost.

[0089] Specifically, by constructing a control model of a thermally activated building system for a corresponding temperature-controlled building, classifying the thermal influence parameters in the model into a state vector matrix and an input vector matrix, and obtaining a first coefficient matrix corresponding to the state vector matrix and a second coefficient matrix corresponding to the input vector matrix by using the heat resistance, heat capacity, and energy balance relationships between the parameters, an accurate temperature control equation for the temperature-controlled building can be formed. Based on the parameter values after the prediction cycle time length, control signals for controlling the supply water temperature and the supply air temperature can be generated, realizing precise control of the temperature of the temperature-controlled building after the prediction cycle time length and avoiding the problems of slow response speed and low accuracy in the control of thermally activated buildings.

[0090] In this embodiment, the construction of the control model of the thermally activated building system for the corresponding temperature-controlled building includes:

[0091] Constructing a building model, a meteorological model, a water supply model, an air supply model, a power statistics model, and an observer in the control model of the thermally activated building system corresponding to the temperature-controlled building;

[0092] Setting a meteorological parameter mode, a sky temperature module, and a solar radiation module in the meteorological model, and associating the meteorological model with the outdoor environmental thermal influence parameters of the building model;

[0093] Setting a heat gain module inside the building, an air source heat pump, a variable frequency water pump, a water supply parameter value module, and a parameter conversion module in the water supply model, and associating the water supply model with the thermal influence parameters of the pipes inside the floor slab of the building model;

[0094] Setting a ventilation parameter value module and a variable frequency fan in the air supply model, and associating the air supply model with the thermal influence parameters of the indoor air conditioning system of the building model;

[0095] Setting a floor heating unit conversion module, a water side power module, and a total heating / cooling power module in the power statistics model, and associating the power statistics model with the thermal influence parameters of the building model;

[0096] Setting a temperature display module, an uncomfortable duration statistics module, a total power display module, and a building load display module in the observer, associating the observer with the thermal influence parameters of the building model, and the observer is used to statistically calculate the control output data of the temperature-controlled building at the current moment and obtain the state vector matrix value at the current moment, the temperature control equation value of the temperature-controlled building at the current moment, and the temperature control equation value of the temperature-controlled building after the prediction cycle time length according to the control output data at the current moment.

[0097] In this embodiment, the construction of the control model of the thermally activated building system for the corresponding temperature-controlled building further includes:

[0098] Set up the TABS module, which is connected to the building model and the parameter conversion module. The data transmitted from the building model to the TABS module includes the star node temperature, the surface temperatures of the floor and the ceiling, and the combined heat fluxes of the floor and the ceiling. The data transmitted from the TABS module to the building model includes the outlet temperature of the fluid, the mass flow rate, the average temperature of the floor slab, and the heat outputs of the floor and the ceiling. The TABS module also transmits the energy stored in the floor slab and the heat transfer coefficient at the end of each time step. The parameter conversion module is used to calculate the heat transfer of each unit by dividing each floor or ceiling into multiple units based on the combined heat fluxes of the floor and the ceiling output by the building model.

[0099] Set up the MPC controller, which is connected to the building model, the TABS module, the water supply model, the air supply model, and the observer. The MPC controller is used to generate control signals for controlling the water supply temperature and the air supply temperature and to control the variable frequency water pump and the variable frequency air blower according to the control signals.

[0100] In this embodiment, the MPC controller is used to generate control signals for controlling the water supply temperature and the air supply temperature and to control the variable frequency water pump and the variable frequency air blower, including:

[0101] Obtain the state vector matrix value at the current moment, the temperature control equation value of the temperature-regulated building at the current moment, and the temperature control equation value of the temperature-regulated building after the prediction period time length, and obtain the parameter values after the prediction period time length;

[0102] According to the state vector matrix value at the current moment, the temperature control equation value of the temperature-regulated building at the current moment, the temperature control equation value of the temperature-regulated building after the prediction period time length, and the parameter values after the prediction period time length, divide the prediction period time length into multiple time periods, generate the input vector matrix value corresponding to each time period, and generate the control signal corresponding to each time period from the input vector matrix value corresponding to each time period;

[0103] Send the control signals of each time period to the variable frequency water pump and the variable frequency air blower in sequence according to the time order.

[0104] In this embodiment, setting the thermal influence parameters in the thermal-activated building system control model and classifying the thermal influence parameters into the state vector matrix and the input vector matrix includes:

[0105] Obtain the state vector matrix X of the temperature-regulated building, and the parameters in the state vector matrix include the average temperature T of the floor slab of the temperature-regulated building avg,concrete , the dry-bulb temperature T of the indoor air air,room , the temperature T of the inner wall of the temperature-regulated building iwand the temperature T of the temperature-controlled building exterior wall ew , then

[0106] Obtain the input vector matrix U of the temperature-controlled building, and the parameters in the input vector matrix include the supply water temperature T of the pipes in the floor of the temperature-controlled building water,supply , the supply air temperature T of the indoor air conditioning system air,supply , the dry bulb temperature T of the outdoor environment amb , the heat dissipation Q of indoor personnel and equipment gain and the solar radiation intensity Q rad , then

[0107] Among them, the average temperature T of the floor of the temperature-controlled building avg,concrete , the dry bulb temperature T of the indoor air air,room , the temperature T of the interior wall of the temperature-controlled building iw and the temperature T of the exterior wall of the temperature-controlled building ew , the supply water temperature T of the pipes in the floor of the temperature-controlled building water,supply , the supply air temperature T of the indoor air conditioning system air,supply , the dry bulb temperature T of the outdoor environment amb are in °C, and the heat dissipation Q of indoor personnel and equipment gain and the solar radiation intensity Q rad are in W.

[0108] In this embodiment, obtaining the first coefficient matrix corresponding to the state vector matrix and the second coefficient matrix corresponding to the input vector matrix according to the heat resistance and heat capacity energy balance relationship between the parameters in the state vector matrix and the parameters in the input vector matrix includes:

[0109] Set heat resistance and heat capacity between the parameters in the state vector matrix and the parameters in the input vector matrix to form a fourth-order RC model; the fourth-order RC model is as Figure 5 shown;

[0110] Write a set of linear differential equations according to the energy balance of different states in the RC model as follows:

[0111]

[0112] Among them, g wd is the total transmittance of the exterior window of the temperature-controlled building, and R1 is the average temperature T of the floor of the temperature-controlled building avg,concrete and the dry bulb temperature T of the indoor air air,room The heat resistance between them, R2 is the supply air temperature T of the indoor air conditioning system air,supply and the dry bulb temperature T of the outdoor environment ambThe thermal resistance between them, R3 is the average temperature T of the temperature-controlled building floor avg,concrete and the supply water temperature T of the pipeline in the temperature-controlled building floor water,supply The thermal resistance between them, R4 is the supply air temperature T of the indoor air conditioning system air,supply and the dry bulb temperature T of the indoor air air,room The thermal resistance between them, R5 is the dry bulb temperature T of the indoor air air,room and the temperature T of the temperature-controlled building exterior wall ew The thermal resistance between them, R6 is the dry bulb temperature T of the outdoor environment amb and the temperature T of the temperature-controlled building exterior wall ew The thermal resistance between them, R7 is the dry bulb temperature T of the indoor air air,room and the temperature T of the temperature-controlled building interior wall iw The thermal resistance between them, C concrete is the average temperature T of the temperature-controlled building floor avg,concrete and the heat capacity between the temperature-controlled building floor, C ew is the temperature T of the temperature-controlled building exterior wall ew and the heat capacity between the temperature-controlled building floor, C iw is the temperature T of the temperature-controlled building interior wall iw and the heat capacity between the temperature-controlled building floor, C air,room is the dry bulb temperature T of the indoor air air,room and the heat capacity between the temperature-controlled building floor;

[0113] Obtaining the first coefficient matrix corresponding to the state vector matrix is:

[0114]

[0115] Obtaining the second coefficient matrix corresponding to the input vector matrix is:

[0116]

[0117] It can be understood that with A and B, Theoretically, the temperature control equation of the temperature-controlled building is complete and can be calculated. Wherein, C is the heat capacity (J / kg·℃) between building components; R is the thermal resistance (℃ / W) between building components.

[0118] In this embodiment, the step of multiplying the state vector matrix by the first coefficient matrix and then summing with the input vector matrix multiplied by the second coefficient matrix to form the temperature control equation of the temperature-controlled building includes: The temperature control equation of the temperature-controlled building is

[0119] Inputting the parameter value after the prediction cycle time length into the temperature control equation of the temperature-regulated building to generate a control signal for controlling the water supply temperature and the air supply temperature with the goal of minimizing the water supply temperature of the fluid in the pipes in the floor of the temperature-regulated building and the energy efficiency cost, the demand deviation cost, and the operation cost includes:

[0120] Setting up an objective function corresponding to the temperature control equation of the temperature-regulated building with the goal of minimizing the water supply temperature of the fluid in the pipes in the floor of the temperature-regulated building and the energy efficiency cost, the demand deviation cost, and the operation cost where J e (k) is the energy efficiency cost at the k-th moment, J d (k) is the demand deviation cost at the k-th moment, C op (k) is the operation cost at the k-th moment, α is the weight coefficient of the energy efficiency cost, β is the weight coefficient of the demand deviation cost, γ is the weight coefficient of the operation cost, Δt concrete is the prediction cycle time length, H p is the number of time periods after dividing the prediction cycle time length into multiple time periods;

[0121] Combining the temperature control equation of the temperature-regulated building with the objective function to obtain Generating a control signal for controlling the water supply temperature and the air supply temperature after solving.

[0122] Among them, in the objective function,

[0123] The energy efficiency cost at the k-th moment is where Q heating is the heating power value of the unit at the k-th moment, Q cooling is the cooling power value of the unit at the k-th moment, COP is the heating coefficient of the unit, and EER is the cooling coefficient of the unit (the unit is an air source heat pump and a variable frequency fan);

[0124] The demand deviation cost at the k-th moment is ε heating =T air,room -T comf,max , ε heating ≥0, ε cooling =T comf,min -T air,room , ε cooling ≥0, where ε heating is the deviation value between the indoor temperature and the upper limit of the thermal comfort temperature at the k-th moment, ε cooling is the deviation value between the indoor temperature and the lower limit of the thermal comfort temperature at the k-th moment;

[0125] The operation cost at the k-th moment is C op (k)=C energy (k)+C maint (k)+Cequip (k), where C energy (k) is the cost of energy consumption at the k-th moment, C maint (k) is the cost of equipment maintenance and servicing at the k-th moment, C equip (k) is the cost of system scheduling and management at the k-th moment.

[0126] Among them, it is preferred that the length of the prediction cycle time is 24 hours, which is divided into 24 time periods, each time period is 1 hour, H p is 24, that is, the prediction is carried out 24 hours after the current moment. The energy efficiency cost J e (k) has the unit of W 2 ); the demand deviation cost J d (k) has the unit of °C 2 ; the operation cost C op (k) has the unit of yuan.

[0127] In summary of the above links, a control framework of the TABS system based on model predictive control is as Figure 6 shown. In this framework, first, the output y(k) is obtained through the measurement system, that is, the air temperature value in the building room, as the feedback of the current state of the system. Since these output data can be directly output from the TRNSYS model, there is no need to use an observer for state estimation. Next, at each time step (i.e., time period), the system predicts future disturbances such as outdoor temperature changes and internal heat gains, and generates corresponding disturbance prediction data, which are used in the calculation of the MPC controller. The MPC controller uses the RC building model to predict the U(k) of the system for a period of time in the future, and the generated control signal is transmitted to the controller in TRNSYS, and the system is adjusted by optimizing the control of the supply water temperature and supply water flow rate to ensure that the desired control objective is achieved within the prediction time range. In this way, the MPC framework can, based on predicting future disturbances and system responses, optimize the water supply regulation in real time, improve the energy efficiency of the TABS system and improve the indoor thermal comfort.

[0128] Figure 6 In, if the indoor comfort temperature at the current k-th moment is T comf (k), and the indoor comfort temperature corresponding to the i-th time period within the prediction cycle time length after the k-th moment is T comf (k + i), that is, the indoor comfort temperature adjustment method known to the MPC controller is T comf (k + i|k); if the dry bulb temperature T amb of the outdoor environment at the current k-th moment, the heat dissipation Q gain of indoor personnel and equipment, and the solar radiation intensity Q rad are grouped as (T amb , Q rad , Qgain )(k), then the dry-bulb temperature T of the outdoor environment corresponding to the i-th time period within the prediction cycle time length after the k-th moment amb , the heat dissipation Q of indoor personnel and equipment gain and the solar radiation intensity Q rad are grouped as (T amb , Q rad , Q gain )(k + i), that is, the dry-bulb temperature T of the outdoor environment known by the MPC controller amb , the heat dissipation Q of indoor personnel and equipment gain and the solar radiation intensity Q rad are grouped with the adjustment method as (T amb , Q rad , Q gain )(k + i|k).

[0129] Analyze the thermally activated building system (TABS) based on model predictive control (MPC) using the previously established TRNSYS simulation platform. Through simulation, detailedly evaluate the change of the total energy consumption during the operation of the TABS system, especially the energy consumption difference caused by MPC and traditional control methods. Verify the actual effect of the MPC method in reducing the total energy consumption on the premise of ensuring indoor thermal comfort, so as to prove its advantages in practical applications.

[0130] As previously mentioned, the heating of the floor and ceiling in the TABS system is realized through the Type360 module in TRNSYS. This module uses the finite difference method (FDM) to solve thermodynamic problems such as heat conduction and heat storage. By decomposing the building floor structure into multiple nodes, the finite difference method discretizes the continuous heat transfer problem. The temperature change of each node is calculated through the heat exchange with adjacent nodes. The division accuracy of the nodes and the selection of the time step directly affect the accuracy and stability of the model. The floor in the verification case is a structure with air blocks, and its structure and dimensions are as Figure 7 shown. The layered structure of the floor is shown in Table 1. According to the requirements of Type360, it is divided into a discretized node network as Figure 8 shown.

[0131] Table 1 Layered structure of the floor

[0132]

[0133] The model of the present invention involves multiple parameters, and the value of each parameter is closely related to the energy transfer and operating efficiency of the system. Especially when applying model predictive control, the setting of these parameters plays a crucial role in the performance and optimization of the system. By reasonably setting the parameters, the response time, temperature regulation accuracy, and energy efficiency level of the system can be controlled more precisely, thereby improving the thermal comfort inside the building and minimizing energy consumption. Table 2 lists the names and values of some key parameters, which play a crucial role in heat transfer and control strategies.

[0134] Table 2 Key Parameter Values of the Model

[0135]

[0136]

[0137] Figure 9 It is a schematic diagram of the simulation results of the heat supply and energy consumption of the heat pump. In the figure, for each set of data from left to right are the heat supply of the heat pump based on rule-based control (RBC), the heat supply of the heat pump based on model predictive control (MPC), the energy consumption of the heat pump based on rule-based control (RBC), and the energy consumption of the heat pump based on model predictive control (MPC). To compare the energy-saving effects of the two control strategies of rule-based control (RBC) and model predictive control (MPC), Figure 9 The heat supply and energy consumption data of the heat pump during 21 days of operation under the heating condition are shown. Through analysis, it is found that the heat pump with the MPC control mode has less heat supply, resulting in lower total energy consumption of the system. For example, on the 8th day of the system operation, the heat supply of the heat pump under the RBC control mode is 110 kWh, while the heat supply under the MPC control mode is about 90 kWh, indicating that the MPC control strategy can reduce the system energy consumption by optimizing the heat supply. For the operation of the heat pump, during the 21-day operation period, the start-stop times of the heat pump based on the two control strategies of RBC and MPC are 336 times and 183 times respectively, and the cumulative operation durations of the heat pump are 204 h and 134 h respectively. The two operation dynamics show that the operation time of the heat pump based on the two control strategies of RBC and MPC each time it starts is 0.61 h and 0.73 h respectively. The heat pump has fewer start-stop times and longer working time each time under the MPC mode, which helps to improve the stability of the system and effectively extend the service life of the heat pump unit.

[0138] Overall, the MPC control strategy shows better performance than the RBC control strategy in terms of heat supply and system energy consumption. Specifically, through accurate load prediction and optimized regulation, MPC not only reduces the start-up frequency of the heat pump but also enables the system to reduce energy consumption while providing stable heat, significantly improving the overall operating efficiency of the system.

[0139] In the above-mentioned heat-activated building control method, a heat-activated building system control model is constructed for the corresponding temperature-adjusting building. The heat influence parameters in the model are classified into a state vector matrix and an input vector matrix, and the first coefficient matrix corresponding to the state vector matrix and the second coefficient matrix corresponding to the input vector matrix are obtained by using the heat resistance, heat capacity, and energy balance relationships between the parameters. An accurate temperature control equation for the temperature-adjusting building can be formed, and control signals for controlling the supply water temperature and the supply air temperature can be generated based on the parameter values after the prediction cycle time length, realizing precise control of the temperature of the temperature-adjusting building after the prediction cycle time length and avoiding the problems of slow response speed and low accuracy in heat-activated building control.

[0140] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the following steps:

[0141] Construct a heat-activated building system control model for the corresponding temperature-adjusting building, set heat influence parameters in the heat-activated building system control model, and classify the heat influence parameters into a state vector matrix and an input vector matrix;

[0142] Obtain the first coefficient matrix corresponding to the state vector matrix and the second coefficient matrix corresponding to the input vector matrix according to the heat resistance, heat capacity, and energy balance relationships between the parameters in the state vector matrix and the parameters in the input vector matrix;

[0143] Multiply the state vector matrix by the first coefficient matrix and sum it with the product of the input vector matrix multiplied by the second coefficient matrix to form a temperature control equation for the temperature-adjusting building;

[0144] Input the parameter values after the prediction cycle time length into the temperature control equation for the temperature-adjusting building, and generate control signals for controlling the supply water temperature and the supply air temperature with the goal of minimizing the lowest supply water temperature of the fluid in the pipes of the temperature-adjusting building floor, as well as the energy efficiency cost, demand deviation cost, and operation cost.

[0145] In one embodiment, when the computer program is executed by a processor, it also implements the following steps:

[0146] The construction of the heat-activated building system control model for the corresponding temperature-adjusting building includes:

[0147] Construct a building model, a meteorological model, a water supply model, a air supply model, a power statistics model, and an observer for the corresponding temperature-adjusting building in the heat-activated building system control model;

[0148] Set a meteorological parameter mode, a sky temperature module, and a solar radiation module in the meteorological model, and associate the meteorological model with the outdoor environmental heat influence parameters of the building model;

[0149] Set up a heat gain module inside the building, an air source heat pump, a variable frequency water pump, a water supply parameter value module, and a parameter conversion module in the water supply model, and associate the water supply model with the fluid heat influence parameters of the pipes inside the floor slab of the building model;

[0150] Set up a ventilation parameter value module and a variable frequency fan in the air supply model, and associate the air supply model with the heat influence parameters of the indoor air conditioning system of the building model;

[0151] Set up a floor heating unit conversion module, a water side power module, and a total heating / cooling power module in the power statistics model, and associate the power statistics model with the heat influence parameters of the building model;

[0152] Set up a temperature display module, an uncomfortable duration statistics module, a total power display module, and a building load display module in the observer, and associate the observer with the heat influence parameters of the building model. The observer is used to count the control output data of the temperature-controlled building at the current moment and obtain the state vector matrix value at the current moment, the temperature control equation value of the temperature-controlled building at the current moment, and the temperature control equation value of the temperature-controlled building after the prediction cycle time length according to the control output data at the current moment.

[0153] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented:

[0154] The heat-activated building system control model for the corresponding temperature-controlled building further includes:

[0155] Set up a TABS module. The TABS module is connected to the building model and the parameter conversion module. The data transmitted from the building model to the TABS module includes the star node temperature, the surface temperatures of the floor and ceiling, and the combined heat fluxes of the floor and ceiling. The data transmitted from the TABS module to the building model includes the outlet temperature of the fluid, the mass flow rate, the average temperature of the floor slab, and the heat outputs of the floor and ceiling. The TABS module also transmits the energy stored in the floor slab and the heat transfer coefficient at the end of each time step. The parameter conversion module is used to calculate the heat transfer of each unit by dividing each floor or ceiling into multiple units according to the combined heat fluxes of the floor and ceiling output by the building model.

[0156] Set up an MPC controller. The MPC controller is connected to the building model, the TABS module, the water supply model, the air supply model, and the observer. The MPC controller is used to generate control signals for controlling the water supply temperature and the air supply temperature and control the variable frequency water pump and the variable frequency fan according to the control signals.

[0157] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented:

[0158] The MPC controller is used to generate control signals for controlling the water supply temperature and the air supply temperature and control the variable frequency water pump and the variable frequency air blower according to the control signals, and includes:

[0159] Obtain the state vector matrix value at the current moment, the temperature control equation value of the temperature-adjusting building at the current moment, and the temperature control equation value of the temperature-adjusting building after the prediction cycle time length, and obtain the parameter values after the prediction cycle time length;

[0160] According to the state vector matrix value at the current moment, the temperature control equation value of the temperature-adjusting building at the current moment, the temperature control equation value of the temperature-adjusting building after the prediction cycle time length, and the parameter values after the prediction cycle time length, divide the prediction cycle time length into multiple time periods, generate the input vector matrix value corresponding to each time period, and generate the control signal corresponding to each time period from the input vector matrix value corresponding to each time period;

[0161] Send the control signals of each time period to the variable frequency water pump and the variable frequency air blower in sequence according to the time sequence.

[0162] In one embodiment, when the computer program is executed by the processor, the following steps are further implemented:

[0163] Setting the thermal influence parameters in the thermal activation building system control model and classifying the thermal influence parameters into a state vector matrix and an input vector matrix includes:

[0164] Obtain the state vector matrix X of the temperature-adjusting building, and the parameters in the state vector matrix include the average temperature T of the floor of the temperature-adjusting building avg,concrete , the dry bulb temperature T of the indoor air air,room , the temperature T of the inner wall of the temperature-adjusting building iw , and the temperature T of the outer wall of the temperature-adjusting building ew , then

[0165] Obtain the input vector matrix U of the temperature-adjusting building, and the parameters in the input vector matrix include the water supply temperature T of the pipeline in the floor of the temperature-adjusting building water,supply , the air supply temperature T of the indoor air conditioning system air,supply , the dry bulb temperature T of the outdoor environment amb , the heat dissipation Q of indoor personnel and equipment gain , and the solar radiation intensity Q rad , then

[0166] In one embodiment, when the computer program is executed by the processor, the following steps are further implemented:

[0167] Obtaining the first coefficient matrix corresponding to the state vector matrix and the second coefficient matrix corresponding to the input vector matrix according to the heat resistance and heat capacity energy balance relationship between the parameters in the state vector matrix and the parameters in the input vector matrix includes:

[0168] Setting heat resistance and heat capacity between the parameters in the state vector matrix and the parameters in the input vector matrix to form a fourth-order RC model;

[0169] Writing a set of linear differential equations according to the energy balance in different states in the RC model as follows:

[0170]

[0171] where g wd is the total transmittance of the temperature-controlled building exterior window, R1 is the thermal resistance between the average temperature T avg,concrete of the temperature-controlled building floor slab and the dry-bulb temperature T air,room of the indoor air, R2 is the thermal resistance between the supply air temperature T air,supply of the indoor air-conditioning system and the dry-bulb temperature T amb of the outdoor environment, R3 is the thermal resistance between the average temperature T avg,concrete of the temperature-controlled building floor slab and the water supply temperature T water,supply of the pipeline in the temperature-controlled building floor slab, R4 is the thermal resistance between the supply air temperature T air,supply of the indoor air-conditioning system and the dry-bulb temperature T air,room of the indoor air, R5 is the thermal resistance between the dry-bulb temperature T air,room of the indoor air and the temperature T ew of the temperature-controlled building exterior wall, R6 is the thermal resistance between the dry-bulb temperature T amb of the outdoor environment and the temperature T ew of the temperature-controlled building exterior wall, R7 is the thermal resistance between the dry-bulb temperature T air,room of the indoor air and the temperature T iw of the temperature-controlled building interior wall, C concrete is the heat capacity between the average temperature T avg,concrete of the temperature-controlled building floor slab and the temperature-controlled building floor slab, C ew is the heat capacity between the temperature T ew of the temperature-controlled building exterior wall and the temperature-controlled building floor slab, C iw is the heat capacity between the temperature T iw of the temperature-controlled building interior wall and the temperature-controlled building floor slab, C air,room is the heat capacity between the dry-bulb temperature T air,room of the indoor air and the temperature-controlled building floor slab;

[0172] The first coefficient matrix corresponding to the state vector matrix is ​​obtained as:

[0173]

[0174] The second coefficient matrix corresponding to the input vector matrix is ​​obtained as:

[0175]

[0176] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0177] The step of multiplying the state vector matrix by the first coefficient matrix and summing the state vector matrix by the input vector matrix multiplied by the second coefficient matrix to form the temperature control equation of the temperature control building comprises: the temperature control equation of the temperature control building is:

[0178] The step of inputting the parameter value after the prediction cycle time length into the temperature control equation of the temperature control building to generate a control signal for controlling the water supply temperature and the air supply temperature according to the goal of minimizing the water supply temperature of the pipeline fluid in the floor of the temperature control building and minimizing the energy efficiency cost, demand deviation cost, and operation cost includes:

[0179] The objective function corresponding to the temperature control equation of the temperature control building is formed according to the goal of minimizing the water supply temperature of the pipe fluid in the floor of the temperature control building and minimizing the energy efficiency cost, demand deviation cost and operation cost. Among them J e (k) is the energy efficiency cost at the kth moment, J d (k) is the demand deviation cost at the kth moment, C op (k) is the operating cost at the kth moment, α is the weight coefficient of energy efficiency cost, β is the weight coefficient of demand deviation cost, γ is the weight coefficient of operating cost, Δt concrete H is the length of the prediction cycle. p The number of time periods after dividing the prediction period into multiple time periods;

[0180] The temperature control equation of the temperature-adjusting building is combined with the objective function to obtain After solving, control signals for controlling the water supply temperature and air supply temperature are generated.

[0181] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0182] In the objective function,

[0183] The energy efficiency cost at the kth moment is Where Q heating is the heating power value of the unit at the kth moment, Q coolingLet \(P_{cool,k}\) be the cooling power value of the unit at the \(k\)-th moment, \(COP\) be the coefficient of performance of the unit for heating, and \(EER\) be the coefficient of performance of the unit for cooling (the unit is an air source heat pump and a variable frequency fan).

[0184] The demand deviation cost at the \(k\)-th moment is \(\varepsilon\) heating \(=T\) air,room \(-T\) comf,max , \(\varepsilon\) heating \(\geq0\), \(\varepsilon\) cooling \(=T\) comf,min \(-T\) air,room , \(\varepsilon\) cooling \(\geq0\), where \(\varepsilon\) heating is the deviation value between the indoor temperature and the upper limit of the thermal comfort temperature at the \(k\)-th moment, and \(\varepsilon\) cooling is the deviation value between the indoor temperature and the lower limit of the thermal comfort temperature at the \(k\)-th moment;

[0185] The operating cost at the \(k\)-th moment is \(C\) op (k)=C energy (k)+C maint (k)+C equip (k), where \(C\) energy (k) is the cost of energy consumption at the \(k\)-th moment, \(C\) maint (k) is the cost of equipment maintenance and repair at the \(k\)-th moment, \(C\) equip (k) is the cost of system scheduling and management at the \(k\)-th moment.

[0186] For the specific limitations on the implementation steps when the computer program is executed by the processor, reference can be made to the limitations on the method of thermally activated building control in the above text, which will not be elaborated here.

[0187] In one embodiment, a computer device is provided. The computer device can be a server, and its internal structure diagram can be as Figure 10 shown. The computer device includes a processor, a memory, a network interface, and a database connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store thermally activated building control data. The network interface of the computer device is used to communicate with external terminals through a network connection. When the computer program is executed by the processor, a method of thermally activated building control is implemented.

[0188] Those skilled in the art can understand that, Figure 10The structure shown is only a block diagram of some structures related to the solution of this application, and does not constitute a limitation on the computer device to which the solution of this application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine some components, or have different component arrangements.

[0189] In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, the following steps are implemented:

[0190] Correspondingly, build a control model of the thermally activated building system for the temperature-controlled building, set thermal influence parameters in the control model of the thermally activated building system, and classify the thermal influence parameters into a state vector matrix and an input vector matrix;

[0191] Obtain a first coefficient matrix corresponding to the state vector matrix and a second coefficient matrix corresponding to the input vector matrix according to the heat resistance, heat capacity energy balance relationship between the parameters in the state vector matrix and the parameters in the input vector matrix;

[0192] Multiply the state vector matrix by the first coefficient matrix and then sum it with the input vector matrix multiplied by the second coefficient matrix to form a temperature-controlled building temperature control equation;

[0193] Input the parameter values after the prediction cycle time length into the temperature-controlled building temperature control equation, and generate a control signal for controlling the water supply temperature and the air supply temperature with the goal of minimizing the lowest water supply temperature of the fluid in the pipes of the temperature-controlled building floor and the energy efficiency cost, demand deviation cost, and operation cost.

[0194] In one embodiment, when the processor executes the computer program, the following steps are further implemented:

[0195] The corresponding construction of the control model of the thermally activated building system for the temperature-controlled building includes:

[0196] Correspondingly build a building model, a meteorological model, a water supply model, an air supply model, a power statistics model, and an observer in the control model of the thermally activated building system for the temperature-controlled building;

[0197] Set a meteorological parameter mode, a sky temperature module, and a solar radiation module in the meteorological model, and associate the meteorological model with the outdoor environmental thermal influence parameters of the building model;

[0198] Set an internal heat gain module of the building, an air source heat pump, a variable frequency water pump, a water supply parameter value module, and a parameter conversion module in the water supply model, and associate the water supply model with the thermal influence parameters of the fluid in the pipes of the floor of the building model;

[0199] Set a ventilation parameter value module and a variable-frequency fan in the air supply model, and associate the air supply model with the indoor air-conditioning system thermal influence parameters of the building model;

[0200] Set a floor heating unit conversion module, a water-side power module, and a total heating / cooling power module in the power statistics model, and associate the power statistics model with the thermal influence parameters of the building model;

[0201] Set a temperature display module, an uncomfortable duration statistics module, a total power display module, and a building load display module in the observer, and associate the observer with the thermal influence parameters of the building model. The observer is used to statistically calculate the control output data of the temperature-controlled building at the current moment and obtain the state vector matrix value at the current moment, the temperature control equation value of the temperature-controlled building at the current moment, and the temperature control equation value of the temperature-controlled building after the prediction cycle time length according to the control output data at the current moment.

[0202] In one embodiment, when the processor executes the computer program, the following steps are further implemented:

[0203] The construction of the corresponding temperature-controlled building for the thermal activation building system control model further includes:

[0204] Set a TABS module. The TABS module is connected to the building model and the parameter conversion module. The data transmitted from the building model to the TABS module includes the star node temperature, the surface temperatures of the floor and the ceiling, and the combined heat fluxes of the floor and the ceiling. The data transmitted from the TABS module to the building model includes the outlet temperature of the fluid, the mass flow rate, the average temperature of the floor slab, and the heat outputs of the floor and the ceiling. The TABS module also transmits the energy stored in the floor slab and the heat transfer coefficient at the end of each time step. The parameter conversion module is used to calculate the heat transfer amount of each unit by dividing each floor or ceiling into multiple units according to the combined heat fluxes of the floor and the ceiling output by the building model.

[0205] Set an MPC controller. The MPC controller is connected to the building model, the TABS module, the water supply model, the air supply model, and the observer. The MPC controller is used to generate control signals for controlling the water supply temperature and the air supply temperature and control the variable-frequency water pump and the variable-frequency fan according to the control signals.

[0206] In one embodiment, when the processor executes the computer program, the following steps are further implemented:

[0207] The MPC controller is used to generate control signals for controlling the water supply temperature and the air supply temperature and control the variable-frequency water pump and the variable-frequency fan, including:

[0208] Obtain the state vector matrix value at the current moment, the temperature control equation value of the temperature-regulated building at the current moment, and the temperature control equation value of the temperature-regulated building after the prediction cycle time length, and obtain the parameter values after the prediction cycle time length;

[0209] According to the state vector matrix value at the current moment, the temperature control equation value of the temperature-regulated building at the current moment, the temperature control equation value of the temperature-regulated building after the prediction cycle time length, and the parameter values after the prediction cycle time length, divide the prediction cycle time length into multiple time periods, generate the input vector matrix value corresponding to each time period, and generate the control signal corresponding to each time period from the input vector matrix value corresponding to each time period;

[0210] Send the control signals of each time period to the variable frequency water pump and the variable frequency fan in sequence according to the time order.

[0211] In one embodiment, when the processor executes the computer program, the following steps are further implemented:

[0212] The setting of the thermal influence parameters in the thermal activation building system control model and the classification of the thermal influence parameters into a state vector matrix and an input vector matrix include:

[0213] Obtain the state vector matrix X of the temperature-regulated building, and the parameters in the state vector matrix include the average temperature T of the floor of the temperature-regulated building avg,concrete , the dry bulb temperature T of the indoor air air,room , the temperature T of the inner wall of the temperature-regulated building iw and the temperature T of the outer wall of the temperature-regulated building ew , then

[0214] Obtain the input vector matrix U of the temperature-regulated building, and the parameters in the input vector matrix include the supply water temperature T of the pipeline in the floor of the temperature-regulated building water,supply , the supply air temperature T of the indoor air conditioning system air,supply , the dry bulb temperature T of the outdoor environment amb , the heat dissipation Q of indoor personnel and equipment gain and the solar radiation intensity Q rad , then

[0215] In one embodiment, when the processor executes the computer program, the following steps are further implemented:

[0216] The obtaining of the first coefficient matrix corresponding to the state vector matrix and the second coefficient matrix corresponding to the input vector matrix according to the thermal resistance and heat capacity energy balance relationship between the parameters in the state vector matrix and the parameters in the input vector matrix includes:

[0217] Set a thermal resistance and a heat capacity between the parameters in the state vector matrix and the parameters in the input vector matrix to form a fourth-order RC model;

[0218] Write a set of linear differential equations according to the energy balance in different states in the RC model as follows:

[0219]

[0220] where g wd is the total transmittance of the temperature-controlled building exterior window, and R1 is the average temperature T of the temperature-controlled building floor avg,concrete and the dry-bulb temperature T of the indoor air air,room between the thermal resistances, R2 is the supply air temperature T of the indoor air conditioning system air,supply and the dry-bulb temperature T of the outdoor environment amb between the thermal resistances, R3 is the average temperature T of the temperature-controlled building floor avg,concrete and the supply water temperature T of the pipes in the temperature-controlled building floor water,supply between the thermal resistances, R4 is the supply air temperature T of the indoor air conditioning system air,supply and the dry-bulb temperature T of the indoor air air,room between the thermal resistances, R5 is the dry-bulb temperature T of the indoor air air,room and the temperature T of the temperature-controlled building exterior wall ew between the thermal resistances, R6 is the dry-bulb temperature T of the outdoor environment amb and the temperature T of the temperature-controlled building exterior wall ew between the thermal resistances, R7 is the dry-bulb temperature T of the indoor air air,room and the temperature T of the temperature-controlled building interior wall iw between the thermal resistances, C concrete is the heat capacity between the average temperature T of the temperature-controlled building floor avg,concrete and the temperature-controlled building floor, C ew is the heat capacity between the temperature T of the temperature-controlled building exterior wall ew and the temperature-controlled building floor, C iw is the heat capacity between the temperature T of the temperature-controlled building interior wall iw and the temperature-controlled building floor, C air,room is the heat capacity between the dry-bulb temperature T of the indoor air air,room and the temperature-controlled building floor;

[0221] Obtain the first coefficient matrix corresponding to the state vector matrix as:

[0222]

[0223] Obtain the second coefficient matrix corresponding to the input vector matrix as:

[0224]

[0225] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0226] The step of multiplying the state vector matrix by the first coefficient matrix and summing the state vector matrix by the input vector matrix multiplied by the second coefficient matrix to form the temperature control equation of the temperature control building comprises: the temperature control equation of the temperature control building is:

[0227] The step of inputting the parameter value after the prediction cycle time length into the temperature control equation of the temperature control building to generate a control signal for controlling the water supply temperature and the air supply temperature according to the goal of minimizing the water supply temperature of the pipeline fluid in the floor of the temperature control building and minimizing the energy efficiency cost, demand deviation cost, and operation cost includes:

[0228] The objective function corresponding to the temperature control equation of the temperature control building is formed according to the goal of minimizing the water supply temperature of the pipe fluid in the floor of the temperature control building and minimizing the energy efficiency cost, demand deviation cost and operation cost. Among them J e (k) is the energy efficiency cost at the kth moment, J d (k) is the demand deviation cost at the kth moment, C op (k) is the operating cost at the kth moment, α is the weight coefficient of energy efficiency cost, β is the weight coefficient of demand deviation cost, γ is the weight coefficient of operating cost, Δt concrete H is the length of the prediction cycle. p The number of time periods after dividing the prediction period into multiple time periods;

[0229] The temperature control equation of the temperature-adjusting building is combined with the objective function to obtain After solving, control signals for controlling the water supply temperature and air supply temperature are generated.

[0230] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0231] In the objective function,

[0232] The energy efficiency cost at the kth moment is Where Q heating is the heating power value of the unit at the kth moment, Q cooling is the cooling power value of the unit at the kth moment, COP is the heating coefficient of the unit, and EER is the cooling coefficient of the unit (the unit is an air source heat pump and a variable frequency fan);

[0233] The demand deviation cost at the kth moment is ε heating =Tair,room -T comf,max , ε heating ≥0, ε cooling = T comf,min -T air,room , ε cooling ≥0, where ε heating is the deviation value between the indoor temperature at the k-th moment and the upper limit of the thermally comfortable temperature, and ε cooling is the deviation value between the indoor temperature at the k-th moment and the lower limit of the thermally comfortable temperature;

[0234] The operating cost at the k-th moment is C op (k) = C energy (k) + C maint (k) + C equip (k), where C energy (k) is the cost of energy consumption at the k-th moment, and C maint (k) is the cost of equipment maintenance and upkeep at the k-th moment, and C equip (k) is the cost of system scheduling and management at the k-th moment.

[0235] For the specific limitations on the steps implemented when the processor executes the computer program, reference can be made to the limitations on the method of thermally activated building control in the above text, which will not be elaborated here.

[0236] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the following steps are implemented:

[0237] Construct a thermally activated building system control model for the temperature-regulating building, set thermal influence parameters in the thermally activated building system control model, and classify the thermal influence parameters into a state vector matrix and an input vector matrix;

[0238] Obtain a first coefficient matrix corresponding to the state vector matrix and a second coefficient matrix corresponding to the input vector matrix according to the thermal resistance, heat capacity, and energy balance relationship between the parameters in the state vector matrix and the parameters in the input vector matrix;

[0239] Multiply the state vector matrix by the first coefficient matrix and then sum it with the product of the input vector matrix multiplied by the second coefficient matrix to form a temperature-regulating building temperature control equation;

[0240] Input the parameter values after the prediction cycle time length into the temperature-regulating building temperature control equation, and generate a control signal for controlling the water supply temperature and the air supply temperature with the goal of minimizing the lowest water supply temperature in the pipes of the temperature-regulating building floor, as well as the energy efficiency cost, demand deviation cost, and operating cost.

[0241] In one embodiment, when the computer program is executed by the processor, the following steps are further implemented:

[0242] The corresponding temperature-adjusting building constructs a control model for a thermally activated building system, including:

[0243] In the control model of the thermally activated building system, a building model, a meteorological model, a water supply model, a ventilation model, a power statistics model, and an observer are constructed corresponding to the temperature-adjusting building;

[0244] In the meteorological model, a meteorological parameter mode, a sky temperature module, and a solar radiation module are set, and the meteorological model is associated with the outdoor environmental thermal influence parameters of the building model;

[0245] In the water supply model, a building internal heat gain module, an air source heat pump, a variable frequency water pump, a water supply parameter value module, and a parameter conversion module are set, and the water supply model is associated with the fluid thermal influence parameters of the pipes in the floor slab of the building model;

[0246] In the ventilation model, a ventilation parameter value module and a variable frequency fan are set, and the ventilation model is associated with the indoor air conditioning system thermal influence parameters of the building model;

[0247] In the power statistics model, a floor heating unit conversion module, a water side power module, and a total heating / cooling power module are set, and the power statistics model is associated with the thermal influence parameters of the building model;

[0248] In the observer, a temperature display module, an uncomfortable duration statistics module, a total power display module, and a building load display module are set, and the observer is associated with the thermal influence parameters of the building model. The observer is used to statistically calculate the control output data of the temperature-adjusting building at the current moment and obtain the state vector matrix value at the current moment, the temperature control equation value of the temperature-adjusting building at the current moment, and the temperature control equation value of the temperature-adjusting building after the prediction cycle time length according to the control output data at the current moment.

[0249] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented:

[0250] The corresponding temperature-adjusting building constructs a control model for a thermally activated building system, further including:

[0251] Set up the TABS module, which is connected to the building model and the parameter conversion module. The data transmitted from the building model to the TABS module includes the star node temperature, the surface temperatures of the floor and the ceiling, and the combined heat fluxes of the floor and the ceiling. The data transmitted from the TABS module to the building model includes the outlet temperature of the fluid, the mass flow rate, the average temperature of the floor slab, and the heat outputs of the floor and the ceiling. The TABS module also transmits the energy stored in the floor slab and the heat transfer coefficient at the end of each time step. The parameter conversion module is used to calculate the heat transfer amount of each unit by dividing each floor or ceiling into multiple units according to the combined heat fluxes of the floor and the ceiling output by the building model.

[0252] Set up the MPC controller, which is connected to the building model, the TABS module, the water supply model, the air supply model, and the observer. The MPC controller is used to generate control signals for controlling the water supply temperature and the air supply temperature and to control the variable frequency water pump and the variable frequency air blower according to the control signals.

[0253] In one embodiment, when the computer program is executed by the processor, the following steps are further implemented:

[0254] The MPC controller is used to generate control signals for controlling the water supply temperature and the air supply temperature and to control the variable frequency water pump and the variable frequency air blower, including:

[0255] Obtain the state vector matrix value at the current moment, the temperature control equation value of the temperature-adjusted building at the current moment, and the temperature control equation value of the temperature-adjusted building after the prediction period time length, and obtain the parameter values after the prediction period time length;

[0256] According to the state vector matrix value at the current moment, the temperature control equation value of the temperature-adjusted building at the current moment, the temperature control equation value of the temperature-adjusted building after the prediction period time length, and the parameter values after the prediction period time length, divide the prediction period time length into multiple time periods, generate the input vector matrix value corresponding to each time period, and generate the control signal corresponding to each time period from the input vector matrix value corresponding to each time period;

[0257] Send the control signals of each time period to the variable frequency water pump and the variable frequency air blower in sequence according to the time order.

[0258] In one embodiment, when the computer program is executed by the processor, the following steps are further implemented:

[0259] The step of setting the thermal influence parameters in the thermal-activated building system control model and classifying the thermal influence parameters into the state vector matrix and the input vector matrix includes:

[0260] Obtain the state vector matrix X of the temperature-controlled building, where the parameters in the state vector matrix include the average temperature T of the floor slab of the temperature-controlled building avg,concrete , the dry-bulb temperature T of the indoor air air,room , the temperature T of the inner wall of the temperature-controlled building iw and the temperature T of the outer wall of the temperature-controlled building ew , then

[0261] Obtain the input vector matrix U of the temperature-controlled building, where the parameters in the input vector matrix include the supply water temperature T of the pipeline in the floor slab of the temperature-controlled building water,supply , the supply air temperature T of the indoor air-conditioning system air,supply , the dry-bulb temperature T of the outdoor environment amb , the heat dissipation Q of indoor personnel and equipment gain and the solar radiation intensity Q rad , then

[0262] In one embodiment, when the computer program is executed by the processor, the following steps are further implemented:

[0263] The obtaining of the first coefficient matrix corresponding to the state vector matrix and the second coefficient matrix corresponding to the input vector matrix according to the thermal resistance and heat capacity energy balance relationship between the parameters in the state vector matrix and the parameters in the input vector matrix includes:

[0264] Set thermal resistance and heat capacity between the parameters in the state vector matrix and the parameters in the input vector matrix to form a fourth-order RC model;

[0265] Write a set of linear differential equations according to the energy balance of different states in the RC model as follows:

[0266]

[0267] where g wd is the total transmittance of the exterior windows of the temperature-controlled building, R1 is the thermal resistance between the average temperature T of the floor slab of the temperature-controlled building avg,concrete and the dry-bulb temperature T of the indoor air air,room , R2 is the thermal resistance between the supply air temperature T of the indoor air-conditioning system air,supply and the dry-bulb temperature T of the outdoor environment amb , R3 is the thermal resistance between the average temperature T of the floor slab of the temperature-controlled building avg,concrete and the supply water temperature T of the pipeline in the floor slab of the temperature-controlled building water,supply , R4 is the thermal resistance between the supply air temperature T of the indoor air-conditioning system air,supply and the dry-bulb temperature T of the indoor air air,roomThe thermal resistance between, R5 is the dry - bulb temperature T of the indoor air air,room and the temperature T of the temperature - controlled building exterior wall ew The thermal resistance between, R6 is the dry - bulb temperature T of the outdoor environment amb and the temperature T of the temperature - controlled building exterior wall ew The thermal resistance between, R7 is the dry - bulb temperature T of the indoor air air,room and the temperature T of the temperature - controlled building interior wall iw The thermal resistance between, C concrete is the average temperature T of the temperature - controlled building floor avg,concrete and the heat capacity between the temperature - controlled building floor, C ew is the temperature T of the temperature - controlled building exterior wall ew and the heat capacity between the temperature - controlled building floor, C iw is the temperature T of the temperature - controlled building interior wall iw and the heat capacity between the temperature - controlled building floor, C air,room is the dry - bulb temperature T of the indoor air air,room and the heat capacity between the temperature - controlled building floor;

[0268] Obtaining the first coefficient matrix corresponding to the state - vector matrix is:

[0269]

[0270] Obtaining the second coefficient matrix corresponding to the input - vector matrix is:

[0271]

[0272] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented:

[0273] The step of summing the product of the state - vector matrix and the first coefficient matrix and the product of the input - vector matrix and the second coefficient matrix to form the temperature - controlled equation of the temperature - controlled building includes: The temperature - controlled equation of the temperature - controlled building is

[0274] The step of inputting the parameter values after the prediction - period time length into the temperature - controlled equation of the temperature - controlled building and generating a control signal for controlling the water - supply temperature and the air - supply temperature with the goal of minimizing the water - supply temperature of the fluid in the pipes in the temperature - controlled building floor and the energy - efficiency cost, the demand - deviation cost, and the operation cost includes:

[0275] Setting an objective function corresponding to the temperature - controlled equation of the temperature - controlled building with the goal of minimizing the water - supply temperature of the fluid in the pipes in the temperature - controlled building floor and the energy - efficiency cost, the demand - deviation cost, and the operation cost where J e (k) is the energy - efficiency cost at the k - th moment, Jd (k) is the demand deviation cost at the kth moment, C op (k) is the operating cost at the kth moment, α is the weight coefficient of energy efficiency cost, β is the weight coefficient of demand deviation cost, γ is the weight coefficient of operating cost, Δt concrete H is the length of the prediction cycle. p The number of time periods after dividing the prediction period into multiple time periods;

[0276] The temperature control equation of the temperature-adjusting building is combined with the objective function to obtain After solving, control signals for controlling the water supply temperature and air supply temperature are generated.

[0277] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0278] In the objective function,

[0279] The energy efficiency cost at the kth moment is Where Q heating is the heating power value of the unit at the kth moment, Q cooling is the cooling power value of the unit at the kth moment, COP is the heating coefficient of the unit, and EER is the cooling coefficient of the unit (the unit is an air source heat pump and a variable frequency fan);

[0280] The demand deviation cost at the kth moment is ε heating =T air,room -T comf,max ,ε heating ≥0,ε cooling =T comf,min -T air,room ,ε cooling ≥0, where ε heating is the deviation between the indoor temperature and the upper limit of thermal comfort temperature at the kth moment, ε cooling is the deviation between the indoor temperature and the lower limit of thermal comfort temperature at the kth moment;

[0281] The operation cost at the kth moment is C op (k) = C energy (k)+C maint (k)+C equip (k), where C energy (k) is the cost of energy consumption at the kth moment, C maint (k) is the cost of equipment maintenance and upkeep at the kth moment, C equip (k) is the cost of system scheduling and management at the kth time.

[0282] For the specific limitations on the steps implemented when a computer program is executed by a processor, reference may be made to the limitations on the method of thermally activated building control in the foregoing text, which will not be elaborated herein.

[0283] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, storage, database, or other medium used in the embodiments provided in this application can include non-volatile and / or volatile memories. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or an external cache. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and Rambus dynamic RAM (RDRAM), etc.

[0284] The technical features of the above embodiments can be combined arbitrarily. For the sake of concise description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope described in this specification.

[0285] The above-described embodiments merely represent several implementation manners of this application. The description is relatively specific and detailed, but it should not be construed as a limitation on the scope of the invention patent. It should be noted that for those of ordinary skill in the art, without departing from the concept of this application, several modifications and improvements can still be made, and these all belong to the protection scope of this application. Therefore, the protection scope of the patent of this application shall be subject to the appended claims.

Claims

1. A thermally activated building control method, characterized in that, Comprising: A control model for a thermally activated building system corresponding to a temperature-regulating building structure. In the control model of the thermally activated building system, thermal influence parameters are set and classified into a state vector matrix and an input vector matrix; According to the heat resistance and heat capacity energy balance relationship between the parameters in the state vector matrix and the parameters in the input vector matrix, a first coefficient matrix corresponding to the state vector matrix and a second coefficient matrix corresponding to the input vector matrix are obtained; The state vector matrix is multiplied by the first coefficient matrix and then summed with the input vector matrix multiplied by the second coefficient matrix to form a temperature-regulating building temperature control equation; The parameter values after the prediction cycle time length are input into the temperature-regulating building temperature control equation, and control signals for controlling the supply water temperature and the supply air temperature are generated with the goal of minimizing the supply water temperature in the pipes of the temperature-regulating building floor and the energy efficiency cost, demand deviation cost, and operation cost; 2. The thermal activation building control method according to claim 1, characterized in that, The control model for a thermally activated building system corresponding to the temperature-regulating building structure includes: In the control model of the thermally activated building system, a building model, a meteorological model, a water supply model, an air supply model, a power statistics model, and an observer are constructed corresponding to the temperature-regulating building; In the meteorological model, a meteorological parameter mode, a sky temperature module, and a solar radiation module are set, and the meteorological model is associated with the outdoor environment thermal influence parameters of the building model; In the water supply model, an internal heat gain module of the building, an air source heat pump, a variable frequency water pump, a water supply parameter value module, and a parameter conversion module are set, and the water supply model is associated with the thermal influence parameters of the pipes in the floor of the building model; In the air supply model, a ventilation parameter value module and a variable frequency fan are set, and the air supply model is associated with the thermal influence parameters of the indoor air conditioning system of the building model; In the power statistics model, a floor heating unit conversion module, a water side power module, and a total heating / cooling power module are set, and the power statistics model is associated with the thermal influence parameters of the building model; In the observer, a temperature display module, an uncomfortable duration statistics module, a total power display module, and a building load display module are set, and the observer is associated with the thermal influence parameters of the building model. The observer is used to statistically analyze the control output data of the temperature-regulating building at the current moment and obtain the state vector matrix value at the current moment, the temperature control equation value of the temperature-regulating building at the current moment, and the temperature control equation value of the temperature-regulating building after the prediction cycle time length; 3. The thermal activation building control method according to claim 2, wherein The control model for a thermally activated building system corresponding to the temperature-regulating building structure further includes: Set up the TABS module. The TABS module is connected to the building model and the parameter conversion module. The data transmitted by the building model to the TABS module includes the star node temperature, the surface temperatures of the floor and the ceiling, and the combined heat fluxes of the floor and the ceiling. The data transmitted by the TABS module to the building model includes the outlet temperature of the fluid, the mass flow rate, the average temperature of the floor slab, and the heat outputs of the floor and the ceiling. The TABS module also transmits the energy stored in the floor slab and the heat transfer coefficient at the end of each time step. The parameter conversion module is used to calculate the heat transfer amount of each unit by dividing each floor or ceiling into multiple units according to the combined heat fluxes of the floor and the ceiling output by the building model. Set up the MPC controller. The MPC controller is connected to the building model, the TABS module, the water supply model, the air supply model, and the observer. The MPC controller is used to generate control signals for controlling the water supply temperature and the air supply temperature and to control the variable frequency water pump and the variable frequency air blower according to the control signals.

4. The thermal activation building control method according to claim 3, characterized in that, The MPC controller is used to generate control signals for controlling the water supply temperature and the air supply temperature and to control the variable frequency water pump and the variable frequency air blower according to the control signals, including: Obtain the value of the state vector matrix at the current moment, the value of the temperature-controlled building temperature control equation at the current moment, and the value of the temperature-controlled building temperature control equation after the prediction period time length, and obtain the parameter values after the prediction period time length; According to the value of the state vector matrix at the current moment, the value of the temperature-controlled building temperature control equation at the current moment, the value of the temperature-controlled building temperature control equation after the prediction period time length, and the parameter values after the prediction period time length, divide the prediction period time length into multiple time periods, generate the input vector matrix value corresponding to each time period, and generate the control signal corresponding to each time period from the input vector matrix value corresponding to each time period; Send the control signals of each time period to the variable frequency water pump and the variable frequency air blower in sequence according to the time order.

5. The thermal activation building control method according to claim 1, characterized in that, The setting of the thermal influence parameters in the thermal activation building system control model and the classification of the thermal influence parameters into the state vector matrix and the input vector matrix include: Obtain the state vector matrix X of the temperature-controlled building, where the parameters in the state vector matrix include the average temperature T of the floor slab of the temperature-controlled building avg,concrete , the dry-bulb temperature T of the indoor air air,room , the temperature T of the interior wall of the temperature-controlled building iw and the temperature T of the exterior wall of the temperature-controlled building ew , then Obtain the input vector matrix U of the temperature-controlled building, and the parameters in the input vector matrix include the supply water temperature T of the pipes in the floor of the temperature-controlled building water,supply , the supply air temperature T of the indoor air conditioning system air,supply , the dry bulb temperature T of the outdoor environment amb , the heat dissipation Q of indoor personnel and equipment gain and the solar radiation intensity Q rad , then 6. The thermally activated building control method according to claim 5, characterized in that, The obtaining of the first coefficient matrix corresponding to the state vector matrix and the second coefficient matrix corresponding to the input vector matrix according to the thermal resistance and heat capacity energy balance relationship between the parameters in the state vector matrix and the parameters in the input vector matrix includes: Set up the thermal resistance and heat capacity between the parameters in the state vector matrix and the parameters in the input vector matrix to form a fourth-order RC model; Write a set of linear differential equations according to the energy balance of different states in the RC model as follows: where g wd is the total transmittance of the temperature - controlled building exterior window, R1 is the thermal resistance between the average temperature T avg,concrete of the temperature - controlled building floor slab and the dry - bulb temperature T air,room of the indoor air, R2 is the thermal resistance between the supply air temperature T air,supply of the indoor air - conditioning system and the dry - bulb temperature T amb of the outdoor environment, R3 is the thermal resistance between the average temperature T avg,concrete of the temperature - controlled building floor slab and the water supply temperature T water,supply of the pipes inside the temperature - controlled building floor slab, R4 is the thermal resistance between the supply air temperature T air,supply of the indoor air - conditioning system and the dry - bulb temperature T air,room of the indoor air, R5 is the thermal resistance between the dry - bulb temperature T air,room of the indoor air and the temperature T ew of the temperature - controlled building exterior wall, R6 is the thermal resistance between the dry - bulb temperature T amb of the outdoor environment and the temperature T ew of the temperature - controlled building exterior wall, R7 is the thermal resistance between the dry - bulb temperature T air,room of the indoor air and the temperature T iw of the temperature - controlled building interior wall, C concrete is the heat capacity between the average temperature T avg,concrete of the temperature - controlled building floor slab and the temperature - controlled building floor slab, C ew is the heat capacity between the temperature T ew of the temperature - controlled building exterior wall and the temperature - controlled building floor slab, C iw is the heat capacity between the temperature T iw of the temperature - controlled building interior wall and the temperature - controlled building floor slab, C air,room is the heat capacity between the dry - bulb temperature T air,room of the indoor air and the temperature - controlled building floor slab; Obtaining the first coefficient matrix corresponding to the state vector matrix is as follows: Obtaining the second coefficient matrix corresponding to the input vector matrix is as follows:

7. According to the thermal activation building control method described in claim 6, characterized in that Said that multiplying the state vector matrix by the first coefficient matrix and then summing it with the input vector matrix multiplied by the second coefficient matrix to form the temperature control equation for the temperature-regulated building includes: The temperature control equation for the temperature-regulated building is The inputting of the parameter values after the prediction period time length into the temperature-controlled building temperature control equation and generating the control signals for controlling the water supply temperature and the air supply temperature with the goal of minimizing the lowest water supply temperature of the pipeline fluid in the temperature-controlled building floor slab and the energy efficiency cost, demand deviation cost, and operation cost includes: Set the objective function corresponding to the temperature control equation of the temperature-controlled building floor with the goal of minimizing the water supply temperature of the pipeline fluid in the temperature-controlled building floor and minimizing the energy efficiency cost, demand deviation cost, and operation cost where J e (k) is the energy efficiency cost at the k-th moment, J d (k) is the demand deviation cost at the k-th moment, C op (k) is the operation cost at the k-th moment, α is the weight coefficient of the energy efficiency cost, β is the weight coefficient of the demand deviation cost, γ is the weight coefficient of the operation cost, Δt concrete is the length of the prediction period, H p is the number of time periods after dividing the length of the prediction period into multiple time periods; The temperature control equation of the temperature-adjusting building is combined with the objective function to obtain After solving, control signals for controlling the water supply temperature and air supply temperature are generated.

8. The thermally activated building control method according to claim 7, characterized in that In the objective function, The energy efficiency cost at the k-th moment is where Q heating is the heating power value of the unit at the k-th moment, Q cooling is the cooling power value of the unit at the k-th moment, COP is the heating coefficient of the unit, and EER is the cooling coefficient of the unit (the unit is an air source heat pump and a variable frequency fan); The demand deviation cost at the k-th moment is ε heating = T air,room - T comf,max , ε heating ≥ 0, ε cooling = T comf,min - T air,room , ε cooling ≥ 0, where ε heating is the deviation value between the indoor temperature and the upper limit of the thermal comfort temperature at the k-th moment, and ε cooling is the deviation value between the indoor temperature and the lower limit of the thermal comfort temperature at the k-th moment; The operating cost at the k-th moment is C op (k) = C energy (k) + C maint (k) + C equip (k), where C energy (k) is the cost of energy consumption at the k-th moment, C maint (k) is the cost of equipment maintenance and repair at the k-th moment, C equip (k) is the cost of system scheduling and management at the k-th moment.

9. A computer device, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, When the processor executes the computer program, the steps of the method according to any one of claims 1 to 8 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, the steps of the method according to any one of claims 1 to 8 are implemented.

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