Heating ventilation and air conditioning system control method, device, equipment and medium

Abstract

The embodiment of the invention discloses a heating ventilation air-conditioning system control method, device and equipment and a medium, and relates to the field of intelligent building environment management.The method comprises the steps that the initial number of people, the initial indoor temperature and the initial indoor carbon dioxide concentration of a building served by a heating ventilation air-conditioning system are determined; determining a predicted number of people at the target time point and a room occupancy state according to the initial number of people; according to the initial indoor temperature, the initial indoor carbon dioxide concentration and the predicted number of people, the predicted indoor temperature and the predicted indoor carbon dioxide concentration at the target time point are determined; inputting the room occupancy state, the predicted indoor temperature and the predicted indoor carbon dioxide concentration into a pre-trained model optimizer to obtain an indoor air temperature set value and fresh air flow of the heating ventilation air conditioning system at a target time point; the optimization target of the model optimizer comprises thermal comfort punishment, indoor air quality punishment and energy consumption punishment. Therefore, efficient balance among energy efficiency optimization, living comfort and air quality can be achieved.

Description

Technical Field

[0001] This disclosure relates to the field of intelligent building environment management technology, specifically to a method, apparatus, equipment, and medium for controlling a heating, ventilation, and air conditioning system. Background Technology

[0002] Traditional Building Automation Systems (BAS) neglect the dynamic variable of human behavior, relying on fixed schedules or simplistic human behavior patterns. This approach easily leads to overcooling, overheating, or overventilation, resulting in significant energy waste. Human behavior is influenced by multiple factors, including culture, psychology, and context, and its high degree of uncertainty makes traditional systems difficult to adapt to. Some control systems incorporate people sensors and employ simple feedback control logic (OBC) based on the number of people, adjusting system setpoints according to real-time people data. However, this control logic lacks comprehensive consideration of people's comfort feedback, real-time people count, people count prediction, and dynamic prediction of the building environment. It cannot resolve the problem of conflicting control objectives, such as achieving a balance between energy efficiency optimization, comfort, and air quality. Although the development of Internet of Things (IoT) technology in recent years has accumulated a large amount of real-time data in the building environment, this data has not yet been systematically analyzed and modeled, and it is difficult to fully utilize it in real-time prediction and control decisions. This lack of human-centered adaptability not only weakens the potential for building energy conservation but may also reduce people's satisfaction. Therefore, existing technologies need further improvement and development. Summary of the Invention

[0003] This disclosure provides a method, apparatus, electronic device, and computer-readable storage medium for controlling a heating, ventilation, and air conditioning system, aiming to at least partially solve one of the technical problems in the related art.

[0004] In a first aspect, embodiments of this disclosure provide a method for controlling a heating, ventilation, and air conditioning system, the method comprising: Determine the initial number of people, initial indoor temperature, and initial indoor carbon dioxide concentration in the building served by the HVAC system; Based on the initial number of people, determine the predicted number of people and room occupancy status at the target time point; Based on the initial indoor temperature, the initial indoor carbon dioxide concentration, and the predicted number of people, determine the predicted indoor temperature and predicted indoor carbon dioxide concentration at the target time point; The room occupancy status, the predicted indoor temperature, and the predicted indoor carbon dioxide concentration are input into a pre-trained model optimizer to obtain the indoor air temperature setpoint and fresh air flow rate of the HVAC system at the target time point. The optimization objectives of the model optimizer include thermal comfort penalty, indoor air quality penalty, and energy consumption penalty.

[0005] Secondly, embodiments of this disclosure also provide a heating, ventilation, and air conditioning system control device, the device comprising: The first determination is used to determine the initial number of people, initial indoor temperature, and initial indoor carbon dioxide concentration in the building served by the HVAC system; The second determination is used to determine the predicted number of people at the target time point and the room occupancy status based on the initial number of people. The third determination is used to determine the predicted indoor temperature and predicted indoor carbon dioxide concentration at the target time point based on the initial indoor temperature, the initial indoor carbon dioxide concentration, and the predicted number of people. The processing module is used to input the room occupancy status, the predicted indoor temperature, and the predicted indoor carbon dioxide concentration into a pre-trained model optimizer to obtain the indoor air temperature setpoint and fresh air flow rate of the HVAC system at the target time point. The optimization objectives of the model optimizer include: thermal comfort penalty, indoor air quality penalty, and energy consumption penalty.

[0006] Thirdly, this disclosure also provides an electronic device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the computer program is executed by the processor, it implements the steps in the above-described HVAC system control method.

[0007] Fourthly, this disclosure also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps in the above-described HVAC system control method.

[0008] Fifthly, embodiments of this disclosure also provide a computer program product or computer program including computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the methods provided in various optional implementations of embodiments of this disclosure.

[0009] In this embodiment, the initial number of occupants, initial indoor temperature, and initial indoor carbon dioxide concentration of the building served by the HVAC system are determined. Based on the initial number of occupants, the predicted number of occupants and room occupancy status at the target time point are determined. Based on the initial indoor temperature, initial indoor carbon dioxide concentration, and predicted number of occupants, the predicted indoor temperature and predicted indoor carbon dioxide concentration at the target time point are determined. The room occupancy status, predicted indoor temperature, and predicted indoor carbon dioxide concentration are input into a pre-trained model optimizer to obtain the indoor air temperature setpoint and fresh air flow rate of the HVAC system at the target time point. The optimization objectives of the model optimizer include: thermal comfort penalty, indoor air quality penalty, and energy consumption penalty. This achieves a highly efficient balance between energy efficiency optimization, residential comfort, and air quality.

[0010] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure. Attached Figure Description

[0011] To more clearly illustrate the technical solutions in this disclosure, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0012] Figure 1 This is a schematic flowchart of a heating, ventilation and air conditioning system control method provided in an embodiment of this disclosure; Figure 2 This is a schematic flowchart of another HVAC system control method provided in this embodiment; Figure 3 This is a schematic diagram of the implementation architecture of the human-in-the-loop model optimization control method based on the Internet of Things (IoT) proposed in this disclosure; Figure 4 This is a schematic diagram of the human-in-the-loop model optimization control scheme based on the integrated random number model, real-time number of people and real-time personnel feedback proposed in this disclosure; Figure 5 It is based on the thermal resistance and thermal capacity diagram of the building's thermal dynamic structure presented in this disclosure; Figure 6 This is a schematic diagram of the operation of a real-time human comfort feedback system based on QR codes, as proposed in this disclosure. Figure 7 This is a schematic diagram of the structure of the HVAC system control device provided in the embodiments of this disclosure; Figure 8 This is a schematic diagram of the structure of the electronic device provided in the embodiments of this disclosure. Detailed Implementation

[0013] Some embodiments of this disclosure will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. Various changes, modifications, and equivalents of the methods, apparatus, and / or systems described herein will become apparent upon understanding this disclosure. For example, the order of operations described herein is merely illustrative and is not limited to those orders set forth herein, but can be changed as will become apparent upon understanding this disclosure, except for operations that must be performed in a particular order. Furthermore, for clarity and brevity, descriptions of features known in the art may be omitted.

[0014] The embodiments described in the following examples of this disclosure are not representative of all embodiments consistent with this disclosure. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this disclosure as detailed in the appended claims.

[0015] It should be noted that the execution subject of the HVAC system control method in this embodiment can be an HVAC system control device, which can be configured in any type of electronic device, and is not limited here.

[0016] In this embodiment, the "heating, ventilation and air conditioning system control device" will be used as the executing entity to describe the "heating, ventilation and air conditioning system control method", and no limitation will be made here.

[0017] It should be noted that the order of description of the following embodiments is not intended to limit the priority of the embodiments.

[0018] Figure 1 This is a flowchart illustrating the control method for a heating, ventilation, and air conditioning system provided according to the first embodiment of this disclosure.

[0019] like Figure 1 As shown, the method includes: Step 101: Determine the initial number of people, initial indoor temperature, and initial indoor carbon dioxide concentration of the building served by the HVAC system.

[0020] The heating, ventilation and air conditioning (HVAC) system can be composed of fan coil units (FCU) and main air handling units (PAU), and the fresh air flow is adjusted through a variable air volume (VAV) system.

[0021] Among them, the initial number of people, the initial indoor temperature, and the initial indoor carbon dioxide concentration refer to the number of people, indoor temperature, and indoor carbon dioxide concentration in the initial state of the building served by the HVAC system before the multi-objective model predictive control is carried out.

[0022] In this embodiment of the disclosure, an indoor temperature sensor, a carbon dioxide sensor, a people sensor, and an IoT thermostat can be configured in the building served by the HVAC system. The indoor temperature sensor and carbon dioxide sensor are used to monitor indoor temperature and carbon dioxide concentration, respectively. Both sensors are installed in the center of the room to ensure the representativeness of the measurement data. The people sensor, a camera-based sensor, is used to detect the number of people in the room in real time. The sensor is installed on the ceiling of the room to ensure coverage of the entire room. The IoT thermostat is used to receive optimized temperature setpoints and control the operation of the FCU. The thermostat is installed on the wall of the room for easy operation.

[0023] In this embodiment of the disclosure, when the initial number of people, initial indoor temperature and initial indoor carbon dioxide concentration of the building served by the HVAC system are determined, reliable initial data support can be provided for subsequent control of the HVAC system.

[0024] Step 102: Based on the initial number of people, determine the predicted number of people at the target time and the room occupancy status.

[0025] The target time point can refer to the time point at which the HVAC system is to be optimized and controlled. In this embodiment of the present disclosure, one or more target time points can be determined within the optimization time range based on a preset time step.

[0026] The predicted number of people refers to the predicted number of people in the building served by the HVAC system at the target time. Room occupancy status indicates whether the building served by the HVAC system is occupied by users at the target time. For example, a room occupancy status of 1 represents a room with people in the building at the target time, while a room occupancy status of 0 represents a room with no people in the building at the target time.

[0027] For example, in this embodiment of the disclosure, when determining the predicted number of people and room occupancy status at a target time point based on the initial number of people, the initial number of people and current time characteristics (such as day of the week, beginning of the month, end of the month, etc.) can be input into a pre-trained machine learning model (which can be trained based on historical number of people change data of the building served by the HVAC system) to determine the predicted number of people and room occupancy status at the target time point. Alternatively, the predicted number of people and room occupancy status at the target time point can be determined based on the initial number of people using any other possible method, without any limitation.

[0028] Optionally, in some embodiments, when determining the predicted number of people and room occupancy status at the target time point based on the initial number of people, the following steps can be taken: The transition probability parameters are dynamically estimated using an adaptive B-spline method, where multiple transition probability parameters jointly form a transition probability matrix; an initial state probability distribution is determined based on the initial number of people; the predicted number of people at the target time point is determined based on the transition probability matrix and the initial state probability distribution; and the room occupancy status at the target time point is determined based on the predicted number of people at the target time point and a preset threshold. Therefore, a non-homogeneous Markov chain (IMC) method can be used for random number modeling, thereby achieving accurate prediction of the number of people and room occupancy status at the target time point.

[0029] Among them, the transition probability parameter ( Let represent the probability of transitioning from state i to state j at time t. Here, the adaptive B-spline method is used to dynamically estimate these time-varying parameters. The transition probability matrix, defined in the non-homogeneous Markov chain (IMC) method, represents the likelihood of state transitions over time, as well as the initial state (or initial distribution) in the state space.

[0030] The preset threshold refers to a pre-configured threshold used to determine the occupancy status of a room. For example, it can be set to 0.5.

[0031] In this embodiment of the disclosure, when the predicted number of people and the room occupancy status at the target time point are determined based on the initial number of people, reliable data support can be provided for subsequently determining the predicted indoor temperature and predicted indoor carbon dioxide concentration at the target time point.

[0032] Step 103: Determine the predicted indoor temperature and predicted indoor carbon dioxide concentration at the target time point based on the initial indoor temperature, the initial indoor carbon dioxide concentration, and the predicted number of people.

[0033] Among them, predicted indoor temperature and predicted indoor carbon dioxide concentration refer to the predicted indoor temperature and indoor carbon dioxide concentration of the building served by the HVAC system at the target time point.

[0034] It is understood that indoor temperature and carbon dioxide concentration may be affected by the number of people. Therefore, the embodiments of this disclosure can combine the initial indoor temperature, the initial indoor carbon dioxide concentration, and the predicted number of people to determine the predicted indoor temperature and predicted indoor carbon dioxide concentration at a target time point. This provides reliable reference information for subsequent HVAC system control.

[0035] Step 104: Input the room occupancy status, predicted indoor temperature, and predicted indoor carbon dioxide concentration into the pre-trained model optimizer to obtain the indoor air temperature setpoint and fresh air flow rate of the HVAC system at the target time point. The optimization objectives of the model optimizer include: thermal comfort penalty, indoor air quality penalty, and energy consumption penalty.

[0036] Among them, the model optimizer can refer to a pre-configured model that controls and optimizes the HVAC system based on multiple optimization objectives such as thermal comfort penalty, indoor air quality penalty, and energy consumption penalty.

[0037] Optionally, in some embodiments, the model optimizer performs multi-objective optimization control based on the following: constructing a first penalty function corresponding to thermal comfort penalty, a second penalty function corresponding to indoor air quality penalty, and a third penalty function corresponding to energy consumption penalty; determining a first importance descriptor value corresponding to thermal comfort penalty, a second importance descriptor value corresponding to indoor air quality penalty, and a third importance descriptor value corresponding to energy consumption penalty; determining a first product value of the first penalty function and the first importance descriptor value, a second product value of the second penalty function and the second importance descriptor value, and a third product value of the third penalty function and the third importance descriptor value; determining a first sum value of the first product value and the second product value, and determining a fourth product value of the first sum value and the room occupancy status; determining a second sum value of the fourth product value and the third product value; minimizing the cumulative sum value of the second sum value corresponding to multiple target time points within the prediction time range to obtain the indoor air temperature setpoint and fresh air flow rate corresponding to each target time point. Thus, weighted processing of multiple optimization objectives can be achieved based on the room occupancy status and the importance descriptor value corresponding to each optimization objective, thereby effectively improving the multi-objective optimization performance of the model optimizer.

[0038] The first importance descriptor value describes the relative importance of thermal comfort penalty among multiple optimization objectives. The second importance descriptor value describes the relative importance of indoor air quality penalty among multiple optimization objectives. The third importance descriptor value describes the relative importance of energy consumption penalty among multiple optimization objectives.

[0039] Optionally, in some embodiments, the first function is used to evaluate the deviation between the indoor air temperature setpoint and the desired reference temperature; the second function is used to indicate when the indoor carbon dioxide concentration exceeds a preset control threshold, wherein the second function takes a value of zero when the indoor carbon dioxide concentration is less than the preset control threshold; and the third function is used to indicate the energy consumption required to cool the room and process and supply fresh air. This effectively improves the indication effect of the first, second, and third functions.

[0040] Among them, the expected reference temperature The default value can be set to 24°C, and then flexibly adjusted according to the application scenario. The preset control threshold can also be flexibly adjusted according to the application scenario; for example, it can be configured to 800ppm.

[0041] Optionally, in some embodiments, comfort feedback data of indoor temperatures corresponding to users within the building served by the HVAC system can also be obtained; the desired reference temperature is updated based on the comfort feedback data; and the desired reference temperature is reset to a preset default value based on a preset time interval. Thus, the desired reference temperature can be updated in real time by combining actual comfort feedback data of users within the building, thereby ensuring that the HVAC system control responds to the personalized thermal comfort of users within the building, and the desired reference temperature is reset to a preset default value based on a preset time interval to effectively prevent temperature preferences from drifting due to long-term accumulation.

[0042] The comfort feedback data can be used to provide users with suggestions regarding indoor temperature comfort, such as increasing the temperature, decreasing the temperature, or maintaining the temperature. The preset time interval can be flexibly configured according to the application scenario; for example, it can refer to 24 hours or 7 days, without limitation.

[0043] In this embodiment, the initial number of occupants, initial indoor temperature, and initial indoor carbon dioxide concentration of the building served by the HVAC system are determined. Based on the initial number of occupants, the predicted number of occupants and room occupancy status at the target time point are determined. Based on the initial indoor temperature, initial indoor carbon dioxide concentration, and predicted number of occupants, the predicted indoor temperature and predicted indoor carbon dioxide concentration at the target time point are determined. The room occupancy status, predicted indoor temperature, and predicted indoor carbon dioxide concentration are input into a pre-trained model optimizer to obtain the indoor air temperature setpoint and fresh air flow rate of the HVAC system at the target time point. The optimization objectives of the model optimizer include: thermal comfort penalty, indoor air quality penalty, and energy consumption penalty. This achieves a highly efficient balance between energy efficiency optimization, residential comfort, and air quality.

[0044] Figure 2 This is a flowchart illustrating the control method for a heating, ventilation, and air conditioning system provided according to the first embodiment of this disclosure.

[0045] like Figure 2 As shown, the method includes: Step 201: Determine the initial number of people, initial indoor temperature, and initial indoor carbon dioxide concentration of the building served by the HVAC system.

[0046] Step 202: Based on the initial number of people, determine the predicted number of people at the target time and the room occupancy status.

[0047] The descriptions of steps 201 to 202 can be found in the above embodiments, and will not be repeated here.

[0048] Step 203: Obtain outdoor weather forecast data for the building served by the HVAC system at the target time point. The outdoor weather forecast data includes solar radiation and outdoor temperature.

[0049] Outdoor meteorological forecast data can be obtained from weather forecasts.

[0050] It is understood that outdoor temperature and solar radiation may affect the environmental parameters inside a building. Therefore, in this embodiment of the disclosure, outdoor meteorological forecast data of the building served by the HVAC system at the target time point can be obtained, thereby providing data support for the outdoor meteorological dimension for the subsequent construction of the building dynamic model.

[0051] Step 204: Based on the predicted number of people, determine the predicted data of the number of people-related disturbances at the target time point. The predicted data of the number of people-related disturbances includes: indoor thermal disturbance data and carbon dioxide generation data.

[0052] Among these, population-related disturbance prediction data can be used to describe the impact of population changes on relevant environmental parameters. Indoor thermal disturbance data refers to changes in indoor heat caused by population changes. Carbon dioxide generation data refers to changes in indoor carbon dioxide concentration caused by population changes.

[0053] In other words, in this embodiment of the present disclosure, after determining the predicted number of people at the target time point, the predicted number of people at the target time point can be used to determine the relevant disturbance prediction data, thereby providing data support for indoor human factors for the subsequent construction of the building dynamic model.

[0054] Step 205: Based on outdoor meteorological forecast data and population-related disturbance forecast data, construct a building dynamic model based on stochastic differential equations. The building dynamic model includes a first model describing indoor temperature changes and a second model describing indoor carbon dioxide concentration changes.

[0055] Among them, the building dynamic model can be used to describe the dynamic evolution of room temperature and carbon dioxide concentration.

[0056] For example, in this embodiment of the disclosure, a building thermal dynamics model can be constructed based on an RC thermal network, and the change in indoor carbon dioxide concentration can be described based on a mass balance equation. The RC thermal network is a network composed of thermal resistance (R) and heat capacity (C). Based on the RC thermal network, the thermal system can be analyzed using circuit analysis methods.

[0057] Optionally, in some embodiments, the first model includes a first control variable describing the room cooling supply, the first control variable being less than or equal to the maximum cooling capacity of the fan coil unit in the HVAC system; the second model includes a second control variable describing the fresh air flow supply, the value of the second control variable falling within a predefined range of air flow values. This allows for constraints on the building's dynamic model, ensuring the accuracy of the descriptions in the resulting first and second models.

[0058] In other words, in this embodiment of the present disclosure, after obtaining outdoor weather forecast data and population-related disturbance forecast data, a building dynamic model based on stochastic differential equations can be constructed based on the outdoor weather forecast data and population-related disturbance forecast data, thereby providing a reliable execution tool for subsequently determining the predicted indoor temperature and predicted indoor carbon dioxide concentration at the target time point.

[0059] Step 206: Based on the first model and the initial indoor temperature, determine the predicted indoor temperature at the target time point.

[0060] Step 207: Based on the second model and the initial indoor carbon dioxide concentration, determine the predicted indoor carbon dioxide concentration at the target time point.

[0061] In other words, in this embodiment of the disclosure, outdoor meteorological forecast data of the building served by the HVAC system at a target time point can be obtained. This outdoor meteorological forecast data includes solar radiation and outdoor temperature. Based on the predicted number of people, predicted disturbance data related to the number of people at the target time point is determined. This disturbance data includes indoor thermal disturbance data and carbon dioxide generation data. Based on the outdoor meteorological forecast data and the predicted disturbance data related to the number of people, a building dynamic model based on stochastic differential equations is constructed. This building dynamic model includes a first model describing changes in indoor temperature and a second model describing changes in indoor carbon dioxide concentration. Based on the first model and the initial indoor temperature, the predicted indoor temperature at the target time point is determined. Based on the second model and the initial indoor carbon dioxide concentration, the predicted indoor carbon dioxide concentration at the target time point is determined. Therefore, a building dynamic model can be constructed based on outdoor meteorological forecast data and predicted disturbance data related to the number of people, thereby achieving accurate prediction of indoor temperature and indoor carbon dioxide concentration at the target time point.

[0062] Step 208: Input the room occupancy status, predicted indoor temperature, and predicted indoor carbon dioxide concentration into the pre-trained model optimizer to obtain the indoor air temperature setpoint and fresh air flow rate of the HVAC system at the target time point. The optimization objectives of the model optimizer include: thermal comfort penalty, indoor air quality penalty, and energy consumption penalty.

[0063] The description of step 208 can be found in the above embodiments, and will not be repeated here.

[0064] In this embodiment, outdoor meteorological forecast data of the building served by the HVAC system at a target time point is acquired. This outdoor meteorological forecast data includes solar radiation and outdoor temperature. Based on the predicted number of people, human-related disturbance forecast data for the target time point is determined. This human-related disturbance forecast data includes indoor thermal disturbance data and carbon dioxide generation data. Based on the outdoor meteorological forecast data and the human-related disturbance forecast data, a building dynamic model based on stochastic differential equations is constructed. This building dynamic model includes a first model describing indoor temperature changes and a second model describing indoor carbon dioxide concentration changes. Based on the first model and the initial indoor temperature, the predicted indoor temperature at the target time point is determined. Based on the second model and the initial indoor carbon dioxide concentration, the predicted indoor carbon dioxide concentration at the target time point is determined. Therefore, a building dynamic model can be constructed based on outdoor meteorological forecast data and human-related disturbance forecast data, thereby achieving accurate prediction of indoor temperature and indoor carbon dioxide concentration at the target time point.

[0065] In summary, the HVAC system control method proposed in this disclosure can employ camera-based personnel sensing technology to capture real-time occupancy data, while simultaneously allowing personnel to provide immediate comfort feedback through a QR code-based voting system. This real-time data is combined with a personnel pattern model trained on historical data. The system can optimize the operation of the HVAC system in real time. The system utilizes cloud and edge computing platforms to collaboratively process data, ensuring real-time responsiveness and control accuracy.

[0066] 1) System Architecture This invention proposes an IoT-supported "human-in-the-loop" multi-objective model optimized control architecture (e.g., Figure 3 As shown, Figure 3 This is a schematic diagram of the implementation architecture of the human-in-the-loop model-based optimized control method based on the Internet of Things (IoT) proposed in this disclosure. The Model Predictive Control (MPC) architecture consists of three layers: a data acquisition and field control layer, a data transmission layer, and an application layer based on cloud-edge collaboration. The core features of the architecture are: (1) Human-centered control loop, including the collection of personnel data, personnel feature modeling and prediction, and model optimization control strategy based on personnel prediction and real-time voting feedback.

[0067] (2) Balancing multiple control objectives, including improving indoor occupant comfort, improving air quality and improving energy efficiency.

[0068] (3) The cloud and edge computing platforms work together to process the data. The cloud devices train models based on historical data, and the edge devices deploy optimization control algorithms based on real-time model predictions and send optimization control signals.

[0069] In the data acquisition and field control layer, this layer mainly realizes two functions: first, it collects real-time data from the physical world through IoT sensors, IoT thermostats, IoT chilled water meters, building automation systems (BAS), and QR codes; second, it transmits the optimized control setpoints to the actuators in the HVAC system through the IoT to realize field control.

[0070] At the data transmission layer, this layer is responsible for real-time data exchange and protocol conversion. It supports multiple communication protocols (such as MQTT, HTTP, and BACnet) through gateways and APIs, ensuring seamless interoperability between different subsystems. The MQTT protocol enables efficient and lightweight communication between IoT gateways and cloud servers and edge computing devices; the HTTP API is used to receive user feedback and weather forecast data; and the BACnet protocol supports bidirectional communication between servers and traditional BAS gateways, facilitating the monitoring and optimized control of HVAC systems.

[0071] The cloud-edge collaborative application layer integrates various software services, including real-time data processing and storage, data visualization, and advanced data analysis and model prediction optimization. Cloud devices train data-driven models based on historical data. Edge devices deploy optimization control algorithms based on real-time model predictions, combining real-time measurement data with data-driven models to dynamically optimize temperature and ventilation setpoints, ensuring the optimal balance between thermal comfort, indoor air quality, and energy efficiency.

[0072] 2) Model Predictive Control (MPC) The "human-in-the-loop" MPC solution proposed in this invention integrates real-time resident numbers, resident feedback, and random resident prediction models (such as...). Figure 4 As shown, Figure 4 This is a schematic diagram of the human-in-the-loop model optimization control scheme based on the integrated random number model, real-time number of people, and real-time personnel feedback proposed in this disclosure.

[0073] 2.1 Random headcount prediction based on non-homogeneous Markov chains (IMC) This invention employs the non-homogeneous Markov chain (IMC) method for modeling random number of people. A key feature of the IMC method is its time-varying transition probability matrix, which defines the probability of state transitions over time, as well as the initial state (or initial distribution) in the state space. State space This represents different ranges of people. At a given time step t, the transition probabilities between states form a transition matrix. As shown in formula (1).

[0074]

[0075] The transition probability parameters are estimated using the adaptive B-spline method. Then, a stochastic crowd control model based on IMC is used for real-time online crowd prediction. Assume the current time is k, and the current crowd measurement corresponds to the state... Then the initial state probability distribution at time k is: ,in and The probability distribution of the number of people at n time steps is then obtained through the forward Kolmogorov equation, as shown in formula (2):

[0076] The expected number of people for the future time step is calculated using formula (3):

[0077] in Indicates time t, t=k+1, k+2… k+n The expected number of people. In addition, the predicted room occupancy status is determined by... This means that if the condition If true, its value is 1; otherwise, it is 0.

[0078]

[0079] In this invention, a threshold α = 0.5 is used to determine the expected room occupancy status.

[0080] 2.2 Building Dynamics Modeling Based on Stochastic Differential Equations (SDE) In building dynamics modeling, this invention employs stochastic differential equations (SDEs) to describe the dynamic evolution of room temperature and carbon dioxide concentration. This invention constructs a building thermal dynamics model based on an RC thermal network and describes indoor carbon dioxide concentration changes based on a mass balance equation. For example... Figure 5 As shown, Figure 5 The thermal resistance and thermal capacity diagrams of the building thermal dynamics structure are presented according to the present disclosure. The SDE model describing the building thermal dynamics is given in equations (5) and (6), while the SDE model for room carbon dioxide concentration is given in equation (7).

[0081] (5) (6) (7) The continuous-time SDE model is discretized to ensure more efficient computation. The stochastic discrete-time model for room temperature and carbon dioxide concentration is as follows:

[0082] in, The state vector representing the room temperature and the external wall temperature at time step t is denoted as . ; It is the interference vector, containing the number of residents, solar radiation, and outdoor temperature, represented as... Online optimization in progress; future user numbers... Predictions were made using the IMC model (Equations 2 and 3), while solar radiation... and outdoor temperature This information can be obtained from weather forecasts. These forecasts form the predictive disturbance vector. . It is a control variable representing the room's cooling supply. This control variable is constrained by the maximum cooling capacity of the FCU.

[0083]

[0084] In practice, indoor air temperature Adjust the temperature setpoint on the FCU temperature controller. To control.

[0085] Modeling carbon dioxide concentration (Equation 9). The system state representing the indoor carbon dioxide concentration at time step t; It is a control variable, representing the fresh air flow rate supply. Fresh air flow is constrained to ensure it remains within predefined minimum and maximum limits.

[0086]

[0087] Major disturbances affecting carbon dioxide concentration Number of people In online optimization, the predicted future number of people... As a confounding variable. , , , , and It is the corresponding matrix of the discrete-time model. and The process noise that indicates indoor air temperature and CO2 concentration.

[0088] 2.3 MPC Structure and Multi-Objective Optimization The optimized controller developed in this invention aims to optimize the prediction range. N Intra-time step t Indoor air temperature setting value ( ) and fresh air flow ( This is achieved by solving a multi-objective rolling optimization problem at each time step. The weighted sum of the following penalty terms is minimized: Thermal comfort penalty (i.e., the first penalty function mentioned above); Indoor air quality penalty (i.e., the second penalty function mentioned above); and Energy consumption penalty (i.e., the third penalty function mentioned above). Parameters (i.e., the first importance descriptive value mentioned above) (i.e., the second importance descriptive value mentioned above) and (i.e., the third importance descriptor value mentioned above) defines the relative importance of each objective, which can be adjusted by operations personnel to reflect a specific priority. The overall MPC structure is shown in formulas (12) and (13). The constraints of the optimization control problem include: occupancy prediction constraints, building dynamic constraints, HVAC system constraints, and initial condition constraints.

[0089]

[0090] in, That is, the first product value mentioned above. That is, the second product value mentioned above. That is, the third product value mentioned above. This refers to the room occupancy status described above. That is, the first sum mentioned above, corresponding to Then it is the fourth product value mentioned above.

[0091] subject to

[0092] The three optimization objectives are as follows.

[0093] (1) Thermal comfort penalty ) Thermal comfort is quantified by a quadratic penalty function, as shown in Equation (14).

[0094]

[0095] This function evaluates the indoor air temperature setpoint and the desired reference temperature. The deviation between them. In this invention, The default value is set to 24°C. However, feedback from people's voting will have an impact. The value of is adjusted to better meet people's preferences.

[0096] (2) Indoor air quality penalty ) Indoor air quality is represented by CO2 concentration, which serves as an indicator of indoor pollutant levels. The cost function for controlling IAQ is shown in formula (15).

[0097]

[0098] in The CO2 control threshold is set to 800 ppm. To ensure computational efficiency, the max operation in the penalty function is implemented using an auxiliary variable. And linearization is performed using additional constraints.

[0099]

[0100]

[0101]

[0102] (3) Energy consumption penalty ) Energy consumption includes the energy required to cool the room and to process and supply fresh air. Energy consumption is defined as shown in Equation (19).

[0103]

[0104] Cooling requirements for fresh air The calculation is based on the enthalpy difference between the supplied fresh air and the outdoor air, and the fresh air flow rate. The product of. Here, and These represent the specific enthalpy of the supplied fresh air and the specific enthalpy of the outdoor air, respectively. Enthalpy of outdoor air. Calculated dynamically using predicted outdoor temperature and RH data. The coefficient of performance (COP) represents the overall efficiency of the chiller unit and auxiliary cooling distribution system.

[0105] As described in equations (12) and (13), the MPC formula is transformed into a quadratic programming (QP) problem and solved using an optimization solver. Model predictive control (MPC) employs an iterative rolling optimization method, optimizing within the moving prediction range. The optimization at each time step generates a series of control signals, with only the first control signal being implemented in the actual system. At the next time step, the optimization is recalculated using updated real-time data.

[0106] 3) Real-time personnel comfort feedback Traditional MPC systems typically assume a fixed comfort temperature range, while this invention introduces a real-time personnel feedback mechanism, incorporating individualized personnel needs into the control loop. The implementation of this system is as follows: Figure 6 As shown, Figure 6 This is a schematic diagram of the operation of a real-time human comfort feedback system based on QR codes, as proposed in this disclosure. Individuals can participate in voting by scanning a QR code, reporting their personalized comfort needs. The system dynamically adjusts the reference temperature based on the voting results. This is incorporated into the MPC optimization process to ensure that the control strategy can respond to personnel preferences in real time. In addition, to prevent temperature preferences from drifting due to long-term accumulation, the system resets the reference temperature to the default value (24°C) at midnight every day.

[0107] 4) Actual deployment and IoT transformation of the laboratory This invention has been successfully deployed in an office living laboratory at a university. The laboratory includes a large office, a small office, and a conference room. The HVAC system consists of fan coil units (FCUs) and main air handling units (PAUs). During the renovation, LoRa-based IoT sensors, thermostats, and a QR code feedback system were installed. The conference room was selected as the target space for testing "human-in-the-loop" MPC due to its highly irregular usage patterns, exhibiting significant randomness and intermittency. The experimental testing period covered three control strategies: traditional fixed setpoint control (FIX), simple rule-based people feedback control (OBC), and the MPC control proposed in this invention. Test results show that this invention demonstrates significant advantages under different living conditions.

[0108] Based on the above embodiments, this disclosure can include at least the following technical effects: (1) By optimizing precooling and dynamically adjusting fresh air flow, the system significantly reduces unnecessary energy consumption; (2) Significantly improved thermal comfort. The system also dynamically adjusts the temperature setpoint based on real-time user feedback to ensure that individual comfort needs are met; (3) It avoids both over-ventilation and under-ventilation. The system dynamically adjusts the fresh air flow to ensure that the indoor air quality is always within a comfortable range; (4) The system responds quickly to personalized comfort feedback through a real-time voting mechanism to ensure a comfortable experience for users. Users can provide feedback by scanning a QR code, and the system dynamically adjusts its control strategy based on the feedback to ensure the system's flexibility and responsiveness.

[0109] To facilitate better implementation of the HVAC system control method of this disclosure, this disclosure also provides an HVAC system control device based on the above-described HVAC system control method. The meanings of the terms used are the same as in the above-described HVAC system control method, and specific implementation details can be found in the descriptions of the method embodiments.

[0110] Please see Figure 7 , Figure 7This is a schematic diagram of the structure of a heating, ventilation, and air conditioning system control device 700 provided in an embodiment of this disclosure. The heating, ventilation, and air conditioning system control device 700 includes: The first determining module 701 is used to determine the initial number of people, initial indoor temperature and initial indoor carbon dioxide concentration of the building served by the HVAC system; The second determining module 702 is used to determine the predicted number of people at the target time point and the room occupancy status based on the initial number of people. The third determining module 703 is used to determine the predicted indoor temperature and predicted indoor carbon dioxide concentration at the target time point based on the initial indoor temperature, the initial indoor carbon dioxide concentration, and the predicted number of people. The processing module 704 is used to input the room occupancy status, predicted indoor temperature, and predicted indoor carbon dioxide concentration into a pre-trained model optimizer to obtain the indoor air temperature setpoint and fresh air flow rate of the HVAC system at the target time point. The optimization objectives of the model optimizer include thermal comfort penalty, indoor air quality penalty, and energy consumption penalty.

[0111] Optionally, the second determining module 702 is specifically used for: An adaptive B-spline method is used to dynamically estimate the transition probability parameters, where multiple transition probability parameters are jointly composed of a transition probability matrix. Determine the initial probability distribution based on the initial number of people; Based on the transition probability matrix and the initial state probability distribution, the predicted number of people at the target time point is determined; Based on the predicted number of people at the target time and the preset threshold, the room occupancy status at the target time is determined.

[0112] Optionally, the third determining module 703 is specifically used for: Obtain outdoor weather forecast data for the building served by the HVAC system at the target time point. The outdoor weather forecast data includes solar radiation and outdoor temperature. Based on the predicted number of people, the predicted data of the number of people-related disturbances at the target time point are determined. The predicted data of the number of people-related disturbances include: indoor thermal disturbance data and carbon dioxide generation data. Based on outdoor meteorological forecast data and population-related disturbance forecast data, a building dynamic model based on stochastic differential equations is constructed. The building dynamic model includes a first model describing indoor temperature changes and a second model describing indoor carbon dioxide concentration changes. Based on the first model and the initial indoor temperature, determine the predicted indoor temperature at the target time point; Based on the second model and the initial indoor carbon dioxide concentration, the predicted indoor carbon dioxide concentration at the target time point is determined.

[0113] Optional, of which, The first model includes a first control variable to describe the room cooling supply, which is less than or equal to the maximum cooling capacity of the fan coil unit in the HVAC system. The second model includes a second control variable to describe the fresh air flow supply, and the value of the second control variable is within a predefined range of air flow values.

[0114] Optionally, the model optimizer performs multi-objective optimization control based on the following method: Construct a first penalty function corresponding to thermal comfort penalty, a second penalty function corresponding to indoor air quality penalty, and a third penalty function corresponding to energy consumption penalty; Determine the first importance descriptive value corresponding to thermal comfort penalty, the second importance descriptive value corresponding to indoor air quality penalty, and the third importance descriptive value corresponding to energy consumption penalty; Determine the first product value of the first penalty function and the first importance descriptor value, the second product value of the second penalty function and the second importance descriptor value, and the third product value of the third penalty function and the third importance descriptor value; Determine the first sum of the first product value and the second product value, and determine the fourth product value of the first sum value and the room occupancy status; Determine the second sum of the fourth product value and the third product value; The sum of the second values ​​corresponding to multiple target time points within the prediction time range is minimized to obtain the indoor air temperature setpoint and fresh air flow rate corresponding to each target time point.

[0115] Optional, of which, The first function is used to evaluate the deviation between the indoor air temperature setpoint and the desired reference temperature; The second function is used to indicate when the indoor carbon dioxide concentration exceeds the preset control threshold. When the indoor carbon dioxide concentration is less than the preset control threshold, the value of the second function is zero. The third function is used to indicate the energy consumption required to cool the room and to process and supply fresh air.

[0116] Optionally, the device also includes: The acquisition module is used to acquire comfort feedback data on indoor temperature for users in buildings served by the HVAC system. The update module is used to update the expected reference temperature based on comfort feedback data; The reset module is used to reset the reference temperature to a preset default value based on a preset time interval.

[0117] In this embodiment, the initial number of occupants, initial indoor temperature, and initial indoor carbon dioxide concentration of the building served by the HVAC system are determined. Based on the initial number of occupants, the predicted number of occupants and room occupancy status at the target time point are determined. Based on the initial indoor temperature, initial indoor carbon dioxide concentration, and predicted number of occupants, the predicted indoor temperature and predicted indoor carbon dioxide concentration at the target time point are determined. The room occupancy status, predicted indoor temperature, and predicted indoor carbon dioxide concentration are input into a pre-trained model optimizer to obtain the indoor air temperature setpoint and fresh air flow rate of the HVAC system at the target time point. The optimization objectives of the model optimizer include: thermal comfort penalty, indoor air quality penalty, and energy consumption penalty. This achieves a highly efficient balance between energy efficiency optimization, residential comfort, and air quality.

[0118] In addition, this disclosure also provides an electronic device, such as Figure 8 As shown, it illustrates a schematic diagram of the structure of the electronic device involved in this disclosure, specifically: The electronic device may include components such as a processor 801 with one or more processing cores, a memory 802 with one or more computer-readable storage media, a power supply 803, and an input unit 804. Those skilled in the art will understand that... Figure 8 The electronic device structure shown does not constitute a limitation on the electronic device and may include more or fewer components than shown, or combine certain components, or have different component arrangements. Wherein: The processor 801 is the control center of the electronic device. It connects various parts of the electronic device via various interfaces and lines. By running or executing software programs and / or modules stored in the memory 802, and by calling data stored in the memory 802, it performs various functions and processes data, thereby providing overall monitoring of the electronic device. Optionally, the processor 801 may include one or more processing cores; preferably, the processor 801 may integrate an application processor and a modem processor, wherein the application processor mainly handles the operating system, user interface, and applications, and the modem processor mainly handles wireless communication. It is understood that the modem processor may not be integrated into the processor 801.

[0119] The memory 802 can be used to store software programs and modules. The processor 801 executes various functional applications and data processing by running the software programs and modules stored in the memory 802. The memory 802 may mainly include a program storage area and a data storage area. The program storage area may store the operating system, application programs required for at least one function (such as sound playback function, image playback function, etc.), etc.; the data storage area may store data created according to the use of the electronic device, etc. In addition, the memory 802 may include high-speed random access memory, and may also include non-volatile memory, such as at least one disk storage device, flash memory device, or other volatile solid-state storage device. Accordingly, the memory 802 may also include a memory controller to provide the processor 801 with access to the memory 802.

[0120] The electronic device also includes a power supply 803 that supplies power to the various components. Preferably, the power supply 803 can be logically connected to the processor 801 through a power management system, thereby enabling functions such as charging, discharging, and power consumption management through the power management system. The power supply 803 may also include one or more DC or AC power supplies, recharging systems, power equipment debugging circuits, power converters or inverters, power status indicators, and other arbitrary components.

[0121] The electronic device may also include an input unit 804, which can be used to receive input digital or character information and generate keyboard, mouse, joystick, optical or trackball signal inputs related to user settings and function control.

[0122] Although not shown, the electronic device may also include a display unit, etc., which will not be described in detail here. Specifically, in this embodiment, the processor 801 in the electronic device loads the executable files corresponding to the processes of one or more application programs into the memory 802 according to the following instructions, and the processor 801 runs the application programs stored in the memory 802, thereby realizing the steps in any of the HVAC system control methods provided in the embodiments of this disclosure.

[0123] For details on the implementation of each of the above operations, please refer to the previous examples, which will not be repeated here.

[0124] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be performed by instructions, or by instructions controlling related hardware. These instructions can be stored in a computer-readable storage medium and loaded and executed by a processor.

[0125] To this end, the present disclosure provides a computer-readable storage medium storing a computer program that can be loaded by a processor to execute the steps of any of the HVAC system control methods provided in the present disclosure.

[0126] For details on the implementation of each of the above operations, please refer to the previous examples, which will not be repeated here.

[0127] The computer-readable storage medium may include: read-only memory (ROM), random access memory (RAM), disk or optical disk, etc.

[0128] Since the instructions stored in the computer-readable storage medium can execute the steps of any of the HVAC system control methods provided in this disclosure, the beneficial effects that any of the HVAC system control methods provided in this disclosure can achieve can be realized, as detailed in the preceding embodiments, and will not be repeated here.

[0129] The above provides a detailed description of a heating, ventilation, and air conditioning system control method, apparatus, electronic device, and computer-readable storage medium provided in this disclosure. Specific examples have been used to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of the present invention. At the same time, those skilled in the art will recognize that, based on the ideas of the present invention, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A control method for a heating, ventilation, and air conditioning system, characterized in that, include: Determine the initial number of people, initial indoor temperature, and initial indoor carbon dioxide concentration in the building served by the HVAC system; Based on the initial number of people, determine the predicted number of people and room occupancy status at the target time point; Based on the initial indoor temperature, the initial indoor carbon dioxide concentration, and the predicted number of people, determine the predicted indoor temperature and predicted indoor carbon dioxide concentration at the target time point; The room occupancy status, the predicted indoor temperature, and the predicted indoor carbon dioxide concentration are input into a pre-trained model optimizer to obtain the indoor air temperature setpoint and fresh air flow rate of the HVAC system at the target time point. The optimization objectives of the model optimizer include thermal comfort penalty, indoor air quality penalty, and energy consumption penalty.

2. The method according to claim 1, characterized in that, The step of determining the predicted number of people and room occupancy status at the target time point based on the initial number of people includes: The transition probability parameters are dynamically estimated using an adaptive B-spline method, wherein multiple transition probability parameters are jointly composed of a transition probability matrix. Based on the initial number of people, determine the initial state probability distribution; Based on the transition probability matrix and the initial state probability distribution, the predicted number of people at the target time point is determined; Based on the predicted number of people at the target time point and a preset threshold, the room occupancy status at the target time point is determined.

3. The method according to claim 1, characterized in that, The step of determining the predicted indoor temperature and predicted indoor carbon dioxide concentration at the target time point based on the initial indoor temperature, the initial indoor carbon dioxide concentration, and the predicted number of people includes: Obtain outdoor weather forecast data for the building served by the HVAC system at the target time point, wherein the outdoor weather forecast data includes: solar radiation and outdoor temperature; Based on the predicted number of people, predictive data on the number of people-related disturbances at the target time point are determined, wherein the predicted data on the number of people-related disturbances includes: indoor thermal disturbance data and carbon dioxide generation data; Based on the outdoor meteorological forecast data and the population-related disturbance forecast data, a building dynamic model based on stochastic differential equations is constructed. The building dynamic model includes: a first model describing indoor temperature changes and a second model describing indoor carbon dioxide concentration changes. Based on the first model and the initial indoor temperature, the predicted indoor temperature at the target time point is determined; Based on the second model and the initial indoor carbon dioxide concentration, the predicted indoor carbon dioxide concentration at the target time point is determined.

4. The method according to claim 3, characterized in that, in, The first model includes a first control variable for describing the room cooling supply, wherein the first control variable is less than or equal to the maximum cooling capacity of the fan coil unit in the HVAC system. The second model includes a second control variable for describing the supply of fresh air flow, and the value of the second control variable is within a predefined range of air flow values.

5. The method according to claim 1, characterized in that, The model optimizer performs multi-objective optimization control based on the following method: Construct a first penalty function corresponding to the thermal comfort penalty, a second penalty function corresponding to the indoor air quality penalty, and a third penalty function corresponding to the energy consumption penalty; Determine the first importance descriptive value corresponding to the thermal comfort penalty, the second importance descriptive value corresponding to the indoor air quality penalty, and the third importance descriptive value corresponding to the energy consumption penalty; Determine the first product value of the first penalty function and the first importance description value, the second product value of the second penalty function and the second importance description value, and the third product value of the third penalty function and the third importance description value; Determine a first sum of the first product value and the second product value, and determine a fourth product value of the first sum value and the room occupancy status; Determine the second sum of the fourth product value and the third product value; The sum of the second sum corresponding to multiple target time points within the prediction time range is minimized to obtain the indoor air temperature setpoint and the fresh air flow rate corresponding to each target time point.

6. The method according to claim 5, characterized in that, in, The first function is used to evaluate the deviation between the indoor air temperature setpoint and the desired reference temperature; The second function is used to indicate when the indoor carbon dioxide concentration exceeds a preset control threshold, wherein when the indoor carbon dioxide concentration is less than the preset control threshold, the second function takes the value of zero; The third function is used to indicate the energy consumption required to cool the room and to process and supply fresh air.

7. The method according to claim 6, characterized in that, The method further includes: Obtain comfort feedback data on indoor temperature for users within the building served by the HVAC system; The desired reference temperature is updated based on the comfort feedback data. The reference temperature is reset to a preset default value based on a preset time interval.

8. A control device for a heating, ventilation, and air conditioning system, characterized in that, include: The first determination module is used to determine the initial number of people, initial indoor temperature, and initial indoor carbon dioxide concentration of the building served by the HVAC system. The second determining module is used to determine the predicted number of people at the target time point and the room occupancy status based on the initial number of people. The third determining module is used to determine the predicted indoor temperature and predicted indoor carbon dioxide concentration at the target time point based on the initial indoor temperature, the initial indoor carbon dioxide concentration, and the predicted number of people. The processing module is used to input the room occupancy status, the predicted indoor temperature, and the predicted indoor carbon dioxide concentration into a pre-trained model optimizer to obtain the indoor air temperature setpoint and fresh air flow rate of the HVAC system at the target time point. The optimization objectives of the model optimizer include: thermal comfort penalty, indoor air quality penalty, and energy consumption penalty.

9. An electronic device, characterized in that, It includes a memory and a processor; the memory stores a computer program, and the processor is configured to run the computer program in the memory to perform the method according to any one of claims 1 to 7.

10. A storage medium, characterized in that, The storage medium is used to store a computer program, which is loaded by a processor to perform the method according to any one of claims 1 to 7.

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