Method, system and device for heating ventilation air conditioning control based on dynamic thermal load prediction

Abstract

The application provides a heating ventilation air conditioning control method and system based on dynamic heat load prediction, a device and a storage medium. The method comprises the following steps: acquiring individual physiological data, environmental parameters and regional personnel distribution data in real time; determining the real-time individual metabolic rate equivalent based on the individual physiological data and the environmental parameters; aggregating the individual metabolic rate equivalent according to the regional personnel distribution data, determining the total heat load and the total heat load change rate of each region in the room, and decomposing the total heat load into sensible heat load and latent heat load; and generating the supply air temperature, supply air volume and humidity control instructions of the heating ventilation air conditioning system for feedforward regulation based on the sensible heat load, the latent heat load and the total heat load change rate. The technical scheme of the application can realize dynamic heat load prediction based on individual physiological data and environmental parameters, quantize the total heat load in the room in real time, and generate feedforward regulation control instructions of the heating ventilation air conditioning system.

Description

Technical Field

[0001] This invention relates to the field of heating, ventilation and air conditioning (HVAC) control, and more particularly to an HVAC control method, system, device, and storage medium based on dynamic heat load prediction. Background Technology

[0002] With increasingly frequent dynamic changes in indoor environments due to human activity, existing HVAC control strategies face certain challenges. While traditional constant-temperature control strategies are well-developed, they lack the ability to sense the actual presence and activity status of people indoors. In places with large fluctuations in occupancy, such as gyms during peak hours, constant-temperature control can easily lead to overheating or undercooling, thereby reducing comfort. At the same time, it may cause energy waste in sparsely populated areas.

[0003] To address this issue, feedback control strategies based on environmental sensors are widely used, with adjustments made based on indoor temperature sensors. However, the inherent response lag of this technology limits its ability to provide immediate adjustments for human thermal comfort. When human activity generates additional heat, it takes time for the air temperature to change sufficiently for the sensor to capture the change; during this period, discomfort may already be felt. In recent years, indoor fresh air control based on carbon dioxide concentration has gained increasing attention. This strategy infers the number of people indoors and adjusts the fresh air volume by monitoring carbon dioxide concentration, but it still has shortcomings in thermal comfort regulation. Changes in carbon dioxide concentration are indirect and lag-dependent, making it difficult to reflect instantaneous changes in the intensity of human activity. Furthermore, carbon dioxide cannot quantify human workload and is difficult to distinguish between sensible heat generated by exercise and latent heat generated by sweat evaporation, significantly limiting its ability to assess heat generation during high-intensity activities. Summary of the Invention

[0004] The purpose of this invention is to provide a heating, ventilation and air conditioning (HVAC) control method, system, equipment, and storage medium based on dynamic heat load prediction, so as to solve the problems of slow response, insufficient thermal comfort, and energy waste in traditional HVAC control under dynamic changes in human activity.

[0005] In a first aspect, the present invention provides a heating, ventilation, and air conditioning (HVAC) control method based on dynamic heat load prediction, comprising the following steps:

[0006] Real-time acquisition of individual physiological data, environmental parameters, and regional population distribution data within the indoor area;

[0007] Based on the individual physiological data and environmental parameters, the real-time individual metabolic rate equivalent is determined;

[0008] Based on the population distribution data of the area, the metabolic rate equivalent of the individuals is aggregated to determine the total heat load and the rate of change of the total heat load in each area of ​​the room, and the total heat load is decomposed into sensible heat load and latent heat load.

[0009] Based on the sensible heat load, latent heat load, and the rate of change of the total heat load, control commands for the supply air temperature, supply air volume, and humidity of the HVAC system are generated for feedforward regulation.

[0010] Optionally, the specific steps for determining the real-time individual metabolic rate equivalent based on the individual physiological data and environmental parameters include:

[0011] Based on the aforementioned environmental parameters, the impact of the indoor environment on individual metabolic rate is assessed, and an environmental correction factor is obtained.

[0012] Identify individual exercise types and determine the corresponding baseline metabolic equivalent and real-time baseline heart rate value based on the exercise type;

[0013] The individual exercise intensity is determined based on the real-time baseline heart rate value and the corresponding baseline heart rate zone;

[0014] The real-time individual metabolic rate equivalent is calculated based on the environmental correction factor, the baseline metabolic equivalent, and the individual exercise intensity.

[0015] Optionally, the formula for calculating the individual metabolic rate equivalent is:

[0016]

[0017] in, Metabolic rate of a single individual at an instant; Environmental correction factor; Individual exercise intensity; Indicates that the type of exercise is At that time, the individual's baseline heart rate was calculated; Indicates that the type of exercise is At that time, the actual baseline value corresponding to the actual activity metabolic rate baseline table.

[0018] Optionally, the specific steps for identifying individual movement types include:

[0019] Acquire triaxial acceleration data from an individual's wearable device and extract features from the triaxial acceleration data;

[0020] The extracted features are input into a pre-trained classification model to obtain motion type information based on the triaxial acceleration data;

[0021] Individuals can select the current activity type from a list of preset activities through the software interface and obtain exercise type information based on the user's selection;

[0022] By combining the motion type information based on the triaxial acceleration data and the motion type information based on the user's selection, the final individual motion type is determined.

[0023] Optionally, the specific steps of aggregating the individual metabolic rate equivalents based on the regional population distribution data to determine the total heat load and the rate of change of the total heat load in each indoor area, and decomposing the total heat load into sensible heat load and latent heat load, include:

[0024] Based on the regional population distribution data, the individual metabolic rate equivalents of each person located in the same area are aggregated to obtain the total heat load of each indoor area;

[0025] The rate of change of the total heat load in each area of ​​the room over time is calculated to obtain the rate of change of the total heat load, so as to quantify the direction and speed of change of the total heat load;

[0026] Based on the total heat load and environmental parameters, the required evaporative heat dissipation for the human body and the maximum evaporative heat dissipation capacity of the area are calculated. The required evaporative heat dissipation for the human body is compared with the maximum evaporative heat dissipation capacity of the area to obtain the final latent heat load.

[0027] Subtracting the latent heat load from the total heat load yields the sensible heat load for each region.

[0028] Optionally, the specific steps of generating control commands for the supply air temperature, supply air volume, and humidity of the HVAC system for feedforward regulation based on the sensible heat load, latent heat load, and the rate of change of the total heat load include:

[0029] Based on the sensible heat load and the current supply air temperature of the HVAC system, the basic supply air temperature offset is determined.

[0030] Based on the latent heat load and the current air supply volume of the HVAC system, the offset of the basic dew point moisture content is determined.

[0031] Based on the total heat load change rate, the basic supply air temperature offset and the basic dew point moisture content offset are corrected by a dynamic gain function to obtain the final supply air temperature offset and dew point moisture content offset.

[0032] By combining the supply air temperature offset, the dew point moisture content offset, and the real-time status of the HVAC system, control commands for the supply air temperature, supply air volume, and humidity of the HVAC system for feedforward regulation are generated.

[0033] Optionally, the HVAC control method further includes:

[0034] It detects indoor environmental deviations caused by unpredictable disturbances or model errors and generates feedback correction instructions based on the detection results.

[0035] By combining the feedback correction command and the supply air temperature, supply air volume and humidity control commands of the HVAC system used for feedforward regulation, the final HVAC system regulation command is generated.

[0036] Secondly, the present invention provides a heating, ventilation, and air conditioning control system based on dynamic heat load prediction, comprising:

[0037] The data acquisition module is used to acquire individual physiological data, environmental parameters, and regional personnel distribution data in real time indoors;

[0038] The human metabolic rate calculation module is used to determine the real-time individual metabolic rate equivalent based on the individual's physiological data and environmental parameters.

[0039] The load prediction module is used to aggregate the individual metabolic rate equivalents based on the regional population distribution data, determine the total heat load and the rate of change of total heat load in each indoor area, and decompose the total heat load into sensible heat load and latent heat load.

[0040] The control module is used to generate control commands for the supply air temperature, supply air volume, and humidity of the HVAC system for feedforward regulation based on the sensible heat load, latent heat load, and the rate of change of the total heat load.

[0041] Thirdly, the present invention provides a device comprising: a processor, a memory, a communication interface, and a communication bus, wherein the processor, the memory, and the communication interface communicate with each other via the communication bus;

[0042] The memory is used to store at least one executable instruction, which causes the processor to perform operations corresponding to the HVAC control method described above.

[0043] Fourthly, the present invention provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the above-described HVAC control method.

[0044] According to the present invention, by acquiring real-time indoor individual physiological data, environmental parameters, and regional personnel distribution data, the total heat load and its rate of change in each area are determined, and decomposed into sensible heat load and latent heat load. This allows for precise characterization of indoor personnel activities and dynamic changes in heat and humidity load. Compared to traditional control methods that rely on temperature or carbon dioxide concentration, the technical solution of the present invention has higher real-time performance and accuracy, directly reflecting the combined effect of personnel quantity and activity intensity. This provides a high-precision data foundation for subsequent HVAC control, thereby significantly improving the foresight and proactiveness of the control strategy.

[0045] Furthermore, by decoupling the heat load to obtain the latent heat load and sensible heat load, the basic control setpoints for supply air temperature and humidity are determined respectively. A dynamic gain function is then introduced using the rate of change of the total heat load for correction, thereby forming forward-looking supply air temperature and dew point humidity offsets. In addition, this invention combines prediction-based feedforward control commands with feedback correction commands generated from real-time detected environmental deviations to form the final HVAC adjustment commands, achieving coordinated operation of rapid response from feedforward regulation and fine compensation from feedback correction. The method of this invention can not only proactively address sudden changes in the number of indoor occupants and activity intensity, suppressing lag in environmental parameter adjustments, but also avoid energy waste caused by excessive or insufficient adjustments, significantly improving indoor thermal comfort while maintaining the high efficiency and stability of the HVAC system.

[0046] The above description is merely an overview of the technical solution of the present invention. In order to better understand the technical means of the present invention and to implement it in accordance with the contents of the specification, the preferred embodiments of the present invention are described in detail below. Attached Figure Description

[0047] Figure 1 A schematic flowchart of a heating, ventilation, and air conditioning control method based on dynamic heat load prediction according to an embodiment of the present invention is shown.

[0048] Figure 2 It shows Figure 1 A schematic flowchart of step S200 for determining real-time individual metabolic rate equivalents based on individual physiological data and environmental parameters;

[0049] Figure 3 It shows Figure 2 A schematic flowchart of step S220 for identifying individual movement types;

[0050] Figure 4 It shows Figure 1The schematic flowchart of step S300, which aggregates individual metabolic rate equivalents based on regional personnel distribution data, determines the total heat load and the rate of change of total heat load in each indoor area, and decomposes the total heat load into sensible heat load and latent heat load;

[0051] Figure 5 It shows Figure 1 A schematic flowchart of step S400 for generating supply air temperature, supply air volume and humidity control commands for feedforward regulation of the HVAC system based on the rate of change of sensible heat load, latent heat load and total heat load.

[0052] Figure 6 A structural block diagram of a heating, ventilation, and air conditioning control system based on dynamic heat load prediction according to an embodiment of the present invention is shown.

[0053] Figure 7 A structural block diagram of a heating, ventilation, and air conditioning (HVAC) control device based on dynamic heat load prediction according to an embodiment of the present invention is shown. Detailed Implementation

[0054] To make the above-mentioned objectives, features, and advantages of this application more apparent and understandable, the specific embodiments of this application will be described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are for illustrative purposes only and are not intended to limit the scope of this application. Furthermore, it should be noted that, for ease of description, only the parts relevant to this application are shown in the accompanying drawings, not the entire structure. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without inventive effort are within the scope of protection of this application.

[0055] The terms “comprising” and “having”, and any variations thereof, used in this application are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the steps or units listed, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to such process, method, product, or apparatus.

[0056] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0057] Figure 1 A schematic flowchart of a heating, ventilation, and air conditioning (HVAC) control method based on dynamic heat load prediction according to an embodiment of the present invention is shown. Figure 1 As shown, the HVAC control method includes:

[0058] Step S100: Real-time acquisition of individual physiological data, environmental parameters, and regional personnel distribution data within the indoor area.

[0059] In this step, real-time acquisition of multi-dimensional data comprehensively perceives the dynamics of indoor occupants and the environmental conditions, laying the foundation for subsequent heat load calculations. In some embodiments, individual physiological data can be acquired through wearable devices such as smartwatches, wristbands, or rings, reflecting individual thermophysiological characteristics such as heart rate, skin temperature, blood oxygen saturation, and exercise status. Environmental parameter data is continuously monitored by indoor temperature, humidity, carbon dioxide, and other air quality sensors, used to describe the overall environmental conditions of the building space. Regional occupant distribution data can be obtained in real time using a Bluetooth beacon-based non-intrusive positioning method, accurately reflecting the number and distribution of people in different functional areas (such as the strength training area, aerobics area, and yoga area in a gym). By simultaneously acquiring the above three types of data, reliable and detailed input information can be provided for the calculation of metabolic rate equivalents and the prediction of dynamic heat load in subsequent steps, thereby avoiding the lag and distortion problems caused by relying on a single measurement indicator.

[0060] Step S200: Determine the real-time individual metabolic rate equivalent based on individual physiological data and environmental parameters.

[0061] In this step, individual real-time metabolic rate equivalents are dynamically calculated by combining individual physiological signals with environmental factors. Compared with methods that rely on empirical estimation or fixed metabolic assumptions, the processing in step S200 can accurately characterize the instantaneous heat production levels of different individuals under different environments, and can automatically adjust with changes in exercise intensity. This not only improves the accuracy of heat load prediction, but also provides high-resolution individual input for subsequent zonal aggregation, enabling control strategies to be adjusted more finely according to actual activity conditions.

[0062] Step S300: Aggregate individual metabolic rate equivalents based on regional personnel distribution data to determine the total heat load and the rate of change of total heat load in each indoor area, and decompose the total heat load into sensible heat load and latent heat load.

[0063] In this step, the introduction of the rate of change reflects the trend and dynamic characteristics of load increases and decreases, helping to predict fluctuations in cooling / heating demand in advance. The separation of sensible heat and latent heat creates conditions for subsequent independent control of supply air temperature and humidity, enabling the system to more effectively address the increase in sensible heat caused by high-intensity activities or the increase in latent heat load caused by excessive sweating, thus balancing comfort and control efficiency.

[0064] Step S400: Based on the sensible heat load, latent heat load, and total heat load change rate, generate control commands for the supply air temperature, supply air volume, and humidity of the HVAC system for feedforward regulation.

[0065] In this step, the sensible heat load, latent heat load, and their rate of change are integrated to generate the supply air temperature, supply air volume, and humidity control commands required for feedforward regulation, achieving proactive response. By directly converting load forecast results into executable control commands, the system can intervene before environmental deviations are caused by changes in human activity, avoiding room temperature or humidity overshoot and significantly reducing response lag. Simultaneously, incorporating the dynamic load change rate into the calculation makes the control output more stable and sensitive, ensuring comfort while reducing energy waste and improving the operating efficiency and stability of the HVAC system.

[0066] According to the above embodiments, by acquiring real-time indoor individual physiological data, environmental parameters, and regional personnel distribution data, the total heat load and its rate of change in each area are determined, and decomposed into sensible heat load and latent heat load. This allows for precise characterization of indoor personnel activities and dynamic changes in heat and humidity load. Compared to traditional control methods that rely on temperature or carbon dioxide concentration, the technical solution of this invention has higher real-time performance and accuracy, directly reflecting the combined effect of personnel quantity and activity intensity. This provides a high-precision data foundation for subsequent HVAC control, thereby significantly improving the foresight and proactiveness of the control strategy.

[0067] Figure 2 It shows Figure 1 A schematic flowchart illustrating step S200, which determines the real-time individual metabolic rate equivalent based on individual physiological data and environmental parameters. (See attached flowchart.) Figure 2 As shown, step S200 includes:

[0068] Step S210: Based on environmental parameters, assess the impact of the indoor environment on individual metabolic rate to obtain environmental correction factors.

[0069] In this step, an overall assessment of environmental parameters such as indoor temperature and humidity is conducted to calculate the actual influencing factors of the environment on the human metabolic rate. In hot and humid environments, the human body needs to exert additional physiological effort to maintain body temperature, such as increasing skin blood flow and perspiration rate. This increases the load on the cardiovascular system, resulting in a higher heart rate and energy consumption at the same exercise output. By quantifying these physiological changes into environmental correction factors, the differences in individual metabolic levels under different environmental conditions can be accurately reflected, providing a more realistic and refined basis for subsequent metabolic rate equivalent calculations.

[0070] In one embodiment, the formula for calculating the environmental correction factor is:

[0071]

[0072] Where, k e This represents the environmental correction factor for quantifying exercise intensity. The model uses 1 as the baseline value, corresponding to the standard metabolic level in a comfortable environment. When environmental conditions deviate from the comfort zone, it will lead to k... e >1; T is the temperature weighting coefficient; T is the actual temperature measured by the environmental monitoring sensor; T C The baseline temperature for comfort. RH is the humidity weighting factor; RH is the relative humidity measured by the environmental monitoring sensor. C The relative humidity is used as a comfort baseline.

[0073] Step S220: Identify the individual's exercise type and determine the corresponding baseline metabolic equivalent and real-time baseline heart rate value based on the exercise type.

[0074] In this step, the heat production characteristics of different types of exercise vary significantly. For example, the heat production mechanisms of aerobic exercise and strength training are different. By identifying the individual type of exercise, it can be ensured that subsequent calculations are based on benchmark data that conforms to the actual exercise pattern, thereby improving the accuracy of metabolic rate estimation.

[0075] Step S230: Determine the individual exercise intensity based on the real-time baseline heart rate value and the corresponding baseline heart rate zone.

[0076] In this step, even when performing the same type of activity (such as aerobic training like running), the actual metabolic rate will vary significantly among individuals due to factors such as exercise pace, health level, fatigue state, and emotional state. Heart rate, as an ideal physiological indicator reflecting these differences, can objectively reflect the load level of the cardiovascular system. To ensure the accuracy of exercise intensity assessment, the collected heart rate data needs to be individualized and standardized. By mapping the real-time heart rate to the effective range from resting to maximum heart rate for each individual, the exercise intensity can be quantified, and the degree to which the cardiovascular system is mobilized can be accurately reflected.

[0077] In one embodiment, the formula for quantifying individual exercise intensity is:

[0078]

[0079] HRR stands for Heart Rate Reserve, calculated as the difference between maximum heart rate and resting heart rate. It quantifies an individual's available heart rate range and uses this to define exercise intensity zones. n This represents the real-time measured heart rate value; HR rest HR represents an individual's heart rate during wakefulness and inactivity. max This represents the maximum heart rate an individual can reach during extreme exercise, and it can usually be estimated using the empirical formula "220 - age".

[0080] Step S240: Based on the environmental correction factor, the baseline metabolic equivalent, and the individual exercise intensity, the real-time individual metabolic rate equivalent is calculated.

[0081] In this step, environmental correction factors, baseline metabolic equivalents, and individual exercise intensity are integrated to calculate the individual's instantaneous metabolic rate equivalent. This comprehensive calculation not only reflects the theoretical metabolic level of an individual at a specific exercise intensity but also takes into account environmental influences and individual differences, thus obtaining a metabolic rate assessment result that closely reflects reality. This result can be directly used for indoor dynamic heat load modeling, improving the timeliness and accuracy of predictions.

[0082] In one embodiment, the formula for calculating individual metabolic rate equivalent is:

[0083]

[0084] in, Metabolic rate of a single individual at an instant; Environmental correction factor; Individual exercise intensity; Indicates that the type of exercise is At that time, the individual's baseline heart rate was calculated; Indicates that the type of exercise is At that time, the actual baseline value corresponding to the actual activity metabolic rate baseline table.

[0085] Figure 3 It shows Figure 2 A schematic flowchart illustrating step S220, which involves identifying the individual's movement type. Figure 3 As shown, step S220 includes:

[0086] Step S221: Obtain the three-axis acceleration data of the wearable device worn by the individual, and extract features from the three-axis acceleration data.

[0087] Step S222: Input the extracted features into the pre-trained classification model to obtain motion type information based on triaxial acceleration data.

[0088] In steps S221 and S222 above, by acquiring triaxial acceleration data from an individual's wearable device and extracting its features, the dynamic patterns and trends of the user's movement in different directions can be captured. This process analyzes the triaxial acceleration signals in real time, transforming the raw motion data into a set of key motion features, which are then automatically identified using a pre-trained machine learning model. The model can output activity labels such as "sitting," "walking," or "running" based on these features, thus providing a reliable input basis for subsequent metabolic rate calculations. Its mathematical formula is expressed as follows:

[0089]

[0090]

[0091] Here, AAC represents the raw triaxial acceleration data collected by the wearable device, which constitutes a three-dimensional vector at any given moment. Used to characterize the motion state of an individual in different directions. This is represented as the recognition time. This is represented as a feature extraction function, used to transform the messy and redundant information in the original acceleration signal into representative key feature indicators; This represents a classification function that identifies and classifies the extracted feature vectors, corresponding to a pre-trained machine learning classification model. This represents the final activity label output by the classification model at time t, such as "sitting", "walking", or "running".

[0092] Step S223: The individual selects the current activity type from the preset activity list through the software interface to obtain exercise type information based on the user's selection.

[0093] In this step, the user actively selects the type of activity they are currently engaged in through a software interface (such as a mobile app or wearable device screen). Users can directly specify their current activity from a preset list of activities (such as "yoga," "spinning," or "strength training"), and the system records this selection as exercise type information. This method relies on active user interaction, ensuring absolute accuracy of the recognition results and effectively distinguishing specific exercise types with similar movement patterns but significant differences in metabolic levels.

[0094] Step S224: Combine motion type information based on triaxial acceleration data and motion type information selected by the user to determine the final individual motion type.

[0095] In this step, the system integrates the automatic identification results based on triaxial acceleration data with the user's manual selection information to determine the final individual exercise type label. Subsequently, it queries the built-in actual activity metabolic rate benchmark table based on this label to obtain the corresponding baseline metabolic equivalent (MET) and baseline heart rate value. The metabolic equivalent varies significantly among different activities; for example, walking has a metabolic equivalent of approximately 2.0–3.8 depending on pace, gymnastics has 3.0–4.0, and basketball has 5.0–7.6. This approach ensures the accuracy of exercise type identification and provides reliable basic data for subsequent metabolic rate calculations and exercise intensity analysis.

[0096] Figure 4 It shows Figure 1In step S300, the aggregated individual metabolic rate equivalents based on regional population distribution data are used to determine the total heat load and rate of change of total heat load in each indoor area. A schematic flowchart illustrating the decomposition of the total heat load into sensible heat load and latent heat load is also provided. Figure 4 As shown, step S300 includes:

[0097] Step S310: Based on the regional personnel distribution data, aggregate the individual metabolic rate equivalents of each person located in the same area to obtain the total heat load of each indoor area.

[0098] In this step, by acquiring real-time data on the distribution of people within each area, the system can identify the individuals belonging to each area and convert each person's metabolic equivalent into absolute thermic rate (in watts) to quantify the total human heat source intensity within the area. Specifically, the DuBois formula is first used to calculate the individual's body surface area, and then a conversion factor is used to convert the individual's metabolic equivalent into absolute thermic rate. The specific formula is as follows:

[0099]

[0100]

[0101] Where A represents the surface area of ​​an individual, W i H represents the weight of individual i. i This represents the height of individual i. This represents the absolute metabolic heat production rate of individual i at time t. This represents the metabolic rate of a single individual at instant t.

[0102] Then, the absolute heat production rates of all people in the same area are summed arithmetically to obtain the total heat load of the area, as shown in the following formula:

[0103]

[0104] in, This represents the current total metabolic heat production rate of region z at time t; This represents the absolute metabolic heat production rate of individual i at time t; It represents the set of all individuals i located in region z at time t. This set is dynamically updated by the real-time positioning system and changes in real time as people enter and leave the region.

[0105] Step S310 can accurately summarize the dynamic physiological information of individuals to the regional level, providing a reliable data foundation for subsequent heat load analysis and feedforward adjustment of HVAC systems, while also realizing the quantitative reflection of heat load differences in different regions.

[0106] Step S320: Calculate the rate of change of the total heat load in each area of ​​the room over time to obtain the rate of change of the total heat load, so as to quantify the direction and speed of change of the total heat load.

[0107] In this step, the rate and direction of change of the total metabolic heat production rate in each region can be quantified by calculating the change over time. Specifically, the backward difference method is used to obtain the time derivative of the total metabolic heat production rate, thereby obtaining the rate of load change. When the rate of change of the total heat load in a certain region shows a large positive value, it indicates that the cooling load in that region is about to increase rapidly. The system can predict and adjust the air conditioning output in advance based on this, realizing proactive control of environmental conditions, improving indoor comfort and avoiding energy waste.

[0108] In one embodiment, the formula for the rate of change of total heat load is:

[0109]

[0110] in, This represents the rate of change of the total heat load in region z over time. This represents the total metabolic heat production rate of region z at time t; This represents the total metabolic heat production rate of the region in the previous calculation period (time t-Δt). This indicates the calculation time step, which is the frequency interval between data sampling and calculation.

[0111] Step S330: Based on the total heat load and environmental parameters, calculate the evaporative heat dissipation required by the human body and the maximum evaporative heat dissipation capacity of the area. Compare the evaporative heat dissipation required by the human body with the maximum evaporative heat dissipation capacity of the area to obtain the final latent heat load.

[0112] Step S340: Subtract the latent heat load from the total heat load to obtain the sensible heat load of each region.

[0113] In indoor environments, the total heat generated by the human body is released in two forms: sensible heat and latent heat. HVAC systems need to adjust for these two types of loads separately. First, the maximum evaporative heat dissipation capacity that the human body can achieve through the skin is calculated based on current environmental parameters, while simultaneously assessing the latent heat loss required for the human body to maintain thermal balance. The latent heat load is then taken as the smaller of the two values ​​to ensure that the air conditioning system does not overestimate the latent heat demand. Subsequently, the actual latent heat load is subtracted from the total human body heat; the remainder is the sensible heat load. This achieves a reasonable division between sensible and latent heat loads, providing a precise control basis for subsequent feedforward regulation.

[0114] First, define the total heat dissipation generated by the human body as the total heat dissipation demand of the area:

[0115]

[0116] in, The total heat dissipation demand for the predicted region; The current total heat load of region z at time t.

[0117] In one embodiment, the formula for calculating the maximum evaporative heat dissipation capacity of the region is:

[0118]

[0119] in, This indicates the maximum evaporative heat dissipation capacity of a region, that is, the maximum latent heat that the environment can remove; It represents the evaporative heat transfer coefficient between the moist skin surface and the surrounding air. This coefficient is an empirical value calibrated based on experimental data, and the overall evaporative heat transfer efficiency is determined in combination with the air flow rate. Indicates the average air velocity in the region; This represents the partial pressure of saturated water vapor at the assumed skin temperature; It represents the partial pressure of water vapor in the ambient air of a region, which is calculated from the ambient temperature T and the ambient humidity RH of the region. The total body surface area of ​​all people in the area.

[0120] In one embodiment, the formula for calculating the evaporative heat loss required by the human body is:

[0121]

[0122]

[0123] in, This represents the amount of heat loss through evaporation required by the human body to maintain thermal balance. This represents the predicted total heat dissipation demand for the region. This indicates the maximum sensible heat that the environment can remove; This represents the combined heat transfer coefficient between the outer surface of the human body (clothing) and the entire indoor environment, and is taken as 7.2 W / (m²). 2 ·K); This represents the total body surface area of ​​all people within the area. This represents the average skin temperature, calculated at 35°C. This represents the dry-bulb temperature of region z.

[0124] In one embodiment, the formulas for determining the final latent heat load and sensible heat load are as follows:

[0125]

[0126]

[0127] in, This represents the final predicted regional latent heat load; This represents the amount of heat loss through evaporation required by the human body to maintain thermal balance. This indicates the maximum latent heat that the environment can remove; Indicates the physiological basis of latent respiratory heat; This represents the final predicted regional sensible heat load; This represents the predicted total heat dissipation demand for the region.

[0128] Figure 5 It shows Figure 1 In step S400, a schematic flowchart is generated to produce control commands for the supply air temperature, supply air volume, and humidity of the HVAC system for feedforward regulation, based on the rate of change of sensible heat load, latent heat load, and total heat load. Figure 5 As shown, step S400 includes:

[0129] Step S410: Based on the sensible heat load and the current supply air temperature of the HVAC system, determine the basic supply air temperature offset.

[0130] In this step, the system calculates the sensible heat load for each area and, in conjunction with the current supply air temperature of the HVAC system, sets the initial supply air temperature. The sensible heat load primarily reflects the perceptible heat demand of the human body and the environment. By determining the baseline supply air temperature offset by referencing the current supply air temperature, it can be ensured that the air conditioning system provides appropriate heat adjustment immediately during the feedforward control phase, thereby quickly responding to indoor temperature demands, improving the thermal comfort of residents, and reducing comfort fluctuations caused by temperature lag.

[0131] Step S420: Based on the latent heat load and the current air supply volume of the HVAC system, determine the offset of the basic dew point moisture content.

[0132] In this step, the latent heat load is addressed, and the baseline dew point moisture content offset is determined in conjunction with the current air supply volume of the HVAC system. The latent heat load mainly corresponds to the heat lost by the human body through sweat evaporation, and its regulation is closely related to air humidity. By accurately calculating the baseline dew point moisture content offset, the system can ensure that indoor humidity is controlled within a reasonable range to meet the human body's evaporative heat dissipation needs, while avoiding discomfort or energy waste caused by excessively high or low humidity.

[0133] Step S430: Based on the rate of change of total heat load, the basic supply air temperature offset and the basic dew point moisture content offset are corrected by a dynamic gain function to obtain the final supply air temperature offset and dew point moisture content offset.

[0134] In this step, based on the calculated rate of change of total heat load, the baseline supply air temperature offset and baseline dew point moisture content offset are corrected using a dynamic gain function to obtain the final supply air temperature offset and dew point moisture content offset. This step achieves proactive optimization of air conditioning regulation by quantifying the direction and rate of heat load change, enabling supply air conditions to adapt to changes in the indoor environment in advance, thereby further improving comfort and reducing energy consumption fluctuations.

[0135] In one embodiment, the offset is calculated using the following formula:

[0136]

[0137]

[0138] in, This indicates the final supply air temperature offset; Indicates the offset of the base supply air temperature; This indicates the final dew point moisture content offset. This indicates the offset of the base dew point moisture content.

[0139] Dynamic gain function For a reason about nonlinear function, when When the preset threshold is exceeded, the function value will be significantly greater than 1, thereby amplifying the adjustment range and response speed of the supply air temperature and dew point humidity, and realizing the proactive suppression of sudden or rapid changes in total heat load.

[0140] Step S440: Combine the supply air temperature offset, dew point humidity offset, and real-time status of the HVAC system to generate supply air temperature, supply air volume, and humidity control commands for feedforward regulation of the HVAC system.

[0141] In this step, the calculated supply air temperature offset and dew point humidity offset are combined with the real-time operating status of the HVAC system to generate the final supply air temperature, supply air volume, and humidity control commands. By comprehensively considering the current system status and predictive load adjustments, the resulting control commands can quickly and accurately respond to indoor heat and humidity demands under feedforward regulation, achieving a dynamic balance between temperature and humidity, improving system operating efficiency, and ensuring the thermal comfort of indoor occupants during various activities.

[0142] In one embodiment, the final formula is expressed as:

[0143]

[0144]

[0145] in, Indicates the final supply air temperature; Indicates the base supply air temperature; This indicates the final supply air temperature offset; Indicates the final dew point moisture content; Indicates the basic dew point moisture content; This indicates the final dew point moisture content offset.

[0146] In one embodiment, the HVAC control method further includes the following steps:

[0147] Step S500: Detect indoor environmental deviations caused by unpredictable disturbances or model errors, and generate feedback correction instructions based on the detection results.

[0148] In this step, deviations in indoor environmental parameters are monitored in real time to identify environmental changes caused by unpredictable disturbances (such as sudden increases or decreases in the number of people, opening of doors and windows, equipment overheating, etc.) or model errors. This step not only quantifies the difference between actual indoor temperature and humidity and predicted values, but also transforms the deviation information into feedback correction signals, providing a reliable basis for subsequent control. By timely detecting and quantifying these unforeseen disturbances, the system can effectively avoid delays or deviations caused by relying solely on feedforward predictions, thereby ensuring the comfort and stability of the indoor environment.

[0149] Step S600: Combine the feedback correction command and the supply air temperature, supply air volume and humidity control commands of the HVAC system used for feedforward regulation to generate the final HVAC system regulation command.

[0150] In this step, feedback correction commands and feedforward adjustment commands are integrated to generate the final HVAC system control commands. Step S600 combines proactive prediction and real-time correction to achieve a combination of proactive and responsive control strategies. Feedforward adjustment ensures the ability to anticipate upcoming changes in heat load, while feedback correction ensures timely compensation for unpredictable disturbances. The resulting control commands precisely control supply air temperature, air volume, and humidity while maintaining indoor thermal comfort and air quality, achieving efficient and dynamically responsive HVAC control.

[0151] According to the above embodiments, latent heat load and sensible heat load are obtained by decoupling the heat load, and the basic control setpoints for supply air temperature and humidity are determined respectively. A dynamic gain function is introduced using the rate of change of the total heat load for correction, thereby forming forward-looking supply air temperature offset and dew point humidity offset. Furthermore, this invention combines the predictive feedforward control command with the feedback correction command generated from real-time detected environmental deviations to form the final HVAC adjustment command, achieving coordinated operation of rapid response of feedforward regulation and fine compensation of feedback correction. The method of this invention can not only anticipate sudden changes in the number of indoor occupants and activity intensity, suppressing the lag in environmental parameter adjustment, but also avoid energy waste caused by excessive or insufficient adjustment, significantly improving indoor thermal comfort while maintaining the high efficiency and stability of the HVAC system.

[0152] To facilitate a more intuitive understanding of the above content of the present invention, a detailed description is provided below with reference to a specific embodiment.

[0153] The implementation space is designed as a modern smart gym with a total building area of ​​500m². 2 This embodiment selects the aerobic training area as the specific analysis object. The building parameters of this area are as follows: area 500m² 2 The room has a ceiling height of 4m and uses an adjustable constant air volume all-air system. The chilled water valve, supply air temperature, fresh air valve, and reheat capacity are all adjustable. The initial environmental conditions of the area are: dry bulb temperature 26.0°C, relative humidity 60%, average wind speed 0.2m / s, basic supply air temperature 18.0°C, and fresh air valve opening 30%.

[0154] User information is as follows: User A, female, 28 years old, weight 60kg, height 1.68m, resting heart rate 70 bpm; User B, male, 40 years old, weight 85kg, height 1.80m, resting heart rate 75 bpm. In the simulated training scenario, User A performs high-intensity training on a treadmill with a real-time heart rate of 150 bpm; User B performs moderate-intensity training on an elliptical machine with a real-time heart rate of 125 bpm. Indoor air quality sensor data shows an area temperature of 26.0°C and relative humidity of 60%. Bluetooth beacon positioning system confirms that both users are located within 500m. 2 Within the aerobic training area.

[0155] For user A, the comfort baseline is set as follows: =22°C; =50%. Weighting coefficient: temperature =0.02; humidity =0.005.

[0156]

[0157] Based on accelerometer data, after feature extraction and machine learning classification, the final activity label is output as "running". This is obtained by querying the actual activity metabolic rate benchmark table. =8; =75%. Simultaneously, based on the user's age, their maximum heart rate was calculated to be 192 bpm.

[0158]

[0159] Calculate the final individual metabolic rate:

[0160]

[0161] For user B, the comfort baseline is set as follows: =22°C; =50%. Weighting coefficient: temperature =0.02; humidity =0.005.

[0162]

[0163] Based on the user's selected activity type, the final activity label is output as "Elliptical Trainer". This is obtained by querying the actual activity metabolic rate benchmark table. =5; =55%. Simultaneously, based on the user's age, their maximum heart rate was calculated to be 180 bpm.

[0164]

[0165] Calculate the final individual metabolic rate:

[0166]

[0167] The system identifies individuals in each region through real-time location tracking and sums their respective metabolic equivalents. This process quantifies the total human heat source intensity within the region by converting real-time physiological metabolic equivalents into individual metabolic heat production rates, measured in W.

[0168] Substituting the values ​​into the formula above, the surface area of ​​user A is calculated to be 1.69 m². 2 User B's body surface area is 2.05m². 2 For user A: =791.1W; For user B: =600.8W.

[0169] The total heat production rate of the polymerization region is calculated as follows:

[0170]

[0171] Assuming the rate of change of regional heat production When the heat production rate in the previous cycle was 100W / s, the system needed to perform a forward-looking response.

[0172] At this point, the total heat dissipation generated by the human body is defined as:

[0173]

[0174] Summing is performed based on the calculation of the human body surface area mentioned above:

[0175]

[0176] Next, based on the evaporation characteristics between the human skin surface and the environment, the partial pressure of water vapor on the skin surface and the partial pressure of water vapor in the current ambient air are retrieved. Assuming a skin temperature of 35°C, the corresponding partial pressure of water vapor on the skin surface is... The ambient air temperature is 24°C and the relative humidity is 60%. The corresponding partial pressure of water vapor in the air can be calculated as follows: .

[0177]

[0178] Based on an understanding of the body's surface heat dissipation capacity and environmental conditions, the required evaporative heat loss for the human body in this area is calculated as follows:

[0179]

[0180] Assuming the latent heat of respiration is 20% of total metabolism:

[0181] For simplicity, let's assume the air conditioning system is operating in steady state before the user begins strenuous exercise, maintaining an indoor environment of 24°C and 60%RH. Let's assume the base supply air temperature is 20°C. At time t, the control strategy module receives the predicted data, and the system allocates the predicted sensible and latent heat loads to different control paths.

[0182] Assuming the air conditioning system delivers 800m³ of air to this area. 3 / h, wind speed is 0.22m / s, then:

[0183]

[0184] Calculate the required moisture difference:

[0185]

[0186] Assuming the rate of change of total metabolic heat production in the region When the heat production rate in the previous cycle was 100 W / s, the system needed to perform a look-ahead response. The system detected that this rate of change triggered a strong response threshold, and based on the built-in dynamic gain function... Overshoot control is performed to address such drastic changes.

[0187] After calculation, assuming the dynamic gain value of temperature at this time is... The dynamic gain with moisture content is .

[0188]

[0189] At this point, the calculated moisture content of the surface cooler section is:

[0190]

[0191] The machine's dew point relative humidity is set to 95%, and the dew point temperature is found to be 13.8°C according to the enthalpy-humidity chart.

[0192] At this point, the target supply air temperature is calculated to be:

[0193]

[0194] At this point, the reheating section needs to be heated to 4.55°C.

[0195] The above generated =13.8°C and =18.35°C is used as the main control command and is immediately sent to the building automation system for execution. Simultaneously, a standard feedback controller continuously monitors the actual indoor temperature and humidity. Assuming that due to the influence of solar radiation from windows not covered by the model, the indoor temperature still deviates by +0.1°C after executing the feedforward command, the PID controller will generate a correction value. Finally, the system sends a set temperature to the reheat coil. , =13.8°C.

[0196] This invention also provides a heating, ventilation, and air conditioning control system based on dynamic heat load prediction, such as... Figure 6As shown, the HVAC control system includes a data acquisition module 101, a human metabolic rate calculation module 102, a load prediction module 103, and a control module 104. The data acquisition module 101 is used to acquire individual physiological data, environmental parameters, and regional personnel distribution data in real time. The human metabolic rate calculation module 102 is used to determine the real-time individual metabolic rate equivalent based on the individual physiological data and environmental parameters. The load prediction module 103 is used to aggregate the individual metabolic rate equivalent according to the regional personnel distribution data, determine the total heat load and the rate of change of the total heat load in each area of ​​the room, and decompose the total heat load into sensible heat load and latent heat load. The control module 104 is used to generate control commands for the supply air temperature, supply air volume, and humidity of the HVAC system for feedforward regulation based on the sensible heat load, latent heat load, and the rate of change of the total heat load.

[0197] The present invention also provides a heating, ventilation and air conditioning control device based on dynamic heat load prediction, comprising: a processor 201, a memory 202, and a computer program stored in the memory 202 and configured to be executed by the processor 201. When the processor 201 executes the computer program, it implements the heating, ventilation and air conditioning control method as described in any of the above embodiments.

[0198] When processor 201 executes a computer program, it implements the steps in the above-described embodiments of the HVAC control method, for example... Figure 1 All steps of the HVAC control method shown. Alternatively, when processor 201 executes a computer program, it implements the functions of each module / unit in the above-described HVAC control system, for example... Figure 6 The functions of each module in the HVAC control system are shown.

[0199] For example, a computer program can be divided into one or more modules, one or more of which are stored in memory 202 and executed by processor 201 to perform the present invention. One or more modules can be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of the computer program in a heating, ventilation, and air conditioning control system.

[0200] The processor 201 may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor. The processor 201 is the control center of the HVAC control system, connecting various parts of the entire HVAC control system through various interfaces and lines.

[0201] The memory 202 can be used to store computer programs and / or modules. The processor 201 implements various functions of the HVAC control system by running or executing the computer programs and / or modules stored in the memory 202 and by calling the data stored in the memory 202. The memory 202 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, etc.; the data storage area may store data created based on the use of the HVAC control system, etc. In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as hard disk, RAM, plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, at least one disk storage device, flash memory device, or other volatile solid-state storage device.

[0202] If the modules / units of the HVAC control system are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc.

[0203] This invention also provides a computer-readable storage medium. The methods described above according to embodiments of the invention can be implemented in hardware or firmware, or implemented as computer code that can be recorded on a storage medium, or implemented as computer code downloaded via a network and originally stored on a remote storage medium or a non-transitory machine-readable storage medium and then stored on a local storage medium. Thus, the methods described herein can be processed by software stored on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. The storage medium can be a magnetic disk, optical disk, read-only memory, random access memory, flash memory, hard disk, or solid-state drive, etc.; further, the storage medium can also include combinations of the above types of memory. It is understood that computers, processors, microprocessor controllers, or programmable hardware include storage components capable of storing or receiving software or computer code, which, when accessed and executed by the computer, processor, or hardware, implements the methods shown in the above embodiments.

[0204] The technical features of the above embodiments can be combined in any way. For the sake of brevity, 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, they should be considered to be within the scope of this specification.

[0205] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these all fall within the protection scope of the present invention. Therefore, the protection scope of this invention patent should be determined by the appended claims.

Claims

1. A heating, ventilation, and air conditioning control method based on dynamic thermal load prediction, characterized by, The method comprises the following steps: Real-time acquisition of individual physiological data, environmental parameters and regional personnel distribution data in the room; Based on the individual physiological data and environmental parameters, the real-time individual metabolic rate equivalent is determined; According to the regional personnel distribution data, the individual metabolic rate equivalent is aggregated to determine the total heat load and the total heat load change rate of each region in the room, and the total heat load is decomposed into sensible heat load and latent heat load; Based on the sensible heat load, latent heat load and total heat load change rate, the supply air temperature, supply air volume and humidity control instruction of the HVAC system for feedforward regulation are generated; The specific steps of aggregating the individual metabolic rate equivalent based on the regional personnel distribution data, determining the total heat load and the total heat load change rate of each region in the room, and decomposing the total heat load into sensible heat load and latent heat load include: Based on the regional personnel distribution data, the individual metabolic rate equivalent of each person located in the same region is aggregated to obtain the total heat load of each region in the room; The change rate of the total heat load of each region in the room over time is calculated to obtain the total heat load change rate, so as to quantify the change direction and speed of the total heat load; According to the total heat load and environmental parameters, the required evaporative heat dissipation of the human body and the maximum evaporative heat dissipation capacity of the region are calculated, and the required evaporative heat dissipation of the human body is compared with the maximum evaporative heat dissipation capacity of the region to obtain the final latent heat load; The total heat load is subtracted by the latent heat load to obtain the sensible heat load of each region; The specific steps of generating the supply air temperature, supply air volume and humidity control instruction of the HVAC system for feedforward regulation based on the sensible heat load, latent heat load and total heat load change rate include: Based on the sensible heat load, the basic supply air temperature offset is determined in combination with the current supply air temperature of the HVAC system; Based on the latent heat load, the basic dew point moisture content offset is determined in combination with the current supply air volume of the HVAC system; Based on the total heat load change rate, the basic supply air temperature offset and the basic dew point moisture content offset are corrected by a dynamic gain function to obtain the final supply air temperature offset and dew point moisture content offset; In combination with the supply air temperature offset, the dew point moisture content offset and the real-time state of the HVAC system, the supply air temperature, supply air volume and humidity control instruction of the HVAC system for feedforward regulation are generated.

2. The HVAC control method of claim 1, wherein, The specific steps of determining the real-time individual metabolic rate equivalent based on the individual physiological data and environmental parameters include: Based on the environmental parameters, the influence of the indoor environment on the individual metabolic rate is evaluated to obtain an environmental correction factor; The individual exercise type is identified, and the corresponding reference metabolic equivalent and real-time reference heart rate value are determined according to the exercise type; According to the real-time reference heart rate value and the corresponding reference heart rate interval, the individual exercise intensity is determined; Based on the environmental correction factor, the reference metabolic equivalent and the individual exercise intensity, the real-time individual metabolic rate equivalent is calculated.

3. The HVAC control method of claim 2, wherein, The calculation formula of the individual metabolic rate equivalent is: wherein, is the metabolic rate of the individual at the instant time; is the environmental correction factor; is the individual exercise intensity; represents the measured individual heart rate reference value when the exercise type is ; represents the corresponding actual reference value in the actual activity metabolic rate reference table when the exercise type is .

4. The HVAC control method of claim 2, wherein, The specific steps of identifying the individual exercise type include: Obtaining triaxial acceleration data of a wearable device worn by an individual, and performing feature extraction on the triaxial acceleration data; Inputting the extracted features into a pre-trained classification model to obtain motion type information based on the triaxial acceleration data; Selecting a current activity type from a pre-set activity list by the individual through a software interface to obtain motion type information based on user selection; Combining the motion type information based on the triaxial acceleration data and the motion type information based on user selection to determine the final individual motion type.

5. The HVAC control method of claim 4, wherein, The HVAC control method further comprises: Detecting indoor environment deviation caused by non-predictive disturbance or model error, and generating feedback correction instructions based on the detection results; Combining the feedback correction instructions and the supply air temperature, supply air volume and humidity control instructions of the HVAC system for feedforward regulation to generate final HVAC system regulation instructions.

6. An HVAC control system employing the HVAC control method based on dynamic thermal load prediction according to any one of claims 1 to 5, characterized in that, Comprise: A data acquisition module for acquiring individual physiological data, environmental parameters and regional personnel distribution data in real time; A human metabolic rate calculation module for determining the real-time individual metabolic rate equivalent based on the individual physiological data and environmental parameters; A load prediction module for aggregating the individual metabolic rate equivalent according to the regional personnel distribution data, determining the total heat load and total heat load change rate of each region in the room, and decomposing the total heat load into sensible heat load and latent heat load; A control module for generating supply air temperature, supply air volume and humidity control instructions of the HVAC system for feedforward regulation based on the sensible heat load, latent heat load and total heat load change rate.

7. A heating, ventilation, and air conditioning control device based on dynamic thermal load prediction, characterized by, Comprise: A processor, a memory, a communication interface and a communication bus, the processor, the memory and the communication interface complete communication with each other through the communication bus; The memory is used to store at least one executable instruction, and the executable instruction makes the processor execute the operation corresponding to the HVAC control method in any one of claims 1-5.

8. A computer-readable storage medium, characterized in that, The computer readable storage medium stores a computer program, and the computer program is executed by the processor to realize the steps of the HVAC control method in any one of claims 1-5. The computer readable storage medium stores a computer program, and the computer program is executed by the processor to realize the steps of the HVAC control method in any one of claims 1-5.

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