An air conditioner regulation method and device based on user comfort and carbon emission optimization

Abstract

The application discloses an air conditioner regulation and control method and device based on user comfort and carbon emission optimization, and the method comprises the following steps: collecting building parameters, indoor temperature, outdoor temperature and air conditioner equipment operation parameters of an area where an air conditioner system is located; taking minimization of carbon emission as a first objective function, and establishing an upper optimization model considering the influence of respiratory heat; taking maximization of user comfort as a second objective function, and establishing a lower optimization model considering the influence of carbon dioxide concentration, different user activities and different types of user thermal comfort preferences; based on the collected data, the upper model and the lower model are solved simultaneously, and an optimal control scheme of an enabling power time sequence of the air conditioner system is output. The application provides an efficient and feasible regulation and control strategy for an intelligent air conditioner system, improves the low-carbon scheduling capability of a building energy system operation, and realizes collaborative optimization of energy consumption control and user comfort.

Description

Technical Field

[0001] This invention relates to the field of air conditioning control technology, and in particular to an air conditioning control method and apparatus optimized based on user comfort and carbon emissions. Background Technology

[0002] Among numerous building energy systems, air conditioning systems, due to their high-frequency operation and large energy consumption, have become a key component in optimizing carbon emission management. However, traditional air conditioning control strategies often use indoor temperature as the sole adjustment indicator, ignoring the dynamic changes in users' actual thermal comfort. This makes it difficult to balance energy conservation and comfort, limiting further optimization of refined control and carbon optimization potential.

[0003] Regarding the construction of indoor thermal balance models, Chinese patent CN119321609A discloses a flexible control method, device, computer equipment, and computer program product for air conditioning. It proposes to provide comfort alarms based on predicted indoor air temperature values ​​and adjust the indoor set temperature and operating power of the air conditioner according to the building thermal balance model to achieve control over the air conditioner power and indoor temperature. Chinese patent CN115392045A discloses a method, device, equipment, and medium for optimizing the operation of air conditioning and heating in temporary buildings. It constructs a temporary building thermodynamic model based on the building structure and heat transfer principles of temporary buildings. This model can perform simple calculations on the heat dissipation of temporary buildings and incorporate environmental factors to accurately calculate the heating effect of air conditioning. However, these existing solutions all neglect the impact of respiratory heat generated by crowd gathering in the building thermal balance model, and the control accuracy needs to be improved.

[0004] Regarding user comfort evaluation, Chinese patent CN119333940A proposes a deep learning-based dynamic optimization method and system for air conditioning. This method adjusts air conditioning equipment in real time through the air conditioning control system based on the user's optimal air conditioning control strategy, thereby dynamically optimizing air conditioning parameters based on user comfort and energy-saving goals. Chinese patent CN120257863A proposes an energy-saving temperature control optimization method and system based on a central air conditioning simulation platform. This method collects the operating parameters of the central air conditioning system in real time through an IoT sensor network, combines historical data and outdoor meteorological data to construct a multi-dimensional dynamic dataset, and uses a simulation engine driven by a hybrid physical model and machine learning to establish a dynamic thermodynamic model of the central air conditioning system. Based on user comfort needs, energy cost constraints, and environmental policy indicators, a multi-objective optimization function is defined for the simulation platform model to optimize energy-saving temperature control. However, these existing solutions do not consider the impact of carbon dioxide concentration on human comfort, ignore the dynamic changes in users' actual thermal comfort, and lack personalized research on different user activities and thermal comfort preferences among different types of users.

[0005] In summary, current air conditioning systems still lack refined consideration in comfort modeling and energy consumption scheduling based on thermal balance models, making it difficult to achieve both energy saving and comfort. The control scheme needs further optimization. Summary of the Invention

[0006] To address the shortcomings of existing technologies, this invention provides an air conditioning control method and apparatus based on user comfort and carbon emission optimization. This provides an efficient and feasible control strategy for intelligent air conditioning systems, enhances the low-carbon scheduling capability of building energy systems, and achieves synergistic optimization of energy consumption control and user comfort.

[0007] The present invention adopts the following technical solution.

[0008] In a first aspect, the present invention provides an air conditioning control method optimized based on user comfort and carbon emissions, the method comprising:

[0009] Collect building parameters, indoor temperature, outdoor temperature, and air conditioning equipment operating parameters for the area where the air conditioning system is located;

[0010] With minimizing carbon emissions as the primary objective function, an upper-level optimization model considering the impact of respiratory heat is established.

[0011] With maximizing user comfort as the second objective function, a lower-level optimization model is established that takes into account the effects of carbon dioxide concentration, different user activities, and different types of user thermal comfort preferences.

[0012] Based on the collected data, the upper-level model and the lower-level model are solved simultaneously to output the optimal control scheme for the start-up power time series of the air conditioning system.

[0013] Optionally, the expression for the first objective function is as follows:

[0014]

[0015] In the formula, To achieve the lowest carbon emissions; The carbon emissions generated per 1 kWh of power consumed; for The operating power at time step D; D is the total number of discrete time steps within the optimization period.

[0016] Optionally, establishing a higher-level optimization model that considers the effects of respiratory heat includes: formulating dynamic equilibrium constraints for thermal inertia that take into account the effects of respiratory heat, the expression of which is as follows:

[0017]

[0018]

[0019]

[0020]

[0021] In the formula, for Room temperature at that moment; for The outdoor temperature at any given time; for Operating power at any given time; The thermal conductivity coefficient of the wall; , and These are three coefficients determined by building parameters; air density; The standard floor area; For floor height; The specific heat capacity of air; The heat storage coefficient of the interior wall surface; This refers to the area of ​​the interior walls; These are thermal conductivity parameters; The thermal conductivity coefficient of the roof; , These are the areas of the walls and the roof, respectively. and These are the cooling load parameters for electrical equipment and lighting systems, respectively. and These are the heat dissipation per unit area for electrical equipment and lighting systems, respectively. The area of ​​the cooling zone; The coefficient of sensible heat dissipation and cooling load of the human body; respectively Sensible and latent heat loss through respiration of a user of type m at any given time during u-activity.

[0022] Optionally, the room temperature in the thermal inertia dynamic equilibrium constraint condition satisfies the following thermal inertia condition:

[0023]

[0024] In the formula, for Room temperature at that moment; For a single discrete time; for The operating power of the air conditioning system at any given time; This refers to the rated power of the air conditioning system.

[0025] Optionally, the expression for the second objective function is as follows:

[0026]

[0027]

[0028] In the formula, Indicates the maximum user comfort level; express The perceived comfort level of the group within the cooling area of ​​the air conditioning system at any given time; L(i) is The number of people within the refrigerated area at any given time; ) express Individual tactile comfort of type m users at any given moment when performing u-activity; To optimize the total number of discrete time steps within a time period.

[0029] Optionally, the individual's perceived comfort The formula for calculating ) is as follows:

[0030]

[0031]

[0032]

[0033] In the formula, express The baseline ergonomic comfort index of type m users at any given time when performing activity u. express The impact of CO2 concentration at any given time on air quality. Indicates the weighting coefficient; Let i be the actual indoor CO2 concentration at time i; express The direction of deviation of the user's perceived comfort at any given moment when performing the activity.

[0034] Optional, user baseline ergonomic comfort index The calculation formula is as follows:

[0035]

[0036] In the formula, This represents the metabolic rate of user type m at time i when performing activity u; The ratio of the area of ​​the human body covered by clothing to the area of ​​the body exposed; h c This refers to the thermal resistance value of the garment. t r The building surfaces surrounding the human body

[0037] The average radiation temperature during radiative heat exchange; t cl Temperature of the outer surface of the garment; The water vapor pressure in the environment; for Room temperature at that moment; Corrected values ​​for respiration heat loss based on CO2 concentration: ; The baseline is the sensible heat loss from respiration; The baseline latent heat of respiration; respectively Sensible and latent heat loss through respiration of a user of type m at any given time during u-activity.

[0038] Optionally, the heat dissipation from respiration. and latent heat of respiration The calculation formulas are as follows:

[0039]

[0040]

[0041]

[0042]

[0043] In the formula, for Room temperature at any given time express The function of human respiratory rate and CO2 concentration for a user of type m at time m performing activity u: The baseline respiratory rate; Let i be the actual indoor CO2 concentration at time i; The baseline CO2 concentration; and Let represent the metabolic rate and respiratory rate sensitivity coefficients of type m user at time i when performing activity u. and They represent Metabolic rate and respiratory rate of user type m at time point. express The ratio of activity type u at any given time to the basal metabolic rate of user type m; express The weighting coefficient of time u-type activity relative to the basic breathing frequency of m-type users.

[0044] Optionally, the constraints of the lower-level optimization model include:

[0045] Individual comfort level upper and lower limits constraints: ;

[0046] Air conditioning start / stop constraints:

[0047]

[0048]

[0049] In the formula; ) express The individual tactile comfort of type m users at any given time when performing activity u. They represent Time and The operating power of the air conditioning system at any given time; Represents a single discrete time; This represents the total number of time intervals between adjacent time points in N discrete time points; For the air conditioning system at all times The enabled status, This indicates that the air conditioner is on. This indicates that the air conditioner is off; This refers to the minimum operating time of the air conditioning system. This is the minimum downtime for the air conditioning system.

[0050] In a second aspect, the present invention provides an air conditioning control device optimized for user comfort and carbon emissions, comprising the steps of the method described in any one of the first aspects of the present invention, the device including:

[0051] The data acquisition unit is used to collect building parameters, indoor temperature, outdoor temperature, and air conditioning equipment operating parameters of the area where the air conditioning system is located.

[0052] The first establishment unit is used to establish an upper-level optimization model that takes into account the effect of respiratory heat, with the minimization of carbon emissions as the first objective function.

[0053] The second establishment unit is used to establish a lower-level optimization model that takes into account the effects of carbon dioxide concentration, different user activities, and different types of user thermal comfort preferences, with the goal of maximizing user comfort as the second objective function.

[0054] The optimization control unit is used to solve the problem by combining the upper-level model and the lower-level model based on the collected data, and output the optimal control scheme for the start-up power time series of the air conditioning system.

[0055] Thirdly, the present invention provides a terminal, including a processor and a storage medium;

[0056] The storage medium is used to store instructions;

[0057] The processor is configured to operate according to the instructions to perform the steps of the method according to any one of the first aspects of the present invention.

[0058] Fourthly, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method described in any one of the first aspects of the present invention.

[0059] The beneficial effects of this invention are compared with those of the prior art:

[0060] This invention addresses the technical problem that current air conditioning systems lack refined consideration in comfort modeling and energy consumption scheduling based on thermal balance models, making it difficult to achieve both energy saving and comfort. By establishing an upper-level optimization model that considers the impact of respiratory heat and a lower-level optimization model that takes into account the effects of carbon dioxide concentration, different user activities, and different types of user thermal comfort preferences, this invention provides an efficient and feasible control strategy for intelligent air conditioning systems, improves the low-carbon scheduling capability of building energy systems, and achieves synergistic optimization of energy consumption control and user comfort.

[0061] This invention introduces respiratory heat influencing factors into the traditional PMV thermal comfort model when measuring user comfort. By quantifying the correlation between sensible and latent heat of respiration and parameters such as metabolic rate and indoor CO2 concentration, it upgrades thermal comfort assessment from simply fitting environmental parameters to a joint calculation of the environment and human physiological mechanisms, which is closer to the actual human experience and overcomes the assessment bias caused by the neglect of key physiological factors in traditional models. At the same time, by combining the differences in basal metabolic rate and respiratory sensitivity among different user types, as well as the correction coefficients for metabolic rate and respiratory rate under different activity states, and with personalized comfort threshold adjustment, it achieves a leap from group average to more accurate individual results, and can dynamically respond to user differences. In addition, through the closed-loop feedback of CO2 concentration and respiratory heat, the synergistic optimization of group comfort, and the dynamic balance of thermal inertia and regulation, it enhances adaptability in complex scenarios such as multiple people living together and diverse activities, and deeply couples precise comfort with carbon emission optimization. By solving the upper and lower layer models together, it supports intelligent regulation upgrades while taking into account comfort and low carbon emissions, significantly improving the practicality and intelligence of the model in practical applications. Attached Figure Description

[0062] Figure 1 This is a flowchart of the air conditioning control method based on user comfort and carbon emission optimization in this invention;

[0063] Figure 2 This is a block diagram illustrating the structural principle of the air conditioning control device optimized for user comfort and carbon emissions in this invention. Detailed Implementation

[0064] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of this invention. The embodiments described in this invention are merely some embodiments of this invention, and not all embodiments. Based on the spirit of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the protection scope of this invention.

[0065] Example 1:

[0066] Reference Figure 1 This invention provides an air conditioning control method optimized based on user comfort and carbon emissions, specifically including the following steps:

[0067] Step S1: Collect building parameters, indoor temperature, outdoor temperature, and air conditioning equipment operating parameters for the area where the air conditioning system is located;

[0068] Step S2: Establish an upper-level optimization model that considers the impact of respiratory heat, with minimizing carbon emissions as the primary objective function;

[0069] Step S3: With maximizing user comfort as the second objective function, establish a lower-level optimization model that takes into account the effects of carbon dioxide concentration, different user activities, and different types of user thermal comfort preferences.

[0070] Step S4: Based on the collected data, solve the problem by combining the upper-level model and the lower-level model, and output the optimal control scheme for the start-up power time series of the air conditioning system.

[0071] Among them, the current room temperature, outdoor temperature, air conditioning equipment operating parameters and building parameters of the regional air conditioning system are collected to provide basic data input for modeling and optimization;

[0072] In this embodiment, relevant parameters are extracted from the air conditioning equipment's control system or operation manual, including the rated cooling capacity of the air conditioning system's chiller, the minimum shutdown time of the chiller set to 5 minutes, the minimum start-up time set to 3 minutes, the cooling capacity per unit area, the coefficient of performance (COP), and relevant data on the indoor area. The COP and building parameters for a specific area are shown in Tables 1 and 2 below:

[0073] Table 1

[0074]

[0075] Table 2

[0076]

[0077] As an embodiment of the present invention, step S2, establishing an upper-level optimization model that considers the influence of respiratory heat, includes:

[0078] S2.1. Establish the first objective function to minimize carbon emissions:

[0079]

[0080] In the formula, To achieve the lowest carbon emissions; The carbon emissions generated per 1 kWh of power consumed; for The operating power at time step D; D is the total number of discrete time steps within the optimization period.

[0081] S2.2, Define the constraints for the upper-level optimization model: thermal inertia dynamic equilibrium constraints and air conditioning power constraints;

[0082] The expression for the air conditioner power constraint is as follows:

[0083]

[0084] In the formula, This refers to the minimum rated cooling load power of the air conditioner. This refers to the rated maximum cooling load power of the air conditioner.

[0085] In this embodiment, the process of constructing the thermal inertia dynamic equilibrium constraint is as follows:

[0086] (1) Establish the function of respiratory rate versus CO2 concentration:

[0087]

[0088] In the formula, The respiratory rate is a function of CO2 concentration; As the baseline respiratory rate, this embodiment sets it to 12 breaths / min; The respiratory rate sensitivity coefficient of a user of type m at time i when performing activity u. Let i be the actual indoor CO2 concentration at time i; As a baseline CO2 concentration, this embodiment is set to... .

[0089] Among them, respiratory rate sensitivity :

[0090]

[0091] In the formula, express The breathing rate of type m users at time m. express The weighting coefficient of time u-type activity relative to the basic breathing frequency of m-type users.

[0092] (2) The dynamic model of respiratory heat dissipation based on the function of respiratory rate and CO2 concentration is as follows:

[0093]

[0094] In the formula: respectively Sensible and latent heat loss through respiration of type m users at any given time during u-activity. This represents the metabolic rate of user type m at time i when performing activity u; The ambient water vapor pressure is set to 2000 Pa in this embodiment;

[0095] Among them, metabolic rate The calculation method is as follows:

[0096]

[0097] In the formula, This represents the metabolic rate of user type m at time i. This represents the ratio of activity type u to the basal metabolic rate of user type m at time point u. (Subscript) These refer to the types of users and the types of activities, respectively. In this embodiment, the types of users include children aged 1-10, teenagers aged 11-20, young adults aged 21-30, middle-aged people aged 31-40, middle-aged and elderly people aged 41-50, elderly people aged 51-70, and elderly people aged 70 and above. The types of activities include: sleeping, sitting, standing in relaxation, brisk walking, jogging, and running.

[0098] Furthermore, examples of human energy metabolism rate and respiratory rate under different types of user activities in this embodiment are shown in Table 3:

[0099] Table 3

[0100]

[0101] (3) Based on the above dynamic model of respiratory heat dissipation, the dynamic equilibrium constraint condition of thermal inertia considering the influence of respiratory heat is formulated, and its expression is as follows:

[0102]

[0103]

[0104]

[0105]

[0106] In the formula, for Room temperature at that moment; for The outdoor temperature at any given time; for Operating power at any given time; The thermal conductivity coefficient of the wall; , and These are three coefficients determined by building parameters; air density; The standard floor area; For floor height; The specific heat capacity of air; The heat storage coefficient of the interior wall surface; This refers to the area of ​​the interior walls; For the heat conduction parameters (a weighted sum of heat conduction coefficients, i.e., the contribution of heat conduction in the roof and exterior walls of a building to temperature changes), this embodiment... The value is 1.8; The thermal conductivity coefficient of the roof; , These are the areas of the walls and the roof, respectively. and These are the cooling load parameters for electrical equipment and lighting systems, respectively. and These are the heat dissipation per unit area for electrical equipment and lighting systems, respectively. The area of ​​the cooling zone; The coefficient of sensible heat dissipation and cooling load of the human body; respectively Sensible and latent heat loss through respiration of a user of type m at any given time during u-activity.

[0107] Furthermore, the room temperature in the thermal inertia dynamic equilibrium constraint condition satisfies the following thermal inertia condition:

[0108]

[0109] In the formula, for Room temperature at that moment; For a single discrete time; for The operating power of the air conditioning system at any given time; This refers to the rated power of the air conditioning system.

[0110] As an embodiment of the present invention, step S3, establishing a lower-level optimization model that takes into account the effects of carbon dioxide concentration, different user activities, and different types of user thermal comfort preferences, includes:

[0111] S3.1. Establish a function with maximizing user comfort as the second objective function:

[0112]

[0113]

[0114] In the formula, Indicates the maximum user comfort level; express The perceived comfort level of the group within the cooling area of ​​the air conditioning system at any given time; L(i) is The number of people within the refrigerated area at any given time; ) express Individual tactile comfort of type m users at any given moment when performing u-activity; To optimize the total number of discrete time steps within a time period.

[0115] S3.2, Define the constraints for the lower-level optimization model: air conditioning start / stop constraints and upper and lower limits of individual physical comfort;

[0116] Among them, the upper and lower limits of individual perceived comfort are: ;

[0117] Furthermore, in this embodiment, individual tactile comfort... The calculation process is as follows:

[0118] (1) Based on the dynamic heat dissipation model established in step S2, the heat loss caused by respiration due to CO2 concentration of individual users is corrected, and its expression is as follows:

[0119]

[0120] In the formula, for Correction value for respiration heat loss due to CO2 concentration for type m users at time m when performing u activities: The baseline is the sensible heat loss from respiration; The baseline latent heat of respiration;

[0121] (2) Introduce the influencing factors of user respiratory heat into the traditional PMV thermal comfort model. To obtain the user's baseline physical comfort index The calculation formula is as follows:

[0122]

[0123] In the formula, This represents the metabolic rate of user type m at time i when performing activity u; The ratio of the area of ​​the human body covered by clothing to the area of ​​the body exposed; h c This refers to the thermal resistance value of the garment. t rThe average radiation temperature is the equivalent temperature corresponding to the combined effect of radiative heat exchange between the human body and all surfaces around the human body (walls, floors, ceilings, windows, equipment, etc.). In this embodiment, the value is taken as 29.7℃. t cl The outer surface temperature of the garment is 32°C in this embodiment.

[0124] (3) Combine CO2 concentration with air quality perception to correct individual perceived comfort. ):

[0125]

[0126]

[0127]

[0128] In the formula, express Air quality at any given time; This represents the weighting coefficient; preferably, in this embodiment, it is set as follows: It is 0.5; Let i be the actual indoor CO2 concentration at time i; express The direction of deviation (towards heat, towards cold, or balanced) of the perceived comfort of a user of type m at any given moment when performing activity u.

[0129] In this embodiment, the expression for the air conditioner start / stop constraint is as follows:

[0130]

[0131]

[0132]

[0133] In the formula, They represent Time and The operating power of the air conditioning system at any given time; Represents a single discrete time; This represents the total number of time intervals between adjacent time points in N discrete time points; For the air conditioning system at all times The enabled status, This indicates that the air conditioner is on. This indicates that the air conditioner is off; This constraint, representing the minimum start-up time for the air conditioning system, ensures that the air conditioner maintains at least [a certain operating temperature] after startup. The runtime is such that the system will not become unstable due to excessively frequent start-ups and shutdowns; This is the minimum downtime for the air conditioning system, ensuring that the system remains idle for a certain period after each shutdown to avoid the negative impacts of frequent start-stop cycles.

[0134] As an embodiment of the present invention, in step S4, based on the currently collected data, the upper-level model and the lower-level model are solved by combining the objective function and the constraint conditions using an optimization algorithm to obtain the optimal control scheme for the start-up power time series of the air conditioning system.

[0135] In summary, using initial values ​​for parameters such as air conditioning power limits, carbon emission factors, user comfort values, operating power rate limits, and start-stop time constraints, a two-level optimization model is employed. The upper-level objective is to minimize the carbon emissions of the air conditioning system, while the lower-level objective is to maximize user comfort. Combining an alternating optimization method, the operating power of the lower level is first fixed, and the power load of the upper level is optimized to minimize carbon emissions; then, the power load of the upper level is fixed, and the operating power of the lower level is optimized to maximize user comfort. Through this alternating optimization strategy, the decision variables are iteratively updated step by step, ultimately solving for the optimal air conditioning start-up time and operating power, ensuring that the air conditioning system achieves optimal control while meeting carbon emission constraints and comfort requirements. It should be further noted that there are many existing algorithms for solving the alternating optimization method of the two-level model; those skilled in the art can choose one of these algorithms to solve the problem. This is not the focus of this invention and will not be elaborated upon further.

[0136] The beneficial effects of this invention are compared with those of the prior art:

[0137] This invention addresses the technical problem that current air conditioning systems lack refined consideration in comfort modeling and energy consumption scheduling based on thermal balance models, making it difficult to achieve both energy saving and comfort. By establishing an upper-level optimization model that considers the impact of respiratory heat and a lower-level optimization model that takes into account the effects of carbon dioxide concentration, different user activities, and different types of user thermal comfort preferences, this invention provides an efficient and feasible control strategy for intelligent air conditioning systems, improves the low-carbon scheduling capability of building energy systems, and achieves synergistic optimization of energy consumption control and user comfort.

[0138] This invention introduces respiratory heat influencing factors into the traditional PMV thermal comfort model when measuring user comfort. By quantifying the correlation between sensible and latent heat of respiration and parameters such as metabolic rate and indoor CO2 concentration, it upgrades thermal comfort assessment from simply fitting environmental parameters to a joint calculation of the environment and human physiological mechanisms, which is closer to the actual human experience and overcomes the assessment bias caused by the neglect of key physiological factors in traditional models. At the same time, by combining the differences in basal metabolic rate and respiratory sensitivity among different user types, as well as the correction coefficients for metabolic rate and respiratory rate under different activity states, and with personalized comfort threshold adjustment, it achieves a leap from group average to more accurate individual results, and can dynamically respond to user differences. In addition, through the closed-loop feedback of CO2 concentration and respiratory heat, the synergistic optimization of group comfort, and the dynamic balance of thermal inertia and regulation, it enhances adaptability in complex scenarios such as multiple people living together and diverse activities, and deeply couples precise comfort with carbon emission optimization. By solving the upper and lower layer models together, it supports intelligent regulation upgrades while taking into account comfort and low carbon emissions, significantly improving the practicality and intelligence of the model in practical applications.

[0139] Example 2:

[0140] like Figure 2 As shown, the present invention provides an air conditioning control device optimized based on user comfort and carbon emissions. The device is used to implement the steps of the method in Embodiment 1 above, and the device specifically includes:

[0141] The data acquisition unit is used to collect building parameters, indoor temperature, outdoor temperature, and air conditioning equipment operating parameters of the area where the air conditioning system is located.

[0142] The first establishment unit is used to establish an upper-level optimization model that takes into account the effect of respiratory heat, with the minimization of carbon emissions as the first objective function.

[0143] The second establishment unit is used to establish a lower-level optimization model that takes into account the effects of carbon dioxide concentration, different user activities, and different types of user thermal comfort preferences, with the goal of maximizing user comfort as the second objective function.

[0144] The optimization control unit is used to solve the problem by combining the upper-level model and the lower-level model based on the collected data, and output the optimal control scheme for the start-up power time series of the air conditioning system.

[0145] The air conditioning control device based on user comfort and carbon emission optimization provided in this embodiment of the invention is based on the same technical concept as the air conditioning control method based on user comfort and carbon emission optimization provided in Embodiment 1, and can produce the beneficial effects described in Embodiment 1. For the contents not described in detail in this embodiment, please refer to Embodiment 1.

[0146] Example 3:

[0147] This invention provides a terminal, including a processor and a storage medium, which is an embedded computer system device. The storage medium stores instructions, and the memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium, and the database stores instruction data. The processor of the terminal operates according to the instructions provided by the storage medium to execute the steps of the air conditioning control method based on user comfort and carbon emission optimization as described in any of the embodiments of this invention.

[0148] Example 4:

[0149] The present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method described in any one of the embodiments.

[0150] This invention can be a system, method, and / or computer program product. A computer program product may include a computer-readable storage medium having computer-readable program instructions loaded thereon for causing a processor to implement various aspects of the invention.

[0151] Computer-readable storage media can be tangible devices capable of holding and storing instructions for use by an instruction execution device. Computer-readable storage media can be, for example—but not limited to—electrical storage devices, magnetic storage devices, optical storage devices, electromagnetic storage devices, semiconductor storage devices, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of computer-readable storage media include: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static random access memory (SRAM), portable compact disc read-only memory (CD-ROM), digital multifunction disc (DVD), memory sticks, floppy disks, mechanical encoding devices, such as punch cards or recessed protrusions storing instructions thereon, and any suitable combination of the foregoing. The computer-readable storage media used herein are not to be construed as transient signals themselves, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (e.g., light pulses through fiber optic cables), or electrical signals transmitted through wires.

[0152] The computer-readable program instructions described herein can be downloaded from computer-readable storage media to various computing / processing devices, or downloaded via a network, such as the Internet, local area network, wide area network, and / or wireless network, to an external computer or external storage device. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. A network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards them to the computer-readable storage media in the respective computing / processing device.

[0153] The computer program instructions used to perform the operations of this invention may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Smalltalk, C++, etc., and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The computer-readable program instructions may be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or may be connected to an external computer (e.g., via the Internet using an Internet service provider). In some embodiments, electronic circuitry, such as programmable logic circuitry, field-programmable gate arrays (FPGAs), or programmable logic arrays (PLAs), is personalized by utilizing state information from the computer-readable program instructions. This electronic circuitry can execute the computer-readable program instructions to implement various aspects of the invention.

[0154] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the protection scope of the claims of the present invention.

Claims

1. A method for air conditioning regulation based on user comfort and carbon emission optimization, the method comprising: receiving a user input; determining a user comfort level; determining a carbon emission level; and regulating the air conditioning based on the user comfort level and the carbon emission level. The application relates to an optimal control method and device for an air conditioning system. Collecting building parameters, indoor temperature, outdoor temperature and air conditioning equipment operation parameters of an area where the air conditioning system is located; An upper optimization model considering the influence of respiratory heat is established by taking the minimization of carbon emission as a first objective function; A lower optimization model is established by taking the maximization of user comfort as a second objective function, and the influence of carbon dioxide concentration, different user activities and different types of user thermal comfort preferences is considered; Based on the collected data, the upper model and the lower model are solved simultaneously to output an optimal control scheme of an activation power time sequence of the air conditioning system.

2. The method for air conditioning regulation based on user comfort and carbon emission optimization according to claim 1, wherein, The expression of the first objective function is as follows: wherein is the lowest carbon emission; is the carbon emission per 1 kWh of power consumed; is is the operating power at the moment; D is the total number of discrete time steps within the optimization period.

3. The method for air conditioning regulation based on user comfort and carbon emission optimization according to claim 1, wherein, The upper optimization model considering the influence of respiratory heat comprises formulating a thermal inertia dynamic balance constraint condition considering the influence of respiratory heat, and the expression is as follows: wherein is the room temperature at the time; is the outdoor temperature at the time; is the operating power at the time; is , and are three coefficients determined by the building parameters, respectively; is the standard floor area; is the air specific heat capacity; is the internal wall area; is the thermal conduction parameter; , are the wall and roof wall areas, respectively; and are the electrical equipment and lighting system cooling load parameters, respectively; and are the electrical equipment and lighting system unit area heat dissipation, respectively; is the refrigeration area; is the respiratory sensible heat and respiratory latent heat dissipation of the m type user at the time when the user is performing the u activity.

4. The method for air conditioning regulation based on user comfort and carbon emission optimization according to claim 3, wherein, The indoor temperature in the thermal inertia dynamic balance constraint condition satisfies the following thermal inertia condition: wherein is the room temperature at the time instant; is a single discrete time; is the air conditioning system activation power at the time instant; is the rated power of the air conditioning system.

5. The method for air conditioning regulation based on user comfort and carbon emission optimization of claim 1, wherein, The expression of the second objective function is as follows: wherein, represents the maximum user comfort level; represents the group thermal comfort level of the group in the air conditioning system cooling area at time i; L(i) is the number of people in the group in the cooling area at time i; ) represents the individual thermal comfort level of the m type user when performing the u activity at time i; is the total number of discrete time steps within the optimization period.

6. The method for air conditioning regulation based on user comfort and carbon emission optimization according to claim 5, wherein, the individual's sensory comfort ) is calculated as follows: wherein represents a user reference somatosensory comfort index of the m type user at the time instant when performing the u activity; represents a CO2 concentration to air quality influence index at the time instant, represents a weighting coefficient; is the actual indoor CO2 concentration at the time instant i; represents a deviation direction of the somatosensory comfort of the m type user at the time instant when performing the u activity.

7. The method for air conditioning regulation based on user comfort and carbon emission optimization according to claim 6, wherein, User reference body feel comfort index The calculation formula is as follows: wherein, represents the metabolic rate of an m type user while performing an u activity at time i; is the ratio of the area of the body covered by the garment to the area of the body exposed; h c is the thermal resistance value of the garment; t r is the body surface area of the body Average radiant temperature of radiation heat exchange; t cl Temperature of the outer surface of the clothing; Water vapor pressure of the environment; Room temperature at the time; Respiratory sensible heat discharge amount correction value based on CO2 concentration; Reference respiratory sensible heat discharge amount; Reference respiratory latent heat discharge amount; Respiratory sensible heat discharge amount and respiratory latent heat discharge amount of the m type user at the time when performing the u activity, respectively. Respiratory sensible heat discharge amount and respiratory latent heat discharge amount of the m type user at the time when performing the u activity, respectively.​​ 8. The method for air conditioning regulation based on user comfort and carbon emission optimization according to claim 3 or 7, characterized in that, The respiratory sensible heat dissipation amount and the respiratory latent heat dissipation amount The calculation formulas are as follows, respectively: wherein, is the room temperature at time i, denotes the respiratory rate of the m-type user at time i while performing the u-activity, is the reference respiratory rate; is the actual indoor CO2 concentration at time i; is the reference CO2 concentration; and denote the metabolic rate and the respiratory rate sensitivity coefficient of the m-type user at time i while performing the u-activity, respectively, and denote the metabolic rate and the respiratory rate of the m-type user at time i, denotes the scaling factor of the u-type activity at time i with respect to the basal metabolic rate of the m-type user; denotes the weighting factor of the u-type activity at time i with respect to the basal respiratory rate of the m-type user.

9. The method for air conditioning regulation based on user comfort and carbon emission optimization as claimed in claim 1 wherein, The constraint condition of the lower optimization model comprises: Upper and lower limits of individual thermal comfort constraints: ； Air conditioner start-stop constraints: In the formula; ) express The individual tactile comfort of type m users at any given time when performing activity u. They represent Time and The operating power of the air conditioning system at any given time; Represents a single discrete time; This represents the total number of time intervals between adjacent time points in N discrete time points; For the air conditioning system at all times The enabled status, This indicates that the air conditioner is on. This indicates that the air conditioner is off; This refers to the minimum operating time of the air conditioning system. This is the minimum downtime for the air conditioning system.

10. An air conditioning regulating device based on user comfort and carbon emission optimization, operating the user comfort and carbon emission optimization air conditioning regulating method as claimed in any one of claims 1 to 9, characterized by, The device comprises: A data collection unit is used for collecting building parameters, indoor temperature, outdoor temperature and air conditioning equipment operation parameters of an area where the air conditioning system is located; A first establishment unit is used for establishing an upper optimization model considering the influence of respiratory heat by taking the minimization of carbon emission as a first objective function; A second establishment unit is used for establishing a lower optimization model by taking the maximization of user comfort as a second objective function, and the influence of carbon dioxide concentration, different user activities and different types of user thermal comfort preferences is considered; An optimal control unit is used for solving the upper model and the lower model simultaneously based on the collected data to output an optimal control scheme of an activation power time sequence of the air conditioning system.

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