Air conditioner regulation and control method and device based on user comfort and carbon emission optimization

Abstract

The invention discloses an air conditioner regulation and control method and device based on user comfort and carbon emission optimization. The method comprises the steps that building parameters, indoor temperature, outdoor temperature and air conditioner equipment operation parameters of an area where an air conditioner system is located are collected; taking the minimum carbon emission as a first objective function, and establishing an upper-layer optimization model considering the respiratory heat influence; taking maximized user comfort as a second objective function, and establishing a lower-layer optimization model considering carbon dioxide concentration, different user activities and different types of user thermal comfort preference influences; and on the basis of the collected data, the upper-layer model and the lower-layer model are combined for solving, and an optimal control scheme of the starting power time sequence of the air conditioning system is output. According to the method, an efficient and feasible regulation and control strategy is provided for the intelligent air conditioning system, the low-carbon dispatching capacity of operation of the building energy consumption system is improved, and collaborative optimization of energy consumption control and user comfort is achieved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of air conditioning regulation, and particularly relates to an air conditioning regulation method and device based on user comfort and carbon emission optimization. BACKGROUND

[0002] Among many building energy systems, air conditioning system becomes the key link of carbon emission optimization scheduling due to its high frequency operation and large energy consumption proportion. However, the traditional air conditioning control strategy often takes indoor temperature as a single regulation index, ignores the dynamic changes of user's real thermal comfort, and is difficult to balance energy saving and comfort, which limits the further optimization of fine control and carbon optimization potential.

[0003] In the aspect of indoor heat balance model construction, the Chinese patent with publication number CN119321609A discloses an air conditioning flexible regulation method and device, computer equipment and computer program product, proposes to perform comfort alarm based on indoor air temperature prediction value, and adjust air conditioning indoor set temperature and air conditioning operation power according to building heat balance model, so as to realize control of air conditioning power and indoor temperature; the Chinese patent with publication number CN115392045A discloses a temporary building air conditioning heating operation optimization method, device, equipment and medium, a temporary building thermodynamic model is built based on the building structure of the temporary building and the heat transfer principle, the heat dissipation of the temporary building can be simply calculated through the model, and environmental factors are introduced, so that the heating effect of the air conditioner can be accurately calculated; however, these existing schemes ignore the influence of respiratory heat generated by crowd gathering in the building heat balance model, and the control precision needs to be improved.

[0004] In the aspect of user comfort evaluation, the Chinese patent with publication number CN119333940A proposes an air conditioning dynamic optimization method and system based on deep learning, adjusts and controls air conditioning equipment in real time through an air conditioning control system according to a user air conditioning optimal control strategy, so as to realize dynamic optimization of air conditioning parameters based on user comfort and energy saving target; the Chinese patent with publication number CN120257863A proposes an energy-saving temperature control optimization method and system based on a central air conditioning simulation platform, collects operation parameters of a central air conditioning system in real time through an Internet of Things sensor network, combines historical data and outdoor meteorological data, constructs a multi-dimensional dynamic data set, adopts a simulation engine driven by a physical model and machine learning, establishes a dynamic thermodynamic model of the central air conditioning system, defines a multi-objective optimization function of the simulation platform model according to user comfort requirements, energy consumption cost constraints and environmental policy indexes, so as to perform optimization of energy-saving temperature control; however, these existing schemes do not consider the influence of carbon dioxide concentration on human comfort, ignore the dynamic changes of user's real thermal comfort, and lack of research on individual differences of different user activities and different types of user thermal comfort preferences.

[0005] In summary, the current air conditioning system still lacks detailed consideration in comfort modeling and energy consumption scheduling based on heat balance model, and it is difficult to realize the double guarantee of energy saving and comfort, and the regulation scheme needs to be further optimized. SUMMARY

[0006] To solve the problems in the prior art, the application provides an air conditioner regulation method and device based on user comfort and carbon emission optimization, provides an efficient and feasible regulation strategy for an intelligent air conditioner system, improves the low-carbon scheduling ability of a building energy system operation, and realizes the collaborative optimization of energy consumption control and user comfort.

[0007] The application adopts the following technical solutions.

[0008] In a first aspect, the application provides an air conditioner regulation method based on user comfort and carbon emission optimization, which comprises the following steps: Collecting building parameters, indoor temperature, outdoor temperature and air conditioner equipment operation parameters in the area where the air conditioning system is located; Establishing an upper optimization model considering the influence of respiratory heat as a first objective function to minimize carbon emissions; Establishing a lower optimization model considering the influence of carbon dioxide concentration, different user activities and different types of user thermal comfort preferences as a second objective function to maximize user comfort; Based on the collected data, the upper model and the lower model are solved simultaneously to output the optimal control scheme of the start-up power time sequence of the air conditioning system.

[0009] Optionally, the expression of the first objective function is as follows:

[0010] In the formula, is the minimum carbon emission; is the carbon emission per 1kWh of power consumption; is the running power at time t; and D is the total discrete time step number in the optimization period.

[0011] Optionally, establishing an 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:

[0012]

[0013]

[0014]

[0015] In the formula,​ is the room temperature at the time t; is the outdoor temperature at the time t; is the running power at the time t; is the running power at the time t; is the running power at the time t; is the running power at the time t; is the heat transfer coefficient of the wall; , and are three coefficients determined by the building parameters, respectively; is the air density; is the standard floor area; is the floor height; is the specific heat capacity of air; is the internal wall surface heat storage coefficient; is the internal wall area; is the heat transfer parameter; is the heat transfer coefficient of the roof; , are the areas of the wall and the roof wall, respectively; and are the cooling load parameters of the electrical equipment and the lighting system, respectively; and are the unit area heat dissipation of the electrical equipment and the lighting system, respectively; is the refrigeration area; is the human sensible heat dissipation cooling load coefficient; respectively are the respiratory sensible heat dissipation and the respiratory latent heat dissipation of the m type user at the time t when the user is engaged in the u activity.

[0016] Optionally, the room temperature in the thermal inertia dynamic balance constraint condition satisfies the following thermal inertia condition:

[0017] In the formula, is the room temperature at the time t; is the room temperature at the time t; is a single discrete time; is the air conditioning system starting power at the time t; is the air conditioning system starting power at the time t; is the rated power of the air conditioning system.

[0018] Optionally, the expression of the second objective function is as follows:

[0019]

[0020] In the formula, represents the maximum user comfort; represents ​​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.

[0021] Optionally, the individual's perceived comfort The formula for calculating ) is as follows:

[0022]

[0023]

[0024] 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.

[0025] Optional, user baseline ergonomic comfort index The calculation formula is as follows:

[0026] 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 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. Optionally, the heat dissipation from respiration. and latent heat of respiration The calculation formulas are as follows:

[0027]

[0028]

[0029]

[0030] 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-type u activity relative to the basic breathing frequency of type m user.

[0031] Optionally, the constraints of the lower-level optimization model include: Individual comfort level upper and lower limits constraints: ; Air conditioning start / stop constraints:

[0032]

[0033]

[0034] In the formula; ) express The individual tactile comfort of type m users at any given time when performing activity u. They represent Time and Time sequence of the enabling power of the air conditioning system; Indicates a single discrete time; Indicates the total number of time periods between adjacent time points in N discrete time points; The minimum start-up time of the air conditioning system is The enabling state of the air conditioning system at time Indicates that the air conditioner is on; Indicates that the air conditioner is off; The minimum start-up time of the air conditioning system is The minimum shutdown time of the air conditioning system is

[0035] In a second aspect, the present application provides an air conditioning control device based on user comfort and carbon emission optimization, which runs the steps of the method according to any one of the first aspect of the present application, and the device comprises: A data acquisition unit for acquiring building parameters, indoor temperature, outdoor temperature and air conditioning equipment operating parameters in the area where the air conditioning system is located; A first establishment unit for establishing an upper optimization model considering the influence of respiratory heat, with minimizing carbon emissions as the first objective function; A second establishment unit for establishing a lower optimization model considering the influence of carbon dioxide concentration, different user activities and different types of user thermal comfort preferences, with maximizing user comfort as the second objective function; An optimization control unit for solving the upper model and the lower model based on the collected data, and outputting the optimal control scheme of the time sequence of the enabling power of the air conditioning system.

[0036] In a third aspect, the present application provides a terminal comprising a processor and a storage medium; The storage medium is used to store instructions; The processor is used to operate according to the instructions to perform the steps of the method according to any one of the first aspect of the present application.

[0037] In a fourth aspect, the present application provides a computer readable storage medium having a computer program stored thereon, which is executed by a processor to implement the steps of the method according to any one of the first aspect of the present application.

[0038] The present application has the following advantages compared with the prior art: The present application aims at the technical problem that current air conditioning systems still lack fine consideration in comfort modeling and energy consumption scheduling based on heat balance model, and it is difficult to realize the double guarantee of energy saving and comfort; by establishing an upper optimization model considering the influence of respiratory heat and establishing a lower optimization model considering the influence of carbon dioxide concentration, different user activities and different types of user thermal comfort preferences, an efficient and feasible control strategy is provided for the intelligent air conditioning system, and the low-carbon scheduling ability of the building energy system operation is improved, realizing the collaborative optimization of energy consumption control and user comfort.

[0039] The present application introduces the user respiratory heat influencing factor in the traditional PMV thermal comfort model when measuring user comfort, quantifies the correlation between respiratory sensible heat and latent heat, metabolic rate, indoor CO2 concentration and other parameters, upgrades the thermal comfort evaluation from pure environmental parameter fitting to joint calculation of environmental and human physiological mechanism, which is more in line with the real human body feeling, and breaks through the evaluation deviation caused by the neglect of key physiological factors in the traditional model; at the same time, combined with the basic metabolic rate, respiratory sensitivity difference of different user types, and the metabolic rate and respiratory frequency correction coefficient under different activity states, and then combined with the adjustment of personalized comfort threshold, the accurate transition from group average to individual is realized, which can dynamically respond to user differences; in addition, through the closed-loop feedback of CO2 concentration and respiratory heat, the collaborative optimization of group comfort and the dynamic balance of thermal inertia and regulation, the adaptability in complex scenes such as multiple people living together and various activities is enhanced, and the accurate comfort and carbon emission optimization are deeply coupled, which is solved by the upper and lower models, supporting the upgrade of intelligent regulation while considering comfort and low carbon, and significantly improving the practicality and intelligence of the model in practical application. BRIEF DESCRIPTION OF DRAWINGS

[0040] Figure 1 The air conditioning regulation method based on user comfort and carbon emission optimization in the present application is a flow chart; Figure 2 The structure principle block diagram of the air conditioning regulation device based on user comfort and carbon emission optimization in the present application. DETAILED DESCRIPTION

[0041] In order to make the purpose, technical scheme and advantages of the present application clearer, the technical scheme of the present application will be described clearly and completely below in combination with the drawings in the embodiments of the present application. The described embodiments of the present application are only a part of the embodiments of the present application, not all the embodiments. All other embodiments obtained by those skilled in the art without creative labor based on the spirit of the present application are within the protection scope of the present application.

[0042] Embodiment one: With reference to Figure 1 The embodiment of the present application provides an air conditioning regulation method based on user comfort and carbon emission optimization, which specifically comprises the following steps: 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; Step S2: Establish an upper-level optimization model that considers the impact of respiratory heat, with minimizing carbon emissions as the primary objective function; 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. 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.

[0043] 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; 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: Table 1

[0044] Table 2

[0045] As an embodiment of the present invention, step S2, establishing an upper-level optimization model that considers the influence of respiratory heat, includes: S2.1. Establish the first objective function to minimize carbon emissions:

[0046] 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.

[0047] S2.2, Define the constraints for the upper-level optimization model: thermal inertia dynamic equilibrium constraints and air conditioning power constraints; The expression for the air conditioner power constraint is as follows:

[0048] In the formula, This refers to the minimum rated cooling load power of the air conditioner. The maximum cooling load power of the air conditioner is rated.

[0049] In this embodiment, the construction process of the thermal inertia dynamic balance constraint is as follows: (1) Establish the breathing frequency and CO2 concentration function:

[0050] In the formula, is the breathing frequency and CO2 concentration function; is the reference breathing frequency, which is set to 12 times / min in this embodiment; is the breathing frequency sensitivity coefficient of the m type user at time i when performing the u activity; is the actual indoor CO2 concentration at time i; is the reference CO2 concentration, which is set to 550 ppm in this embodiment. .

[0051] wherein the breathing frequency sensitivity :

[0052] In the formula, represents the breathing frequency of the m type user at time i, represents the weighting coefficient of the u type activity relative to the basic breathing frequency of the m type user at time i.

[0053] (2) The breathing heat dissipation dynamic model is established based on the breathing frequency and CO2 concentration function as follows:

[0054] In the formula: respectively the respiratory sensible heat dissipation and the respiratory latent heat dissipation of the m type user at time i when performing the u activity; represents the metabolic rate of the m type user at time i when performing the u activity; is the water vapor pressure of the environment, which is set to 2000 Pa in this embodiment; wherein the metabolic rate is calculated as follows:

[0055] In the formula, represents the metabolic rate of the m type user at time i, represents the proportional coefficient of the u type activity relative to the basic metabolic rate of the m type user at time i. The 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.

[0056] Furthermore, examples of human energy metabolism rate and respiratory rate under different types of user activities in this embodiment are shown in Table 3: Table 3

[0057] (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:

[0058]

[0059]

[0060]

[0061] 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 respectively, the heat dissipation per unit area of the electrical equipment and the lighting system; is the area of the refrigeration region; is the sensible heat dissipation load coefficient of the human body; respectively, is the respiratory sensible heat dissipation and the respiratory latent heat dissipation of the m-type user at the time t when the user is performing the u activity.

[0062] Further, the room temperature in the thermal inertia dynamic balance constraint condition satisfies the following thermal inertia condition:

[0063] wherein, is is the room temperature at the time t; is a single discrete time; is is the air conditioning system enabling power at the time t; is the rated power of the air conditioning system.

[0064] As an embodiment of the present application, the step S3 of establishing the lower optimization model considering the influence of the carbon dioxide concentration, different user activities and different type user thermal comfort preferences comprises: S3.1, establishing a second objective function of maximizing user comfort:

[0065]

[0066] wherein, represents the maximum user comfort; represents is the group thermal comfort of the group in the refrigeration area of the air conditioning system at the time t; L(i) is is the number of the group in the refrigeration area at the time t; represents is the individual thermal comfort of the m-type user when performing the u activity at the time t; is the total number of the discrete time steps in the optimization period.

[0067] S3.2, formulating the constraint conditions of the lower optimization model: the air conditioning start-stop constraint and the upper and lower limit constraints of the individual thermal comfort; wherein, the upper and lower limit constraints of the individual thermal comfort are: ; Further, the calculation process of the individual thermal comfort in the embodiment is as follows: (1) Based on the heat dissipation dynamic model established in the step S2, the respiratory heat dissipation of the individual user caused by the carbon dioxide concentration is corrected, and the expression is as follows:

[0068] In the formula, The respiratory heat dissipation amount correction value of the m type user at the i moment based on the CO2 concentration when performing the u activity: is the reference respiratory sensible heat dissipation amount; is the reference respiratory latent heat dissipation amount; (2) Introducing the influencing factor of user respiratory heat in the traditional PMV thermal comfort model , the calculation formula of the user reference thermal comfort index is as follows:

[0069] In the formula, represents the metabolic rate of the m type user at the i moment when performing the u activity; is the ratio of the clothing covered area to the naked area; h c is the clothing thermal resistance value; t r is the average radiation temperature, that is, the equivalent temperature corresponding to the comprehensive effect of radiation heat exchange of all surfaces (walls, floors, ceilings, windows, equipment, etc.) around the human body on the human body, and the value in the embodiment is 29.7℃; t cl is the clothing outer surface temperature, and the value in the embodiment is 32℃.

[0070] (3) Combining the CO2 concentration to superimpose the air quality feeling correction individual thermal comfort ):

[0071]

[0072]

[0073] In the formula, represents the air quality at the i moment; represents a weighting coefficient, and preferably, the embodiment sets to 0.5; is the actual indoor CO2 concentration at the i moment; represents the deviation direction (biased hot, biased cold, or balanced) of the thermal comfort of the m type user at the i moment when performing the u activity. In the embodiment, the expression of the air conditioner start-stop constraint is as follows:

[0074] ​​

[0075]

[0076]

[0077] In the formula, respectively represent the on time and the on power of the air conditioning system at time represent a single discrete time; represent the total number of time periods between adjacent time points in N discrete time points; the on state of the air conditioning system at time , representing the air conditioning being on, representing the air conditioning being off; the minimum start-up time of the air conditioning system, which ensures that the air conditioning system maintains a running time of after starting up, and does not cause instability due to too frequent start-up and shutdown; the minimum shutdown time of the air conditioning system, which ensures that the system maintains a certain shutdown time after each shutdown, avoiding the negative effects of frequent start-up and shutdown.

[0078] As an embodiment of the present application, in step S4, the current collected data is used to solve the upper model and the lower model by using an optimization algorithm in combination with the objective function and the constraint condition, to obtain the optimal control scheme of the on power time sequence of the air conditioning system.

[0079] In summary, by inputting the initial values of the air conditioning power limit, the carbon emission factor, the user comfort value, the on power limit, and the start-stop time constraint, etc., a double-layer optimization model is used, the upper layer objective is to minimize the carbon emission of the air conditioning system, the lower layer objective is to maximize the user comfort, and an alternating optimization method is used to fix the on power of the lower layer, optimize the power load of the upper layer to minimize the carbon emission, and then fix the power load of the upper layer, optimize the on power of the lower layer to maximize the user comfort. Through this alternating optimization strategy, the decision variables are gradually updated and iterated, and finally the optimal air conditioning on time and on power are obtained, ensuring that the air conditioning system realizes optimal control under the premise of meeting the carbon emission constraint and comfort requirement. It should be further explained that there are many related existing algorithms for solving the alternating optimization method of the double-layer model, and a person skilled in the art can select one of the algorithms for solving, which is not the focus of the present application and will not be described here.

[0080] The present application has the following advantages compared with the prior art: The present application aims at the technical problem that current air conditioning systems still lack fine consideration in comfort modeling and energy consumption scheduling based on heat balance model, and it is difficult to realize the double guarantee of energy saving and comfort; by establishing an upper optimization model considering the influence of respiratory heat and a lower optimization model considering the influence of carbon dioxide concentration, different user activities and different types of user thermal comfort preference, an efficient and feasible regulation and control strategy is provided for the intelligent air conditioning system, the low-carbon scheduling ability of the building energy system operation is improved, and the collaborative optimization of energy consumption control and user comfort is realized.

[0081] The present application introduces the user respiratory heat influencing factor in the traditional PMV thermal comfort model when measuring the user comfort, quantifies the correlation between respiratory sensible heat and latent heat and parameters such as metabolic rate and indoor CO2 concentration, upgrades the thermal comfort evaluation from pure environmental parameter fitting to joint calculation of environmental and human physiological mechanism, which is more in line with the real human body feeling, and breaks through the evaluation deviation caused by the neglect of key physiological factors in the traditional model; at the same time, combined with the basic metabolic rate and respiratory sensitivity difference of different user types, as well as the metabolic rate and respiratory frequency correction coefficient under different activity states, and then combined with the adjustment of personalized comfort threshold, the accurate individual crossing from group average is realized, which can dynamically respond to user differences; in addition, through the closed-loop feedback of CO2 concentration and respiratory heat, the collaborative optimization of group comfort and the dynamic balance of thermal inertia and regulation, the adaptability in complex scenes such as multiple people living together and various activities is enhanced, and the accurate comfort and carbon emission optimization are deeply coupled, which supports the upgrade of intelligent regulation and control while considering comfort and low carbon, and significantly improves the practicability and intelligence of the model in actual application.

[0082] Embodiment two: As shown in the embodiment one, Figure 2 The present application provides an air conditioning regulation and control device based on user comfort and carbon emission optimization, the device is used to realize the steps of the method in the above embodiment one, and the device specifically comprises: A data acquisition unit is configured to acquire building parameters, indoor temperature, outdoor temperature and air conditioning equipment operation parameters of an area where an air conditioning system is located; A first establishing unit is configured to establish an upper optimization model considering the influence of respiratory heat by taking the minimization of carbon emission as a first objective function; A second establishing unit is configured to establish a lower optimization model considering the influence of carbon dioxide concentration, different user activities and different types of user thermal comfort preference by taking the maximization of user comfort as a second objective function; An optimization control unit is configured to solve the upper model and the lower model based on the acquired data, and output an optimal control scheme of an activation power time sequence of the air conditioning system.

[0083] The air conditioner regulating device based on user comfort and carbon emission optimization provided by the embodiment of the present application and the air conditioner regulating method based on user comfort and carbon emission optimization provided by the embodiment one are based on the same technical concept, and can produce the beneficial effects as described in the embodiment one, and the contents not described in detail in the present embodiment can be referred to the embodiment one.

[0084] Embodiment three: The terminal provided by the embodiment of the present application includes a processor and a storage medium, and the terminal is an embedded computer system device. The storage medium of the terminal is used for storing instructions, and the storage medium includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operating system and the computer program in the non-volatile storage medium to run. The database is used for storing instruction data. The processor of the terminal is used for operating according to the instructions provided by the storage medium to perform the steps of the air conditioner regulating method based on user comfort and carbon emission optimization according to any one of the embodiments one.

[0085] Embodiment four: The computer readable storage medium provided by the embodiment of the present application has a computer program stored thereon. The program is executed by a processor to implement the steps of the method according to any one of the embodiments one.

[0086] The present application can be a system, a method, and / or a computer program product. The computer program product can include a computer readable storage medium (or media) having computer readable program instructions thereon for causing a processor to carry out aspects of the present application.

[0087] The computer readable storage medium can be a tangible device that can retain and store instructions for use by an instruction execution device. The computer readable storage medium can be, for example, but is not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium include the following: a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanically encoded device such as punch-cards or punched-tape, and any suitable combination of the foregoing. A computer readable storage medium, as used herein, is not to be construed as being transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission media (e.g., light pulses passing through a fiber-optic cable), or electrical signals transmitted through a wire.

[0088] Computer readable program instructions described herein can be downloaded to respective computing / processing devices from a computer readable storage medium or to an external computer or external storage device via a network, for example, the Internet, a local area network, a wide area network and / or a wireless network. The network can comprise copper transmission cables, optical transmission fibers, wireless transmission, routers, firewalls, switches, gateway computers and / or edge servers. A network adapter card or network interface in each computing / processing device receives computer readable program instructions from the network and forwards the computer readable program instructions for storage in a computer readable storage medium within the respective computing / processing device.

[0089] Computer readable program instructions for carrying out operations of the present application can be assembler instructions, instruction-set-architecture (ISA) instructions, machine instructions, machine dependent instructions, microcode, firmware instructions, state-setting data, or either source code or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C++ or the like, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The computer readable program instructions can execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection can be made to an external computer (for example, through the Internet using an Internet Service Provider). In some embodiments, electronic circuitry including, for example, programmable logic circuitry, field-programmable gate array (FPGA), or programmable logic array (PLA) can execute the computer readable program instructions by utilizing state information of the computer readable program instructions to personalize the electronic circuitry, in order to perform aspects of the present application.

[0090] Finally, it should be noted that the above-mentioned embodiments illustrate rather than limit the application, and that those skilled in the art will be able to design many alternative embodiments without departing from the scope of the appended claims. Accordingly, the legal scope of the application is defined only by the appended claims.

Claims

1. An air conditioning control method based on user comfort and carbon emission optimization, characterized in that the method... include: Collect building parameters, indoor temperature, outdoor temperature, and air conditioning equipment operating parameters for the area where the air conditioning system is located; With minimizing carbon emissions as the primary objective function, an upper-level optimization model considering the impact of respiratory heat is established. 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. 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.

2. The air conditioning control method based on user comfort and carbon emission optimization according to claim 1, characterized in that, The expression for the first objective function is as follows: 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.

3. The air conditioning control method based on user comfort and carbon emission optimization according to claim 1, characterized in that, 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: 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.

4. The air conditioning control method based on user comfort and carbon emission optimization according to claim 3, characterized in that, The room temperature in the thermal inertia dynamic equilibrium constraint condition satisfies the following thermal inertia condition: 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.

5. The air conditioning control method based on user comfort and carbon emission optimization according to claim 1, characterized in that, The expression for the second objective function is as follows: 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.

6. The air conditioning control method based on user comfort and carbon emission optimization according to claim 5, characterized in that, Individual physical comfort The formula for calculating ) is as follows: 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.

7. The air conditioning control method based on user comfort and carbon emission optimization according to claim 6, characterized in that, User benchmark physical comfort index The calculation formula is as follows: 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 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.

8. The air conditioning control method based on user comfort and carbon emission optimization according to claim 3 or 7, characterized in that, The heat dissipation of respiratory sensible heat and latent heat of respiration The calculation formulas are as follows: 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-type u activity relative to the basic breathing frequency of type m user.

9. The air conditioning control method based on user comfort and carbon emission optimization according to claim 1, characterized in that, The constraints of the lower-level optimization model include: Individual comfort level upper and lower limits constraints: ； Air conditioning 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 control device based on user comfort and carbon emission optimization, operating the air conditioning control method based on user comfort and carbon emission optimization as described in any one of claims 1-9, characterized in that, The device includes: 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. 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. 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. 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.

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