Air conditioning system flexible adjustment potential quantification method and device, terminal and storage medium

Abstract

The invention provides an air conditioning system flexible adjustment potential quantification method and device, a terminal and a storage medium, and belongs to the technical field of building energy management.The method comprises the steps that indoor and outdoor environment data and air conditioning system operation data of a target building are continuously collected, and a first training data subset and a second training data subset are screened out; constructing a room temperature prediction model; sequentially determining air conditioning system thermal resistance, a thermal resistance and thermal capacity parameter combination and an indoor heating timetable in the room temperature prediction model, and taking a calculation result as a model fixed parameter after each calculation; obtaining a room temperature prediction curve; and determining the maximum adjustable duration as the flexible adjustment potential of the air conditioning system. According to the method, the operation data of the air conditioning system are considered when the room temperature prediction model is constructed, the model is used for solving the maximum response duration of the air conditioning system during demand response, the quantification precision of the flexible adjustment potential of the air conditioning system can be effectively improved, and accurate data support can be provided for a demand response strategy.

Description

Technical Field

[0001] The present invention relates to the technical field of building energy management, and in particular to a method, device, terminal and storage medium for quantifying the flexible regulation potential of an air-conditioning system. Background Art

[0002] At present, the proportion of renewable energy generation, dominated by wind power and photovoltaic power, continues to rise. However, the volatility and intermittent characteristics of renewable energy generation have aggravated the pressure on the balance of supply and demand of the power grid, resulting in the contradiction between power supply and demand during peak and valley periods. Demand response (DR) technology realizes the paradigm shift from "source follows load" to "load follows source" by dynamically adjusting the user-side load, and has become a key technical path to improve the elasticity of the power grid and promote the consumption of new energy. As the main energy consumer, the air-conditioning system of public buildings accounts for up to 70% of the energy consumption in summer. Since the energy storage of the building itself and the thermal inertia of the air-conditioning allow short-term load reduction, the impact on human thermal comfort is small, so the air-conditioning system has become a highly potential flexible regulation resource. By accurately quantifying the flexible regulation potential of the air-conditioning system, the peak-shaving and valley-filling benefits of the power grid can be maximized while minimizing the user's perceived disturbance, and the dual goals of building energy conservation and emission reduction and power grid stability can be promoted in coordination.

[0003] In the existing technology, a thermal resistance and heat capacity model based on the equivalent thermal network theory is usually constructed as a room temperature prediction model. The thermal transfer process of the building system is described by thermal resistance and heat capacity parameters, which can simulate dynamic characteristics such as indoor and outdoor heat exchange and provide theoretical support for flexible quantification. However, since the model does not take into account the heat storage of the air-conditioning system itself and its enthalpy and humidity dynamic characteristics, the determination result of the flexible adjustment potential of the air-conditioning system is not accurate enough, making it difficult to provide accurate data support for demand response strategies.

[0004] Therefore, the prior art has defects and needs to be improved and developed. Summary of the invention

[0005] The technical problem to be solved by the present invention is that, in view of the above-mentioned defects of the prior art, a method, device, terminal and storage medium for quantifying the flexible adjustment potential of an air-conditioning system are provided, aiming to solve the problem in the prior art that it is difficult to provide accurate data support for demand response strategies.

[0006] The technical solution adopted by the present invention to solve the technical problem is as follows: In a first aspect, an embodiment of the present invention provides a method for quantifying the flexible adjustment potential of an air conditioning system, the method comprising: Using a plurality of preset sensors to continuously collect indoor and outdoor environmental data and air conditioning system operation data of the target building, and screening out a first training data subset and a second training data subset from historically collected time-series indoor and outdoor environmental data and time-series air conditioning system operation data; Using the first training data subset and the second training data subset, a room temperature prediction model is constructed, wherein the room temperature prediction model is used to predict changes in temperature in a target building after a demand response is initiated; Calculate the thermal resistance of the air conditioning system, the combination of thermal resistance and heat capacity parameters, and the indoor heating schedule in the room temperature prediction model in sequence, and use the calculation results as fixed parameters of the model after each calculation; When a demand response instruction is received, the wall temperature at the time of demand response initiation is calculated, and the wall temperature and the indoor and outdoor environment data and air conditioning system operation data collected last time before the demand response initiation are substituted into the room temperature prediction model with fixed parameters to obtain the room temperature prediction curve; Based on the demand response start time, the preset temperature threshold and the room temperature prediction curve, the maximum adjustable time is determined and used as the flexible adjustment potential of the air-conditioning system.

[0007] In one embodiment, the indoor and outdoor environmental data include indoor air temperature and outdoor air dry bulb temperature, and the air conditioning system operation data include chilled water outlet temperature, chilled water return temperature, return air temperature, supply air temperature, chilled water flow rate and air humidity; from the historically collected time-series indoor and outdoor environmental data and time-series air conditioning system operation data, a first training data subset and a second training data subset are screened out, including: From the historically collected time-series indoor and outdoor environmental data and time-series air conditioning system operation data, the indoor air temperature, outdoor air dry-bulb temperature, chilled water outlet temperature, chilled water return temperature, return air temperature, supply air temperature, chilled water flow rate and air humidity collected in the first historical time period are selected as the first training data subset. In the first historical time period, the target building is in a state of no internal heat, the air conditioning compressor is turned off, and the water pump and the terminal air handling unit are running; From the historically collected time-series indoor and outdoor environmental data and the time-series air-conditioning system operation data, the indoor air temperature, outdoor air dry-bulb temperature, chilled water outlet temperature, chilled water return temperature, return air temperature, supply air temperature, chilled water flow rate and air humidity collected in the second historical time period are selected as the second training data subset. In the second historical time period, the target building is in an internal heat state and the air-conditioning system operates stably.

[0008] In one embodiment, constructing a room temperature prediction model using the first training data subset and the second training data subset includes: Filter out the chilled water outlet temperature, chilled water return temperature and indoor air temperature at a target time from the first training data subset, wherein the target time is the time when the air conditioning system temperature reaches the dew point temperature when the target building is in a state of no internal heat, the air conditioning compressor is turned off, and the water pump and the terminal air handling unit are running; Obtaining a first air conditioning system temperature based on a chilled water outlet temperature and a chilled water return temperature at a target time, and obtaining a temperature difference coefficient based on the first air conditioning system temperature and an indoor air temperature at a target time; obtaining a return air enthalpy value based on the return air temperature and the indoor humidity of the second training data subset, and obtaining a supply air enthalpy value based on the supply air temperature and the indoor humidity of the second training data subset; Obtaining a preset specific heat capacity of air, and obtaining a sensible heat ratio based on the specific heat capacity of air, the return air temperature of the second training data subset, the supply air temperature of the second training data subset, the return air enthalpy value, and the supply air enthalpy value; Based on the sensible heat ratio, latent heat and sensible heat ratio are obtained, and based on the chilled water outlet temperature and the chilled water return temperature of the second training data subset, a second air conditioning system temperature is obtained; Obtaining a proportionality coefficient based on the ratio of latent heat to sensible heat, the indoor air temperature of the second training data subset, the second air conditioning system temperature, and the temperature difference coefficient; Based on the temperature difference coefficient and the proportionality coefficient, the heat storage characteristics of the target building and the air-conditioning system are coupled to construct a room temperature prediction model.

[0009] In one embodiment, calculating the thermal resistance of the air conditioning system in the room temperature prediction model includes: Filtering out several groups of target data collected in a target historical time period from the second training data subset, each group of target data includes chilled water flow, chilled water return temperature, chilled water outlet temperature and indoor air temperature; Based on the chilled water return temperature and the chilled water outlet temperature in each set of target data, a third air conditioning system temperature is obtained; Based on the chilled water flow rate, chilled water return temperature and chilled water outlet temperature in each set of target data, the heat transfer amount between the indoor air and the air conditioning system is obtained; Based on the heat transfer amount and the sensible heat ratio between the indoor air and the air conditioning system, obtaining the sensible heat transfer amount between the indoor air and the air conditioning system; The thermal resistance of the air conditioning system is obtained based on the third air conditioning system temperature, the sensible heat transfer amount between the indoor air and the air conditioning system, and the indoor air temperature in each set of target data.

[0010] In one embodiment, calculating the thermal resistance and heat capacity parameter combination in the room temperature prediction model includes: Calculate a first wall temperature corresponding to the first training data subset based on the first training data subset, where the first wall temperature is the wall temperature corresponding to the initial acquisition moment in the first training data subset; Substituting the first wall temperature and the first training data subset into a room temperature prediction model with a fixed thermal resistance of the air conditioning system, taking the thermal resistance and heat capacity parameter combination to be determined in the room temperature prediction model as a variable to be optimized, performing a first preset round of optimization on it using a genetic algorithm, and calculating the value of the first objective function corresponding to each round of optimization; Sort the values ​​of the first objective function of all rounds, select a preset number of groups of thermal resistance and heat capacity parameter combinations corresponding to the first objective function from small to large, and calculate the deviation value corresponding to each selected thermal resistance and heat capacity parameter combination; Select a set of thermal resistance and heat capacity parameter combinations with the smallest deviation value as the final thermal resistance and heat capacity parameter combination; The thermal resistance and heat capacity parameter combination includes: the resistance of heat transfer between the building envelope and indoor air, the resistance of heat transfer between the building envelope and outdoor air, the heat capacity of indoor air, the heat capacity of the air conditioning system and the heat capacity of the building envelope.

[0011] In one embodiment, calculating the first wall temperature corresponding to the first training data subset includes: Taking the indoor air temperature at the initial collection time in the first training data subset as the starting point and the outdoor air dry-bulb temperature at the initial collection time as the end point, generating a candidate wall temperature sequence according to a preset fixed step size, wherein the candidate wall temperature sequence includes a plurality of candidate wall temperatures; Substituting each candidate wall temperature and the first training data subset into a room temperature prediction model for which the thermal resistance of the air conditioning system has been determined, and optimizing the thermal resistance and heat capacity parameter combinations to be determined in the room temperature prediction model by using a genetic algorithm for a second preset round to obtain a plurality of groups of thermal resistance and heat capacity parameter combinations corresponding to each candidate wall temperature; Using a preset deviation function to evaluate several thermal resistance and heat capacity parameter combinations corresponding to each candidate wall temperature, and selecting the thermal resistance and heat capacity parameter combination with the smallest deviation as the candidate thermal resistance and heat capacity parameter combination corresponding to the candidate wall temperature; Select the time-series indoor air temperature and the time-series outdoor dry-bulb air temperature collected during the second target historical time period from the historically collected time-series indoor and outdoor environmental data; Substituting the time-series indoor air temperature, the time-series outdoor dry-bulb air temperature and the candidate thermal resistance and heat capacity parameter combination corresponding to each candidate wall temperature into the room temperature prediction model of the determined air conditioning system thermal resistance, and obtaining several wall estimated temperatures through calculation; The error between each candidate wall temperature and its corresponding estimated wall temperature is calculated, and the candidate wall temperature with the smallest error is selected as the first wall temperature.

[0012] In one embodiment, calculating an indoor heat gain schedule in a room temperature prediction model includes: Substituting the second training data subset into a room temperature prediction model for which the thermal resistance and thermal resistance and heat capacity parameter combination of the air conditioning system has been determined, taking the indoor heat gain hour by hour within a preset time range in the room temperature prediction model as a variable to be optimized, and iteratively adjusting its value through a genetic algorithm to minimize the second objective function; When the preset end condition is reached, an indoor heating schedule consisting of the indoor heat gain every hour within the preset time range is obtained.

[0013] In a second aspect, an embodiment of the present invention further provides a device for quantifying the flexible adjustment potential of an air conditioning system, comprising: A data acquisition module is used to continuously collect indoor and outdoor environmental data and air conditioning system operation data of a target building using a plurality of preset sensors, and to select a first training data subset and a second training data subset from the historically collected time-series indoor and outdoor environmental data and time-series air conditioning system operation data; A model building module, used to build a room temperature prediction model using the first training data subset and the second training data subset, wherein the room temperature prediction model is used to predict the change of the temperature in the target building after the demand response is started; A parameter determination module, used to sequentially calculate the thermal resistance of the air conditioning system, the combination of thermal resistance and heat capacity parameters, and the indoor heating schedule in the room temperature prediction model, and use the calculation results as model fixed parameters after each calculation; The curve generation module is used to calculate the wall temperature at the time of demand response start when receiving the demand response instruction, and substitute the wall temperature and the indoor and outdoor environment data and air conditioning system operation data collected last time before the demand response start into the room temperature prediction model with fixed parameters to obtain the room temperature prediction curve; The duration determination module is used to determine the maximum adjustable duration based on the demand response start time, the preset temperature threshold and the room temperature prediction curve, and use it as the flexible adjustment potential of the air-conditioning system.

[0014] In a third aspect, an embodiment of the present invention further provides a terminal, comprising: a memory, a processor, and an air-conditioning system flexible adjustment potential quantification program stored in the memory and executable on the processor, wherein the air-conditioning system flexible adjustment potential quantification program implements the steps of the air-conditioning system flexible adjustment potential quantification method as described above when executed by the processor.

[0015] In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium, which stores a program for quantifying the flexible adjustment potential of an air-conditioning system, and the program for quantifying the flexible adjustment potential of an air-conditioning system can be executed to implement the steps of the method for quantifying the flexible adjustment potential of an air-conditioning system as described above.

[0016] Beneficial effects of the present invention: The present invention collects indoor and outdoor environmental data and air-conditioning system operation data in the target building, and screens out a first training data subset and a second training data subset; constructs a room temperature prediction model; sequentially determines the air-conditioning system thermal resistance, thermal resistance and heat capacity parameter combination, and indoor heating schedule in the room temperature prediction model, and uses the calculation results as model fixed parameters after each calculation; obtains a room temperature prediction curve; determines the maximum adjustable time, and uses it as the flexible adjustment potential of the air-conditioning system. The present invention takes the air-conditioning system operation data into consideration when constructing the room temperature prediction model, and uses the model to solve the maximum response time of the air-conditioning system during demand response, which can effectively improve the quantification accuracy of the flexible adjustment potential of the air-conditioning system and provide accurate data support for demand response strategies. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is a flow chart of a preferred embodiment of the method for quantifying the flexible regulation potential of an air conditioning system in the present invention.

[0018] Figure 2 It is a structural diagram of the room temperature prediction model in the present invention.

[0019] Figure 3 It is a schematic diagram of the relationship between the actual value and the predicted value of the indoor air temperature in the present invention.

[0020] Figure 4 It is a schematic diagram of the relationship between the actual value and the predicted value of the temperature of the air-conditioning system in the present invention.

[0021] Figure 5 It is a schematic diagram of indoor heat gain time in the present invention.

[0022] Figure 6 It is a schematic diagram of the comparison results between the present invention and the two thermal resistances + two thermal capacitances model and the actual value.

[0023] Figure 7 It is a structural schematic diagram of a preferred embodiment of the device for quantifying the flexible regulation potential of an air-conditioning system in the present invention.

[0024] Figure 8 It is a terminal principle block diagram of the present invention. DETAILED DESCRIPTION

[0025] In order to make the purpose, technical solution and advantages of the present invention clearer and more specific, the present invention is further described in detail below with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0026] At present, the proportion of renewable energy generation, dominated by wind power and photovoltaic power, continues to rise. However, the volatility and intermittent characteristics of renewable energy generation have aggravated the pressure on the balance of supply and demand of the power grid, resulting in the contradiction between power supply and demand during peak and valley periods. Demand response (DR) technology realizes the paradigm shift from "source follows load" to "load follows source" by dynamically adjusting the user-side load, and has become a key technical path to improve the elasticity of the power grid and promote the consumption of new energy. As the main energy consumer, the air-conditioning system of public buildings accounts for up to 70% of the energy consumption in summer. Since the energy storage of the building itself and the thermal inertia of the air-conditioning allow short-term load reduction, the impact on human thermal comfort is small, so the air-conditioning system has become a highly potential flexible regulation resource. By accurately quantifying the flexible regulation potential of the air-conditioning system, the peak-shaving and valley-filling benefits of the power grid can be maximized while minimizing the user's perceived disturbance, and the dual goals of building energy conservation and emission reduction and power grid stability can be promoted in coordination.

[0027] In the existing technology, a thermal resistance and heat capacity model based on the equivalent thermal network theory is usually constructed. The heat transfer process of the building system is described by thermal resistance and heat capacity parameters, which can simulate dynamic characteristics such as indoor and outdoor heat exchange and provide theoretical support for flexible quantification. This model can be used to evaluate the flexible adjustment potential of the air-conditioning system in demand response. However, since the model does not consider the heat storage of the air-conditioning system itself and its enthalpy and humidity dynamic characteristics, the quantitative results of the flexible adjustment potential of the air-conditioning system are not accurate enough, making it difficult to provide accurate data support for demand response strategies.

[0028] In view of the above-mentioned defects of the prior art, the present invention provides a method, device, terminal and storage medium for quantifying the flexible adjustment potential of an air-conditioning system. The method includes: continuously collecting indoor and outdoor environmental data and air-conditioning system operation data in a target building, and screening out a first training data subset and a second training data subset; constructing a room temperature prediction model; sequentially determining the air-conditioning system thermal resistance, thermal resistance and heat capacity parameter combination and indoor heating timetable in the room temperature prediction model, and using the calculation results as fixed parameters of the model after each calculation; obtaining a room temperature prediction curve; and determining the maximum adjustable time as the flexible adjustment potential of the air-conditioning system. The present invention takes the air-conditioning system operation data into consideration when constructing the room temperature prediction model. The model is used to solve the maximum response time of the air-conditioning system during demand response, which can effectively improve the accuracy of quantifying the flexible adjustment potential of the air-conditioning system and provide accurate data support for demand response strategies.

[0029] See also Figure 1 The method for quantifying the flexible regulation potential of an air conditioning system according to an embodiment of the present invention comprises the following steps: Step S100: Utilize a plurality of preset sensors to continuously collect indoor and outdoor environmental data and air conditioning system operation data of a target building, and select a first training data subset and a second training data subset from historically collected time-series indoor and outdoor environmental data and time-series air conditioning system operation data.

[0030] Specifically, the indoor and outdoor environmental data include indoor air temperature and outdoor air dry bulb temperature. The air conditioning system operation data include chilled water outlet temperature, chilled water return temperature, return air temperature, supply air temperature, chilled water flow rate and air humidity. A first training data subset and a second training data subset are screened out from the historically collected time-series indoor and outdoor environmental data and the time-series air-conditioning system operation data, including: from the historically collected time-series indoor and outdoor environmental data and the time-series air-conditioning system operation data, the indoor air temperature, outdoor air dry-bulb temperature, chilled water outlet temperature, chilled water return temperature, return air temperature, supply air temperature, chilled water flow rate and air humidity collected in a first historical time period are screened out as the first training data subset, and in the first historical time period, the target building is in a state without internal heat, the air-conditioning compressor is turned off, and the water pump and the terminal air handling unit are running; from the historically collected time-series indoor and outdoor environmental data and the time-series air-conditioning system operation data, the indoor air temperature, outdoor air dry-bulb temperature, chilled water outlet temperature, chilled water return temperature, return air temperature, supply air temperature, chilled water flow rate and air humidity collected in a second historical time period are screened out as the second training data subset, and in the second historical time period, the target building is in a state with internal heat and the air-conditioning system is stably operating. The demand response involved in the present invention is that when the target building has internal heat, the air-conditioning compressor is turned off and the water pump and the terminal air handling unit are running. At this time, this method can fully release the remaining cold capacity in the air-conditioning system pipeline and enhance the flexible adjustment ability of the air-conditioning system.

[0031] See also Figure 1 The method for quantifying the flexible regulation potential of an air conditioning system according to an embodiment of the present invention comprises the following steps: Step S200: construct a room temperature prediction model using the first training data subset and the second training data subset, wherein the room temperature prediction model is used to predict changes in temperature in a target building after demand response is initiated.

[0032] Specifically, the coefficients required to construct the room temperature prediction model are gradually solved using the first training data subset and the second training data subset, and then these coefficients are used to couple the heat storage characteristics of the target building and the air-conditioning system to construct a room temperature prediction model that characterizes the heat exchange process of the target building's envelope structure, air-conditioning system and indoor air.

[0033] In one implementation, constructing a room temperature prediction model using the first training data subset and the second training data subset includes: Filter out the chilled water outlet temperature, chilled water return temperature and indoor air temperature at a target time from the first training data subset, wherein the target time is the time when the air conditioning system temperature reaches the dew point temperature when the target building is in a state of no internal heat, the air conditioning compressor is turned off, and the water pump and the terminal air handling unit are running; Obtaining a first air conditioning system temperature based on a chilled water outlet temperature and a chilled water return temperature at a target time, and obtaining a temperature difference coefficient based on the first air conditioning system temperature and an indoor air temperature at a target time; obtaining a return air enthalpy value based on the return air temperature and the indoor humidity of the second training data subset, and obtaining a supply air enthalpy value based on the supply air temperature and the indoor humidity of the second training data subset; Obtaining a preset specific heat capacity of air, and obtaining a sensible heat ratio based on the specific heat capacity of air, the return air temperature of the second training data subset, the supply air temperature of the second training data subset, the return air enthalpy value, and the supply air enthalpy value; Based on the sensible heat ratio, latent heat and sensible heat ratio are obtained, and based on the chilled water outlet temperature and the chilled water return temperature of the second training data subset, a second air conditioning system temperature is obtained; Obtaining a proportionality coefficient based on the ratio of latent heat to sensible heat, the indoor air temperature of the second training data subset, the second air conditioning system temperature, and the temperature difference coefficient; Based on the temperature difference coefficient and the proportionality coefficient, the heat storage characteristics of the target building and the air-conditioning system are coupled to construct a room temperature prediction model.

[0034] Specifically, when the air conditioning compressor is turned off and the water pump and the terminal air handling unit are running, the evaporator and refrigerant circulation stops, the dehumidification function is interrupted, and the moisture cannot continue to condense, and the latent heat accumulation is reduced. Due to the thermal inertia of the system and the building, the indoor air temperature rises slower than the humidity rises, and the dew point temperature of the indoor air gradually approaches the system chilled water temperature, resulting in the inability of water vapor in the air to condense and the air conditioning system to dehumidify. Calculate the average of the chilled water outlet temperature and the chilled water return temperature at the target time to obtain the first air conditioning system temperature The formula for calculating the temperature difference coefficient is as follows: , where is the indoor air temperature at the target time.

[0035] In one implementation, the indoor air temperature at the target time is 23.977°C, the temperature of the first air conditioning system is 19.518°C, and the calculated temperature difference coefficient is The value is 4.459.

[0036] The formula for calculating the sensible heat fraction is as follows: , where is the sensible heat ratio, is the preset specific heat capacity of air, the value is 1.007 , is the average return air temperature in the second training data subset, is the average value of the supply air temperature in the second training data subset, is the return air enthalpy, is the air supply enthalpy value. When the sensible heat ratio is obtained, the latent heat ratio is calculated based on the sensible heat ratio, and then the latent heat and sensible heat ratio are obtained based on the sensible heat ratio and the latent heat ratio. The formula for calculating the latent heat ratio is as follows: The formula for calculating the ratio of latent heat to sensible heat of the target building under internal heat conditions is as follows: .

[0037] In one implementation, the ratio of latent heat to sensible heat is 0.565.

[0038] Calculate the scale factor The formula is as follows: ; In the formula, is the indoor air temperature when the target building is in an internal heat state and the air conditioning system is in a stable operation state, which is obtained by averaging all indoor air temperatures in the second training data subset. The temperature of the air conditioning system when the target building has internal heat and the air conditioning system is in stable operation is obtained by averaging all chilled water outlet temperatures and all chilled water return temperatures in the second training data subset. When the temperature difference coefficient and proportionality coefficient are obtained, the room temperature prediction model can be constructed by coupling the heat storage characteristics of the target building and the air conditioning system. The mathematical expression of the room temperature prediction model is as follows: ; ; ; ; ; ; ; ; ; ; In the formula, is the ratio of latent heat to sensible heat at any point in time, is the indoor air temperature, is the air conditioning system temperature, is the thermal resistance of the air conditioning system, Represents the sensible heat transfer between indoor air and air conditioning system. Represents the amount of latent heat transfer between indoor air and the air conditioning system. Represents the amount of heat transfer between indoor air and the air conditioning system. represents the amount of heat transfer between the building envelope and the indoor air, represents the wall temperature, It represents the resistance of heat transfer between the building envelope (i.e. the wall) and the indoor air. represents the heat transfer between the outdoor air and the external envelope structure, represents the outdoor air dry bulb temperature, It represents the resistance of heat transfer between the building envelope (i.e. the wall) and the outdoor air. represents the temperature of the air conditioning system at time i+1, Indicates the cooling capacity provided by the air conditioning system. Represents the time step, which is set to one minute in the present invention. represents the heat capacity of the air conditioning system, represents the temperature of the air conditioning system at time i, represents the temperature of the indoor air at time i+1, Indicates indoor heat gain. is the indoor air heat capacity, represents the temperature of the indoor air at time i, represents the wall temperature at time i+1, represents the thermal capacity of the building envelope, represents the wall temperature at time i, H is a time variable, representing the hour of the day, used for function input. h is a subscript, representing a specific hour. represents the indoor heat gain in h hours, is a mapping model that represents the relationship between indoor heat gain (i.e., internal heat gain) and the number of hours in a day. The structural diagram of the room temperature prediction model is shown in Figure 2 shown.

[0039] The current assessment of the flexible regulation potential of building air-conditioning systems mainly relies on three types of room temperature prediction models: white-box models based on physical mechanisms (such as EnergyPlus), data-driven black-box models (such as Long Short-Term Memory, LSTM), and gray-box models represented by the two-resistance + two-capacitance (2R+2C) model. White-box models based on physical mechanisms require complete building geometry parameters and have a long modeling cycle, making it difficult to respond to regulation needs quickly. Although data-driven black-box models avoid complex modeling processes, they have defects such as poor adaptability to working conditions, weak interpretability, and dependence on high computing power, which can easily lead to significant evaluation deviations in sudden demand response events. The gray box model simplifies the thermodynamic representation through the thermal resistance (R) and heat capacity (C) parameters, achieving a balance between computational efficiency and physical interpretability, but it has the following limitations: First, the 2Resistance+2Capacitance (2R+2C) model only considers the thermal inertia of the building envelope, and does not establish a mathematical representation of the thermal resistance and heat capacity of the air-conditioning system, which makes it impossible to quantify the storage and transfer of cold energy in the system, hindering the analysis of the mechanism affecting room temperature; second, the 2Resistance+2Capacitance (2R+2C) model does not consider the dynamic characteristics of the enthalpy and humidity of the air-conditioning system, and ignores the influence of the latent heat of the system on the room temperature, resulting in limited model prediction accuracy; third, the 2Resistance+2Capacitance (2R+2C) model adopts a single-stage global optimization algorithm to jointly train the thermal resistance and heat capacity (RC) parameters, resulting in strong coupling between the model parameters, making it difficult to accurately fit the thermal resistance and heat capacity (RC) parameters, resulting in inaccurate flexible quantification. The above defects restrict the accurate quantification of the flexible adjustment potential of the building air conditioning system, resulting in the problem of insufficient matching between the virtual power plant dispatch instructions and the actual response time. The present invention constructs a room temperature prediction model that includes dynamic adjustment of the enthalpy and humidity of the air conditioning system. On the basis of the two thermal resistances + two thermal capacitances model, the thermal capacitance of the air conditioning system and the thermal resistance of heat transfer between the air conditioning system and the indoor air temperature are added. It is referred to as the three thermal resistances + three thermal capacitances (3Resistance+3Capacitance, 3R+3C) model, which can more accurately calculate the maximum response time of the air conditioner.

[0040] By constructing a room temperature prediction model, the present invention can predict the indoor air temperature after demand response, and then obtain the maximum adjustable time, that is, the time when the indoor air temperature of the target building reaches the preset temperature threshold after demand response. This maximum adjustable time can be fed back to the power grid as the flexible adjustment potential of the air-conditioning system, so as to be used by the power grid to better coordinate and dispatch power resources. Demand response in this article refers to the air-conditioning compressor being turned off and the water pump and the terminal air handling unit running. Since the building has a certain heat storage capacity, even in this case, it still takes a certain amount of time for the indoor air temperature to reach the preset temperature threshold (i.e., 26 degrees Celsius). By calculating the time required to reach 26 degrees Celsius, that is, the maximum response time, it can be fed back to the power grid as the basis for power regulation and scheduling.

[0041] See also Figure 1 The method for quantifying the flexible regulation potential of an air conditioning system according to an embodiment of the present invention further comprises the following steps: Step S300, sequentially calculating the thermal resistance of the air conditioning system, the thermal resistance and heat capacity parameter combination, and the indoor heating schedule in the room temperature prediction model, and using the calculation results as fixed parameters of the model after each calculation.

[0042] Specifically, the present invention determines the unknown parameters in the room temperature prediction model in sequence, and uses an unknown parameter as a fixed parameter of the model each time it is determined, so that the complexity of the calculation can be reduced by solving it step by step. The present invention adopts a two-step identification framework. In the first step, the thermal resistance of the air-conditioning system is fixed, and then the thermal resistance and heat capacity parameter combination is trained using the first training data subset (i.e., without internal heat data); in the second step, the indoor heating time parameters are optimized and fitted using the second training data subset (i.e., with internal heat data), so as to realize efficient and accurate identification of model parameters.

[0043] In one implementation, calculating the thermal resistance of the air conditioning system in the room temperature prediction model includes: Filtering out several groups of target data collected in a target historical time period from the second training data subset, each group of target data includes chilled water flow, chilled water return temperature, chilled water outlet temperature and indoor air temperature; Based on the chilled water return temperature and the chilled water outlet temperature in each set of target data, a third air conditioning system temperature is obtained; Based on the chilled water flow rate, chilled water return temperature and chilled water outlet temperature in each set of target data, the heat transfer amount between the indoor air and the air conditioning system is obtained; Based on the heat transfer amount and the sensible heat ratio between the indoor air and the air conditioning system, obtaining the sensible heat transfer amount between the indoor air and the air conditioning system; The thermal resistance of the air conditioning system is obtained based on the third air conditioning system temperature, the sensible heat transfer amount between the indoor air and the air conditioning system, and the indoor air temperature in each set of target data.

[0044] Specifically, after the room temperature prediction model is constructed, the unknown parameters in the model need to be solved one by one. The first thing solved in the present invention is the thermal resistance of the air conditioning system. . Under normal conditions of large central air conditioning systems, the chilled water flow and inlet and outlet water temperatures fluctuate slightly, and the system average temperature is approximately constant. Therefore, the amount of cooling load stored in the system is relatively stable and easy to calculate through the chilled water flow and inlet and outlet water temperature difference. When the air conditioning system is in an internal heat state for the target building, It is the cooling load stored in the system. Select any hour within the data collection time range of the second training data subset as the target historical time period, and obtain the chilled water flow, chilled water return temperature, chilled water outlet temperature and indoor air temperature in this time period as the target data. Then calculate the heat transfer between the indoor air and the air conditioning system , the calculation formula is as follows: , where Represents the specific heat capacity of water, which is 4.18 ; Indicates the average value of chilled water flow in the target historical time period; Indicates the average value of the chilled water return temperature during the target historical time period; The average value of the chilled water outlet temperature during the target historical period. The sensible heat transfer between the indoor air and the air conditioning system is calculated using the following formula: , Among them, sensible heat accounts for The value has been obtained when building the room temperature prediction model, so it can be used directly here. Then the thermal resistance of the air conditioning system is calculated using the following formula: In the formula, Indicates to find the average value of the data in the target historical time period, and substitute the average value of the indoor air temperature in the target historical time period into , substitute the average value of the air temperature of the third air conditioning system during the target historical period into The third air conditioning system temperature is obtained by averaging the chilled water return temperature and chilled water outlet temperature in each set of target data. After that, it is used as a fixed parameter of the room temperature prediction model and subsequently used to solve other unknown parameters.

[0045] In one implementation, calculating the thermal resistance and heat capacity parameter combination in the room temperature prediction model includes: Calculate a first wall temperature corresponding to the first training data subset based on the first training data subset, where the first wall temperature is the wall temperature corresponding to the initial acquisition moment in the first training data subset; Substituting the first wall temperature and the first training data subset into a room temperature prediction model with a fixed thermal resistance of the air conditioning system, taking the thermal resistance and heat capacity parameter combination to be determined in the room temperature prediction model as a variable to be optimized, performing a first preset round of optimization on it using a genetic algorithm, and calculating the value of the first objective function corresponding to each round of optimization; Sort the values ​​of the first objective function of all rounds, select a preset number of groups of thermal resistance and heat capacity parameter combinations corresponding to the first objective function from small to large, and calculate the deviation value corresponding to each selected thermal resistance and heat capacity parameter combination; Select a set of thermal resistance and heat capacity parameter combinations with the smallest deviation value as the final thermal resistance and heat capacity parameter combination; The thermal resistance and heat capacity parameter combination includes: the resistance of heat transfer between the building envelope and indoor air, the resistance of heat transfer between the building envelope and outdoor air, the heat capacity of indoor air, the heat capacity of the air conditioning system and the heat capacity of the building envelope.

[0046] Specifically, when the thermal resistance of the air conditioning system is solved After that, it is used as a fixed parameter of the model. On this basis, in order to calculate the thermal resistance and heat capacity parameter combination, it is necessary to first calculate the wall temperature corresponding to the initial acquisition time in the first training data subset (i.e., the first wall temperature), and then substitute the first wall temperature into the room temperature prediction model to determine the final thermal resistance and heat capacity parameter combination. The thermal resistance and heat capacity parameter combination of the present invention includes the resistance value of heat transfer between the building envelope and the indoor air , the resistance of heat transfer between the building envelope and the outdoor air , indoor air heat capacity 、Air conditioning system heat capacity and building envelope thermal capacity . The genetic algorithm uses the above five parameters as chromosomes in the optimization process, randomly initializes them within a preset order of magnitude, and generates an initial population. The genetic algorithm then optimizes the population through operations such as selection, crossover, and mutation. After substituting the first training data subset into the room temperature prediction model, the genetic algorithm is used to optimize the first prediction round, and the first preset round can be 5000 times. When the number of iterations reaches the preset number, the genetic algorithm optimization is stopped. Each round of optimization calculates the value of the corresponding first objective function. The expression of the first objective function is: ; In the formula, is the weighted sum of the indoor air temperature and the air conditioning system temperature obtained from the first training data subset, that is, the actual value. is the weighted sum of the predicted values ​​of the indoor air temperature and the air conditioning system temperature obtained by the room temperature prediction model, and n is the number of time steps for collecting the first data training subset. The calculation formula is as follows: . In the formula, r represents the ratio of the indoor air temperature range to the air conditioning system temperature fluctuation range, and the value is between 0 and 1. The indoor air temperature range is the difference between the average temperature of the room when the air conditioner is stopped and the average temperature when the air conditioner is running stably, and the air conditioning system temperature fluctuation range is the difference between the average temperature of the air conditioner when the air conditioner is stopped and the average temperature when the air conditioner is running stably. By calculating the historical data, the ratio r of the indoor air temperature range to the air conditioning system temperature fluctuation range can be obtained. After 5000 iterations, due to the uncertainty of the genetic algorithm, 500 sets of thermal resistance and heat capacity parameter combinations with the smallest first objective function value are selected, and then the deviation values ​​of these 500 sets of thermal resistance and heat capacity parameters are calculated.

[0047] The calculation formula of the deviation value is as follows: ; In the formula, Indicates the deviation value of the thermal resistance and heat capacitance parameter combination, Represents the average value of the resistance to heat transfer between the building envelope and the indoor air in all thermal resistance and heat capacity combinations. Represents the average value of the resistance to heat transfer between the building envelope and the outdoor air in all thermal resistance and heat capacity combinations. Represents the average value of the heat capacity of the air conditioning system in all thermal resistance and heat capacity combinations, Represents the average value of indoor air heat capacity in all thermal resistance and heat capacity combinations, Represents the average value of the building envelope heat capacity among all thermal resistance and heat capacity combinations.

[0048] The mathematical expression for selecting the thermal resistance and heat capacity parameter combination with the smallest deviation value is as follows: ; In the formula, is the final thermal resistance and heat capacity parameter combination of the room temperature prediction model, It is the thermal resistance and heat capacitance parameter combination with the smallest deviation.

[0049] In one implementation, .

[0050] In one implementation, the specific value of the thermal resistance and thermal capacitance parameter combination finally determined is: , , , , .

[0051] After the thermal resistance and heat capacity parameters are determined through training, they are fixed in the room temperature prediction model as model fixed parameters.

[0052] After the first preset round of training, the relationship between the actual value and the predicted value of indoor air temperature is shown in the figure below: Figure 3 As shown in the figure, the relationship between the actual value and the predicted value of the air conditioning system temperature is shown in Figure 4 The root mean square error (RMSE) between the actual value and the predicted value of the indoor air temperature is 0.036℃, and the root mean square error (RMSE) between the actual value and the predicted value of the air conditioning system temperature is 0.416℃.

[0053] In one implementation, calculating the first wall temperature corresponding to the first training data subset based on the first training data subset includes: Taking the indoor air temperature at the initial collection time in the first training data subset as the starting point and the outdoor air dry-bulb temperature at the initial collection time as the end point, generating a candidate wall temperature sequence according to a preset fixed step size, wherein the candidate wall temperature sequence includes a plurality of candidate wall temperatures; Substituting each candidate wall temperature and the first training data subset into a room temperature prediction model for which the thermal resistance of the air conditioning system has been determined, and optimizing the thermal resistance and heat capacity parameter combinations to be determined in the room temperature prediction model by using a genetic algorithm for a second preset round to obtain a plurality of groups of thermal resistance and heat capacity parameter combinations corresponding to each candidate wall temperature; Using a preset deviation function to evaluate several thermal resistance and heat capacity parameter combinations corresponding to each candidate wall temperature, and selecting the thermal resistance and heat capacity parameter combination with the smallest deviation as the candidate thermal resistance and heat capacity parameter combination corresponding to the candidate wall temperature; Select the time-series indoor air temperature and the time-series outdoor dry-bulb air temperature collected during the second target historical time period from the historically collected time-series indoor and outdoor environmental data; Substituting the time-series indoor air temperature, the time-series outdoor dry-bulb air temperature and the candidate thermal resistance and heat capacity parameter combination corresponding to each candidate wall temperature into the room temperature prediction model of the determined air conditioning system thermal resistance, and obtaining several wall estimated temperatures through calculation; The difference between each candidate wall temperature and its corresponding estimated wall temperature is calculated, and the candidate wall temperature with the smallest difference is selected as the first wall temperature.

[0054] Specifically, since both the indoor air temperature and the outdoor air dry-bulb temperature can be collected by sensors, but the wall temperature is not easy to measure. The setting of the wall temperature in the model has a great influence on the subsequent identification of other unknown parameters. Therefore, the present invention uses the first training data subset to perform multiple rounds of iterative training to optimize the calculation of the wall temperature corresponding to the initial collection time in the first training data subset.

[0055] Taking the indoor air temperature at the initial collection time in the first training data subset as the starting point and the outdoor air dry bulb temperature at the initial collection time as the end point, the exhaustive method is used to select values ​​in a fixed step of 0.1 degrees Celsius to obtain a candidate wall temperature sequence. For example, if the first training data subset collects indoor air temperature and outdoor air dry bulb temperature from 21:00 on August 1, 2024. The indoor air temperature is 26.5 degrees and the outdoor air dry bulb temperature is 27.5 degrees. Then a candidate wall temperature sequence in the form of (26.5, 26.6, 26.7, 26.8, 26.9, 27.0, 27.1, 27.2, 27.3, 27.4, 27.5) is generated.

[0056] Substitute each candidate wall temperature and the first training data subset into the room temperature prediction model, and use the thermal resistance and heat capacity parameter combination to be determined in the room temperature prediction model (i.e., the resistance of heat transfer between the building envelope and indoor air, the resistance of heat transfer between the building envelope and outdoor air, the heat capacity of indoor air, the heat capacity of the air conditioning system, and the heat capacity of the building envelope) as the variable to be optimized, and use the genetic algorithm to perform the second preset round of optimization, which can be 100. After the above optimization, several groups of thermal resistance and heat capacity combinations can be obtained. Then use the preset deviation function to evaluate these combinations, and select the thermal resistance and heat capacity parameter combination with the smallest deviation as the candidate thermal resistance and heat capacity parameter combination. The formula of the deviation function is: ; The mathematical expression for selecting the thermal resistance and heat capacitance parameter combination with the smallest deviation as the candidate thermal resistance and heat capacitance parameter combination is: , where is the candidate thermal resistance and heat capacity parameter combination corresponding to a candidate wall temperature, It should be noted that the thermal resistance and heat capacity parameter combination with the smallest deviation selected this time is only used for the subsequent calculation of the first wall temperature, and is not the final thermal resistance and heat capacity parameter combination of the temperature prediction model.

[0057] The second target historical time period is the time interval formed by tracing back 30 hours from the initial collection time of the first training data subset. For example, if the initial collection time of the first training data set is 21:00 on August 1, 2024, the target historical time period is 15:00 on July 31, 2024 to 21:00 on August 1, 2024.

[0058] At this time, the indoor air temperature ranked first in the time series indoor air temperature and the outdoor dry-bulb air temperature ranked first in the time series outdoor dry-bulb air temperature are averaged to obtain the wall temperature corresponding to the initial collection time in the second target historical time period. Then, the wall temperature corresponding to the initial collection time in the second target historical time period, the indoor air temperature ranked first, and the candidate thermal resistance and heat capacity parameter combination corresponding to each candidate wall temperature are substituted into the wall estimated temperature formula in the temperature prediction model of the determined air conditioning system thermal resistance and thermal resistance and heat capacity parameter combination for calculation. Among them, the wall estimated temperature formula is as follows: ; ; ; Specifically, when i=0, substitute the wall temperature corresponding to the initial collection time in the second target historical time period into and , substitute the first indoor air temperature in the time series into , substitute the outdoor dry-bulb air temperature that ranks first in the time series into , the resistance of heat transfer between the building envelope and indoor air, the resistance of heat transfer between the building envelope and outdoor air, and the heat capacity of the building envelope in the candidate thermal resistance and heat capacity parameter combination are also substituted into the formula , the wall temperature corresponding to the next acquisition time can be obtained by calculation In the next calculation, substitute the second indoor air temperature in the time series into , substitute the second outdoor dry-bulb air temperature in the time series into ,Will As the wall temperature at the second collection time in the second target historical time period, Maintaining the same state, the calculation is performed again to obtain the wall temperature at the third collection moment in the second target historical time period. The above calculation process is repeated in this way until the estimated wall temperature at the last data collection moment in the second target historical time period (that is, the initial collection moment of the first training data set) is obtained. It can be understood that the above calculation process fixedly uses a candidate thermal resistance and heat capacity parameter combination corresponding to a candidate wall temperature. The above calculation needs to be performed for each candidate thermal resistance and heat capacity parameter combination corresponding to each candidate wall temperature. After the calculation of all wall estimated temperatures is completed, the difference between each candidate wall temperature and its corresponding wall estimated temperature is calculated, and the candidate wall temperature with the smallest difference is selected as the first wall temperature.

[0059] In one implementation, calculating an indoor heat gain schedule in a room temperature prediction model includes: Substituting the second training data subset into a room temperature prediction model for which the thermal resistance and thermal resistance and heat capacity parameter combination of the air conditioning system has been determined, taking the indoor heat gain hour by hour within a preset time range in the room temperature prediction model as a variable to be optimized, and iteratively adjusting its value through a genetic algorithm to minimize the second objective function; When the preset end condition is reached, an indoor heating schedule consisting of the indoor heat gain every hour within the preset time range is obtained.

[0060] Specifically, when the thermal resistance and thermal resistance and heat capacity combination of the air conditioning system are determined, these two are used as fixed parameters of the model to solve the indoor heating schedule , that is, the indoor heat gain corresponding to H in each hour of the day. The expression of the second objective function is: ; In the formula, represents the indoor heat gain at time i, is the indoor heat gain at time i predicted by the room temperature prediction model. Genetic algorithm is used to randomly generate multiple groups , then calculate the second objective function of each set of solutions, retain the result with smaller error, and generate new solutions through combination and random perturbation until the preset number of iterations is reached or the second objective function converges to the preset value, and select the solution corresponding to the smallest second objective function value The indoor heating schedule generated by the present invention is as follows: Figure 5 shown.

[0061] While considering the cooling capacity of buildings and air conditioning systems, the present invention focuses on the close relationship between the cooling capacity of the system and the changes in air temperature and humidity. value, and a dual-objective penalty function is used to balance the relationship between the air conditioning system temperature and the indoor air temperature, so as to obtain a stable combination of thermal resistance and heat capacity parameters and an indoor heating timetable. The room temperature prediction model of the present invention is used to predict the change process of the indoor air temperature of the target building during demand response (that is, when the air conditioning compressor is turned off and the water pump and the terminal air handling unit are running in the internal heat state). By constructing an experiment with two days of different outdoor temperature scenarios, the root mean square error (RMSE) of the indoor air temperature is 0.061°C and 0.174°C, respectively, and the root mean square error (RMSE) of the air conditioning system temperature is 0.347°C and 0.697°C, respectively. Based on the prediction results, the maximum allowable adjustment time is further quantified to achieve an accurate evaluation of the flexible adjustment potential of the air conditioning system, providing a basis for the optimization of the demand response strategy.

[0062] See also Figure 1 The method for quantifying the flexible regulation potential of an air conditioning system according to an embodiment of the present invention further comprises the following steps: Step S400: When a demand response instruction is received, the wall temperature at the time of demand response start is calculated, and the wall temperature and the indoor and outdoor environmental data and air conditioning system operation data collected last time before the demand response start are substituted into a room temperature prediction model with fixed parameters to obtain a room temperature prediction curve.

[0063] Specifically, when a demand response instruction issued by the power grid is received, the demand response instruction usually includes the start time of the demand response. It is necessary to calculate the wall temperature at the start time of the demand response to calculate how long it takes for the target building to reach the preset temperature threshold when the demand response is actually performed. The indoor and outdoor environmental data and the air-conditioning system operation data collected 30 hours before the start time of the demand response are obtained as calculation-related data. Substitute the room temperature prediction model that has fixed the air-conditioning system thermal resistance, thermal resistance and heat capacity parameter combination, and indoor heat gain schedule, and use the temperature calculation formula in the room temperature prediction model to calculate the wall temperature at the start time of the demand response. Subsequently, it and the indoor and outdoor environmental data and air-conditioning system operation data collected for the last time before the start time of the demand response are substituted into the room temperature prediction model with fixed parameters (i.e., air-conditioning system thermal resistance, thermal resistance and heat capacity parameter combination, and indoor heat gain schedule) to obtain a room temperature prediction curve. The present invention innovatively proposes a method for calculating wall temperature, which can improve the accuracy of parameter order of magnitude estimation.

[0064] See also Figure 1 The method for quantifying the flexible regulation potential of an air conditioning system according to an embodiment of the present invention further comprises the following steps: Step S500: Based on the demand response start time, the preset temperature threshold and the room temperature prediction curve, determine the maximum adjustable time and use it as the flexible adjustment potential of the air-conditioning system.

[0065] Specifically, determine the arrival time when the room temperature prediction curve reaches the preset temperature threshold for the first time , subtract the demand response start time from the arrival time , get the maximum adjustable time This duration reflects the flexible adjustment potential of the air conditioning system. The maximum adjustable duration can be reported to the power grid so that the grid can make decisions on power allocation.

[0066] In order to verify the effect of the room temperature prediction model in the present invention. The accuracy is verified by using the data when the compressor is turned off and the water pump and the terminal air handling unit are kept running in the demand response event. The root mean square error between the room temperature prediction value and the true value calculated by the room temperature prediction model of the present invention is 0.174°C, and the root mean square error between the air conditioning system temperature prediction value and the true value is 0.697°C. The model shows good prediction effect.

[0067] In addition, the room temperature prediction model of the present invention is compared with the 2Resistance+2Capacitance (2R+2C) model in the prior art that only considers the thermal process of the building. The training and verification of the 2Resistance+2Capacitance model use the same data of the present invention and set the same genetic algorithm operating parameters. The room temperature prediction model of the present invention and the 2Resistance+2Capacitance model are verified using the same verification set. The comparison results between the present invention and the 2Resistance+2Capacitance room temperature prediction model (i.e., 2R+2C model) and the true value are shown in the attached figure. Figure 6 As shown. The root mean square error between the indoor air temperature prediction value and the true value of the two thermal resistances + two heat capacitances model is calculated to be 0.453°C, while the root mean square error between the indoor air temperature prediction value and the true value of the room temperature prediction model of the present invention is 0.174°C, which is 0.279°C lower. This shows that the room temperature prediction model in the present invention effectively captures the heat transfer process of the air-conditioning system in the demand response event and its impact on indoor temperature and humidity, thereby significantly improving the room temperature prediction accuracy of the model.

[0068] In one implementation, the flexible regulation potential calculated by the room temperature prediction model of the present invention is 76 minutes, and the actual regulation potential calculated based on the data collected by the collector is 80 minutes. In comparison, the flexible regulation potential calculated by the two-resistance + two-capacitance (2Resistance+2Capacitance, 2R+2C) model is 63 minutes. This shows that the flexibility quantification accuracy of the temperature prediction results based on the present invention is improved by more than 70% compared with the traditional method. This improved method accurately evaluates the flexible regulation capability of the building air-conditioning system during demand response through physical modeling, providing strong support for the optimal scheduling of the building virtual power plant.

[0069] In summary, the present invention proposes a room temperature prediction (3Resistance+3Capacitance, 3R+3C) model, which characterizes the cold storage capacity of the air conditioning system and its heat exchange path with indoor air by introducing the heat capacity and thermal resistance of the air conditioning system, and combines the dynamic coupling mechanism of sensible heat and latent heat, quantifies the proportion of latent heat through system coefficients α and β, and optimizes the mathematical expression of the heat transfer process. Compared with the two-thermal resistance + two-thermal capacity model that only considers the thermal storage characteristics of the building in the prior art, the model significantly improves the accuracy of room temperature prediction in demand response events, and the root mean square error (RMSE) is reduced by more than 60%. The present invention proposes a method for directly calculating and fixing the thermal resistance of the air conditioning system based on the chilled water flow rate and the difference between the chilled water return temperature and the outlet water temperature. Different from the traditional two-thermal resistance + two-thermal capacity model parameter full optimization method, this method simplifies the complexity of parameter identification by fixing the thermal resistance of the air conditioning system, shortens the model training time, and avoids the error accumulation caused by parameter coupling, thereby improving the robustness and prediction accuracy of the model. In order to solve the problem that the wall temperature cannot be measured, this paper proposes an initial wall temperature optimization method that combines exhaustive method with historical data. The initial temperature sequence is generated by exhaustive method, and the deviation function ( ) to screen the corresponding optimal parameter combination, and calculate the initial wall temperature in combination with historical data. This method effectively solves the prediction inaccuracy problem caused by the initial wall temperature setting deviation of the traditional model. Experimental verification shows that the prediction error can be controlled within 0.2℃, which significantly improves the prediction accuracy and stability of the model. The present invention proposes a flexible quantification method based on accurate room temperature prediction, which improves the room temperature prediction accuracy by integrating the heat storage and enthalpy-humidity dynamic characteristics of the air-conditioning system, thereby accurately evaluating the flexible adjustment potential of the building air-conditioning system. Traditional flexible quantification methods mainly rely on simulation software or simplified thermal resistance and heat capacity (RC) models, which are difficult to accurately reflect the thermal inertia of the air-conditioning system and its dynamic impact on the room temperature, resulting in large errors in the evaluation of the flexible adjustment duration. The present invention optimizes the heat transfer path of the thermal resistance + heat capacity model, accurately predicts the room temperature changes during the demand response period, and calculates the maximum adjustable potential in combination with the user's thermal comfort threshold, thereby providing a more accurate basis for flexible quantification. Experimental verification shows that the flexibility quantification accuracy of this method is improved by more than 70% compared with the traditional method, which can provide a refined control basis for the scheduling of building virtual power plants and the optimization of power grid demand response.

[0070] In one embodiment, if Figure 7 As shown, based on the above-mentioned method for quantifying the flexible adjustment potential of an air-conditioning system, the present invention also provides a device for quantifying the flexible adjustment potential of an air-conditioning system, including: The data acquisition module 100 is used to continuously collect the indoor and outdoor environment data and the air conditioning system operation data of the target building by using a plurality of preset sensors, and to filter out a first training data subset and a second training data subset from the historically collected time-series indoor and outdoor environment data and time-series air conditioning system operation data; A model building module 200, for building a room temperature prediction model using the first training data subset and the second training data subset, wherein the room temperature prediction model is used to predict changes in temperature in a target building after a demand response is initiated; The parameter determination module 300 is used to sequentially calculate the thermal resistance of the air conditioning system, the thermal resistance and heat capacity parameter combination, and the indoor heat gain schedule in the room temperature prediction model, and use the calculation results as the model fixed parameters after each calculation; The curve generation module 400 is used to calculate the wall temperature at the time of starting the demand response when receiving the demand response instruction, and substitute the wall temperature and the indoor and outdoor environment data and air conditioning system operation data collected last time before the demand response starts into the room temperature prediction model with fixed parameters to obtain the room temperature prediction curve; The duration determination module 500 is used to determine the maximum adjustable duration based on the demand response start time, the preset temperature threshold and the room temperature prediction curve, and use it as the flexible adjustment potential of the air-conditioning system.

[0071] It should be noted that the above explanation of the embodiment of the method for quantifying the flexible adjustment potential of an air-conditioning system is also applicable to the device for quantifying the flexible adjustment potential of an air-conditioning system of this embodiment, and will not be repeated here.

[0072] Based on the above embodiment, the present invention further provides a terminal, whose principle block diagram can be shown as follows: Figure 8 As shown. The terminal includes a processor, a memory, a network interface and a display screen connected via a device bus. The processor of the terminal is used to provide computing and control capabilities. The memory of the terminal includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating device and an air-conditioning system flexible adjustment potential quantification program. The internal memory provides an environment for the operation of the operating device and the air-conditioning system flexible adjustment potential quantification program in the non-volatile storage medium. The network interface of the terminal is used to communicate with an external terminal via a network connection. When the air-conditioning system flexible adjustment potential quantification program is executed by the processor, the steps of any one of the above-mentioned air-conditioning system flexible adjustment potential quantification methods are implemented. The display screen of the terminal can be a liquid crystal display screen or an electronic ink display screen.

[0073] Those skilled in the art will understand that Figure 8The principle block diagram shown in the figure is only a block diagram of a partial structure related to the scheme of the present invention, and does not constitute a limitation on the terminal to which the scheme of the present invention is applied. The specific terminal may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0074] In one embodiment, a terminal is provided, which includes a memory, a processor, and an air-conditioning system flexible adjustment potential quantification program stored in the memory and executable on the processor. When the air-conditioning system flexible adjustment potential quantification program is executed by the processor, the steps of any one of the air-conditioning system flexible adjustment potential quantification methods provided in the embodiments of the present invention are implemented.

[0075] An embodiment of the present invention also provides a computer-readable storage medium, on which a program for quantifying the flexible adjustment potential of an air-conditioning system is stored. When the program for quantifying the flexible adjustment potential of an air-conditioning system is executed by a processor, the steps of any one of the methods for quantifying the flexible adjustment potential of an air-conditioning system provided in an embodiment of the present invention are implemented.

[0076] It should be understood that the serial numbers of the steps in the above embodiments do not imply a sequence of execution, and the execution sequence of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0077] Those skilled in the art can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the above-mentioned device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of the present invention. The specific working process of the units and modules in the above-mentioned device can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0078] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0079] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0080] In the embodiments provided by the present invention, it should be understood that the disclosed apparatus / terminal equipment and method can be implemented in other ways. For example, the apparatus / terminal equipment embodiments described above are only illustrative, for example, the division of the above modules or units is only a logical function division, and in actual implementation, other division methods can be used, for example, multiple units or components can be combined or integrated into another apparatus, or some features can be ignored or not executed.

[0081] The embodiments described above are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, it should be understood by those skilled in the art that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features therein may be replaced by equivalents. However, these modifications or replacements do not deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should be included in the protection scope of the present invention.

Claims

1. A method for quantifying the flexible regulation potential of an air conditioning system, characterized in that: The method comprises: Using a plurality of preset sensors to continuously collect indoor and outdoor environmental data and air conditioning system operation data of the target building, and screening out a first training data subset and a second training data subset from historically collected time-series indoor and outdoor environmental data and time-series air conditioning system operation data; Using the first training data subset and the second training data subset, a room temperature prediction model is constructed, wherein the room temperature prediction model is used to predict a change in temperature in a target building after a demand response is initiated; Calculate the thermal resistance of the air conditioning system, the combination of thermal resistance and heat capacity parameters, and the indoor heating schedule in the room temperature prediction model in sequence, and use the calculation results as fixed parameters of the model after each calculation; When a demand response instruction is received, the wall temperature at the time of demand response initiation is calculated, and the wall temperature and the indoor and outdoor environment data and air conditioning system operation data collected last time before the demand response initiation are substituted into the room temperature prediction model with fixed parameters to obtain the room temperature prediction curve; Based on the demand response start time, the preset temperature threshold and the room temperature prediction curve, the maximum adjustable time is determined and used as the flexible adjustment potential of the air-conditioning system.

2. The method for quantifying the flexible regulation potential of an air conditioning system according to claim 1, characterized in that: The indoor and outdoor environmental data include indoor air temperature and outdoor air dry bulb temperature, and the air conditioning system operation data include chilled water outlet temperature, chilled water return temperature, return air temperature, supply air temperature, chilled water flow rate and air humidity; from the historically collected time-series indoor and outdoor environmental data and time-series air conditioning system operation data, a first training data subset and a second training data subset are screened out, including: From the historically collected time-series indoor and outdoor environmental data and time-series air conditioning system operation data, the indoor air temperature, outdoor air dry-bulb temperature, chilled water outlet temperature, chilled water return temperature, return air temperature, supply air temperature, chilled water flow rate and air humidity collected in the first historical time period are selected as the first training data subset. In the first historical time period, the target building is in a state of no internal heat, the air conditioning compressor is turned off, and the water pump and the terminal air handling unit are running; From the historically collected time-series indoor and outdoor environmental data and the time-series air-conditioning system operation data, the indoor air temperature, outdoor air dry-bulb temperature, chilled water outlet temperature, chilled water return temperature, return air temperature, supply air temperature, chilled water flow rate and air humidity collected in the second historical time period are selected as the second training data subset. In the second historical time period, the target building is in an internal heat state and the air-conditioning system operates stably.

3. The method for quantifying the flexible regulation potential of an air conditioning system according to claim 2, characterized in that: Constructing a room temperature prediction model using the first training data subset and the second training data subset includes: Filter out the chilled water outlet temperature, chilled water return temperature and indoor air temperature at a target time from the first training data subset, wherein the target time is the time when the air conditioning system temperature reaches the dew point temperature when the target building is in a state of no internal heat, the air conditioning compressor is turned off, and the water pump and the terminal air handling unit are running; Obtaining a first air conditioning system temperature based on a chilled water outlet temperature and a chilled water return temperature at a target time, and obtaining a temperature difference coefficient based on the first air conditioning system temperature and an indoor air temperature at a target time; obtaining a return air enthalpy value based on the return air temperature and the indoor humidity of the second training data subset, and obtaining a supply air enthalpy value based on the supply air temperature and the indoor humidity of the second training data subset; Obtaining a preset specific heat capacity of air, and obtaining a sensible heat ratio based on the specific heat capacity of air, the return air temperature of the second training data subset, the supply air temperature of the second training data subset, the return air enthalpy value, and the supply air enthalpy value; Based on the sensible heat ratio, latent heat and sensible heat ratio are obtained, and based on the chilled water outlet temperature and the chilled water return temperature of the second training data subset, a second air conditioning system temperature is obtained; Obtaining a proportionality coefficient based on the ratio of latent heat to sensible heat, the indoor air temperature of the second training data subset, the second air conditioning system temperature, and the temperature difference coefficient; Based on the temperature difference coefficient and the proportionality coefficient, the heat storage characteristics of the target building and the air-conditioning system are coupled to construct a temperature prediction model.

4. The method for quantifying the flexible regulation potential of an air conditioning system according to claim 3, characterized in that: Calculate the thermal resistance of the air conditioning system in the room temperature prediction model, including: Filtering out several groups of target data collected in a target historical time period from the second training data subset, each group of target data includes chilled water flow, chilled water return temperature, chilled water outlet temperature and indoor air temperature; Based on the chilled water return temperature and the chilled water outlet temperature in each set of target data, a third air conditioning system temperature is obtained; Based on the chilled water flow rate, chilled water return temperature and chilled water outlet temperature in each set of target data, the heat transfer amount between the indoor air and the air conditioning system is obtained; Based on the heat transfer amount and the sensible heat ratio between the indoor air and the air conditioning system, obtaining the sensible heat transfer amount between the indoor air and the air conditioning system; The thermal resistance of the air conditioning system is obtained based on the third air conditioning system temperature, the sensible heat transfer amount between the indoor air and the air conditioning system, and the indoor air temperature in each set of target data.

5. The method for quantifying the flexible regulation potential of an air conditioning system according to claim 1, characterized in that: Calculate the thermal resistance and heat capacity parameter combinations in the room temperature prediction model, including: Calculate a first wall temperature corresponding to the first training data subset based on the first training data subset, where the first wall temperature is the wall temperature corresponding to the initial acquisition moment in the first training data subset; Substituting the first wall temperature and the first training data subset into a room temperature prediction model with a fixed thermal resistance of the air conditioning system, taking the thermal resistance and heat capacity parameter combination to be determined in the room temperature prediction model as a variable to be optimized, performing a first preset round of optimization on it using a genetic algorithm, and calculating the value of the first objective function corresponding to each round of optimization; Sort the values ​​of the first objective function of all rounds, select a preset number of groups of thermal resistance and heat capacity parameter combinations corresponding to the first objective function from small to large, and calculate the deviation value corresponding to each selected thermal resistance and heat capacity parameter combination; Select a set of thermal resistance and heat capacity parameter combinations with the smallest deviation value as the final thermal resistance and heat capacity parameter combination; The thermal resistance and heat capacity parameter combination includes: the resistance of heat transfer between the building envelope and indoor air, the resistance of heat transfer between the building envelope and outdoor air, the heat capacity of indoor air, the heat capacity of the air conditioning system and the heat capacity of the building envelope.

6. The method for quantifying the flexible regulation potential of an air conditioning system according to claim 5, characterized in that: Calculating a first wall temperature corresponding to the first training data subset based on the first training data subset includes: Taking the indoor air temperature at the initial collection time in the first training data subset as the starting point and the outdoor air dry-bulb temperature at the initial collection time as the end point, generating a candidate wall temperature sequence according to a preset fixed step size, wherein the candidate wall temperature sequence includes a plurality of candidate wall temperatures; Substituting each candidate wall temperature and the first training data subset into a room temperature prediction model for which the thermal resistance of the air conditioning system has been determined, and optimizing the thermal resistance and heat capacity parameter combinations to be determined in the room temperature prediction model by using a genetic algorithm for a second preset round to obtain a plurality of thermal resistance and heat capacity parameter combinations corresponding to each candidate wall temperature; Using a preset deviation function to evaluate several thermal resistance and heat capacity parameter combinations corresponding to each candidate wall temperature, and selecting the thermal resistance and heat capacity parameter combination with the smallest deviation as the candidate thermal resistance and heat capacity parameter combination corresponding to the candidate wall temperature; Select the time-series indoor air temperature and the time-series outdoor dry-bulb air temperature collected during the second target historical time period from the historically collected time-series indoor and outdoor environmental data; Based on the time-series indoor air temperature, the time-series outdoor dry-bulb air temperature and the candidate thermal resistance and heat capacity parameter combination corresponding to each of the candidate wall temperatures, a plurality of estimated wall temperatures are obtained; The difference between each candidate wall temperature and its corresponding estimated wall temperature is calculated, and the candidate wall temperature with the smallest difference is selected as the first wall temperature.

7. The method for quantifying the flexible regulation potential of an air conditioning system according to claim 1, characterized in that: Calculate the indoor heat gain schedule in the room temperature prediction model, including: Substituting the second training data subset into a room temperature prediction model for which the thermal resistance and thermal resistance and heat capacity parameter combination of the air conditioning system has been determined, taking the indoor heat gain hour by hour within a preset time range in the room temperature prediction model as a variable to be optimized, and iteratively adjusting its value through a genetic algorithm to minimize the second objective function; When the preset end condition is reached, an indoor heating schedule consisting of the indoor heat gain every hour within the preset time range is obtained.

8. A device for quantifying the flexible regulation potential of an air conditioning system, characterized in that: include: A data acquisition module is used to continuously collect indoor and outdoor environmental data and air conditioning system operation data of a target building using a plurality of preset sensors, and to select a first training data subset and a second training data subset from the historically collected time-series indoor and outdoor environmental data and time-series air conditioning system operation data; A model building module, used to build a room temperature prediction model using the first training data subset and the second training data subset, wherein the room temperature prediction model is used to predict the change of the temperature in the target building after the demand response is started; A parameter determination module, used to sequentially calculate the thermal resistance of the air conditioning system, the combination of thermal resistance and heat capacity parameters, and the indoor heating schedule in the room temperature prediction model, and use the calculation results as model fixed parameters after each calculation; The curve generation module is used to calculate the wall temperature at the time of demand response start when receiving the demand response instruction, and substitute the wall temperature and the indoor and outdoor environment data and air conditioning system operation data collected last time before the demand response start into the room temperature prediction model with fixed parameters to obtain the room temperature prediction curve; The duration determination module is used to determine the maximum adjustable duration based on the demand response start time, the preset temperature threshold and the room temperature prediction curve, and use it as the flexible adjustment potential of the air-conditioning system.

9. A terminal, characterized in that: The terminal includes a memory, a processor, and an air-conditioning system flexible adjustment potential quantification program stored in the memory and executable on the processor. When the air-conditioning system flexible adjustment potential quantification program is executed by the processor, the steps of the air-conditioning system flexible adjustment potential quantification method as described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores an air conditioning system flexible adjustment potential quantification program, and when the air conditioning system flexible adjustment potential quantification program is executed by the processor, the steps of the air conditioning system flexible adjustment potential quantification method according to any one of claims 1 to 7 are implemented.

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