Building heat conduction parameter identification method and device, computer equipment and storage medium

Abstract

The invention relates to a building heat conduction parameter identification method and device, computer equipment and a storage medium, and relates to the technical field of virtual power plants. The method comprises the following steps: acquiring an analytic recursive model of a second-order equivalent heat conduction parameter model of a target temperature control device; the analytic recursive model comprises a heat conduction parameter to be identified; respectively obtaining a size parameter and a state measurement data set of the building space and a current regulation and control parameter of the virtual power plant; determining a target value range of the heat conduction parameter according to the size parameter and the current regulation and control parameter; and determining a target value of the heat conduction parameter according to the state measurement data set, the analytic recursion model, the target value range and a preset termination condition. By adopting the method, the identification accuracy of the building heat conduction parameters can be improved.

Description

Technical Field

[0001] This application relates to the field of virtual power plant technology, and in particular to a method, apparatus, computer equipment, and storage medium for identifying building heat conduction parameters. Background Technology

[0002] In virtual power plant operation, building air conditioning systems, as typical temperature-controlled loads, directly impact the economic viability of virtual power plants participating in electricity spot market bidding and ancillary service market response due to their aggregated control capabilities. However, the heat transfer parameters of air conditioning systems (such as thermal resistance and heat capacity) often deviate from design values ​​due to equipment aging, dynamic environmental changes (temperature and humidity fluctuations, personnel movement), or operating mode switching. This leads to a decrease in the prediction accuracy of traditional fixed-parameter models, resulting in energy efficiency losses. Therefore, research on parameter identification methods for equivalent heat transfer models of building air conditioning systems has significant practical implications. Summary of the Invention

[0003] Therefore, it is necessary to provide a method, device, computer equipment, and storage medium for identifying building heat conduction parameters to address the aforementioned technical problems, thereby improving the accuracy of building heat conduction parameter identification.

[0004] In a first aspect, this application provides a method for identifying building heat conduction parameters, applied to a virtual power plant, wherein the temperature control device cluster in the load aggregation system of the virtual power plant includes target temperature control devices within the building space to be identified; the method includes:

[0005] An analytical recursive model for obtaining the second-order equivalent heat conduction parameter model of the target temperature control device is obtained; the analytical recursive model includes the heat conduction parameters to be identified.

[0006] The dimensional parameters and status measurement datasets of the building space, as well as the current control parameters of the virtual power plant, are obtained respectively.

[0007] Based on the size parameters and the current control parameters, determine the target range of the heat conduction parameters;

[0008] The target value of the heat conduction parameter is determined based on the state measurement dataset, the analytical recursive model, the target value range, and the preset termination condition.

[0009] In one embodiment, determining the target value of the heat conduction parameter based on the state measurement dataset, the analytical recursive model, the target value range, and the preset termination condition includes:

[0010] Candidate values ​​for the heat conduction parameter are generated based on the target value range;

[0011] performing recursive processing according to the state measurement data set, the candidate value and the analytical recursive model to obtain a state fitting data set;

[0012] obtaining a comprehensive fitting error corresponding to the candidate value according to the state fitting data set, the state measurement data set and a preset target function;

[0013] in a case where neither the comprehensive fitting error nor the number of recursions satisfies a preset termination condition, returning to perform the step of generating the candidate value of the heat conduction parameter according to the target value range until the comprehensive fitting error and / or the number of recursions satisfies the preset termination condition; the target value of the heat conduction parameter is the candidate value corresponding to the minimum comprehensive fitting error under the condition of satisfying the preset termination condition.

[0014] In one of the embodiments, the state measurement data set includes an electrical power measurement sequence of the target temperature control device, and an indoor air temperature measurement sequence and an outdoor air temperature measurement sequence of the building space; the state fitting data set includes an indoor air temperature fitting sequence, an indoor solid air temperature fitting sequence and a heat power fitting sequence of the building space; the step of performing recursive processing according to the state measurement data set, the candidate value and the analytical recursive model to obtain a state fitting data set includes:

[0015] performing forward recursion according to the indoor air temperature measurement sequence, the outdoor air temperature measurement sequence, the electrical power measurement sequence, the candidate value and the analytical recursive model to obtain the indoor air temperature fitting sequence and the indoor solid air temperature fitting sequence;

[0016] performing backward estimation according to the indoor air temperature fitting sequence, the indoor solid air temperature fitting sequence, the outdoor air temperature measurement sequence and the candidate value to obtain the heat power fitting sequence.

[0017] In one of the embodiments, each measurement sequence includes a plurality of measurement data sampled at a preset period; and the step of performing backward estimation according to the indoor air temperature fitting sequence, the indoor solid air temperature fitting sequence, the outdoor air temperature measurement sequence and the candidate value to obtain the heat power fitting sequence includes:

[0018] for any sampling time, obtaining heat power fitting data of the current sampling time according to indoor air temperature measurement data, indoor solid air temperature fitting data and outdoor air temperature measurement data of the current sampling time, and indoor air temperature measurement data of the next sampling time and the candidate value; wherein the indoor solid air temperature fitting data of the current sampling time is obtained according to heat power fitting data, indoor solid air temperature fitting data, indoor air temperature measurement data and outdoor air temperature measurement data of the previous sampling time, and the candidate value.

[0019] In one of the embodiments, the obtaining of the comprehensive fitting error corresponding to the candidate value according to the state fitting data set, the state measurement data set and a preset target function comprises:

[0020] The indoor air temperature fitting error is obtained according to the indoor air temperature measurement sequence and the indoor air temperature fitting sequence.

[0021] The thermal power fitting error is obtained according to the electric power measurement sequence and the thermal power fitting sequence.

[0022] The indoor air temperature fitting error and the thermal power fitting error are input into the target function to obtain the comprehensive fitting error corresponding to the candidate value; the comprehensive fitting error is positively correlated with the indoor air temperature fitting error and the thermal power fitting error respectively.

[0023] In one of the embodiments, the heat conduction parameters comprise a thermal resistance parameter and a thermal capacity parameter, and a target value range of the heat conduction parameters comprises a target value range of the thermal resistance parameter and a target value range of the thermal capacity parameter; wherein the target value range of the heat conduction parameters is determined according to the size parameter and the current regulation parameter, comprising:

[0024] The initial value range of the thermal capacity parameter is determined according to the size parameter.

[0025] The initial value range of the thermal resistance parameter is determined according to the initial constraint condition of the thermal resistance parameter.

[0026] The target value range of the thermal capacity parameter and the target value range of the thermal resistance parameter are respectively determined according to the current regulation parameter, the initial value range of the thermal capacity parameter and the initial value range of the thermal resistance parameter.

[0027] In one of the embodiments, the current regulation parameter comprises a current electricity price and a regulation instruction of the virtual power plant; and the target value range of the thermal capacity parameter and the target value range of the thermal resistance parameter are respectively determined according to the current regulation parameter, the initial value range of the thermal capacity parameter and the initial value range of the thermal resistance parameter, comprising:

[0028] In the case that the current electricity price is greater than a reference electricity price and the regulation instruction is a load reduction instruction, the target value range of the thermal resistance parameter is determined according to the current electricity price and the initial value range of the thermal resistance parameter; wherein the upper limit value of the target value range of the thermal resistance parameter is greater than the upper limit value of the initial value range of the thermal resistance parameter, and the target value range of the thermal capacity parameter is the initial value range of the thermal capacity parameter; or,

[0029] In a case where the current electricity price is less than the reference electricity price and the regulation instruction is a matching instruction, a target value range of the thermal capacity parameter is determined according to the current electricity price and an initial value range of the thermal capacity parameter; wherein a lower limit value of the target value range of the thermal capacity parameter is greater than a lower limit value of the initial value range of the thermal capacity parameter, and a target value range of the thermal resistance parameter is the initial value range of the thermal resistance parameter.

[0030] In a second aspect, the present application provides a building heat conduction parameter identification device, applied to a virtual power plant, a temperature control device cluster in a load aggregation system of the virtual power plant including a target temperature control device in a building space to be identified; the device includes:

[0031] An acquisition module is configured to acquire an analytical recursive model of a second-order equivalent heat conduction model of the target temperature control device; acquire size parameters and state measurement data sets of the building space, and current regulation parameters of the virtual power plant; the analytical recursive model includes heat conduction parameters to be identified;

[0032] A determination module is configured to determine a target value range of the heat conduction parameters according to the size parameters and the current regulation parameters;

[0033] An identification module is configured to determine a target value of the heat conduction parameters according to the state measurement data sets, the analytical recursive model, the target value range, and a preset termination condition.

[0034] In a third aspect, the present application provides a computer device, including a memory and a processor, the memory stores a computer program, and the processor implements the steps of the above method when executing the computer program.

[0035] In a fourth aspect, the present application provides a computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to implement the steps of the above method.

[0036] The building heat conduction parameter identification method, device, computer equipment and storage medium described above, obtain an analytical recursive model of a second-order equivalent heat conduction parameter model of the target temperature control device; the analytical recursive model includes a heat conduction parameter to be identified; obtain size parameters and state measurement data sets of the building space, and current regulation parameters of the virtual power plant, respectively; determine a target value range of the heat conduction parameter according to the size parameters and the current regulation parameters; and determine a target value of the heat conduction parameter according to the state measurement data set, the analytical recursive model, the target value range and a preset termination condition. In this way, by obtaining the analytical recursive model of the second-order equivalent heat conduction parameter model of the target temperature control device, the target value range of the heat conduction parameter is determined in combination with the size parameters of the building space and the current regulation parameters of the virtual power plant, and then the target value of the heat conduction parameter is identified based on the state measurement data set, the analytical recursive model and the preset termination condition. Compared with the identification methods of related technologies such as least square method and Kalman filtering which depend on steady-state assumption or periodic data, the present method breaks through the limitations brought by noise interference and parameter time-varying characteristics under non-stationary working conditions, can dynamically adapt to the regulation requirements of the virtual power plant, improves the accuracy of heat conduction parameter identification, and then optimizes the prediction accuracy of the building air conditioning system model, reduces the energy efficiency loss and cost of the virtual power plant caused by model mismatch, and improves the economy of the virtual power plant aggregation regulation. BRIEF DESCRIPTION OF DRAWINGS

[0037] Figure 1 A flowchart of a building heat conduction parameter identification method in an embodiment is shown;

[0038] Figure 2 A heat conduction circuit diagram of a target temperature control device in an embodiment is shown;

[0039] Figure 3 A flowchart of a building heat conduction parameter identification method in an embodiment is shown;

[0040] Figure 4 A flowchart of a building heat conduction parameter identification method in another embodiment is shown;

[0041] Figure 5 A flowchart of a building heat conduction parameter identification method in another embodiment is shown;

[0042] Figure 6 A flowchart of a building heat conduction parameter identification method in another embodiment is shown;

[0043] Figure 7 A flowchart of a building heat conduction parameter identification method in another embodiment is shown;

[0044] Figure 8 A flowchart of a building heat conduction parameter identification method in another embodiment is shown;

[0045] Figure 9 a structural block diagram of a building heat conduction parameter identification device in an embodiment;

[0046] Figure 10 an internal structure diagram of a computer device in an embodiment. DETAILED DESCRIPTION

[0047] In order to make the purposes, technical solutions and advantages of the present application clearer, the present application will be further described in detail below with reference to the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and not used to limit the present application.

[0048] The building heat conduction parameter identification method provided by the embodiment of the present application can be applied to a terminal, a server, a system of the terminal and the server, and is realized through the interaction of the terminal and the server.

[0049] In some embodiments, as shown in Figure 1 , a building heat conduction parameter identification method is provided, which is applied to a virtual power plant, and a temperature control device cluster in a load aggregation system of the virtual power plant includes a target temperature control device in a building space to be identified. Wherein, the temperature control devices in the temperature control device cluster are used as temperature control loads to regulate the building temperature. The target temperature control device is one of the temperature control device cluster and is used to regulate the temperature in the building space to be identified. For example, the temperature control device is an air conditioner. In combination with Figure 1 , the building heat conduction parameter identification method includes the following S102 to S108.

[0050] S102, obtaining an analytical recursive model of a second-order equivalent thermal conduction parameter model of the target temperature control device; the analytical recursive model includes the heat conduction parameter to be identified.

[0051] Please refer to Figure 2 , Figure 2 A heat conduction circuit diagram of the target temperature control device is provided. Based on Figure 2 , the second-order equivalent thermal conduction parameter (ETP) model is given for two nodes T a (t) and T m (t) in the circuit diagram as follows:

[0052] (1)

[0053] (2)

[0054] According to the above-mentioned second-order ETP model, the analytical form of T m (t) is obtained, that is, Tm The general solution of (t) is homogeneous solution plus particular solution:

[0055] (3)

[0056] Where, T m is the indoor solid gas temperature; T o is the outdoor air temperature; R a is the thermal resistance between indoor gas and the outside; Q is the air conditioning heat power; t is the time variable; C1, C2 are intermediate variables in the derivation process, which are specifically represented as:

[0057] (4)

[0058] (5)

[0059] Where, C m is the heat capacity of the solid; R m is the thermal resistance between indoor solid and indoor gas; t k is the discrete time.

[0060] (6)

[0061] (7)

[0062] (8)

[0063] (9)

[0064] (10)

[0065] In the formula, △t is the sampling interval; k indicates the sampling point serial number, k=0, 1, 2, 3…….

[0066] According to the conversion of the above-mentioned second-order ETP model, the analytical form of T a (t) is obtained, that is, the general solution of T a (t) is homogeneous solution plus particular solution:

[0067] (11)

[0068] Where, T a is the indoor air temperature.

[0069] Combined with the initial value condition at t k time, the analytical form of T m (t) and T a (t), the second-order ETP model is expressed in matrix form:

[0070] (12)

[0071] (13)

[0072] (14)

[0073] (15)

[0074] (16)

[0075] wherein, the heat conduction parameters to be identified include R a , R m , C a , C m ; C a is the indoor gas heat capacity.

[0076] S104, respectively, acquire the size parameters and the state measurement data set of the building space, and the current regulation parameters of the virtual power plant.

[0077] The size parameters are used to represent the size of the building space, including but not limited to the area S, the height h, etc. The state measurement data set is used to represent the data set measured by measuring the current environment of the building space, which refers to the data that can be directly measured or acquired. The state measurement data set includes but is not limited to the indoor air temperature, the outdoor air temperature, the power of the target temperature control device, etc. The current regulation parameters are used to represent the current regulation situation of the virtual power plant, including but not limited to the real-time electricity price, the regulation instruction, etc.

[0078] S106, determine the target value range of the heat conduction parameters according to the size parameters and the current regulation parameters.

[0079] The target value range of the heat conduction parameters refers to the value range determined in real time based on the size parameters of the building space and the current regulation parameters of the virtual power plant, which is used to constrain the final value of the heat conduction parameters. The target value range of the heat conduction parameters includes the target value range of the thermal resistance R a and R m , and the target value range of the heat capacity C a and C m .

[0080] S108, determine the target value of the heat conduction parameters according to the state measurement data set, the analytical recursive model, the target value range and the preset termination condition.

[0081] The preset termination condition is preset, and the preset termination condition is used to determine the target value of the heat conduction parameter. The target value of the heat conduction parameter meets a target value range of the heat conduction parameter, or in other words, the target value of the heat conduction parameter is determined from the target value range. The analytical recursive model is used to perform analytical recursive calculation based on the state measurement data set, and the recursive calculation is terminated according to the preset termination condition, so as to obtain the target value.

[0082] In the application, a candidate value can be randomly generated based on the target value range of the heat conduction parameter, the candidate value is substituted into the analytical recursive model, forward recursion and backward estimation are completed in combination with the state measurement data set, and a state fitting data set is obtained; a comprehensive fitting error corresponding to the candidate value is calculated through the target function, if the comprehensive fitting error or the number of recursions meets the preset termination condition, the candidate value is determined as the target value of the heat conduction parameter, otherwise, the candidate value is re-generated and the above process is repeated until the termination condition is met.

[0083] The application obtains an analytical recursive model of a second-order equivalent heat conduction parameter model of a target temperature control device, determines a target value range of a heat conduction parameter based on building space size parameters and current regulation parameters of a virtual power plant, and identifies a target value of the heat conduction parameter based on a state measurement data set, the analytical recursive model, and a preset termination condition. Compared with the identification method in the related art, such as the least square method and the Kalman filter, which depend on a steady-state assumption or periodic data, the method breaks through the limitation caused by noise interference and parameter time-varying characteristics under a non-stationary working condition, can dynamically adapt to the regulation demand of the virtual power plant, improves the accuracy of identification of the heat conduction parameter, and further optimizes the prediction accuracy of the building air conditioning system model, reduces the energy efficiency loss and cost of the virtual power plant caused by model mismatch, and improves the economy of the aggregated regulation of the virtual power plant.

[0084] In some embodiments, as shown in FIG. 1, Figure 3 the heat conduction parameter includes a thermal resistance parameter R a and R m , and a thermal capacity parameter C a and C m . Wherein, R a represents the thermal resistance between the indoor gas and the outside world; R m represents the thermal resistance between the indoor solid and the indoor gas; C a represents the thermal capacity of the gas; and C m represents the thermal capacity of the solid.

[0085] The target value range of the heat conduction parameter includes a target value range of the thermal resistance parameter and a target value range of the thermal capacity parameter. That is, the target value range of the heat conduction parameter includes a target value range of R a , a target value range of R m , a target value range of C a , and a target value range of C mThe target value range of the heat transfer parameter. Wherein, S106, according to the size parameter and the current regulation parameter, determines the target value range of the heat transfer parameter, including the following S302 to S306.

[0086] S302, according to the size parameter, determines the initial value range of the heat capacity parameter.

[0087] S304, according to the initial constraint condition of the thermal resistance parameter, determines the initial value range of the thermal resistance parameter.

[0088] S306, according to the current regulation parameter, the initial value range of the heat capacity parameter and the initial value range of the thermal resistance parameter, respectively determines the target value range of the heat capacity parameter and the target value range of the thermal resistance parameter.

[0089] The size parameter includes the area S and the height h of the building space. Then, in the ideal state, if the room is completely filled with air, the area is S, the height is h, the air density is p, and the specific heat capacity is C, it can be known that the ideal gas heat capacity C a * is:

[0090] (17)

[0091] Assuming that the indoor is completely filled with air, C a equals the energy required for the heat source to raise the indoor air temperature by 1℃. Since there are furniture and other objects occupying space in the actual room, the gas heat capacity under the ideal model should be lower than the theoretical calculation value of the above formula.

[0092] In addition, the thermal dynamic characteristics of the actual space are different from the ideal second-order ETP model, and the identification result of the parameter C a cannot completely correspond to the physical heat capacity, so the upper limit of its constraint can be appropriately relaxed, that is, the target value range of C a is:

[0093] 0≤C a ≤C a * (18)

[0094] The indoor solid mass of a general space is much larger than the gas, which is the main heat medium in the building. In addition, according to engineering experience, the value of C m / C a is between 10 and 100. That is, the target value range of C m is:

[0095] 10C a ≤C m ≤100C a (19)

[0096] In addition, under the same temperature difference, the smaller the thermal resistance between substances, the faster the heat conduction. Due to the existence of walls, windows and other partitions, the difficulty of heat convection and heat conduction between indoor and outdoor gases is higher than that between indoor gases and indoor solids. Therefore, the initial constraint conditions between R a and R m are:

[0097] R a ≥ R m (20)

[0098] The virtual power plant needs to adjust the building load according to the real-time electricity price and the grid dispatching instruction, and needs to dynamically match the parameter physical constraint and the regulation and control target. Therefore, the heat conduction model parameter has the characteristics of dynamic change at different load demand periods. In the demand response period, the upper limit of the thermal resistance (R m , R a ) is relaxed to allow greater heat exchange flexibility, or the range of the heat capacity (C m , C a ) is limited to match the load reduction target, so as to determine the target value range of each parameter.

[0099] In this embodiment, the initial value range of the heat capacity parameter is determined based on the physical size of the building space, and the initial constraint condition and the initial value range of the thermal resistance parameter are determined based on the physical characteristics of the heat conduction. Then, the target value range of the heat conduction parameter is determined by combining the dynamic adjustment of the regulation and control parameters of the virtual power plant. The target value range of the heat conduction parameter not only fits the physical characteristics of the building space and avoids parameter solutions without physical meaning, but also flexibly matches the regulation and control target of different load demand periods of the virtual power plant. This lays a foundation for subsequent accurate identification of the heat conduction parameter and improves the engineering practicability of the parameter identification result.

[0100] In some embodiments, as shown in Figure 4 , the current regulation and control parameters include the current electricity price and the regulation and control instruction of the virtual power plant. S206, according to the current regulation and control parameters, the initial value range of the heat capacity parameter and the initial value range of the thermal resistance parameter, the target value range of the heat capacity parameter and the target value range of the thermal resistance parameter are determined respectively, including the following S402 or S404.

[0101] S402, in the case that the current electricity price is greater than the reference electricity price and the regulation and control instruction is a load reduction instruction, the target value range of the thermal resistance parameter is determined according to the current electricity price and the initial value range of the thermal resistance parameter.

[0102] S404, in the case that the current electricity price is less than the reference electricity price and the regulation and control instruction is a matching instruction, the target value range of the heat capacity parameter is determined according to the current electricity price and the initial value range of the heat capacity parameter.

[0103] The upper limit value of the target value range of the thermal resistance parameter is greater than the upper limit value of the initial value range of the thermal resistance parameter, and the target value range of the thermal capacity parameter is the initial value range of the thermal capacity parameter. The lower limit value of the target value range of the thermal capacity parameter is greater than the lower limit value of the initial value range of the thermal capacity parameter, and the target value range of the thermal resistance parameter is the initial value range of the thermal resistance parameter. In the application, when the current electricity price is the reference electricity price, the initial value range can be used as the target value range of the heat transfer parameter.

[0104] In the high electricity price period (such as peak time), in order to increase the heat exchange flexibility and quickly respond to the load reduction instruction, the upper limit of the thermal resistance is relaxed:

[0105] (21)

[0106] (22)

[0107] In the formula, R a,max , R m,max is the upper limit of the thermal resistance (R a , R m ) before adjustment; R a,adj , R m,adj is the upper limit of the thermal resistance adjusted according to the virtual power plant regulation demand; P price is the real-time electricity price; P base is the reference electricity price; is an adjustment coefficient, which is determined according to the virtual power plant strategy and the tension degree of power supply and demand; R a,limit , R m,limit is the absolute upper limit of the thermal resistance allowed by the system.

[0108] In the low electricity price period (such as valley time), in order to match the pre-charging or pre-heating demand, the thermal capacity range can be limited:

[0109] (23)

[0110] (24)

[0111] In the formula, C a,min , C m,min is the lower limit of (C a , C m ); C a,adj , C m,adj is the lower limit of the thermal capacity adjusted according to the virtual power plant regulation demand; C a,base , C m,base is the reference thermal capacity value; is an adjustment coefficient, which is determined according to the virtual power plant strategy and the tension degree of power supply and demand.

[0112] The minimum value (absolute lower limit allowed by the system) and the maximum value (absolute upper limit allowed by the system) of the heat conduction parameter can be set empirically or experimentally, and typical ranges of each parameter are shown in Table 1. Among them, the absolute value range of the thermal resistance R a is 5-50℃ KW -1 . The absolute value range of the heat capacity C a is 0.1-1℃ -1 KW. The absolute value range of the thermal resistance R m is 1-10℃ KW -1 . The absolute value range of the heat capacity C m is 1-100℃ -1 KW.

[0113] Table 1

[0114]

[0115] In this embodiment, the value range of the thermal resistance or the heat capacity parameter is adjusted differently for different electricity price periods and regulation instructions of the virtual power plant. The upper limit of the thermal resistance is relaxed in the high electricity price load reduction period, the heat exchange flexibility is improved to quickly respond to the load reduction instruction, and the air conditioning system temperature adjustment response time is shortened. The lower limit of the heat capacity is increased in the low electricity price pre-charging / pre-heating period, the heat storage capacity of the building space is enhanced to match the energy storage regulation target of the virtual power plant, and the value range of the heat conduction parameter is dynamically adapted to the real-time operation demand of the virtual power plant. The matching degree of the subsequent parameter identification result and the regulation scene is further improved.

[0116] In some embodiments, as shown in Figure 5 S108, the target value of the heat conduction parameter is determined according to the state measurement data set, the analytical recursive model, the target value range and the preset termination condition, including the following S502-S508.

[0117] S502, generating a candidate value of the heat conduction parameter according to the target value range.

[0118] In applications, a group of candidate values can be generated in a random sampling manner according to the target value range of the heat conduction parameters R a , R m , C a , C m .

[0119] S504, performing recursive processing according to the state measurement data set, the candidate value and the analytical recursive model to obtain a state fitting data set.

[0120] The state measurement dataset is used to represent the real-time state of the target temperature control device and the building space, and can be obtained by measurement. The data indexes in the state measurement dataset include, but are not limited to, the electric power of the target temperature control device, the indoor air temperature of the building space, the outdoor air temperature of the building space, and the like. Exemplarily, the state measurement dataset includes an electric power measurement sequence P(t) of the target temperature control device, and indoor air temperature measurement sequences T a,meas (t) and outdoor air temperature measurement sequence T o (t) of the building space.

[0121] The state fitting dataset is used to represent the fitting data corresponding to various measurement data in the state measurement dataset. Exemplarily, the state fitting dataset includes an indoor air temperature fitting sequence T a,est (t), an indoor solid air temperature fitting sequence T m,est (t) and a heat power fitting sequence Q est (t) of the building space.

[0122] In the application, the candidate value and the recursive initial value can be substituted into the analytical recursive model to complete the forward recursion and the backward estimation in combination with the state measurement dataset, so as to generate the state fitting dataset.

[0123] It can be understood that, since the identified target parameters cannot be obtained by direct measurement, in order to measure the accuracy of the identification method, the application converts the parameters into fitting sequences of other measurable variables. Then, the difference between the fitting sequence and the measurement sequence is compared to obtain an adaptability evaluation index of the parameter, that is, an objective function of parameter optimization, which is an indoor air temperature fitting error.

[0124] S506, according to the state fitting dataset, the state measurement dataset and the preset target function, obtaining a comprehensive fitting error corresponding to the candidate value.

[0125] Exemplarily, the comprehensive fitting error Fc can be used to represent the comprehensive error of the indoor air temperature fitting error F ISE and the heat power fitting error F Q .

[0126] S508, in the case that neither the comprehensive fitting error nor the recursion times satisfies the preset termination condition, returning to execute the step of generating the candidate value of the heat conduction parameter according to the target value range, until the comprehensive fitting error and / or the recursion times satisfies the preset termination condition; the target value of the heat conduction parameter is the candidate value corresponding to the minimum value of the comprehensive fitting error under the condition that the preset termination condition is satisfied.

[0127] The preset termination condition can include that the comprehensive fitting error is less than a preset error threshold, that is, Fc<ε, and / or the recursion times reaches a preset number threshold, that is, the maximum iteration number. The preset error threshold ε and the preset number threshold are preset respectively, and can be set according to experiments or experience, which are not limited here.

[0128] If the overall fitting error is greater than or equal to the preset error threshold Fc≥ε, and the number of iterations has not reached the preset number threshold, it indicates that the current candidate value of the heat conduction parameter is not the optimal value. In this case, return to S502, regenerate a new set of candidate values ​​based on the target value range, and perform the next iteration until at least one of the overall fitting error and the number of iterations meets the preset termination condition, i.e., the overall fitting error is less than the preset error threshold, and / or the number of iterations reaches the preset number threshold. Therefore, the target value for the heat conduction parameter is the candidate value corresponding to an overall fitting error (which is the minimum value) that is less than the preset error threshold, or the target value for the heat conduction parameter is the candidate value corresponding to the minimum overall fitting error when the number of iterations reaches the preset number threshold.

[0129] In this embodiment, the target value of the heat conduction parameter is identified through iterative optimization. The candidate value generation is constrained by the target value range to avoid invalid solutions without physical meaning. The dual-path fitting verification is performed through forward recursion and backward estimation. The comprehensive fitting error is quantified by weighted objective function. The iteration endpoint is determined by a clear termination condition. This can accurately select the parameter value with the highest matching degree with the actual working condition. Compared with the traditional fixed parameter model, the identification accuracy is significantly improved, and the prediction error of the air conditioning system model is effectively reduced.

[0130] In some embodiments, such as Figure 6 As shown, the state measurement dataset includes the electrical power measurement sequence P(t) of the target temperature control device and the indoor air temperature measurement sequence T of the building space. a,meas (t) and outdoor temperature measurement sequence T o (t). The state-fitted dataset includes the fitted sequence T of indoor air temperature in the building space. a,est (t), Indoor solid temperature fitting sequence T m,est (t) and thermal power fitting sequence Q est (t).

[0131] S504 involves recursively processing the state measurement dataset, candidate values, and analytical recursive model to obtain the state fitting dataset, which includes the following S602 and S604.

[0132] S602, based on the indoor temperature measurement sequence, outdoor temperature measurement sequence, electric power measurement sequence, candidate values ​​and analytical recursive model, perform forward recursion to obtain the indoor temperature fitting sequence and the indoor solid temperature fitting sequence.

[0133] S604. Based on the indoor temperature fitting sequence, indoor solid temperature fitting sequence, outdoor temperature measurement sequence and candidate values, back estimation is performed to obtain the thermal power fitting sequence.

[0134] A set of candidate values ​​R for heat conduction parameters generated based on S502 a R m C a C m And obtain the average energy efficiency ratio C op,ave The indoor temperature measurement value T at the initial time t0 a,meas (t0), outdoor temperature measurement value T o (t0) and indoor solid temperature T m (t0), where T a (t0), T o (t0) can be obtained by sensor measurement; T m (t0) or denoted as T m0 It can be preset; as shown in Table 1, T m0 The value ranges from 10 to 25℃. Average energy efficiency ratio C op,ave It can be obtained from historical operational data.

[0135] Based on the candidate value of the heat conduction parameter R a R m C a C m With the current fitting interval Δt k Determine matrices A and B, and input the current sampling time t. k The fitted value of indoor temperature T a,est (t k ), indoor solid temperature fitting value T m,est (t k Outdoor temperature measurement value T o (t k ), thermal power measurement value Q meas (t k ), to obtain the next sampling time t k+1 The fitted value of indoor temperature T a,est (t k+1 ), indoor solid temperature fitting value T m,est (t k+1 The fitting interval can also be understood as the sampling interval, with a one-to-one correspondence between the fitting time and the sampling time.

[0136] (25)

[0137] In the formula, P(t) k ) for target temperature control devices such as air conditioners at t k The measured value of electrical power at a given time.

[0138] Specifically, at time t0 (initialization), the initial value T is obtained. a (t0), T m (t0), To (t0) and P(t0), a set of candidate values R a , R m , C a , C m . The first step of recursion, i.e. from t0 to t1, according to the input initial value and the candidate values of the heat conduction parameters, the A and B matrices are calculated (given △t k ), and then Q meas (t0) = P(t0)C op,ave are input, and the T a,est (t1), T m,est (t1) are obtained by recursion. The second step of recursion, i.e. from t1 to t2, first, the Q a,meas (t0) is calculated by reverse estimation according to the measured value T est (t1), and then the fitting values T a,est (t2), T m,est (t2) are calculated by forward recursion, and the cycle continues until the sampling ends.

[0139] Based on the above cycle until the sampling ends, the indoor air temperature fitting sequence T a,est (t) and the heat power fitting sequence Q est (t) are finally obtained. Thus, the indoor air temperature fitting error F a,est can be calculated according to the indoor air temperature fitting sequence T a,meas (t) and the indoor air temperature measured sequence T ISE (t). And the heat power measured sequence Q meas (t) can be obtained according to the electric power measured sequence P(t), so that the heat power fitting error F est can be calculated according to the heat power fitting sequence Q meas (t) and the heat power measured sequence Q Q (t). Further, the objective function F ISE can be updated according to the indoor air temperature fitting error F Q and the heat power fitting error F c , and if the updated objective function value F c still does not satisfy the termination condition, a new set of candidate values of the heat conduction parameters R a , R m , C a , C m is updated until the termination condition is satisfied.

[0140] In this embodiment, the dynamic change process of indoor air temperature and solid air temperature is reconstructed by forward recursion, and the fitted value of thermal power is estimated and derived in reverse. This forms a two-dimensional fitting verification mechanism of air temperature and thermal power. Compared with the single-dimensional fitting method, it can more comprehensively verify the rationality of the candidate parameter values, significantly improve the accuracy of the state fitting dataset, and provide reliable data support for subsequent comprehensive fitting error calculation.

[0141] In some embodiments, each measurement sequence includes multiple measurement data sampled at a preset period. Specifically, in step S604, back estimation is performed based on the indoor temperature fitting sequence, the indoor solid temperature fitting sequence, the outdoor temperature measurement sequence, and candidate values ​​to obtain the thermal power fitting sequence. This includes: for any sampling time, obtaining the thermal power fitting data for the current sampling time based on the indoor temperature measurement data, indoor solid temperature fitting data, and outdoor temperature measurement data at the current sampling time, as well as the indoor temperature measurement data and candidate values ​​at the next sampling time. The indoor solid temperature fitting data at the current sampling time is obtained based on the thermal power fitting data, indoor solid temperature fitting data, indoor temperature measurement data, and outdoor temperature measurement data from the previous sampling time, as well as candidate values.

[0142] For the current sampling time t k and the next sampling time t k+1 First, based on t k Indoor temperature measurement value T at any time a,meas (t k ),t k+1 Indoor temperature measurement value T at any time a,meas (t k+1 ),t k outdoor temperature measurement T at time o (t k ) and the t obtained by recursion k Time-bound indoor solid fitting value T m,est (t k ), obtain The thermal power fitting value Q at time 1 est (t k ):

[0143] (26)

[0144] in, , .

[0145] Then, combine Timing power fitting value Q est (t k ), Indoor temperature measurement value T at any time a,meas (t k ), Solid temperature measurement value T at time m,est (t) and, obtain Fitted value of indoor solid temperature T at time 1 m,est (t k+1 ), and then use it as input to calculate The fitted value of the heat power at time t. Repeating the above process, the fitted heat power sequence Q can be obtained. est (t).

[0146] (27)

[0147] In this embodiment, based on the correlation of time series data, the heat power fitting value is derived by back estimation. By making full use of the fitting data of the previous moment and the measured data of the current moment, the dynamic change law of heat power over time can be accurately restored. The obtained heat power fitting sequence is highly consistent with the actual operating conditions, effectively reducing the heat power fitting error and improving the accuracy of evaluating candidate values ​​of heat conduction parameters.

[0148] In some embodiments, such as Figure 7 As shown in S506, based on the state fitting dataset, the state measurement dataset, and the preset objective function, the comprehensive fitting error corresponding to the candidate value is obtained, including the following S702 to S706.

[0149] S702, based on the indoor temperature measurement sequence and the indoor temperature fitting sequence, obtain the indoor temperature fitting error.

[0150] S704: Obtain the thermal power fitting error based on the electrical power measurement sequence and the thermal power fitting sequence.

[0151] S706, input the indoor temperature fitting error and the thermal power fitting error into the objective function to obtain the comprehensive fitting error corresponding to the candidate value; the comprehensive fitting error is positively correlated with the indoor temperature fitting error and the thermal power fitting error, respectively.

[0152] The indoor temperature measurement sequence is T a,meas (t), the indoor temperature fitted sequence is T a,est (t), then the indoor temperature fitting error F ISE for:

[0153] (28)

[0154] In the formula, T a,est (t) is the value of t calculated based on the analytical recursive model. k Fitted value of indoor temperature at time T; a,meas (t) is t k Indoor temperature measurement at any given time; T0 is the initial indoor solid temperature at t0, which is obtained by sensor measurement; Cavg is the average energy efficiency ratio, which is obtained according to historical operation data.

[0155] The electric power measurement sequence is P(t), and the heat power measurement sequence Q meas (t) is:

[0156] (29)

[0157] In the formula, P set is the set power, which can be predicted; P R is the electric auxiliary heat power, which can be obtained from a target temperature control device such as an air conditioner nameplate. The average energy efficiency ratio C op,ave is:

[0158] (30)

[0159] The heat power fitting error F Q is:

[0160] (31)

[0161] In the formula, Q est (t k ) is the heat power fitting value at t k in the heat power fitting sequence; F Q is the relative error between the measured heat power and the estimated heat power.

[0162] The objective function is:

[0163] (32)

[0164] In the formula, a is a weight factor that distinguishes the importance of F ISE and F Q to the objective function, and can be 0.5.

[0165] In this embodiment, the indoor air temperature fitting deviation is quantified by the error square integral criterion, and the heat power fitting deviation is quantified by the relative error criterion, which can accurately reflect the matching degree of the fitting sequence and the measured sequence. In addition, the objective function is constructed in combination with the weight factor, the error contributions of the two dimensions are balanced, the one-sidedness of single-dimensional evaluation is avoided, the rationality of the parameter candidate value can be more comprehensively and objectively evaluated, and accurate judgment basis is provided for iterative optimization.

[0166] In some embodiments, as Figure 8As shown, a building heat conduction parameter identification method is provided, applied to a virtual power plant, and a temperature control device cluster in a load aggregation system of the virtual power plant includes a target temperature control device such as an air conditioner in a building space to be identified. The method includes the following S802 to S816.

[0167] S802, obtaining an analytical recursive model of a second-order equivalent heat conduction parameter model of the target temperature control device; the analytical recursive model includes the heat conduction parameter to be identified.

[0168] The obtaining process of the analytical recursive model can refer to the aforementioned formulas (1) to (16).

[0169] S804, respectively obtaining size parameters and state measurement data sets of the building space, and current control parameters of the virtual power plant.

[0170] The size parameters include area S and layer height h. The state measurement data sets include an electric power measurement sequence P(t) of the target temperature control device, and an indoor air temperature measurement sequence T a,meas (t) and an outdoor air temperature measurement sequence T o (t) of the building space. The current control parameters include real-time electricity price and control instructions.

[0171] S806, determining a target value range of the heat conduction parameter according to the size parameters and the current control parameters.

[0172] The determination process of the target value range can refer to the aforementioned formulas (17) to (24) and Table 1.

[0173] S808, generating a candidate value of the heat conduction parameter according to the target value range.

[0174] A group of candidate values of the heat conduction parameter can be randomly generated according to the determined target value range, as a basis for calculating a comprehensive fitting error.

[0175] S810, performing forward recursion according to the indoor air temperature measurement sequence, the outdoor air temperature measurement sequence, the electric power measurement sequence, the candidate value and the analytical recursive model, to obtain an indoor air temperature fitting sequence and an indoor solid air temperature fitting sequence.

[0176] S812, for any sampling time, obtaining heat power fitting data of the current sampling time according to indoor air temperature measurement data, indoor solid air temperature fitting data and outdoor air temperature measurement data of the current sampling time, and indoor air temperature measurement data of the next sampling time and the candidate value.

[0177] Wherein, the indoor solid air temperature fitting data of the current sampling time is obtained according to the heat power fitting data, the indoor solid air temperature fitting data, the indoor air temperature measurement data and the outdoor air temperature measurement data of the previous sampling time, and the candidate value.

[0178] The specific calculation process of the fitting sequence can be seen from the foregoing formulas (25) to (27).

[0179] S814, the indoor air temperature fitting error is obtained according to the indoor air temperature measurement sequence and the indoor air temperature fitting sequence, the heat power fitting error is obtained according to the electric power measurement sequence and the heat power fitting sequence, and the indoor air temperature fitting error and the heat power fitting error are input into the objective function to obtain the comprehensive fitting error corresponding to the candidate value.

[0180] The specific calculation process of the comprehensive fitting error can be seen from the foregoing formulas (28) to (32).

[0181] S816, whether the comprehensive fitting error and the recursive number meet the preset termination condition is judged. If neither meets the preset termination condition, the step S808 is returned. If one of them meets the preset termination condition, the step S818 is executed.

[0182] S818, the target value of the heat conduction parameter is output, and the target value is the candidate value corresponding to the minimum comprehensive fitting error when the preset termination condition is met.

[0183] Next, taking the air conditioning system of a certain commercial building as an object, the method provided in the present application is used to identify the heat conduction parameter. Among them, the building area S = 500 m², the floor height h = 3 m. The sampling interval△t = 5 minutes is selected, and a total of 24 hours is sampled, that is, t0= 0, t1= 5 min, t2= 10 min, …, t 288 =1440min.

[0184] Then, in the ideal state, if the room is completely filled with air, the area is S = 500 m², the height is h = 3 m, the air density is p = 1.2 kg / m³, and the specific heat capacity is C = 1.005 kJ / (kg℃), it can be known that the gas heat capacity is:

[0185] C a *=500m²*3m*1.2kg / m3*1.005kJ / (kg℃) =0.5025 kWh / ℃

[0186] Then the initial value range of the gas heat capacity C a is: 0≤C a ≤C a *=0.5025 kWh / ℃.

[0187] Then the initial value range of the gas heat capacity C m is: 10C a ≤C m ≤100C a .

[0188] And the thermal resistance R mR a ≥R a ≥R m .

[0189] If in the high electricity price period (such as peak electricity price 0.8 yuan / kWh), in order to increase the heat exchange flexibility and quickly respond to the load reduction instruction, the upper limit of the thermal resistance is relaxed, and the benchmark electricity price is set to 0.5 yuan / kWh:

[0190] R a,adj =min(50, 10.02) = 10.02℃·kW⁻¹

[0191] R m,adj =min(10, 5.9) = 5.9℃·kW⁻¹

[0192] In the formula, R a,max , R m,max are the upper limits of the thermal resistance (R a , R m ) before adjustment; R a,adj , R m,adj are the upper limits of the thermal resistance adjusted according to the regulation and control demand of the virtual power plant; P price is the real-time electricity price; P base is the benchmark electricity price, which is 0.5 yuan / kWh in this embodiment; is an adjustment coefficient, which is determined according to the virtual power plant strategy and the tightness of power supply and demand, and is set to 0.2 in this embodiment; R a,limit , R m,limit are the absolute upper limits of the thermal resistance allowed by the system.

[0193] If in the low electricity price period (such as valley electricity price 0.3 yuan / kWh), in order to match the pre-charging or pre-heating demand, the heat capacity range can be limited:

[0194] C a,adj =0.1 + 0.2 × (0.5 - 0.3) × 0.5025=0.1201 kWh / ℃

[0195] C m,adj =1.0 + 0.2 × (0.5 - 0.3) × 100= 5.0kWh / ℃

[0196] In the formula, C a,min , C m,min are the lower limits of (C a , C m ); C a,adj , C m,adj are the lower limits of the heat capacity adjusted according to the regulation and control demand of the virtual power plant; C a,base , C m,base are the benchmark heat capacity values. The adjustment coefficient is 0.2, which is determined according to the virtual power plant strategy and the tension of power supply and demand.

[0197] According to the above determination of the heat conduction parameter R a , R m , C a , C m , a set of candidate values are randomly generated in the target value range, and then the generated heat conduction parameter candidate values are substituted into the analytical recursive model, and the indoor air temperature fitting value is calculated by forward recursion and the heat power fitting value is calculated by backward estimation.

[0198] Specifically, at the initial time t0, that is, the initialization process, the input T a,meas (t0)=23℃, T o (t0)=30℃, P(t0)=2.5kW. Then, the heat conduction parameters are randomly generated, C a =0.35kWh / ℃, C m =25kWh / ℃, R a =12℃·kW⁻¹, R m =4.0℃·kW⁻¹. Set the initial value of the indoor solid temperature T m0 =20℃. Then, the first step recursion, that is, from t0 to t1, calculate Q meas (t0)=P(t0)C op,ave =2.5×3.5=8.75kW, wherein the average energy efficiency ratio C op,ave is 3.5. Then, substitute into the second-order ETP model, we have:

[0199] dT a / dt=(32-23) / 12+8.75 / 0.35-(23-20) / (0.35×4)=0.75+25-2.14=23.61℃ / h,

[0200] dT m / dt= (23-20) / (25×4)=0.03℃ / h.

[0201] Then calculate the fitting value and the error of indoor air temperature:

[0202] T a,est (t1) = 23+ (5 / 60)×23.61 = 24.97℃,

[0203] T m,est (t1) = 20 + (5 / 60)×0.03 = 20.00℃,

[0204] F ISE =1.77℃.

[0205] Then, the fitting value of the heating amount is calculated by inverse estimation, that is, the input parameters include: the indoor air temperature measurement value T a,meas (t k ) = 23℃, the outdoor air temperature measurement value T o (t k ) = 32℃, the indoor air temperature measurement value T a,meas (t k+1 ) = 23.2℃, the indoor solid temperature fitting value T m,est (t k ) = 20℃, and the fitting value of the heat power is calculated according to these input parameters:

[0206] Q est (t k ) = 0.35×(23.2−23.0) / (5 / 60) + (23.0−32.0) / 12.0 + (23.0−20.0) / 4.0= 0.84kW.

[0207] Then, the indoor solid temperature fitting value is calculated according to the fitting value of the heat power:

[0208] T m,est (t k+1 ) =0.4140×23 + 0.5709×22.5 + 0.0151×(32 + (-0.585)×8.5)=22.776℃.

[0209] Then, the relative error F Q = 0.904.

[0210] Then, the comprehensive fitting error F C =0.5×1.77 + 0.5×0.904= 1.337<0.01.

[0211] Since the termination condition is not met, the candidate value of the heat conduction parameter is updated according to the target value range of the heat conduction parameter, and the corresponding comprehensive fitting error is calculated according to the new candidate value. After 47 iterations, the algorithm converges, and the optimal parameters are obtained as shown in Table 2, and the final F C = 0.00012<0.01, wherein R a is 12.3℃ KW -1 , C a is 0.35℃ -1 KW, R m is 4.2℃KW -1 , and C m is 26.8℃ -1 KW.

[0212] Table 2

[0213]

[0214] The application provides a building heat conduction parameter identification method for virtual power plant regulation and control requirements. Through analysis of recursive models and double-path fitting verification, the method overcomes the limitations of related technologies in non-steady state conditions, makes the parameter identification results more in line with actual dynamic characteristics, and improves the model accuracy. Moreover, based on dynamic adjustment of parameter ranges of virtual power plants, such as relaxing the upper limit of thermal resistance or raising the lower limit of heat capacity during demand response period, the model flexibly matches the regulation and control targets of load reduction or flexibility improvement, and enhances the regulation and control adaptability. In addition, the reduced model prediction error directly improves the accuracy of virtual power plant market clearing, reduces the cost caused by model mismatch, improves the aggregator's income, and optimizes the economic benefits. In addition, the parameter range setting is combined with physical significance (such as heat capacity / thermal resistance constraints) to avoid parameter solutions without physical significance, ensure that the results can be applied to actual control systems, and have better engineering practicability.

[0215] It should be understood that, although each step in the flowchart involved in each of the above embodiments is displayed in sequence according to the arrow, these steps are not necessarily executed in sequence according to the arrow. Unless otherwise specified herein, the execution of these steps is not strictly limited in sequence, and these steps can be executed in other sequences. Moreover, at least part of the steps in the flowchart involved in each of the above embodiments can include multiple steps or stages, which are not necessarily executed at the same time, but can be executed at different times, and the execution sequence of these steps or stages is not necessarily sequential, but can be executed in rotation or alternation with at least part of other steps or steps or stages in other steps.

[0216] Based on the same inventive concept, the application also provides a building heat conduction parameter identification device for implementing the above-mentioned building heat conduction parameter identification method. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme described in the above method, so the specific limitations in one or more building heat conduction parameter identification device embodiments provided below can refer to the limitations of the building heat conduction parameter identification method in the above text, which will not be repeated here.

[0217] In some embodiments, as Figure 9As shown, an architectural heat conduction parameter identification device 900 is provided, comprising: an acquisition module 901 configured to acquire an analytical recursive model of a second-order equivalent heat conduction model of a target temperature control device; acquire size parameters and state measurement data sets of an architectural space, and current regulation parameters of a virtual power plant, respectively; the analytical recursive model comprises a heat conduction parameter to be identified. A determination module 902 is configured to determine a target value range of the heat conduction parameter according to the size parameters and the current regulation parameters. An identification module 903 is configured to determine a target value of the heat conduction parameter according to the state measurement data sets, the analytical recursive model, the target value range, and a preset termination condition.

[0218] In some embodiments, the identification module is further configured to generate a candidate value of the heat conduction parameter according to the target value range; perform recursive processing according to the state measurement data sets, the candidate value, and the analytical recursive model to obtain a state fitting data set; obtain a comprehensive fitting error corresponding to the candidate value according to the state fitting data set, the state measurement data set, and a preset target function; in a case where neither the comprehensive fitting error nor the number of recursions satisfies the preset termination condition, return to performing the step of generating the candidate value of the heat conduction parameter according to the target value range until the comprehensive fitting error and / or the number of recursions satisfies the preset termination condition; and the target value of the heat conduction parameter is the candidate value corresponding to the minimum value of the comprehensive fitting error under the condition that the preset termination condition is satisfied.

[0219] In some embodiments, the state measurement data set comprises an electrical power measurement sequence of the target temperature control device, and an indoor air temperature measurement sequence and an outdoor air temperature measurement sequence of the architectural space; the state fitting data set comprises an indoor air temperature fitting sequence, an indoor solid air temperature fitting sequence, and a heat power fitting sequence of the architectural space; and the identification module is further configured to perform forward recursion according to the indoor air temperature measurement sequence, the outdoor air temperature measurement sequence, the electrical power measurement sequence, the candidate value, and the analytical recursive model to obtain the indoor air temperature fitting sequence and the indoor solid air temperature fitting sequence; and perform backward estimation according to the indoor air temperature fitting sequence, the indoor solid air temperature fitting sequence, the outdoor air temperature measurement sequence, and the candidate value to obtain the heat power fitting sequence.

[0220] In some embodiments, each measurement sequence comprises a plurality of measurement data sampled at a preset period; and the identification module is further configured to: for any sampling time, obtain heat power fitting data of the current sampling time according to indoor air temperature measurement data of the current sampling time, indoor solid air temperature fitting data, and outdoor air temperature measurement data, and indoor air temperature measurement data of the next sampling time and the candidate value; and the indoor solid air temperature fitting data of the current sampling time is obtained according to heat power fitting data of the previous sampling time, indoor solid air temperature fitting data, indoor air temperature measurement data, and outdoor air temperature measurement data, and the candidate value.

[0221] In some embodiments, the identification module is further configured to: obtain an indoor air temperature fitting error according to the indoor air temperature measurement sequence and the indoor air temperature fitting sequence; obtain a thermal power fitting error according to the electric power measurement sequence and the thermal power fitting sequence; input the indoor air temperature fitting error and the thermal power fitting error into the objective function to obtain a comprehensive fitting error corresponding to the candidate value; and the comprehensive fitting error is positively correlated with the indoor air temperature fitting error and the thermal power fitting error respectively.

[0222] In some embodiments, the heat conduction parameter includes a thermal resistance parameter and a thermal capacity parameter, and a target value range of the heat conduction parameter includes a target value range of the thermal resistance parameter and a target value range of the thermal capacity parameter; and the determination module is further configured to: determine an initial value range of the thermal capacity parameter according to the size parameter; determine an initial value range of the thermal resistance parameter according to the initial constraint condition of the thermal resistance parameter; and determine the target value range of the thermal capacity parameter and the target value range of the thermal resistance parameter according to the current regulation parameter, the initial value range of the thermal capacity parameter and the initial value range of the thermal resistance parameter respectively.

[0223] In some embodiments, the current regulation parameter includes a current electricity price and a regulation instruction of the virtual power plant; and the determination module is further configured to: in a case where the current electricity price is greater than a reference electricity price and the regulation instruction is a load reduction instruction, determine the target value range of the thermal resistance parameter according to the current electricity price and the initial value range of the thermal resistance parameter; wherein an upper limit value of the target value range of the thermal resistance parameter is greater than an upper limit value of the initial value range of the thermal resistance parameter, and the target value range of the thermal capacity parameter is the initial value range of the thermal capacity parameter; or in a case where the current electricity price is less than the reference electricity price and the regulation instruction is a matching instruction, determine the target value range of the thermal capacity parameter according to the current electricity price and the initial value range of the thermal capacity parameter; wherein a lower limit value of the target value range of the thermal capacity parameter is greater than a lower limit value of the initial value range of the thermal capacity parameter, and the target value range of the thermal resistance parameter is the initial value range of the thermal resistance parameter.

[0224] Each of the above modules in the apparatus can be implemented totally or partially by software, hardware and a combination thereof. Each of the above modules can be embedded in or independent of a processor in the computer device in a hardware form, or stored in a memory in the computer device in a software form, so as to be called and executed by the processor to perform the operations corresponding to each of the above modules.

[0225] In one embodiment, a computer device, which can be a server, is provided, and an internal structure diagram of the computer device can be as shown in Figure 10As shown. The computer device includes a processor, a memory and a network interface connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operating system and the computer program in the non-volatile storage medium to run. The database of the computer device is used to store data. The network interface of the computer device is used to communicate with external terminals through network connection. The computer program is executed by the processor to implement the above method.

[0226] Those skilled in the art can understand that, Figure 10 The structure shown in the figure is only a block diagram of part of the structure related to the scheme of the present application, and does not constitute a limitation on the computer device to which the scheme of the present application is applied. The specific computer device can include more or fewer components than those shown in the figure, or combine certain components, or have a different component arrangement.

[0227] In one embodiment, a computer device is provided, including a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the steps of the foregoing method.

[0228] In one embodiment, a computer readable storage medium is provided, which stores a computer program, and the computer program is executed by a processor to implement the steps of the foregoing method.

[0229] In one embodiment, a computer program product is provided, including a computer program, and the computer program is executed by a processor to implement the steps of the foregoing method.

[0230] It should be noted that the user information (including but not limited to user equipment information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in the present application are all information and data authorized by the user or authorized by all parties.

[0231] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer readable storage medium, and when the computer program is executed, the processes of the above-mentioned embodiments of the methods can be included. Any reference to memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical storage, high-density embedded non-volatile memory, resistive memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration but not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc. The database involved in the embodiments provided in the present application can include at least one of a relational database and a non-relational database. The non-relational database can include a distributed database based on a block chain, etc., without being limited thereto. The processor involved in the embodiments provided in the present application can be a general-purpose processor, a central processing unit, a graphics processing unit, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., without being limited thereto.

[0232] Any combination of the technical features of the above embodiments can be made. In order to make the description simple, all possible combinations of the technical features in the above embodiments are not described, however, as long as the combination of the technical features does not exist, it should be considered as the scope of the present application.

[0233] The above embodiments only express several implementation manners of the present application, and the description is more specific and detailed, but it should not be understood as a limitation on the scope of the patent of the present application. It should be pointed out that for ordinary skilled in the art, without departing from the concept of the present application, a number of modifications and improvements can be made, which are within the scope of protection of the present application. Therefore, the protection scope of the present application should be subject to the appended claims.

Claims

1. A method for identifying building heat transfer parameters, characterized in that, Applied to a virtual power plant, the temperature control device cluster in the load aggregation system of the virtual power plant includes target temperature control devices within the building space to be identified; the method includes: An analytical recursive model for obtaining the second-order equivalent heat conduction parameter model of the target temperature control device is obtained; the analytical recursive model includes the heat conduction parameters to be identified. The dimensional parameters and status measurement datasets of the building space, as well as the current control parameters of the virtual power plant, are obtained respectively. Based on the size parameters and the current control parameters, determine the target range of the heat conduction parameters; The target value of the heat conduction parameter is determined based on the state measurement dataset, the analytical recursive model, the target value range, and the preset termination condition.

2. The method according to claim 1, characterized in that, The step of determining the target value of the heat conduction parameter based on the state measurement dataset, the analytical recursive model, the target value range, and the preset termination condition includes: Candidate values ​​for the heat conduction parameter are generated based on the target value range; The state fitting dataset is obtained by performing recursive processing based on the state measurement dataset, the candidate values, and the analytical recursive model. Based on the state fitting dataset, the state measurement dataset, and the preset objective function, obtain the comprehensive fitting error corresponding to the candidate value; If neither the overall fitting error nor the number of iterations meets the preset termination condition, the process returns to the step of generating candidate values ​​for the heat conduction parameter based on the target value range until the overall fitting error and / or the number of iterations meets the preset termination condition; the target value for the heat conduction parameter is the candidate value corresponding to the minimum value of the overall fitting error under the preset termination condition.

3. The method according to claim 2, characterized in that, The state measurement dataset includes the electrical power measurement sequence of the target temperature control device, as well as the indoor and outdoor temperature measurement sequences of the building space; the state fitting dataset includes the indoor temperature fitting sequence, indoor solid temperature fitting sequence, and thermal power fitting sequence of the building space. The step of performing recursive processing based on the state measurement dataset, the candidate values, and the analytical recursive model to obtain the state fitting dataset includes: Based on the indoor temperature measurement sequence, the outdoor temperature measurement sequence, the electric power measurement sequence, the candidate values, and the analytical recursive model, a forward recursion is performed to obtain the indoor temperature fitting sequence and the indoor solid temperature fitting sequence; The thermal power fitting sequence is obtained by performing inverse estimation based on the indoor temperature fitting sequence, the indoor solid temperature fitting sequence, the outdoor temperature measurement sequence, and the candidate values.

4. The method according to claim 3, characterized in that, Each measurement sequence includes multiple measurement data sampled at a preset period; wherein, the step of obtaining the thermal power fitting sequence by performing back estimation based on the indoor temperature fitting sequence, the indoor solid temperature fitting sequence, the outdoor temperature measurement sequence, and the candidate values ​​includes: For any sampling time, the thermal power fitting data for the current sampling time is obtained based on the indoor air temperature measurement data, indoor solid air temperature fitting data, and outdoor air temperature measurement data at the current sampling time, as well as the indoor air temperature measurement data at the next sampling time and the candidate value; wherein, the indoor solid air temperature fitting data for the current sampling time is obtained based on the thermal power fitting data, indoor solid air temperature fitting data, indoor air temperature measurement data, and outdoor air temperature measurement data at the previous sampling time, as well as the candidate value.

5. The method according to claim 2, characterized in that, The step of obtaining the comprehensive fitting error corresponding to the candidate value based on the state fitting dataset, the state measurement dataset, and the preset objective function includes: The indoor temperature fitting error is obtained based on the indoor temperature measurement sequence and the indoor temperature fitting sequence. The thermal power fitting error is obtained based on the electrical power measurement sequence and the thermal power fitting sequence. The indoor temperature fitting error and the thermal power fitting error are input into the objective function to obtain the comprehensive fitting error corresponding to the candidate value; the comprehensive fitting error is positively correlated with the indoor temperature fitting error and the thermal power fitting error, respectively.

6. The method according to any one of claims 1-5, characterized in that, The thermal conductivity parameters include thermal resistance parameters and heat capacity parameters, and the target value range of the thermal conductivity parameters includes the target value range of the thermal resistance parameters and the target value range of the heat capacity parameters; wherein, determining the target value range of the thermal conductivity parameters based on the size parameters and the current control parameters includes: The initial range of values ​​for the heat capacity parameter is determined based on the dimensional parameters; Based on the initial constraints of the thermal resistance parameter, determine the initial range of the thermal resistance parameter. Based on the current control parameter, the initial value range of the heat capacity parameter, and the initial value range of the thermal resistance parameter, the target value range of the heat capacity parameter and the target value range of the thermal resistance parameter are determined respectively.

7. The method according to claim 6, characterized in that, The current control parameters include the current electricity price and control instructions of the virtual power plant; determining the target value ranges of the heat capacity parameter and the thermal resistance parameter based on the current control parameters, the initial value range of the heat capacity parameter, and the initial value range of the thermal resistance parameter includes: When the current electricity price is greater than the benchmark electricity price, and the control instruction is a load reduction instruction, the target value range of the thermal resistance parameter is determined based on the current electricity price and the initial value range of the thermal resistance parameter; wherein, the upper limit of the target value range of the thermal resistance parameter is greater than the upper limit of the initial value range of the thermal resistance parameter, and the target value range of the heat capacity parameter is the initial value range of the heat capacity parameter; or, When the current electricity price is less than the benchmark electricity price and the control instruction is a matching instruction, the target value range of the heat capacity parameter is determined based on the current electricity price and the initial value range of the heat capacity parameter; wherein, the lower limit of the target value range of the heat capacity parameter is greater than the lower limit of the initial value range of the heat capacity parameter, and the target value range of the thermal resistance parameter is the initial value range of the thermal resistance parameter.

8. A device for identifying building heat conduction parameters, characterized in that, Applied to a virtual power plant, the temperature control device cluster in the load aggregation system of the virtual power plant includes target temperature control devices within the building space to be identified; the devices include: The acquisition module is used to acquire the analytical recursive model of the second-order equivalent heat conduction model of the target temperature control device; acquire the size parameters and state measurement dataset of the building space, as well as the current control parameters of the virtual power plant; the analytical recursive model includes the heat conduction parameters to be identified; The determining module is used to determine the target range of the heat conduction parameter based on the size parameter and the current control parameter; The identification module is used to determine the target value of the heat conduction parameter based on the state measurement dataset, the analytical recursive model, the target value range, and the preset termination condition.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 7.

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