Uncertainty estimation method and system for nonlinear dynamic model of heating building

Abstract

The present invention provides an uncertainty estimation method and system for a nonlinear dynamic model of a heating building, which relates to the technical field of building modeling, and includes: constructing a nonlinear heat transfer equation between three independent heat storage units, namely a hot water radiator, a building envelope structure and indoor air, and building a nonlinear dynamic model of the heating building according to the nonlinear heat transfer equation between the three independent heat storage units; acquiring actual building data, and estimating an optimal parameter vector of the nonlinear dynamic model according to the actual building data and based on a genetic algorithm; designing a membership function based on fuzzy set theory, and using the membership function to measure a basic element set consisting of an optimal parameter vector and all possible optimal parameter vectors, and determining a fuzzy level value, thereby obtaining a fuzzy cut set as an uncertainty range of the nonlinear dynamic model.

Description

Technical Field

[0001] The present disclosure relates to the technical field of building modeling, and in particular to an uncertainty estimation method and system for a nonlinear dynamic model of a heating building. Background Art

[0002] The statements in this section merely provide background information related to the present disclosure and do not necessarily constitute prior art.

[0003] Buildings are the main contributors to global energy consumption and carbon emissions. According to the International Energy Agency, the building industry accounts for more than 30% of the world's total final energy consumption and energy-related carbon dioxide emissions. Winter heating energy consumption accounts for a large proportion of building energy consumption, and continues to grow with the continuous increase in building area and people's demand for thermal comfort. At present, many studies aim to improve the energy efficiency of heating buildings to reduce energy consumption, focusing on building renovation, building load forecasting, advanced temperature control and renewable energy applications. However, these technologies and methods require knowledge of building thermodynamics to be effectively implemented. Therefore, accurately establishing a nonlinear dynamic model of heating buildings is the mainstay of building applications and is of great significance to achieving the major goals of energy conservation and emission reduction.

[0004] The established nonlinear dynamic model of heating buildings has uncertainty. It mainly comes from three aspects. First, the dynamic model is essentially a simplified representation of the real physical system. It only uses some key model parameters to reproduce the complex nonlinear thermal dynamic process in the heating building; second, the factors affecting the modeling of heating buildings are uncertain, such as occupancy, weather conditions, thermal performance of enclosure structures and indoor equipment, etc.; third, the input and output variables of the heating building may be interfered by unknown distributed noise during the measurement process. The uncertainty of the nonlinear dynamic model specifically refers to the non-uniqueness of the model parameter values, which is mainly reflected in the existence of multiple suboptimal model parameter values ​​that are very close to the optimal model parameter values. They can also well describe the nonlinear dynamic change process of the heating building. These optimal and suboptimal model parameter values ​​form a multi-dimensional spatial region, which is called the model uncertainty range. When implementing optimization, control and prediction schemes for heating buildings based on nonlinear dynamic models, considering the model uncertainty range can give reliable operating equipment, controller parameter intervals and future prediction output boundaries, improve the indoor environmental quality and energy efficiency of heating buildings, and reduce energy consumption.

[0005] At present, probabilistic methods are widely used to deal with model uncertainty, that is, the parameter vector of a nonlinear dynamic model is considered to be a random variable, and its uncertainty is described by probability distribution. For nonlinear dynamic models with special structures, such as the Hammerstein model and the Wiener model, probabilistic methods can derive an analytical expression for the probability distribution of the model parameter vector under the assumption that the noise follows a Gaussian distribution. For general nonlinear dynamic models, a commonly used method is to randomly sample and evaluate the parameter vector, and use the posterior distribution of the sample to approximate the probability distribution of the parameter vector.

[0006] However, the uncertainty calculation of the above methods relies on a likelihood function that assumes that the noise follows a Gaussian distribution. This assumption may not hold true when there are model structure deviations and unknown noise distributions, making it difficult for probabilistic methods to obtain accurate model uncertainty measurements. Summary of the invention

[0007] In order to solve the above problems, the present invention proposes a method and system for estimating the uncertainty of a nonlinear dynamic model of a heating building. The nonlinear heat transfer process of three independent heat storage units in the heating building is considered to establish a nonlinear dynamic model of the heating building. Based on fuzzy set theory, the uncertainty of the nonlinear dynamic model is estimated by using multiple suboptimal models that reproduce certain measurement outputs and are close to the optimal model. Without the restrictive assumption that the noise follows a Gaussian distribution, the uncertainty range of the model composed of suboptimal models is given.

[0008] According to some embodiments, the present disclosure adopts the following technical solutions:

[0009] A method for estimating uncertainty of a nonlinear dynamic model of a heating building, comprising:

[0010] Construct the nonlinear heat transfer equations between the three independent heat storage units of hot water radiator, building envelope and indoor air, and build the nonlinear dynamic model of the heating building according to the nonlinear heat transfer equations between the three independent heat storage units;

[0011] Acquire actual building data, and estimate the optimal parameter vector of the nonlinear dynamic model based on the actual building data and a genetic algorithm;

[0012] Based on fuzzy set theory, a membership function is designed, and the membership function is used to measure the basic element set consisting of the optimal parameter vector and all possible optimal parameter vectors to determine the fuzzy level value, thereby obtaining a fuzzy cut set as the uncertainty range of the nonlinear dynamics model.

[0013] According to some embodiments, the present disclosure adopts the following technical solutions:

[0014] An uncertainty estimation system for a nonlinear dynamic model of a heating building, comprising:

[0015] A data acquisition and transmission unit, which is used to construct a nonlinear heat transfer equation between three independent heat storage units, namely, a hot water radiator, a building envelope and indoor air, and to construct a nonlinear dynamic model of a heating building according to the nonlinear heat transfer equation between the three independent heat storage units;

[0016] A nonlinear dynamic model identification unit, which is used to obtain actual building data and estimate the optimal parameter vector of the nonlinear dynamic model based on the actual building data and a genetic algorithm;

[0017] The model uncertainty estimation unit is used to design a membership function based on fuzzy set theory, use the membership function to measure the basic element set consisting of the optimal parameter vector and all possible optimal parameter vectors, determine the fuzzy level value, and thus obtain a fuzzy cut set as the uncertainty range of the nonlinear dynamics model.

[0018] According to some embodiments, the present disclosure adopts the following technical solutions:

[0019] A non-transitory computer-readable storage medium is used to store computer instructions. When the computer instructions are executed by a processor, the uncertainty estimation method of a nonlinear dynamic model of a heating building is implemented.

[0020] According to some embodiments, the present disclosure adopts the following technical solutions:

[0021] An electronic device comprises: a processor, a memory and a computer program; wherein the processor is connected to the memory, the computer program is stored in the memory, and when the electronic device is running, the processor executes the computer program stored in the memory so that the electronic device implements the uncertainty estimation method of the nonlinear dynamic model of a heating building.

[0022] Compared with the prior art, the present invention has the following beneficial effects:

[0023] The present invention discloses an uncertainty estimation method for a nonlinear dynamic model of a heating building. The method takes into account the nonlinear heat transfer process between independent heat storage units of the heating building, constructs a nonlinear heat transfer equation between three independent heat storage units, namely a hot water radiator, a building envelope and indoor air, and constructs a nonlinear dynamic model of the heating building according to the nonlinear heat transfer equation between the three independent heat storage units, thereby overcoming the problem of large modeling errors of the linear dynamic model of the building.

[0024] The present invention discloses an uncertainty estimation method for a nonlinear dynamic model of a heating building, which estimates the modeling uncertainty of the nonlinear dynamic model with model structural errors and unknown noise distribution. Based on fuzzy set theory, a membership function is designed, and the membership function is used to measure a basic element set consisting of an optimal parameter vector and all possible optimal parameter vectors to determine a fuzzy level value, thereby obtaining a fuzzy cutoff set as the uncertainty range of the nonlinear dynamic model, without considering the restrictive assumptions of existing methods, thereby ensuring the efficiency and performance of the optimization, prediction and control technology implementation of the heating building. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] The accompanying drawings constituting a part of the present disclosure are used to provide a further understanding of the present disclosure. The illustrative embodiments of the present disclosure and their descriptions are used to explain the present disclosure and do not constitute an improper limitation on the present disclosure.

[0026] Figure 1 It is a flow chart of an uncertainty estimation method of a nonlinear dynamic model of a heating building according to an embodiment of the present disclosure;

[0027] Figure 2 An exterior photo of a residential building and a floor plan of a heated room according to an embodiment of the present disclosure;

[0028] Figure 3 It is a time series diagram for estimating parameter vectors of a nonlinear heating building model according to an embodiment of the present disclosure;

[0029] Figure 4 A density map of the model simulation output corresponding to the estimated model uncertainty range of the embodiment of the present disclosure. DETAILED DESCRIPTION

[0030] The present disclosure is further described below in conjunction with the accompanying drawings and embodiments.

[0031] It should be noted that the following detailed descriptions are all illustrative and are intended to provide further explanation of the present disclosure. Unless otherwise specified, all technical and scientific terms used herein have the same meanings as those commonly understood by those skilled in the art to which the present disclosure belongs.

[0032] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present disclosure. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprising" and / or "including" are used in this specification, it indicates the presence of features, steps, operations, devices, components and / or combinations thereof.

[0033] Example 1

[0034] In one embodiment of the present disclosure, a method for estimating uncertainty of a nonlinear dynamic model of a heating building is provided. The uncertainty range finally estimated can be used for optimization, control and prediction of the heating building, and reliable operating equipment and controller parameter intervals and future prediction outputs can be given, thereby improving the indoor environmental quality and energy efficiency of the heating building. The method includes the following steps:

[0035] Step 1: Construct a nonlinear heat transfer equation between the three independent heat storage units of hot water radiator, building envelope and indoor air, and build a nonlinear dynamic model of the heating building based on the nonlinear heat transfer equation between the three independent heat storage units;

[0036] Step 2: Acquire actual building data, and estimate the optimal parameter vector of the nonlinear dynamic model based on the actual building data and a genetic algorithm;

[0037] Step 3: Based on fuzzy set theory, a membership function is designed, and the membership function is used to measure the basic element set consisting of the optimal parameter vector and all possible optimal parameter vectors, and the fuzzy level value is determined, thereby obtaining a fuzzy cut set as the uncertainty range of the nonlinear dynamic model.

[0038] As an embodiment, the implementation process of an uncertainty estimation method for a nonlinear dynamic model of a heating building is combined with Figure 1 , as follows:

[0039] S1: Developing a nonlinear dynamic model of a heated building

[0040] Specifically, a nonlinear heat transfer equation between three independent heat storage units, namely, a hot water radiator, a building envelope and indoor air, is constructed. According to the nonlinear heat transfer equation between the three independent heat storage units, a nonlinear dynamic model of the heating building is constructed, including:

[0041] Based on the law of conservation of energy and the nonlinear heat transfer process between the three independent heat storage units, the nonlinear differential equations of the hot water radiator, building envelope and indoor air are established respectively. The nonlinear heat transfer process between the three independent heat storage units is:

[0042] Among them, for hot water radiators, it obtains heat energy from imported hot water and transfers the heat energy to the Transferred to the indoor air. and The mechanism formula is:

[0043] (1)

[0044] (2)

[0045] in, is the specific heat capacity of the imported hot water, is the water inlet flow rate, is the inlet water temperature, is the radiator surface temperature, is the total area of ​​the radiator, is the indoor temperature. is the heat transfer coefficient of the radiator, which is the surface temperature of the radiator and indoor temperature The nonlinear function of the temperature difference between The expression of ,in and are the fitting parameters.

[0046] For the building envelope, it receives heat energy from the indoor air by linear convection , and the heat to the outside air, where and Respectively expressed as:

[0047] (3)

[0048] (4)

[0049] in, and are the heat transfer coefficient and total area of ​​the building interior walls, and are the heat transfer coefficient and total area of ​​the building exterior wall, is the surface temperature of the building envelope.

[0050] For indoor air, it gets heat energy directly from hot water radiators and transfers the heat energy through linear convection Transfer to the walls of the heated building and transfer the heat The heat energy is transferred to the windows of the heated building, It is expressed as:

[0051] (5)

[0052] in, and are the total area of ​​the windows and the heat transfer coefficients respectively.

[0053] Furthermore, the nonlinear heat transfer equations between the three independent heat storage units are combined to establish a nonlinear dynamic model of the heating building. According to the heat transfer equations between the three independent heat storage units in formulas (1)-(5),

[0054] The nonlinear dynamic model of the heating building can be established as:

[0055] (6)

[0056] in, , and are the equivalent heat capacities of hot water, building envelope and indoor air, respectively. , and are the specific heat capacities of these three parts, , and is their density, , and is its volume. Combining and sorting out the three sub-equations in formula (6), we get,

[0057] (7)

[0058] Among them, the model parameters that need to be identified are:

[0059]

[0060] The nonlinear dynamic model parameters to be identified are represented by a model parameter vector To express.

[0061] S2: Estimation of the optimal parameter vector of the nonlinear dynamic model based on genetic algorithm and actual building data;

[0062] The actual building data is obtained, and the optimal parameter vector of the nonlinear dynamic model is estimated based on the actual building data and a genetic algorithm, including: determining a reasonable search range of the parameters based on the mechanism knowledge of the unknown nonlinear dynamic model parameters, constructing a modeling template function, designing a fitness function of the genetic algorithm according to the modeling objective function, using the genetic algorithm and the actual building data to find the chromosome that satisfies the maximum fitness function after the population evolution as the optimal parameter vector of the nonlinear dynamic model, saving all evolved populations and their corresponding fitness function values, and then sorting them according to the fitness function values ​​to obtain a suboptimal parameter vector set, which can be used to determine all possible optimal parameter vectors, and all possible optimal parameter vectors constitute a basic element set, which is a subset of the suboptimal parameter vector set.

[0063] Specifically, based on the mechanism knowledge of the model parameters to be identified, a reasonable search range of the parameters is determined, that is, , whose search range is expressed as Based on the nonlinear dynamic model of formula (7), the input variable is the inlet flow rate , Inlet water temperature , and outdoor temperature , the output variable is the indoor temperature Output variables During the measurement process, it will be disturbed by noise of unknown distribution, which can be expressed as:

[0064]

[0065] in, is a bounded random noise. The input and output variables are collected in a data sample set, namely , the length of the data sample is The modeling objective function is selected as the sum of square errors between the model simulation output and the measured output:

[0066] (8)

[0067] The model fitting output is , which is obtained by substituting the input variables and parameter vectors into the nonlinear dynamic model. Based on the modeling objective function, the fitness function of the genetic algorithm is constructed:

[0068]

[0069] Optimal parameter vector Estimation is done by genetic algorithm. is the chromosome with the largest fitness function among all evolving populations, namely:

[0070] (9)

[0071] in, Is related to the evolutionary population The corresponding fitness function set. The scale is , the number of population evolutions is All the evolving populations in the genetic algorithm and their fitness function values ​​can be represented by the set and At the same time, the model simulation output corresponding to the evolutionary population is saved, that is, ,in , .

[0072] For the fitness set , sort them in descending order from large to small, and the sorted set is:

[0073]

[0074] According to the fitness set The sorting order of and the model simulation output set ,in Population collection That is the set of suboptimal parameter vectors defined.

[0075] S3: Measuring the uncertainty of the optimal parameter vector of nonlinear dynamic models based on fuzzy set theory

[0076] Based on fuzzy set theory, a membership function is designed, and the membership function is used to measure the basic element set consisting of the optimal parameter vector and all possible optimal parameter vectors, including:

[0077] For the measurement output n data samples, if the n Data samples and simulation output The residual between is zero, then is called the possible optimal parameter vector; the optimal parameter vector corresponds to All possible optimal parameter vectors of data samples constitute the basic element set.

[0078] Specifically, the optimal parameter vector and the basic element set of all possible optimal parameter vectors are constructed, and the membership function is designed based on the fuzzy set theory to measure the membership value of the basic element set, determine the fuzzy level value, and obtain a fuzzy cutoff set as the model uncertainty range. The details are as follows:

[0079] For the measurement output Data samples ,if With analog output The residual between If is zero, is called the possible optimal parameter vector. The optimal parameter vector Corresponding to All possible optimal parameter vectors of data samples constitute the basic element set:

[0080]

[0081] in, , A collection of basic elements The number of parameter vectors in . The basic element set The parameter vector in usually spans a large range to ensure that the residuals of all data samples are zero. This inevitably leads to poor overall fitting results and fails to meet the goal of minimizing the deviation between the simulated output and the measured output. Therefore, the degree of deviation between the simulated output and the measured output is measured by using fuzzy sets. A membership function is constructed based on the modeling objective function in formula (8):

[0082] (10)

[0083] in, , . Then define a fuzzy set through the membership function:

[0084] For a collection of basic elements Fuzzy domain , Yes The membership function mapped to [0,1] is Therefore, a fuzzy set Can be Uniquely define

[0085] (11)

[0086] This fuzzy set Re-describe The possibility that the parameter vector in is the true parameter vector is measured using fuzzy theory. Then, the fuzzy level value To obtain an acceptable fuzzy cut set as the uncertainty range of the model. , there is a fuzzy level value , if the membership function The value is greater than , it means that in The fuzziness of the fuzzy vector in is acceptable. Therefore, is called the fuzzy level cut set, that is

[0087] (12)

[0088] Fuzzy Level The value of determines the size of the model uncertainty range, which is determined according to the compactness principle between the simulation output and the measurement output. The specific process is as follows:

[0089] (1) Collection according to Sort the values ​​in descending order, Re-expressed as , and set the analog output to Rearrange the order to form a new set ,in ;

[0090] (2) Participate in The set of model parameter vectors and simulation outputs of the iterations are recorded as and , analog output set The upper and lower bounds at all sampling points are expressed as,

[0091] (13)

[0092] calculate The upper and lower bounds of are distance functions from the measured output:

[0093] (14)

[0094] Therefore, we can get the sample index set containing the measured output for

[0095] (15)

[0096] in, is the sample index set Length.

[0097] (3) If and , then continue to iterate, the number of iterations increases, that is, ; Repeat steps (2) and (3) until full Therefore, the blur level The value of can be determined as:

[0098] (16)

[0099] Blur Level The value is a lower limit of the membership function value, which is used to determine an acceptable model uncertainty range. Get the fuzzy level After the value is set, the blur level is used According to formula (12), we get the set , which is the uncertainty range of the nonlinear dynamic model. Ultimately, the estimated uncertainty range is used for the optimization, control and prediction of heating buildings, which can provide reliable operating equipment, controller parameter ranges and future prediction outputs, thereby improving the indoor environmental quality and energy efficiency of heating buildings.

[0100] Simulation experiment

[0101] Furthermore, in order to prove the effectiveness of the solution described in this embodiment, the following experimental verification was carried out in this embodiment:

[0102] The verification process specifically includes the following steps:

[0103] Step 1: Based on the nonlinear heat transfer equations between the three independent heat storage units, namely, the hot water radiator, the building envelope and the indoor air, a nonlinear dynamic model of equation (7) was constructed using MATLAB / Simulink software to simulate the nonlinear heat transfer process of the heating building and verify the effectiveness of the method described in the present invention. This embodiment uses the heating room in an actual residential building as the modeling object for research. The exterior photos of the residential building and the floor plan of the heating room are shown in Figure 1. Figure 2 As shown. The heating room is divided into 5 small rooms, with a total of 4 hot water radiators. The hot water regulating valve and calorimeter are installed on the water inlet and outlet pipes at the entrance of the heating room, which are used to control the change of water inlet flow and measure the water inlet flow and water inlet temperature respectively. The outdoor temperature and the outdoor temperature can be measured by the temperature sensor. The collected time series diagram for the parameter vector estimation of the nonlinear heating building model is shown in Figure 3 As shown, they are the inlet flow, inlet temperature, outdoor temperature, and indoor temperature, respectively. The data length is 671 and the sampling period is 1 minute.

[0104] Step 2: Using genetic algorithms and Figure 2 The optimal parameter vector of the nonlinear dynamic model is , and its corresponding modeling objective function value In the genetic algorithm search process, a total of A collection of suboptimal models and the set of simulated outputs corresponding to the suboptimal fuzzy set .

[0105] Step 3: Optimal parameter vector Together with all possible optimal parameter vectors, it forms a basic element set ,in The membership function in formula (10) is used to measure the basic element set The probability that the parameter vector in is the true vector. By executing steps (1)-(3), the fuzzy level value is determined , thus forming a fuzzy level cut set according to formula (12) as the uncertainty range of the model. Figure 4 The model uncertainty range is given Density plot of the model simulation output corresponding to the intrinsic parameter vector, where lighter colors indicate higher density. In addition, the distances between the upper and lower bounds of the model simulation output and the measured output at certain sampling points are calculated, represented by error bars. It can be seen that the proposed method can give an accurate and compact measure of model uncertainty.

[0106] Example 2

[0107] In one embodiment of the present disclosure, a system for estimating uncertainty of a nonlinear dynamic model of a heating building is provided, comprising:

[0108] A data acquisition and transmission unit, which is used to construct a nonlinear heat transfer equation between three independent heat storage units, namely, a hot water radiator, a building envelope and indoor air, and to construct a nonlinear dynamic model of a heating building according to the nonlinear heat transfer equation between the three independent heat storage units;

[0109] Furthermore, it can collect and transmit the real-time operation data of the water inlet flow, water inlet temperature, outdoor temperature and indoor temperature involved in the modeling of the heating building, use the single-chip microcomputer as the MCU controller, and use RS-485 communication to upload the collected data to the human-computer interaction unit. At the same time, the host computer can send control instructions to the single-chip microcomputer to realize the water inlet flow control of the hot water regulating valve in the heating building;

[0110] A nonlinear dynamic model identification unit, which is used to obtain actual building data and estimate the optimal parameter vector of the nonlinear dynamic model based on the actual building data and a genetic algorithm;

[0111] The model uncertainty estimation unit is used to design a membership function based on fuzzy set theory, use the membership function to measure the basic element set consisting of the optimal parameter vector and all possible optimal parameter vectors, determine the fuzzy level value, and thus obtain a fuzzy cut set as the uncertainty range of the nonlinear dynamics model.

[0112] In addition, it also includes a human-computer interaction unit, which is used to display the real-time operating data of the inlet water flow, inlet water temperature, outdoor temperature, and indoor temperature involved in the heating building modeling, and the model simulation output corresponding to the uncertainty range of the nonlinear dynamic model, as well as the distance between the upper and lower bounds of the model simulation output and the measured output, so as to facilitate operators to observe the accuracy of the heating building modeling and model uncertainty estimation.

[0113] Furthermore, the system described in this embodiment corresponds to the method described in Example 1, and its technical details have been described in detail in Example 1, so they will not be repeated here.

[0114] Example 3

[0115] In one embodiment of the present disclosure, a non-transitory computer-readable storage medium is provided, wherein the non-transitory computer-readable storage medium is used to store computer instructions. When the computer instructions are executed by a processor, the uncertainty estimation method of the nonlinear dynamic model of a heating building is implemented.

[0116] Example 4

[0117] In one embodiment of the present disclosure, an electronic device is provided, comprising: a processor, a memory and a computer program; wherein the processor is connected to the memory, the computer program is stored in the memory, and when the electronic device is running, the processor executes the computer program stored in the memory, so that the electronic device executes the uncertainty estimation method for the nonlinear dynamic model of a heating building.

[0118] The present disclosure is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present disclosure. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of the processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0119] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0120] Although the above describes the specific implementation methods of the present disclosure in conjunction with the accompanying drawings, it is not intended to limit the scope of protection of the present disclosure. Technical personnel in the relevant field should understand that on the basis of the technical solution of the present disclosure, various modifications or variations that can be made by those skilled in the art without creative work are still within the scope of protection of the present disclosure.

Claims

1. A method for estimating uncertainty of a nonlinear dynamic model of a heating building, characterized in that: include: Construct the nonlinear heat transfer equations between the three independent heat storage units of hot water radiator, building envelope and indoor air, and build the nonlinear dynamic model of the heating building according to the nonlinear heat transfer equations between the three independent heat storage units; Acquire actual building data, and estimate the optimal parameter vector and all possible optimal parameter vectors of the nonlinear dynamic model based on the actual building data and a genetic algorithm; The method of obtaining actual building data and estimating the optimal parameter vector of the nonlinear dynamic model and all possible optimal parameter vectors based on the actual building data and the genetic algorithm includes: determining a reasonable search range of the parameters based on the mechanism knowledge of the unknown nonlinear dynamic model parameters, constructing a modeling objective function, designing a fitness function of the genetic algorithm according to the modeling objective function, finding the chromosome that satisfies the maximum fitness function after population evolution as the optimal parameter vector of the nonlinear dynamic model by using the genetic algorithm and the actual building data, saving all evolved populations and their corresponding fitness function values, and obtaining a suboptimal parameter vector set after sorting according to the fitness function values; Based on fuzzy set theory, a membership function is designed, and the membership function is used to measure the basic element set consisting of the optimal parameter vector and all possible optimal parameter vectors, and the fuzzy level value is determined, so as to obtain a fuzzy cut set as the uncertainty range of the nonlinear dynamics model; The membership function is designed based on fuzzy set theory, and the membership function is used to measure the basic element set composed of the optimal parameter vector and all possible optimal parameter vectors, including: For the measurement output n data samples, if the n Data samples and simulation output The residual between is zero, then is called the possible optimal parameter vector; the optimal parameter vector corresponds to All possible optimal parameter vectors of data samples constitute the basic element set; The deviation between the simulation output and the measured output is measured by using fuzzy sets, a membership function is constructed based on the modeling objective function, and a fuzzy set is defined by the membership function. Then, an acceptable fuzzy cutoff is obtained as the uncertainty range of the model through the fuzzy level value. For the fuzzy vector, there is a fuzzy level value. If the value of the membership function is greater than the fuzzy level value, it means that the fuzziness of the fuzzy vector is acceptable. The fuzzy level value is determined by the compactness principle between the simulation output and the measured output.

2. The uncertainty estimation method of the nonlinear dynamic model of a heating building according to claim 1, characterized in that: Constructing nonlinear heat transfer equations between three independent heat storage units, namely, a hot water radiator, a building envelope structure and indoor air, including: based on the law of conservation of energy and the nonlinear heat transfer process between the three independent heat storage units, establishing nonlinear differential equations for the hot water radiator, the building envelope structure and indoor air respectively.

3. The uncertainty estimation method of the nonlinear dynamic model of a heating building according to claim 1, characterized in that: The nonlinear heat transfer process between the three independent heat storage units is as follows: for the hot water radiator, it obtains heat energy from the imported hot water and transfers the heat energy to the indoor air by nonlinear heat convection; For the building envelope, it receives heat energy from indoor air by linear convection and transfers heat energy to outdoor air; For indoor air, it obtains heat energy directly from the hot water radiator and transfers the heat energy to the walls and windows of the heated building respectively by linear thermal convection.

4. The uncertainty estimation method of the nonlinear dynamic model of a heating building according to claim 1, characterized in that: The nonlinear heat transfer equations between the three independent heat storage units are combined to establish the nonlinear dynamic model of the heating building: in, , and are the equivalent heat capacities of the hot water radiator, building envelope and indoor air, respectively. , and are the specific heat capacities of these three parts, , and is their density, , and is its volume; The heat energy that indoor air obtains directly from the hot water radiator. is the heat energy obtained by the hot water radiator from the imported hot water, is the radiator surface temperature, The building envelope receives heat energy from the indoor air in a linear convection manner. To transfer heat energy to the outdoor air, For the heat energy transferred to the windows of a heated building, is the indoor temperature.

5. An uncertainty estimation system for a nonlinear dynamic model of a heating building, characterized in that: include: A data acquisition and transmission unit, which is used to construct a nonlinear heat transfer equation between three independent heat storage units, namely, a hot water radiator, a building envelope and indoor air, and to construct a nonlinear dynamic model of a heating building according to the nonlinear heat transfer equation between the three independent heat storage units; A nonlinear dynamic model identification unit, which is used to obtain actual building data, and estimate the optimal parameter vector and all possible optimal parameter vectors of the nonlinear dynamic model based on the actual building data and a genetic algorithm; The method of obtaining actual building data and estimating the optimal parameter vector of the nonlinear dynamic model and all possible optimal parameter vectors based on the actual building data and the genetic algorithm includes: determining a reasonable search range of the parameters based on the mechanism knowledge of the unknown nonlinear dynamic model parameters, constructing a modeling objective function, designing a fitness function of the genetic algorithm according to the modeling objective function, finding the chromosome that satisfies the maximum fitness function after population evolution as the optimal parameter vector of the nonlinear dynamic model by using the genetic algorithm and the actual building data, saving all evolved populations and their corresponding fitness function values, and obtaining a suboptimal parameter vector set after sorting according to the fitness function values; A model uncertainty estimation unit is used to design a membership function based on fuzzy set theory, and use the membership function to measure a basic element set consisting of an optimal parameter vector and all possible optimal parameter vectors to determine a fuzzy level value, thereby obtaining a fuzzy cut set as the uncertainty range of the nonlinear dynamics model; The membership function is designed based on fuzzy set theory, and the membership function is used to measure the basic element set composed of the optimal parameter vector and all possible optimal parameter vectors, including: For the measurement output n data samples, if the n Data samples and simulation output The residual between is zero, then is called the possible optimal parameter vector; the optimal parameter vector corresponds to All possible optimal parameter vectors of data samples constitute the basic element set; The deviation between the simulation output and the measured output is measured by using fuzzy sets, a membership function is constructed based on the modeling objective function, and a fuzzy set is defined by the membership function. Then, an acceptable fuzzy cutoff is obtained as the uncertainty range of the model through the fuzzy level value. For the fuzzy vector, there is a fuzzy level value. If the value of the membership function is greater than the fuzzy level value, it means that the fuzziness of the fuzzy vector is acceptable. The fuzzy level value is determined by the compactness principle between the simulation output and the measured output.

6. A non-transitory computer-readable storage medium, characterized in that: The non-transitory computer-readable storage medium is used to store computer instructions, and when the computer instructions are executed by a processor, an uncertainty estimation method for a nonlinear dynamic model of a heating building as described in any one of claims 1 to 4 is implemented.

7. An electronic device, characterized in that: include: A processor, a memory and a computer program; wherein the processor is connected to the memory, the computer program is stored in the memory, and when the electronic device is running, the processor executes the computer program stored in the memory so that the electronic device executes the uncertainty estimation method for the nonlinear dynamic model of a heating building as described in any one of claims 1 to 4.

Images