A building thermal mass flexibility capacity rapid estimation method, system and device applied to a building cluster and a storage medium
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- TIANJIN UNIV
- Filing Date
- 2026-03-06
- Publication Date
- 2026-06-09
Smart Images
Figure CN122174652A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of building energy flexibility technology, specifically to a method, system, device, and storage medium for rapid estimation of building thermal quality flexibility capacity applied to building clusters. Background Technology
[0002] Buildings, accounting for over one-third of total electricity consumption, are typically clustered within cities and subject to unified regulation. They can participate in demand response to transfer electricity across time and space, making them a valuable flexible resource for balancing grid supply and demand. Several indicators characterize the flexibility of building clusters, with defining their total flexibility capacity being crucial. Firstly, this provides a reference for energy planning, integrating building flexibility into a broader energy storage system. Based on a clear understanding of their flexibility capacity, rational energy planning for the entire region can be implemented. For clustered communities, this reduces investment in other distributed energy facilities, effectively saving economic costs and engineering design and implementation difficulties, which is of great significance. Secondly, it provides load aggregators with total scalable capacity when building clusters participate in demand response events, facilitating capacity application and subsequent operational strategy formulation.
[0003] The generalized energy storage flexibility resources within building clusters mainly include thermal inertia energy storage coupled with the building's thermal capacity and HVAC system, as well as mobile energy storage units composed of electric vehicles and charging piles, and energy-storable electrical equipment within the building. Among these, the building itself plus HVAC is present in every building and represents the most widely used, flexible, and convenient generalized energy storage resource in building cluster scenarios. It utilizes the attenuation and delay characteristics of the building's thermal mass to store cooling capacity and thus adjust electricity consumption patterns. However, compared to traditional energy storage technologies such as electrochemical energy storage, physical energy storage, and electromagnetic energy storage, its potential is not immediately apparent. Therefore, conducting a correct quantitative assessment of the flexibility capacity of resources such as the building itself plus HVAC is a crucial and challenging step.
[0004] The flexibility capacity (E) of building structure plus HVAC resources is of immense value. However, methods for quantifying flexibility capacity in large-scale building clusters still face the following challenges: 1. Quantification methods based on field experiments or simulation models are more suitable for detailed scenarios involving individual buildings, but are costly and difficult to scale. Large-scale building cluster scenarios require simplified quantification. 2. Existing aggregation-simplification modeling methods for building HVAC clusters use a top-down random assignment mechanism for the RC model, making it difficult to accurately represent the individual characteristics of different building rooms, thus failing to provide accurate flexibility capacity for the building structure plus HVAC. 3. The idea of equating buildings with batteries can better help integrate building clusters into energy storage systems for collaborative planning and scheduling, but its focus is primarily on proposing an indicator representation. Regarding the quantification of flexibility capacity (E), its heat capacity value is also directly given in the research using the RC model assignment method, lacking a more accurate and convenient rapid quantification method. Summary of the Invention
[0005] In view of the above-mentioned problems, the present invention is proposed.
[0006] Therefore, the technical problem solved by this invention is that existing methods for quantifying the flexibility capacity in building clusters are costly and difficult to scale up, struggle to accurately represent the individual characteristics of different building rooms, and cannot provide accurate flexibility capacity for the building itself plus HVAC systems. The invention also addresses the question of how to achieve more accurate, simple, and rapid quantification.
[0007] To address the aforementioned technical problems, this invention provides the following technical solution: a method for rapidly estimating the building thermal mass flexibility capacity applied to building clusters, comprising: constructing a building shoebox model; applying a temperature excitation signal to the building shoebox model to obtain a basic dataset; obtaining a large-scale dataset of building parameters and corresponding equivalent thermal capacity values based on the basic dataset; filtering key feature parameters of the large-scale dataset to construct a simplified equivalent thermal capacity model; inputting the geometric and thermal parameters of the building to be evaluated into the simplified equivalent thermal capacity model; outputting the building thermal mass equivalent thermal capacity; and calculating and outputting the building flexibility capacity with a preset temperature comfort range.
[0008] As a preferred embodiment of the rapid estimation method for building thermal mass flexibility capacity applied to building clusters described in this invention, the acquisition of the basic dataset includes: establishing a building shoebox model and setting constant outdoor meteorological parameters and internal disturbance parameters; constructing a temperature step change as a temperature excitation signal and calculating the equivalent heat capacity value of a single building based on the load response; and constructing a basic dataset containing diverse building parameters and corresponding equivalent heat capacity values by setting the range of building geometric parameters and thermal parameters and performing random sampling.
[0009] As a preferred embodiment of the rapid estimation method for building thermal mass flexibility capacity applied to building clusters as described in this invention, the construction of the building shoebox model includes: first, establishing a standard rectangular cuboid geometric space and defining the corresponding window-to-wall ratio; second, assigning specific material construction layers and corresponding thermophysical properties to the exterior walls, roof, floors, and windows of the geometry in each orientation; and finally, configuring an ideal HVAC system model and an air conditioning operation schedule.
[0010] As a preferred embodiment of the rapid estimation method for building thermal mass flexibility capacity applied to building clusters described in this invention, the key feature parameter screening of the dataset includes: splitting the equivalent heat capacity of building thermal mass into three parts, namely indoor air, walls and floors; integrating the density, specific heat capacity, thermal conductivity and thickness in the building thermal parameters into heat storage coefficient and heat transfer coefficient; and combining the length, width and height and window-to-wall ratio in the building geometric parameters to construct a set of key feature parameters.
[0011] As a preferred embodiment of the rapid estimation method for building thermal mass flexibility capacity applied to building clusters described in this invention, the construction of the simplified equivalent thermal capacity model includes: using a multiple linear regression method to fit parameters, fitting the contribution weights of the three components of the building thermal mass equivalent thermal capacity to the equivalent thermal capacity, and establishing a simplified calculation model of the equivalent thermal capacity with key parameters as input.
[0012] As a preferred embodiment of the rapid estimation method for building thermal mass flexibility capacity applied to building clusters described in this invention, the equivalent heat capacity value is expressed as: , in, For equivalent heat capacity, This represents the weight of indoor air in the equivalent heat capacity of a building's thermal mass. This represents the weight of the wall in the equivalent heat capacity of the building's thermal mass. This represents the weight of the floor slab in the equivalent heat capacity of the building's thermal mass. For the length of the building, For the width of the building, For the height of the building, The heat storage coefficient of the wall. The heat transfer coefficient of the wall is... For the window-to-wall ratio, The floor slab heat storage coefficient, The heat transfer coefficient of the floor slab.
[0013] As a preferred embodiment of the rapid estimation method for building thermal mass flexibility capacity applied to building clusters as described in this invention, the building flexibility capacity includes: the maximum amount of cold energy that can be stored in the building thermal mass when the internal temperature setpoint changes, relying on the heat capacity of the building thermal mass; and the integral of the difference between the instantaneous cooling load during the cooling process and the cooling load after the cooling ends over the cooling time is the building's flexibility capacity during the cooling process.
[0014] Another objective of this invention is to provide a rapid estimation system for building thermal mass flexibility capacity in building clusters. This system can obtain a dataset of building parameters and corresponding equivalent thermal capacity values based on a basic dataset, and then filter key feature parameters of the dataset to construct a simplified equivalent thermal capacity model. This solves the problem that current methods for quantifying flexibility capacity in building clusters contain distortions in equivalent thermal capacity values, which cause the flexibility capacity to deviate from the true value.
[0015] As a preferred embodiment of the rapid estimation system for building thermal mass flexibility capacity applied to building clusters according to the present invention, the system includes: a data acquisition module, a model building module, a feature selection and modeling module, and a model application and capacity calculation module. The data acquisition module collects building geometric and thermal parameters, including the building's length, width, height, window-to-wall ratio, and the heat storage and heat transfer coefficients of the building envelope materials. The model building module constructs a building shoebox model based on the parameters collected by the data acquisition module, sets constant outdoor meteorological parameters, obtains a basic dataset by applying a temperature excitation signal, and acquires a dataset of building parameters and corresponding equivalent heat capacity values. The feature selection and modeling module decomposes the equivalent heat capacity based on building thermophysical mechanisms, constructs a set of key feature parameters, and uses a multiple linear regression method to construct a simplified equivalent heat capacity model. The model application and capacity calculation module inputs the building's geometric and thermal parameters into the simplified equivalent heat capacity model to output the building's thermal mass equivalent heat capacity, and calculates the building flexibility capacity against a preset temperature comfort range.
[0016] Another object of the present invention is to provide a device for rapid estimation of building thermal quality flexibility capacity for building clusters, comprising a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement a method for rapid estimation of building thermal quality flexibility capacity for building clusters.
[0017] Another object of the present invention is to provide a storage medium for rapid estimation of building thermal mass flexibility capacity for building clusters, wherein a computer program is stored thereon, and when the computer program is executed by a processor, the steps of a method for rapid estimation of building thermal mass flexibility capacity for building clusters are implemented.
[0018] The beneficial effects of this invention are: The present invention provides a rapid estimation method for building thermal mass flexibility capacity applied to building clusters. By constructing a building shoebox model and applying a temperature excitation signal to obtain a basic dataset, it solves the problems of high cost and difficulty in large-scale promotion of field experiments, achieving low-cost and high-efficiency data acquisition. By filtering key feature parameters of the dataset, 13 basic physical parameters are simplified to 6 conventional parameters that can be directly obtained from building drawings, solving the problems of redundant input parameters and high data acquisition difficulty in existing simplified models, significantly improving the engineering universality of the method. By constructing an equivalent heat capacity simplified model, the estimation error is controlled within 20%, solving the problem of the flexibility capacity deviating from the true value due to the distortion of equivalent heat capacity in existing empirical assignment methods, ensuring the accuracy of the quantitative results. The present invention achieves better results in terms of method universality, result accuracy, and engineering operability. Attached Figure Description
[0019] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0020] Figure 1 This is an overall flowchart of a method for rapidly estimating the building thermal mass flexibility capacity of a building cluster, as provided in Embodiment 1 of the present invention.
[0021] Figure 2 This is a building shoebox model diagram of a rapid estimation method for building thermal mass flexibility capacity applied to building clusters, provided in Embodiment 1 of the present invention.
[0022] Figure 3 This is a schematic diagram of the flexibility capacity during the cooling process of a rapid estimation method for building thermal mass flexibility capacity applied to building clusters, as provided in Embodiment 1 of the present invention.
[0023] Figure 4 This is a temperature excitation experiment temperature change sampling diagram for a rapid estimation method of building thermal mass flexibility capacity applied to building clusters, provided in Embodiment 2 of the present invention.
[0024] Figure 5 This is a fitting curve of the equivalent heat capacity of a building thermal mass, which is provided in Embodiment 2 of the present invention for a rapid estimation method of building thermal mass flexibility capacity applied to building clusters.
[0025] Figure 6 This is a graph showing the random sampling results of parameters for a rapid estimation method of building thermal mass flexibility capacity applied to building clusters, provided in Embodiment 2 of the present invention.
[0026] Figure 7 The graph shows the correlation analysis results of the total equivalent heat capacity and 13 batch simulation input features of a rapid estimation method for building thermal mass flexibility capacity applied to building clusters, as provided in Embodiment 2 of the present invention.
[0027] Figure 8 The graph shows the correlation analysis results of the total equivalent heat capacity and the three sub-items of equivalent heat capacity of indoor air, walls, and floors (roofs) in a rapid estimation method for building thermal mass flexibility capacity applied to building clusters, as provided in Embodiment 2 of the present invention.
[0028] Figure 9 This is a performance diagram of the model training set for a rapid estimation method of building thermal mass flexibility capacity applied to building clusters, provided in Embodiment 2 of the present invention.
[0029] Figure 10 This is a performance diagram of a model test set for a rapid estimation method of building thermal mass flexibility capacity applied to building clusters, provided in Embodiment 2 of the present invention.
[0030] Figure 11 This is a case study of a method for rapidly estimating the building thermal mass flexibility capacity of a building cluster, as provided in Embodiment 2 of the present invention, with a diagram showing the building and room numbers.
[0031] Figure 12 This is a comparison chart of the estimated equivalent heat capacity of a building room and the simulation value for a case study of a rapid estimation method for building thermal mass flexibility capacity applied to building clusters, provided in Embodiment 2 of the present invention.
[0032] Figure 13 This is a case study of the error distribution diagram of a building room in an embodiment 2 of the present invention, which provides a method for rapidly estimating the building thermal mass flexibility capacity of a building cluster.
[0033] Figure 14 This is a distribution diagram of C and E values for different energy-saving steps and room types in a building thermal mass flexibility capacity rapid estimation method for building clusters, provided in Embodiment 2 of the present invention.
[0034] Figure 15 This is a diagram showing the average equivalent heat capacity of residential buildings with different unit sizes for each step of a rapid estimation method for building thermal mass flexibility capacity applied to building clusters, as provided in Embodiment 2 of the present invention.
[0035] Figure 16 This is a diagram showing the average equivalent heat capacity of residential buildings with different unit sizes for each step of a rapid estimation method for building thermal mass flexibility capacity applied to building clusters, as provided in Embodiment 2 of the present invention. Detailed Implementation
[0036] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.
[0037] Example 1, referring to Figures 1-3 As one embodiment of the present invention, a method for rapidly estimating the building thermal mass flexibility capacity applied to building clusters is provided, comprising: S1: Construct a building shoebox model 100, and obtain the basic dataset 200 by applying a temperature excitation signal M through the building shoebox model 100.
[0038] Specifically, the basic dataset 200 includes the following: by establishing a building shoebox model 100 and setting constant outdoor meteorological parameters and internal disturbance parameters, constructing a temperature step change as a temperature excitation signal M and calculating the equivalent heat capacity C value of a single building based on the load response; by setting the range of building geometric parameters G and thermal parameters P and performing random sampling, a basic dataset 200 containing diverse building parameters and corresponding equivalent heat capacity C values is constructed.
[0039] The construction of the building shoebox model 100 includes, first, establishing a standard rectangular cuboid geometric space and defining the corresponding window-to-wall ratio; second, assigning specific material construction layers and corresponding thermophysical properties to the exterior walls, roof, floors, and windows of each orientation of the geometry; and finally, configuring an ideal HVAC system model and an air conditioning operation schedule.
[0040] Furthermore, a simulation-based approach was employed to obtain a foundational dataset of 200. First, a building shoebox model 100 was established, with constant outdoor meteorological parameters and internal disturbance parameters set. By applying an indoor temperature excitation signal M to the building model and calculating its load response, the equivalent heat capacity of the building was determined. Based on this, batch random simulations were performed. By setting reasonable ranges for building shape parameters and thermal parameters P and conducting random sampling, a large-scale dataset containing diverse building parameters and their corresponding equivalent heat capacity values was constructed, providing data support for subsequent model construction.
[0041] It should be noted that the architectural shoebox model 100 was built using SketchUp, such as... Figure 2As shown, a standard rectangular cuboid geometry is created in SketchUp as the basic mapping of the physical environment. The corresponding window-to-wall ratio is defined and saved as an IDF file. The IDF file is opened in Energyplus, and specific material construction layers and their corresponding thermophysical properties (including thickness, thermal conductivity, density, and specific heat capacity) are assigned to the exterior walls, roof, floors, and windows of the geometry in each orientation. Then, it is configured with an ideal HVAC system model and an air conditioning operation schedule. This standardized shoebox model, which removes complex internal partitions, is designed to accurately extract and characterize the core thermodynamic characteristics of a single room or functional area within a building, thereby providing an efficient and reliable model foundation for subsequent large-scale parametric batch simulations.
[0042] The model will be simulated in Energyplus to simulate the change of indoor temperature setpoint, thereby obtaining the flexibility capacity. Because this invention only explores the flexibility capacity provided by physical structures such as building thermal mass, internal disturbances such as occupants, lighting, and electrical appliances are not included in the batch simulation. To avoid interference from meteorological changes, constant outdoor meteorological parameters are maintained. These constant outdoor meteorological parameters are determined with reference to the stable peak conditions of a typical summer extreme cooling design day; for example, the dry-bulb temperature is fixed at 32.5℃, and the total solar radiation energy is set to a constant value of 800W / ㎡. This constant value is selected to construct a stable and unfluctuating outdoor background heat source environment for the simulation experiment. If real dynamic meteorological data were used, the periodic changes in diurnal temperature range and solar radiation would be superimposed on the step response decay curve of the air conditioning cooling load in the form of a sine wave, causing serious interference. Using constant parameters can effectively eliminate the interference of environmental fluctuations on the building's thermal response signal, ensuring that the cooling load change curve during the cooling process shows a monotonous and smooth decay trend, thus providing clean and noise-free basic data support for the subsequent accurate extraction of the maximum flexibility capacity (Emax) and equivalent heat capacity (Ceq).
[0043] It should also be noted that a temperature excitation experiment is designed in the building operation settings. In the building operation settings, the present invention designs a special temperature excitation experiment to obtain the building's equivalent heat capacity. The temperature excitation signal M mainly refers to the step change signal of the indoor set temperature applied to the ideal air conditioning system (for example, suddenly dropping the set temperature from high to low), the purpose of which is to trigger the thermal response and cold storage process of the building's physical structure.
[0044] To this end, the experiment used random sampling to generate six temperature sequences totaling 8760 hours (6 × 8760 h), which served as the operating schedule for the ideal air conditioning system's temperature setpoint. In arranging the schedule, the EnergyPlus simulation engine's year-round operating architecture was fully utilized, dividing the 8760 hours of each year into 40 consecutive experimental cycles, with a temperature change occurring every 219 hours. This setup seamlessly integrates 40 independent tests with different temperature stimuli into a single year-long simulation, avoiding the enormous computational cost of frequently restarting the simulation engine and significantly improving the throughput efficiency for generating large-scale datasets in batches.
[0045] Within each 219-hour cycle, the moment when the indoor setpoint temperature begins to decrease is marked as the cooling start moment. Considering the significant thermal inertia of heavy building envelopes, which require a considerable amount of time to reach thermodynamic steady state after a temperature abrupt change, this study sets 175 hours after the cooling start moment as the integration time to reach steady state (i.e., the cooling end moment). The time constant of heavy building envelopes generally does not exceed 50 hours, and 175 hours is more than three times the time constant. This setting ensures that building models under most thermal parameter combinations P can fully complete cold storage and reach a new steady state, guaranteeing the integrity of subsequent calculations of flexibility capacity through the integration of cooling load differences and avoiding distortion in the calculated equivalent heat capacity C due to an excessively short integration cutoff time.
[0046] After completing real-time indoor temperature and cooling load simulations in EnergyPlus, the system filters out processes where temperature changes result in cooling for targeted analysis. For all selected cooling processes, the difference between the real-time cooling load and the steady-state cooling load after cooling is uniformly calculated and integrated over a 175-hour interval. The result is the flexibility capacity of a single cooling process. Through the simulation of the above randomly sampled sequence, each baseline building will generate approximately 100 or more effective cooling processes. Finally, the flexibility capacity of these hundreds of experiments is linearly fitted with the air temperature difference before and after cooling to obtain a relationship curve. The slope of this curve is the equivalent heat capacity C of the target building's thermal mass.
[0047] S2: Obtain a large-scale dataset of building parameters and corresponding equivalent heat capacity values based on the basic dataset 200, and filter key feature parameters of the large-scale dataset to construct a simplified equivalent heat capacity model 300.
[0048] Specifically, the key feature parameter screening for large-scale datasets includes splitting the equivalent heat capacity of building thermal mass into three parts: indoor air, walls, and floors; integrating the density, specific heat capacity, thermal conductivity, and thickness in the building thermal parameters P into heat storage coefficient and heat transfer coefficient; and combining them with the length, width, height, and window-to-wall ratio in the building geometric parameters G to construct a set of key feature parameters.
[0049] The construction of the simplified equivalent heat capacity model 300 includes using the multiple linear regression method to fit the parameters, fitting the contribution weights of the three components of the building thermal mass equivalent heat capacity to the equivalent heat capacity C, and establishing a simplified calculation model 300 of the equivalent heat capacity with key parameters as input.
[0050] The equivalent heat capacity is expressed as: , in, For equivalent heat capacity, This represents the weight of indoor air in the equivalent heat capacity of a building's thermal mass. This represents the weight of the wall in the equivalent heat capacity of the building's thermal mass. This represents the weight of the floor slab in the equivalent heat capacity of the building's thermal mass. For the length of the building, For the width of the building, For the height of the building, The heat storage coefficient of the wall. The heat transfer coefficient of the wall is... For the window-to-wall ratio, The floor slab heat storage coefficient, The heat transfer coefficient of the floor slab.
[0051] The three weight values are not fixed empirical constants, but rather obtained by dynamically fitting multiple linear regression using ordinary least squares (OLS) with a 'large-scale batch simulation dataset' acquired in this invention. After processing and fitting cooling simulation data from 800 baseline buildings, the specific weight coefficient values are as follows: the comprehensive weight coefficient for indoor air is approximately 3.15, the comprehensive weight coefficient for walls is approximately 2.51, and the comprehensive weight coefficient for floors and roofs is approximately 0.77. This data-driven fitting of weights most accurately reflects the dynamic contribution of each physical component to the actual cooling process.
[0052] Furthermore, based on the large-scale dataset obtained in the first step, the input variables are first determined. Feature extraction and dimensionality reduction are then performed based on the derivation of building physics mechanism formulas. Specifically, the equivalent heat capacity is first divided into three parts: indoor air heat capacity, interior and exterior wall heat capacity, and floor (roof) heat capacity. Expressions for the heat capacity of these three parts are listed, and the value of the equivalent heat capacity is equal to the weighted sum of the heat capacity of the three parts. According to the physical mechanism of equivalent heat capacity, the basic physical parameters affecting heat capacity include length, width, height, window-to-wall ratio, and the thermal parameters P of the building envelope material. Multiple basic physical thermal parameters P (density, specific heat capacity, thermal conductivity, thickness) are integrated into two comprehensive engineering parameters: the material's "heat storage coefficient S" and "heat transfer coefficient U". The equivalent heat capacity expression is simplified using these more convenient comprehensive engineering parameters. The beneficial effect of this mechanism-based screening and dimensionality reduction lies in simplifying the originally complex 13 input variables into 6 conventional parameters directly obtainable from architectural drawings (length, width, height, heat storage coefficient, heat transfer coefficient, and window-to-wall ratio). This simplification primarily involves cleverly introducing two standard engineering parameters for formula substitution. Based on the expressions for the material's heat storage coefficient S and heat transfer coefficient U, the required thickness, heat capacity, and density of the interior and exterior wall materials, as well as the floor and roof materials, are obtained through combination and multiplication. Therefore, these parameters do not require additional acquisition; the heat storage coefficient and heat transfer coefficient provided in the drawings can be used as model input. This not only effectively eliminates strong coupling and collinearity among multiple variables but also significantly reduces the difficulty of data acquisition and computational complexity, thereby significantly improving the engineering universality of the quantitative evaluation method. The selected... These six key characteristic parameters that have a significant impact are mathematically represented as Subsequently, multiple linear regression was used to fit the parameters, determining the contribution weights of the three component heat capacities to the equivalent heat capacity, and establishing a simplified calculation model for the equivalent heat capacity using these key parameters as inputs. And a flexible capacity calculation model.
[0053] This invention utilizes the Energyplus-JEPlus-Eppy toolchain to generate datasets using a batch simulation method. Energyplus is used to create 100 building shoebox models and an ideal air conditioning system model. Numerical random sampling is performed on 13 parameters across two main categories within their value ranges to generate 800 baseline buildings with different thermal parameters P and morphological parameters. Specifically, JEplus is used to copy the template building IDF files, and Python's Eppy tool is used for random sampling of influencing factor characteristics.
[0054] It should be noted that the multiple linear regression method aims to quantify the relationship between independent variables (explanatory variables) and dependent variables (response variables). It is a generalization of univariate linear regression, allowing for the simultaneous examination of the independent effects of multiple explanatory variables on the response variable, expressed as: , in, As the dependent variable, The intercept term represents the expected value of the response variable when all explanatory variables are zero. For the corresponding regression coefficients, for There are 1 independent variables.
[0055] Each coefficient The dependent variable was quantified while controlling for the effects of other independent variables within the model. The response variable caused by the unit change The average expected change. It is estimated using ordinary least squares, aiming to find the combination of parameter values that minimizes the sum of squared residuals between the observed values and the model predictions. This is expressed as: , in, For the first The true observed value of each sample For the first The predicted value for each sample, For the first The first to the second independent variable samples The specific values of each independent variable This represents the number of data samples.
[0056] This invention employs a multiple linear regression method, combined with mechanism-based input variable settings, using the derived formula of building thermodynamics (i.e., the total equivalent heat capacity equals the sum of the heat capacities of indoor air, walls, floors, and roof) as the underlying physical structure of the regression model. Based on this, simplified terms of these three physical components are used as three composite independent variables for linear fitting in the multiple linear regression, thereby determining the weight coefficient of each characteristic term in the total heat capacity. This mechanism, deeply integrated with the setting of building physical parameters, not only ensures the statistical model has clear physical interpretability and effectively avoids the blindness of purely data-driven models, but also greatly improves the fitting accuracy of the multiple linear regression and the model's generalization ability in complex engineering scenarios. The relationship between the influencing input variables and the flexibility capacity under numerous cooling events is fitted to construct a building thermal mass equivalent heat capacity estimation model. Furthermore, by substituting building parameters into the building thermal mass equivalent heat capacity estimation model to calculate the equivalent heat capacity, and then multiplying it by a pre-defined temperature comfort range, the building flexibility capacity 301 under the specified comfort requirement can be obtained, thus enabling rapid estimation of building flexibility potential.
[0057] To evaluate the building thermal mass equivalent heat capacity estimation model fitted by the multiple linear regression method, this study uses MAE, MAPE, RMSE, and CVRMSE as the model scoring criteria. The specific application of these four scoring criteria is as follows: the building equivalent heat capacity calculated through multiple physical simulations is used as the actual observed value, while the equivalent heat capacity estimated by the multiple linear regression model is used as the model prediction value. These values are then substituted into their respective formulas to quantitatively evaluate the model error. In the field of large-scale engineering evaluation of building energy consumption and flexible heat capacity prediction, it is generally considered that a model error controlled within 20% is sufficient to meet the macro-level estimation accuracy requirements at the engineering level. This fully demonstrates that the model can be widely applied as a qualified and rapid engineering evaluation tool, specifically expressed as follows: , , , , in, The number of data samples. For the first The true observed value of each sample For the first The predicted value for each sample.
[0058] S3: Input the geometric parameters G and thermal parameters P of the building to be evaluated into the simplified equivalent heat capacity model 300, output the equivalent heat capacity of the building thermal mass, and calculate and output the building flexibility capacity 301 with the preset temperature comfort range.
[0059] Specifically, the building flexibility capacity 301 includes the maximum amount of cold energy that can be stored in the building's thermal mass when the internal temperature setpoint changes, relying on the heat capacity of the building's thermal mass; and the integral of the difference between the instantaneous cooling load during the cooling process and the cooling load after the cooling ends over the cooling time is the building's flexibility capacity during the cooling process.
[0060] Furthermore, this invention defines building flexibility capacity 301 as the maximum amount of cold energy that can be stored in the building's thermal mass when the internal temperature setpoint changes, relying on the heat capacity of the building's thermal mass, with units of [unit missing]. or From an experimental perspective, the measurement of flexibility capacity can be clearly demonstrated during the cooling process, such as... Figure 3The diagram illustrates the real-time changes in temperature and cooling load throughout the entire process. Before cooling begins, the cooling load remains stable at a certain value. After the start of cooling, the cooling load experiences a sharp jump within a short period, reaching a peak load. It then gradually decreases, stabilizing at the end of the cooling process. After stabilization, it will maintain a stable cooling load value higher than before cooling. The difference between the instantaneous cooling load during the cooling process and the cooling load value after cooling ends, integrated over the cooling time, represents the building's flexibility capacity during the cooling process. The flexibility capacity of a single cooling process obtained experimentally is expressed as: , in, For flexible capacity, For the instantaneous cooling load during the cooling process, This represents the cooling load value after the cooling process ends. The moment when the temperature begins to drop. This marks the end of the cooling process.
[0061] It should be noted that, starting from the definition of equivalent heat capacity, it can be found that it mainly comes from the mass heat capacity and mass of indoor air and the building envelope. Mass can be further decomposed into density and volume. Therefore, the influencing factors of equivalent heat capacity can be derived from the mechanism, falling into two main categories: building thermal parameters P and building morphological parameters. These include the mass heat capacity, thermal conductivity, density, and thickness of building materials, as well as the length, width, height, window-to-wall ratio, and number of walls with windows of the entire building.
[0062] This invention considers two types of building thermal mass: indoor air and building envelope. The building envelope is further divided into walls and floors (roof). Therefore, according to the formula and mechanism of equivalent heat capacity, the equivalent heat capacity of building thermal mass can be decomposed into three parts: indoor air, walls, and floors (roof), where the heat capacity of the three parts is expressed as: , , , in, The total heat capacity of indoor air. The total mass heat capacity of indoor air. The total density of indoor air. For the length of the building, For the width of the building, For the height of the building, The total heat capacity of the wall. For the total mass heat capacity of the wall, The total density of the wall. The total thickness of the wall. For the window-to-wall ratio, For the total heat capacity of the floor slab, The total mass heat capacity of the floor slab, The total density of the floor slab. This represents the total thickness of the floor slab.
[0063] However, in real building construction, the building envelope is typically composed of multiple materials. To facilitate calculation and reduce the dimensionality of the input features, the material heat storage coefficient is introduced as follows: , in, The heat storage coefficient of the material, The thermal conductivity of the material For the heat capacity of the material, For the density of the material, The cycle is 24 hours.
[0064] Introducing the material's heat transfer coefficient, the internal surface heat transfer resistance is considered in the calculation. Thermal resistance of outer surface heat transfer There are two cases: one surface is an inner surface, one surface is an outer surface, and both surfaces are inner surfaces. Therefore, the average surface heat transfer resistance for both cases is given as: The heat transfer coefficient of a material is expressed as: , in, The heat transfer coefficient of the material, For thermal resistance, For material thickness, is the thermal conductivity of the material.
[0065] Therefore, only six parameters readily available from architectural drawings—length, width, height, heat storage coefficient, heat transfer coefficient, and window-to-wall ratio—are needed to obtain the equivalent heat capacity of a building's thermal mass. However, considering that building structures consist of multiple layers of materials, the following corrections are required when inputting the data into the model for calculation: The window-to-wall ratio is corrected by dividing the total area of all windows by the total area of all walls. For single structures such as floors, roofs, interior walls, and exterior walls, the total heat transfer coefficient and total heat storage coefficient are calculated using a thickness-weighted average. However, since the total area of combined structures has a much greater impact on heat transfer and heat storage than the total thickness, the total heat transfer coefficient and total heat storage coefficient for combined structures such as floors + roofs and exterior walls + interior walls are calculated using a weighted average of the areas of their constituent structures.
[0066] It should also be noted that, in order to verify the accuracy of the simplified model, typical case buildings were selected, and their equivalent heat capacity values were calculated using the simulation method in the first step and the simplified model in the second step, respectively. The two sets of results were then compared to verify the calculation accuracy and reliability of the simplified model. The verified model was then applied to the actual evaluation.
[0067] Example 2, refer to Figures 4-16 As an embodiment of the present invention, a method for rapid estimation of building thermal mass flexibility capacity applied to building clusters is provided. To verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculations and simulation experiments.
[0068] First, according to the design method of the temperature excitation experiment of the present invention, for a case building, the temperature is controlled between 16°C and 26°C, and a temperature sequence of 10*8760h is randomly sampled.
[0069] Temperature excitation experiments were conducted in the building. Figure 4 The experiment demonstrates the changes in real-time indoor temperature and cooling load during one of the temperature excitation processes. After obtaining the experimental data, the cooling process was selected, the temperature change values during the cooling were recorded, and the flexibility capacity was integrally calculated. Figure 5 The fitting relationship between the flexibility capacity and the corresponding temperature change value is shown. It can be seen that the flexibility capacity during the cooling process is directly proportional to the corresponding temperature change value, that is, its slope is a fixed value, which is the total equivalent heat capacity of the building.
[0070] Relevant standards were consulted to determine the range of values for building morphology parameters and building thermal parameters, as shown in Table 1.
[0071] Table 1. Values of influencing factors used to generate batch simulation datasets.
[0072] Using the Eppy library in Python, 13 features were randomly sampled to generate 800 template buildings, which were then used as input features for the simulation model. The range of values for the random sampling parameters is shown below. Figure 6 As shown, the equivalent heat capacity values of the building thermal mass of 800 buildings, obtained from batch simulations of the cooling process using the Energyplus-JEPlus-Eppy toolchain, and their corresponding simulation input parameters are compiled. For example... Figure 7 The diagram shows the correlation heatmap between various parameters and their corresponding equivalent heat capacity values. It can be seen from the graph that, among the 13 batch simulation input features, except for length, width, and height, the correlation characteristics of other parameters with the total equivalent heat capacity are not obvious, requiring feature combination and dimensionality reduction. The equivalent heat capacity components for three main categories—indoor air, walls, and floors (roofs)—are calculated. These are then statistically analyzed using a scatter plot, as shown below. Figure 8 As shown, it is clear that the heat capacity of each major category is linearly correlated with the equivalent heat capacity.
[0073] Regarding the model construction process, the comparison between estimated and simulated values on the training and test sets, model evaluation metrics such as CVRMSE, RMSE, MAE, and MAPE, and error distribution are presented. Figure 9 and Figure 10 As shown in the results, the model performs well and the error is within the acceptable range for engineering applications. The equivalent heat capacity model of building thermal mass fitted by the multiple linear regression method can basically reflect the total equivalent heat capacity of the building thermal mass composed of indoor air and building envelope. The model proposed in this invention can be used as an estimate of the equivalent heat capacity of building thermal mass in engineering applications.
[0074] To verify the accuracy and application value of the model method proposed in this invention, we built a three-story building using SketchUp and OpenStudio, with each floor consisting of 9 [units / items]. The building comprises 27 rooms, with a window-to-wall ratio of 0.4. The rooms serve different floors and zones within the building, allowing for comprehensive verification. To facilitate differentiation and comparison of results, each room is assigned a number. The room numbering layout is as follows: Figure 11 As shown.
[0075] The calculated heat storage coefficient and heat transfer coefficient, along with the corresponding room's length, width, height, and window-to-wall ratio, are input into the building thermal mass equivalent heat capacity estimation model to obtain the estimated building thermal mass equivalent heat capacity for each room. Similarly, using the temperature random sampling and simulation methods proposed in this invention, 100 cooling processes of indoor temperature and real-time cooling load simulations are performed on the case building in Energyplus to calculate the flexibility capacity of each room for each cooling process, such as... Figure 12 The simulation value of the equivalent heat capacity of the building thermal mass is then calculated from an experimental perspective and compared with the estimated value from the model.
[0076] The results show that the model proposed in this invention is suitable for estimating the shoebox-shaped rooms with different zones and window types, and the estimation error can be controlled within 20%. It is applicable to both inner and outer zones, as well as rooms with different window types. It is particularly effective in estimating the equivalent heat capacity of buildings in rooms on intermediate floors. Figure 13 The error distribution shows that the error is larger on the first floor, followed by the top floor, while the errors on the middle floors are generally smaller. The large error in estimating the equivalent heat capacity of the building's thermal mass on the first or top floor is considered to be due to the simplified estimation mechanism of the input parameters, which leads to larger errors in the input thermal parameters when the building envelope exchanges heat with the ground or outside air. However, these errors are still within the acceptable range for engineering purposes.
[0077] Residential buildings, representing a high percentage of building types, are an excellent resource for demand response users. Residential building users often use split-system air conditioners to create personalized thermal comfort spaces. Different users have different response preferences, and the potential for individual building users is relatively small. Therefore, for residential buildings to participate in demand response, a synergistic control approach is suitable. Thus, assessing the building thermal quality flexibility capacity of residential building users and exploring their response characteristics is fundamental to their better participation in synergistic control and the development of more appropriate response strategies.
[0078] To better facilitate the practical application of the model and demonstrate its value, user-level flexibility capacity and equivalent heat capacity of building thermal mass are estimated. The heat transfer coefficient, heat storage coefficient, and window-to-wall ratio limits for different energy-saving steps in residential buildings, along with the length, width, and height ranges, are obtained from standards and specifications and input into the building thermal mass equivalent heat capacity estimation model. This yields the range of equivalent heat capacity values, which is then multiplied by a 4°C thermal comfort range to obtain the range of building thermal mass flexibility capacity values for energy-saving steps one through four. The results are as follows: Figure 14 As shown in Table 2, it can be seen that from one-step to four-step energy conservation, the requirements for the thermal insulation performance of building envelope materials in the standards are increasing. Therefore, the C-value (E-value) generally shows an upward trend. The data value for three-step energy conservation has decreased because lightweight insulation materials began to be used on a large scale during the three-step energy conservation period. These materials have a lower heat storage coefficient than traditional heavy materials. Moreover, after the start of the three-step energy conservation, the restrictions on the window-to-wall ratio have been weakened, and windows occupy a larger proportion, hindering the storage of cold energy. Due to the increase in large-sized buildings and more diverse building geometry after the two-step energy conservation, the range covered by the C-value (E-value) is also wider. Houses with two exterior walls have greater flexibility than houses with one exterior wall because the exterior walls have higher thermal insulation performance.
[0079] Table 2. Range of C and E values for energy conservation in building steps one through four.
[0080] Buildings with a floor area of less than 60 square meters are classified as small units, those between 60 and 100 square meters as medium units, and those greater than 100 square meters as large units. A window-to-wall ratio of 0.15 is used as the dividing line, categorizing buildings into large and small window-to-wall ratio buildings. Based on this, data analysis was conducted to explore the average equivalent heat capacity of buildings with different geometric parameters at each energy-saving stage. It can be seen that, under the same window-to-wall ratio level and energy-saving stage, the larger the unit size, the greater the average equivalent heat capacity. This demonstrates a positive correlation between length and width parameters and the equivalent heat capacity. The jump in average equivalent heat capacity from the third to the fourth energy-saving stage is greater than in the previous stages, due to the use of high-performance thermal bridge-free insulation materials and window and door systems, which significantly reduces the heat transfer coefficient, enabling buildings to move towards ultra-low energy consumption.
[0081] The average equivalent heat capacity of different apartment types with large window-to-wall ratios is calculated by subtracting the corresponding small window-to-wall ratios from the building's average heat capacity, and a heat map of the difference is plotted as follows: Figure 16 As shown in the figure, positive values indicate that buildings with a larger window-to-wall ratio have a higher average equivalent heat capacity than those with a smaller window-to-wall ratio, while negative values have the opposite. It can be seen that for smaller apartments, the smaller the window-to-wall ratio, the higher the equivalent heat capacity, following a negative correlation between the window-to-wall ratio and equivalent heat capacity. However, for larger apartments, the equivalent heat capacity value tends to be more directly proportional to the window-to-wall ratio. This is because, for larger apartments, the building's length and width have a more significant impact on the equivalent heat capacity result, outweighing the influence of the window-to-wall ratio.
[0082] Example 3, an embodiment of the present invention, provides a rapid estimation system for building thermal mass flexibility capacity applied to building clusters, including a data acquisition module, a model building module, a feature selection and modeling module, and a model application and capacity calculation module.
[0083] The data acquisition module is used to collect building geometric parameters G and thermal parameters P, including the building's length, width, height, window-to-wall ratio, and the heat storage coefficient and heat transfer coefficient of the building envelope materials.
[0084] The model building module is used to construct a building shoebox model 100 based on the parameters collected by the data acquisition module, set constant outdoor meteorological parameters, obtain a basic dataset 200 by applying a temperature excitation signal M, and obtain a large-scale dataset of building parameters and corresponding equivalent heat capacity values.
[0085] The feature selection and modeling module is used to decompose the equivalent heat capacity C based on the building thermophysical mechanism, construct a set of key feature parameters, and use the multiple linear regression method to construct a simplified model 300 of the equivalent heat capacity.
[0086] The model application and capacity calculation module is used to input the geometric parameters G and thermal parameters P of the building to be evaluated into the simplified equivalent heat capacity model 300, output the equivalent heat capacity of the building's thermal mass, and calculate and output the building's flexibility capacity 301 with the preset temperature comfort range.
[0087] This embodiment also provides a computer device, including a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, it implements the rapid estimation method for building thermal mass flexibility capacity applied to building clusters as proposed in the above embodiment.
[0088] This embodiment also provides a computer-readable storage medium storing a computer program thereon. When the computer program is executed by a processor, it implements the rapid estimation method for building thermal mass flexibility capacity applied to building clusters as proposed in the above embodiments.
[0089] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, essentially, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.
[0090] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-including system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.
[0091] More specific examples of computer-readable media (a non-exhaustive list) include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.
[0092] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.
[0093] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.
Claims
1. A method for rapid estimation of building thermal mass flexibility capacity applied to a building cluster, characterized in that, include: Construct a building shoebox model (100), and obtain the basic dataset (200) by applying a temperature excitation signal (M) through the building shoebox model (100). Based on the basic dataset (200), a large-scale dataset (200') of building parameters and corresponding equivalent heat capacity values is obtained, and key feature parameters are selected from the large-scale dataset (200') to construct a simplified equivalent heat capacity model (300). Input the geometric parameters (G) and thermal parameters (P) of the building to be evaluated into the simplified model of equivalent heat capacity (300), output the equivalent heat capacity of the building thermal mass, and calculate the building flexibility capacity (301) with the preset temperature comfort range.
2. The rapid estimation method for building thermal mass flexibility capacity applied to building clusters as described in claim 1, characterized in that: The acquisition of the basic dataset (200) includes, By establishing a building shoebox model (100) and setting constant outdoor meteorological parameters and internal disturbance parameters, a temperature step change is constructed as a temperature excitation signal (M), and the parameter equivalent heat capacity value of a single building is calculated based on the load response. By setting the range of building geometric parameters (G) and thermal parameters (P) and performing random sampling, a basic dataset (200) containing diverse building parameters and corresponding equivalent heat capacity (C) values is constructed.
3. The rapid estimation method for building thermal mass flexibility capacity applied to building clusters as described in claim 1 or 2, characterized in that: The construction of the architectural shoebox model (100) includes, First, establish a standard rectangular cuboid geometric space and define the corresponding window-to-wall ratio; Secondly, specific material construction layers and corresponding thermophysical properties are assigned to the exterior walls, roof, floors and windows of the geometry in each orientation; Finally, configure the ideal HVAC system model and air conditioning operation schedule.
4. The rapid estimation method for building thermal mass flexibility capacity applied to building clusters as described in claim 3, characterized in that: The process of filtering key feature parameters in the dataset includes, The equivalent heat capacity of building thermal mass is divided into three parts: indoor air, walls and floors. The density, specific heat capacity, thermal conductivity and thickness in the building thermal parameters (P) are integrated into the heat storage coefficient and heat transfer coefficient. Combined with the length, width and height and window-to-wall ratio in the building geometric parameters (G), a set of key characteristic parameters is constructed.
5. The rapid estimation method for building thermal mass flexibility capacity applied to building clusters as described in claim 1, 2, or 4, characterized in that: The construction of the simplified equivalent heat capacity model (300) includes, The parameters were fitted using a multiple linear regression method to fit the contribution weights of the three components of the equivalent heat capacity of the building thermal mass to the equivalent heat capacity (C), and a simplified calculation model (300) of the equivalent heat capacity was established with key parameters as input.
6. The rapid estimation method for building thermal mass flexibility capacity applied to building clusters as described in claim 5, characterized in that: The equivalent heat capacity value is expressed as: ， in, For equivalent heat capacity, This represents the weight of indoor air in the equivalent heat capacity of a building's thermal mass. This represents the weight of the wall in the equivalent heat capacity of the building's thermal mass. This represents the weight of the floor slab in the equivalent heat capacity of the building's thermal mass. For the length of the building, For the width of the building, For the height of the building, The heat storage coefficient of the wall. The heat transfer coefficient of the wall is... For the window-to-wall ratio, The floor slab heat storage coefficient, The heat transfer coefficient of the floor slab.
7. The rapid estimation method for building thermal mass flexibility capacity applied to building clusters as described in claim 1, 2, 4, or 6, characterized in that: The building flexibility capacity (301) includes, When the internal temperature setpoint of a building changes, the maximum amount of cold energy that can be stored in the building's thermal mass can be stored by relying on the heat capacity of the building's thermal mass. The difference between the instantaneous cooling load during the cooling process and the cooling load after the cooling process ends, and the integral of the difference over the cooling time, is the building's flexibility capacity during the cooling process.
8. A rapid estimation system for building thermal mass flexibility capacity applied to building clusters, employing the rapid estimation method for building thermal mass flexibility capacity applied to building clusters as described in any one of claims 1 to 7, characterized in that: It includes a data acquisition module, a model building module, a feature selection and modeling module, and a model application and capacity calculation module; The data acquisition module is used to collect building geometric parameters (G) and thermal parameters (P), including the building's length, width, height, window-to-wall ratio, and the heat storage coefficient and heat transfer coefficient of the building envelope material; The model building module is used to build a building shoebox model (100) based on the parameters collected by the data acquisition module, set constant outdoor meteorological parameters, obtain a basic dataset (200) by applying a temperature excitation signal (M), and obtain a dataset of building parameters and corresponding equivalent heat capacity (C) values. The feature selection and modeling module is used to decompose the equivalent heat capacity (C) based on the building thermophysical mechanism, construct a set of key feature parameters, and use the multiple linear regression method to construct a simplified model of the equivalent heat capacity (300). The model application and capacity calculation module is used to input the geometric parameters (G) and thermal parameters (P) of the building to be evaluated into the equivalent heat capacity simplified model (300) to output the equivalent heat capacity of the building thermal mass, and calculate the building flexibility capacity (301) with the preset temperature comfort range.
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 rapid estimation method for building thermal mass flexibility capacity applied to building clusters as described in 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 the processor, it implements the steps of the rapid estimation method for building thermal mass flexibility capacity applied to building clusters as described in any one of claims 1 to 7.