Building energy consumption prediction method and device
By establishing a building model and using Planck's formula and the state-space method to simulate the dynamic heat transfer process of the building, the problem of poor accuracy in building energy consumption prediction in existing technologies has been solved, achieving high-precision energy consumption prediction and energy-saving effects.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- TSINGHUA UNIVERSITY
- Filing Date
- 2023-11-28
- Publication Date
- 2026-07-21
AI Technical Summary
Existing technologies cannot effectively reflect the dynamic response characteristics of building heat transfer, resulting in poor accuracy in building energy consumption prediction. Furthermore, they cannot uniformly handle surfaces with different spectral characteristics, increasing simulation errors.
A building model is established, atmospheric optical property parameters and radiation film type are set, and the dynamic heat transfer process of the building is simulated using Planck's formula and state-space method. The building surface temperature is calculated iteratively by long-wave radiation value, and the energy consumption simulation is adapted to spectrally selective materials.
It improves the accuracy of building energy consumption prediction and can be used quickly and efficiently for energy consumption prediction of various building shapes, with an energy saving potential of approximately 14-42%.
Smart Images
Figure CN117592285B_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the fields of new materials and energy-saving technology, and in particular to a method and apparatus for predicting building energy consumption. Background Technology
[0002] This section is intended to provide background or context for the embodiments of the invention set forth in the claims. The description herein is not an admission that it is prior art simply because it is included in this section.
[0003] Currently, most research on building energy consumption uses effective sky temperature combined with average emissivity for simulation calculations. The basic principle is to determine an effective radiative temperature by constructing a diffuse gray surface to simulate heat exchange between the Earth's surface and the atmosphere, and then use this temperature to calculate the building surface energy consumption. However, a unified averaging method cannot be found for surfaces with different spectral characteristics, and the selectivity of long-wave heat transfer cannot be expressed using average emissivity, thus increasing the error in building energy consumption simulations.
[0004] In addition, existing technologies often use short step sizes and oversimplify the heat transfer process on the building side by using integral methods, which fails to reflect the dynamic response characteristics of building heat transfer. Summary of the Invention
[0005] This invention provides a building energy consumption prediction method to dynamically reflect the building heat transfer process and improve the accuracy of building energy consumption prediction. The method includes:
[0006] A building model is established based on a building whose energy consumption is to be predicted. The building model is configured with atmospheric optical property parameters and the type of radiation film on the building surface. The radiation film type includes radiation materials that utilize optical properties to achieve long-wave heat exchange.
[0007] Within a preset time period, sampling times are taken at a set frequency and in chronological order, starting from the first sampling time. For each sampling time, based on the building surface temperature, building model, and type of radiant film on the building surface at that sampling time, the long-wave radiation value of the building model at that sampling time is determined using Planck's formula. Using the state-space method, the dynamic heat transfer process of the building is simulated and calculated over time using the long-wave radiation value of the building model at that sampling time, and the building surface temperature at the next sampling time is output, until the long-wave radiation value and building surface temperature of the building model at the last sampling time are output.
[0008] This invention also provides a building energy consumption prediction device to dynamically reflect the building heat transfer process and improve the accuracy of building energy consumption prediction. The device includes:
[0009] The building model building module is used to build a building model based on the building whose energy consumption is to be predicted. The building model sets atmospheric optical property parameters and the type of radiation film on the building surface. The radiation film type includes radiation materials that use optical properties to achieve long-wave heat exchange.
[0010] The building energy consumption calculation module is used to sample time points at a set frequency within a preset time period. Starting from the first sampling time point, it determines the long-wave radiation value of the building model at each sampling time point based on Planck's formula, according to the building surface temperature, building model, and type of radiation film on the building surface at that sampling time point. Using the state-space method, it simulates and calculates the dynamic heat transfer process of the building over time using the long-wave radiation value of the building model at that sampling time point, and outputs the building surface temperature at the next sampling time point, until the long-wave radiation value and building surface temperature of the building model at the last sampling time point are output.
[0011] This invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the above-described building energy consumption prediction method.
[0012] This invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described building energy consumption prediction method.
[0013] This invention also provides a computer program product, which includes a computer program that, when executed by a processor, implements the above-described building energy consumption prediction method.
[0014] In this embodiment of the invention, atmospheric optical property parameters and the type of radiation film on the building surface are set for the building model. Based on the building model and the type of radiation film on the building surface, the long-wave radiation value of the building model at the sampling time is determined according to Planck's formula. Then, the state-space method is used to simulate and calculate the dynamic heat transfer process of the building over time using the long-wave radiation value of the building model at the sampling time, and output the building surface temperature at the next sampling time. This simulation of time flow is repeated iteratively until the long-wave radiation value and building surface temperature of the building model at the last sampling time within the preset time period are output. This can dynamically reflect the building heat transfer process and is well adapted to the energy consumption simulation calculation of spectrally selective materials in building energy consumption simulation. It greatly improves the accuracy of building energy consumption prediction, has strong robustness, and can be used quickly and efficiently for energy consumption prediction of various building shapes. Attached Figure Description
[0015] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. 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. In the drawings:
[0016] Figure 1 This is a flowchart illustrating the building energy consumption prediction method in an embodiment of the present invention;
[0017] Figure 2 This is a schematic diagram of the long-wave radiation heat transfer principle in an embodiment of the present invention;
[0018] Figure 3 This is a schematic diagram of the building model setup in an embodiment of the present invention. Figure 1 ;
[0019] Figure 4 This is a schematic diagram of the building model setup in an embodiment of the present invention. Figure 2 ;
[0020] Figure 5 This is a specific embodiment of the building energy consumption prediction method in the present invention;
[0021] Figure 6 This is a specific embodiment of the building energy consumption prediction method in the present invention;
[0022] Figure 7 This is a specific embodiment of the building energy consumption prediction method in the present invention;
[0023] Figure 8 This is a schematic diagram of a building energy consumption prediction device in an embodiment of the present invention. Detailed Implementation
[0024] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the embodiments of the present invention will be further described in detail below with reference to the accompanying drawings. Here, the illustrative embodiments of the present invention and their descriptions are used to explain the present invention, but are not intended to limit the present invention.
[0025] The applicant found that existing technologies cannot reflect the dynamic response characteristics of building heat transfer, resulting in poor accuracy in building energy consumption prediction. Therefore, the applicant proposed a building energy consumption prediction method.
[0026] The acquisition, storage, use, and processing of data in this application all comply with the relevant provisions of national laws and regulations.
[0027] Figure 1 This is a flowchart illustrating the building energy consumption prediction method in an embodiment of the present invention, as shown below. Figure 1As shown, the method includes:
[0028] Step 101: Establish a building model based on the building whose energy consumption is to be predicted; set the atmospheric optical property parameters and the type of radiation film on the building surface in the building model, wherein the radiation film type includes radiation materials that utilize optical properties to achieve long-wave heat exchange.
[0029] Step 102: Within a preset time period, sample the time at a set frequency. Starting from the first sampling time, for each sampling time, determine the long-wave radiation value of the building model at that sampling time based on Planck's formula, according to the building surface temperature, building model, and type of radiation film on the building surface at that sampling time. Using the state-space method, simulate and calculate the dynamic heat transfer process of the building over time using the long-wave radiation value of the building model at that sampling time, and output the building surface temperature at the next sampling time, until the long-wave radiation value and building surface temperature of the building model at the last sampling time are output.
[0030] from Figure 1 As shown in the flowchart, in this embodiment of the invention, atmospheric optical property parameters and the type of radiation film on the building surface are set for the building model. Based on the building model and the type of radiation film on the building surface, the long-wave radiation value of the building model at the sampling time is determined according to Planck's formula. Then, using the state-space method, the long-wave radiation value of the building model at the sampling time is used to simulate and calculate the dynamic heat transfer process of the building over time, and the building surface temperature at the next sampling time is output. This process is repeated to simulate the flow of time and iterate until the long-wave radiation value and building surface temperature of the building model at the last sampling time within the preset time period are output. This can dynamically reflect the building heat transfer process and is well adapted to the energy consumption simulation calculation of spectrally selective materials in building energy consumption simulation. It greatly improves the accuracy of building energy consumption prediction, has strong robustness, and can be used quickly and efficiently for energy consumption prediction of various building shapes.
[0031] The building energy consumption prediction method in the embodiments of the present invention will be explained in detail below.
[0032] First, a building model is created using the building whose energy consumption is to be predicted as a prototype.
[0033] For example, taking a certain experimental chamber as an example, a building model is established using AutoCAD and DeST building energy consumption simulation software. The building model is configured with atmospheric optical property parameters and the type of radiation film on the building surface. The radiation film type includes radiation materials that utilize optical properties to achieve long-wave heat transfer. The atmospheric optical property parameters include the absorptivity of various wavelengths in the atmosphere. During implementation, the building model is pre-linked with atmospheric data to specify the absorptivity of various wavelengths of the atmosphere in the simulation calculation.
[0034] Radiation film types include one or any combination of the following: narrow-band radiation film, wide-band radiation film, and selective radiation film.
[0035] Wherein, the narrow-band radiation film indicates that the emissivity of the radiation film in the atmospheric window region is higher than a preset threshold; the wide-band radiation film indicates that the emissivity of the radiation film in the atmospheric window region and multiple other bands is higher than a preset threshold; the selective radiation film indicates that the emissivity of the radiation film in multiple specified bands is higher than a preset threshold.
[0036] To reduce building energy consumption, this embodiment of the invention sets up a radiation film type for the building model, achieving spectrally selective radiative cooling through the unique properties of the radiation material. The radiation material in this embodiment is prepared using the material's spectral selectivity, exhibiting high reflectivity in the solar radiation band and high emissivity in the thermal infrared band. This allows for daytime isolation from solar radiation while utilizing background radiation from outer space for cooling. Its long-wave radiation heat transfer principle is as follows: Figure 2 As shown, Figure 2 This is a schematic diagram illustrating the long-wave radiation heat transfer principle in an embodiment of the present invention. When the radiative film on a building surface is exposed to strong solar radiation, a lower surface temperature can be achieved through enhanced spectral selective radiation cooling, which involves techniques such as increasing albedo and infrared emissivity. High albedo is achieved to reflect incident solar radiation as much as possible, primarily in the visible and near-infrared bands. Simultaneously, high emissivity should be achieved in the long-wave infrared band to transfer heat to outer space, which is crucial for cooling below ambient temperature.
[0037] In practice, the corresponding numbers of the radiation film surfaces are recorded on the building model. First, radiation film types are added to the building material library. Examples include narrow-band radiation films (those with low emissivity except in the atmospheric window region (8-13 μm) and high emissivity only in the atmospheric window); wide-band radiation films (those with high emissivity in both the atmospheric window and other bands); and selective radiation films (those with high emissivity in multiple bands and low emissivity in others). The spectral characteristics of the radiation film to be calculated are recorded in the building material library. Then, the optical properties of the building surface to be set as the radiation film surface are set to the corresponding radiation film type in the building material library. Figure 3 This is a schematic diagram of the building model setup in an embodiment of the present invention. Figure 1 This shows a data link for constructing a radiant membrane in the database of the DeST building energy simulation software; Figure 4 This is a schematic diagram of the building model setup in an embodiment of the present invention. Figure 2 This illustrates setting the surface that needs to be changed to spectrally selective as a specific radiation film type.
[0038] Once at least the aforementioned preliminary work is completed, the calculations can begin.
[0039] In step 102, within a preset time period, sampling times are taken at a set frequency. Starting from the first sampling time, for each sampling time, the long-wave radiation value of the building model at that sampling time is determined based on Planck's formula, according to the building surface temperature, building model, and type of radiation film on the building surface at that sampling time. Using the state-space method, the long-wave radiation value of the building model at that sampling time is used to simulate and calculate the dynamic heat transfer process of the building over time, and the building surface temperature at the next sampling time is output, until the long-wave radiation value and building surface temperature of the building model at the last sampling time are output.
[0040] Specifically, two functional modules can be constructed. One is a long-wave radiation calculation module, which determines the long-wave radiation value of the building model at the sampling time based on Planck's formula, taking into account the building surface temperature, building model, and type of radiant film on the building surface at the sampling time. The other is a building dynamic heat transfer kernel, which uses the state-space method to simulate the building's dynamic heat transfer process over time using the long-wave radiation value of the building model at the sampling time, and outputs the building surface temperature at the next sampling time. First, the long-wave radiation value is calculated by the long-wave radiation calculation module. After obtaining the long-wave radiation value, it is used as input data for the building dynamic heat transfer kernel to simulate the building's dynamic heat transfer, outputting the radiant film surface temperature at the next sampling time, i.e., the temperature of the building surface. Then, iterative calculations are performed with the long-wave radiation calculation module until the long-wave radiation value and building surface temperature of the building model at the last sampling time are output.
[0041] Figure 5 This is a specific embodiment of the building energy consumption prediction method in the present invention, such as... Figure 5As shown, using the building surface temperature at midnight (20xx:00:00) on the first day of 20xx, and based on the building model and the type of radiant film on the building surface, the long-wave radiation value of the building model at 20xx:00:00 is determined using Planck's formula. Using the state-space method, the dynamic heat transfer process of the building is simulated and calculated over time using the long-wave radiation value of the building model at 20xx:00:00, outputting the building surface temperature at 20xx:01:00. Using the building surface temperature at 20xx:01:00, and based on the building model and the type of radiant film on the building surface, the building surface temperature at 20xx:01:00 is determined using Planck's formula. The long-wave radiation value of the model is calculated using the state-space method. The dynamic heat transfer process of the building model at 20xx:01:00 is simulated over time using the long-wave radiation value, outputting the building surface temperature at 20xx:02:00. Using the building surface temperature at 20xx:02:00, based on the building model and the type of radiant film on the building surface, the long-wave radiation value of the building model at 20xx:02:00 is determined using Planck's formula. The state-space method is then used to simulate the dynamic heat transfer process of the building model at 20xx:02:00 over time using the long-wave radiation value, outputting the building surface temperature at 20xx:03:00. This process continues until the calculation for the entire year of 20xx is completed. It should be noted that in addition to the primary building surface temperature, the building dynamic heat transfer kernel will also output other data, such as relative humidity.
[0042] In one embodiment, determining the longwave radiation value of the building model at the sampling time based on Planck's formula, according to the building surface temperature at that sampling time, the building model, and the type of radiation film on the building surface, may include:
[0043] Using the building surface temperature at the sampling time, and based on the building model and the type of radiation film on the building surface, the monochromatic radiation values of the building model at the sampling time are determined according to Planck's formula.
[0044] The longwave radiation value of the building model at that sampling time is output by integrating the multiple monochromatic band radiation values of the building model, atmospheric ambient temperature, surface sky visibility coefficient, precipitable water, and atmospheric emissivity.
[0045] In this example, Planck's formula is mainly used to calculate the monochromatic radiance, and then integration is performed to output the long-wavelength radiance value.
[0046] In one embodiment, based on the building surface temperature at the sampling time, the building model, and the type of radiation film on the building surface, the long-wave radiation value of the building model at the sampling time is determined using Planck's formula, including:
[0047] Using the building surface temperature at the sampling time, and based on the building model and the type of radiation film on the building surface, the long-wave radiation value of the building model at the sampling time is determined according to Planck's formula, using the following formulas (1) and (2):
[0048]
[0049]
[0050] In the formula, U λ (λ,T) represents the monochromatic λ-band radiance, where λ is the wavelength, π is pi, h is Planck's constant, c is the speed of light, T is the Kelvin temperature, k is the Boltzmann constant, and P... atm T represents the longwave radiation value of the building model at that sampling time. amb Here, θ represents the ambient atmospheric temperature, pw represents the visible angle coefficient of the sky at the surface, and ε represents the precipitable water content. atm ε is the atmospheric emissivity. rcm Emissivity is specifically an emissivity curve that varies with increasing spectrum.
[0051] First, the monochromatic radiance is calculated using Planck's formula (1). Then, the long-wave radiation value is calculated by integration using formula (2). In the first calculation, the building surface temperature is set arbitrarily, or it can be set according to the season.
[0052] In this embodiment of the invention, the calculation of the above-mentioned long-wave radiation value was experimentally verified and analyzed, such as... Figure 6 As shown, Figure 6 This is a specific embodiment of the building energy consumption prediction method in the present invention. It can be seen that the temperature data calculated by simulation is basically consistent with the temperature data of the actual experimental chamber.
[0053] In one embodiment, the state-space method is used to simulate and calculate the dynamic heat transfer process of the building over time using the long-wave radiation value of the building model at the sampling moment, and output the building surface temperature at the next sampling moment, which may include:
[0054] Determine the heat transfer parameters of various heat transfer processes within the building model, and the weighting coefficients of multiple heat flux values within the building model; the heat transfer processes include heat conduction, heat radiation, and heat convection.
[0055] A state-space heat transfer model is established using heat transfer parameters of various heat transfer processes within the building model and weighting coefficients of multiple heat flux values within the building model. The inputs to the state-space heat transfer model are the long-wave radiation of the building model, multiple temperatures within the building model, and multiple heat flux values within the building model. The output is the building surface temperature after a preset time. The preset time is set in advance.
[0056] Based on the building model, determine multiple temperatures and multiple heat flux values within the building model;
[0057] Input the long-wave radiation value of the building model at the sampling time, multiple temperatures within the building model, and multiple heat flux values within the building model into the state space heat transfer model, and output the building surface temperature at the next sampling time.
[0058] The state-space method is one of the important methods for describing dynamic processes in linear systems. In this example, firstly, using an actual experimental chamber, the heat transfer parameters of various heat transfer processes within the building model and the weighting coefficients of multiple heat flux values within the building model were determined through experimental analysis. Based on existing methods, a state-space heat transfer model was established using the heat transfer parameters of various heat transfer processes within the building model and the weighting coefficients of multiple heat flux values within the building model. Finally, a dynamic heat transfer kernel for the building was constructed and iteratively coupled with the long-wave radiation calculation module to achieve dynamic simulation and coupled calculation of the building's thermal processes.
[0059] In one embodiment, after outputting the longwave radiation value and building surface temperature of the building model up to the last sampling time, the method further includes:
[0060] By using the long-wave radiation value and building surface temperature of the building model at each sampling time, the electrical energy consumption value of the building whose energy consumption is to be predicted is determined.
[0061] By obtaining the long-wave radiation value and building surface temperature of the building model at each sampling time, the energy consumption values of the building to be predicted, such as air conditioning power consumption or other power consumption, are determined.
[0062] In one embodiment, the building energy consumption prediction method is implemented based on DeST building energy consumption simulation software.
[0063] In this invention, data exchange utilizes the universal standard interface FMI / FMU. This interface is used for exchanging the surface temperature and long-wave radiation values of spectrally selective materials, with one data exchange completed at each sampling moment in the hourly calculation. In this embodiment, the long-wave radiation value is calculated using a long-wave radiation calculation module. The building surface temperature parameters used are obtained from the building dynamic heat transfer kernel via the FMI / FMU standard interface. FMU (Functional Mock-up Unit) is a standardized model exchange format used to exchange models between different simulation tools. FMI (Functional Mock-up Interface) is a standardized interface specification used to connect different simulation tools and support data exchange between different FMUs. An FMU is typically a file containing model code, parameters, state variables, and other necessary information, and can be used in multiple simulation tools. FMI defines a universal interface specification that allows different simulation tools to exchange data and connect with each other.
[0064] Figure 7 This is a specific embodiment of the building energy consumption prediction method in the present invention, such as... Figure 7 As shown, the overall process of the building energy consumption prediction method in this embodiment of the invention is demonstrated. A building radiation film database is built, a building model is constructed based on the DeST building energy consumption simulation software, necessary parameters such as radiation film type are added to the building model, time flow is simulated, the building surface temperature and long-wave radiation value are iteratively calculated, and finally the building energy consumption prediction value is output.
[0065] The embodiments of the present invention have the following beneficial effects:
[0066] 1. It features intuitive operation, automation, and strong robustness, and can be used quickly and efficiently for building energy consumption simulation with spectrally selective materials. It also has high accuracy in long-wave radiation calculation and is well-suited for building energy consumption simulation with spectrally selective materials, showing great potential for engineering applications.
[0067] 2. Based on calculations of a shopping mall building, the coupled simulation calculation method of this embodiment of the invention, compared with the fixed operation strategy in the prior art, gives an energy-saving potential of approximately 14-42% for air conditioning energy consumption.
[0068] This invention also provides a building energy consumption prediction device, as described in the following embodiments. Since the principle by which this device solves the problem is similar to that of the building energy consumption prediction method, the implementation of this device can refer to the implementation of the building energy consumption prediction method; repeated details will not be elaborated further.
[0069] Figure 8This is a schematic diagram of a building energy consumption prediction device in an embodiment of the present invention, such as... Figure 8 As shown, the device includes:
[0070] Building model construction module 801 is used to build a building model based on the building whose energy consumption is to be predicted; the building model sets atmospheric optical property parameters and the type of radiation film on the building surface, wherein the radiation film type includes radiation materials that use optical properties to achieve long-wave heat exchange.
[0071] The building energy consumption calculation module 802 is used to sample time at a set frequency within a preset time period, and in chronological order, starting from the first sampling time, for each sampling time, based on the building surface temperature, building model, and type of radiation film on the building surface at that sampling time, determine the long-wave radiation value of the building model at that sampling time based on Planck's formula; using the state-space method, it uses the long-wave radiation value of the building model at that sampling time to simulate and calculate the dynamic heat transfer process of the building over time, and outputs the building surface temperature at the next sampling time, until the long-wave radiation value and building surface temperature of the building model at the last sampling time are output.
[0072] In one embodiment, the radiation film type includes one or any combination of the following:
[0073] Narrow-band radiation film, wide-band radiation film, selective radiation film;
[0074] The narrow-band radiation film indicates that the emissivity of the radiation film in the atmospheric window region is higher than a preset threshold.
[0075] The wideband radiation film indicates that the emissivity of the radiation film in the atmospheric window region and multiple other bands is higher than a preset threshold.
[0076] The selective radiation film indicates that the emissivity of the radiation film is higher than a preset threshold in multiple specified wavelength bands.
[0077] In one embodiment, the building energy consumption calculation module 802 is specifically used for:
[0078] Using the building surface temperature at the sampling time, and based on the building model and the type of radiation film on the building surface, the monochromatic radiation values of the building model at the sampling time are determined according to Planck's formula.
[0079] The longwave radiation value of the building model at that sampling time is output by integrating the multiple monochromatic band radiation values of the building model, atmospheric ambient temperature, surface sky visibility coefficient, precipitable water, and atmospheric emissivity.
[0080] In one embodiment, the building energy consumption calculation module 802 is specifically used for:
[0081] Using the building surface temperature at the sampling time, and based on the building model and the type of radiation film on the building surface, determine the long-wave radiation value of the building model at the sampling time using Planck's formula:
[0082]
[0083]
[0084] In the formula, U λ (λ,T) represents the monochromatic λ-band radiance, where λ is the wavelength, π is pi, h is Planck's constant, c is the speed of light, T is the Kelvin temperature, k is the Boltzmann constant, and P... atm T represents the longwave radiation value of the building model at that sampling time. amb Here, θ represents the ambient atmospheric temperature, pw represents the visible angle coefficient of the sky at the surface, and ε represents the precipitable water content. atm ε is the atmospheric emissivity. rcm Emission rate.
[0085] In one embodiment, the building energy consumption calculation module 802 is specifically used for:
[0086] Determine the heat transfer parameters of various heat transfer processes within the building model, and the weighting coefficients of multiple heat flux values within the building model; the heat transfer processes include heat conduction, heat radiation, and heat convection.
[0087] A state-space heat transfer model is established using heat transfer parameters of various heat transfer processes within the building model and weighting coefficients of multiple heat flux values within the building model. The inputs to the state-space heat transfer model are the long-wave radiation of the building model, multiple temperatures within the building model, and multiple heat flux values within the building model. The output is the building surface temperature after a preset time. The preset time is set in advance.
[0088] Based on the building model, determine multiple temperatures and multiple heat flux values within the building model;
[0089] Input the long-wave radiation value of the building model at the sampling time, multiple temperatures within the building model, and multiple heat flux values within the building model into the state space heat transfer model, and output the building surface temperature at the next sampling time.
[0090] In one embodiment, the device further includes:
[0091] The power consumption value determination module is used to determine the power consumption value of the building to be predicted by using the long-wave radiation value and building surface temperature of the building model at each sampling time after the building energy consumption calculation module 802 outputs the long-wave radiation value and building surface temperature of the building model at the last sampling time.
[0092] In one embodiment, the building energy consumption prediction method is implemented based on DeST building energy consumption simulation software.
[0093] This invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the above-described building energy consumption prediction method.
[0094] This invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described building energy consumption prediction method.
[0095] This invention also provides a computer program product, which includes a computer program that, when executed by a processor, implements the above-described building energy consumption prediction method.
[0096] In this embodiment of the invention, atmospheric optical property parameters and the type of radiation film on the building surface are set for the building model. Based on the building model and the type of radiation film on the building surface, the long-wave radiation value of the building model at the sampling time is determined according to Planck's formula. Then, the state-space method is used to simulate and calculate the dynamic heat transfer process of the building over time using the long-wave radiation value of the building model at the sampling time, and output the building surface temperature at the next sampling time. This simulation of time flow is repeated iteratively until the long-wave radiation value and building surface temperature of the building model at the last sampling time within the preset time period are output. This can dynamically reflect the building heat transfer process and is well adapted to the energy consumption simulation calculation of spectrally selective materials in building energy consumption simulation. It greatly improves the accuracy of building energy consumption prediction, has strong robustness, and can be used quickly and efficiently for energy consumption prediction of various building shapes.
[0097] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.
[0098] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.
[0099] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.
[0100] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.
[0101] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.
Claims
1. A method for predicting building energy consumption, characterized in that, include: A building model is created based on the building whose energy consumption is to be predicted. The architectural model sets atmospheric optical property parameters and the type of radiation film on the building surface. The radiation film type includes radiation materials that utilize optical properties to achieve long-wave heat exchange. Within a preset time period, sampling times are taken at a set frequency and in chronological order, starting from the first sampling time. For each sampling time, the long-wave radiation value of the building model is determined based on Planck's formula, according to the building surface temperature, building model, and type of radiation film on the building surface at that sampling time. Using the state-space method, the dynamic heat transfer process of the building is simulated and calculated over time using the long-wave radiation value of the building model at the sampling time, and the building surface temperature at the next sampling time is output, until the long-wave radiation value and building surface temperature of the building model at the last sampling time are output. Specifically, based on the building surface temperature at the sampling time, the building model, and the type of radiation film on the building surface, the long-wave radiation value of the building model at the sampling time is determined using Planck's formula, including: Using the building surface temperature at the sampling time, and based on the building model and the type of radiation film on the building surface, the monochromatic radiation values of the building model at the sampling time are determined according to Planck's formula. The longwave radiation value of the building model at that sampling time is output by integrating multiple monochromatic band radiation values of the building model, atmospheric ambient temperature, surface sky visibility angle coefficient, precipitable water, and atmospheric emissivity.
2. The method as described in claim 1, characterized in that, The radiation film type includes one or any combination of the following: Narrow-band radiation film, wide-band radiation film, selective radiation film; The narrow-band radiation film indicates that the emissivity of the radiation film in the atmospheric window region is higher than a preset threshold. The wideband radiation film indicates that the emissivity of the radiation film in the atmospheric window region and multiple other bands is higher than a preset threshold. The selective radiation film indicates that the emissivity of the radiation film is higher than a preset threshold in multiple specified wavelength bands.
3. The method as described in claim 1, characterized in that, Based on the building surface temperature at the sampling time, the building model, and the type of radiation film on the building surface, the long-wave radiation value of the building model at the sampling time is determined using Planck's formula, including: Using the building surface temperature at the sampling time, and based on the building model and the type of radiation film on the building surface, determine the long-wave radiation value of the building model at the sampling time using Planck's formula: In the formula, Monochrome λ Band radiation value, λ For wavelength, π Pi h Let be Planck's constant. c At the speed of light, T Kelvin temperature k Boltzmann's constant, This represents the longwave radiation value of the building model at that sampling time. Atmospheric ambient temperature The visible angle coefficient of the sky on the surface. pw Precipitation volume, Atmospheric emissivity, Emission rate.
4. The method as described in claim 1, characterized in that, Using the state-space method, the dynamic heat transfer process of the building is simulated and calculated over time using the long-wave radiation value of the building model at the sampling time, and the building surface temperature at the next sampling time is output, including: Determine the heat transfer parameters of various heat transfer processes within the building model, and the weighting coefficients of multiple heat flux values within the building model; the heat transfer processes include heat conduction, heat radiation, and heat convection. A state-space heat transfer model is established using heat transfer parameters of various heat transfer processes within the building model and weighting coefficients of multiple heat flux values within the building model. The inputs to the state-space heat transfer model are the long-wave radiation of the building model, multiple temperatures within the building model, and multiple heat flux values within the building model. The output is the building surface temperature after a preset time. The preset time is set in advance. Based on the building model, determine multiple temperatures and multiple heat flux values within the building model; Input the long-wave radiation value of the building model at the sampling time, multiple temperatures within the building model, and multiple heat flux values within the building model into the state space heat transfer model, and output the building surface temperature at the next sampling time.
5. The method as described in claim 1, characterized in that, After outputting the long-wave radiation value and building surface temperature of the building model at the last sampling time, the following is also included: By using the long-wave radiation value and building surface temperature of the building model at each sampling time, the electrical energy consumption value of the building whose energy consumption is to be predicted is determined.
6. The method as described in claim 1, characterized in that, The building energy consumption prediction method is implemented based on the DeST building energy consumption simulation software.
7. A building energy consumption prediction device, characterized in that, include: The building model building module is used to create a building model based on the building whose energy consumption is to be predicted. The architectural model sets atmospheric optical property parameters and the type of radiation film on the building surface. The radiation film type includes radiation materials that utilize optical properties to achieve long-wave heat exchange. The building energy consumption calculation module is used to sample time at a set frequency within a preset time period. Starting from the first sampling time, it determines the long-wave radiation value of the building model at each sampling time based on Planck's formula, according to the building surface temperature, building model, and type of radiation film on the building surface at that sampling time. Using the state-space method, the dynamic heat transfer process of the building is simulated and calculated over time using the long-wave radiation value of the building model at the sampling time, and the building surface temperature at the next sampling time is output, until the long-wave radiation value and building surface temperature of the building model at the last sampling time are output. Specifically, the building energy consumption calculation module is used for: Using the building surface temperature at the sampling time, and based on the building model and the type of radiation film on the building surface, the monochromatic radiation values of the building model at the sampling time are determined according to Planck's formula. The longwave radiation value of the building model at that sampling time is output by integrating multiple monochromatic band radiation values of the building model, atmospheric ambient temperature, surface sky visibility angle coefficient, precipitable water, and atmospheric emissivity.
8. The apparatus as claimed in claim 7, characterized in that, The radiation film type includes one or any combination of the following: Narrow-band radiation film, wide-band radiation film, selective radiation film; The narrow-band radiation film indicates that the radiation film has a high emissivity in the atmospheric window region, which is higher than a preset threshold. The wideband radiation film indicates that the emissivity of the radiation film in the atmospheric window region and multiple other bands is higher than a preset threshold. The selective radiation film means that the radiation film has a high emissivity of more than a preset threshold in multiple specified wavelength bands.
9. The apparatus as claimed in claim 7, characterized in that, The building energy consumption calculation module is specifically used for: Using the building surface temperature at the sampling time, and based on the building model and the type of radiation film on the building surface, determine the long-wave radiation value of the building model at the sampling time using Planck's formula: In the formula, Monochrome λ Band radiation value, λ For wavelength, π Pi h Let be Planck's constant. c At the speed of light, T Kelvin temperature k Boltzmann's constant, This represents the longwave radiation value of the building model at that sampling time. Atmospheric ambient temperature The visible angle coefficient of the sky on the surface. pw Precipitation volume, Atmospheric emissivity, Emission rate.
10. The apparatus as claimed in claim 7, characterized in that, The building energy consumption calculation module is specifically used for: Determine the heat transfer parameters of various heat transfer processes within the building model, and the weighting coefficients of multiple heat flux values within the building model; the heat transfer processes include heat conduction, heat radiation, and heat convection. A state-space heat transfer model is established using heat transfer parameters of various heat transfer processes within the building model and weighting coefficients of multiple heat flux values within the building model. The inputs to the state-space heat transfer model are the long-wave radiation of the building model, multiple temperatures within the building model, and multiple heat flux values within the building model. The output is the building surface temperature after a preset time. The preset time is set in advance. Based on the building model, determine multiple temperatures and multiple heat flux values within the building model; Input the long-wave radiation value of the building model at the sampling time, multiple temperatures within the building model, and multiple heat flux values within the building model into the state space heat transfer model, and output the building surface temperature at the next sampling time.
11. The apparatus as claimed in claim 7, characterized in that, Also includes: The power consumption value determination module is used to determine the power consumption value of the building to be predicted by using the long-wave radiation value and building surface temperature of the building model at each sampling time after the building energy consumption calculation module outputs the long-wave radiation value and building surface temperature of the building model at the last sampling time.
12. The apparatus as claimed in claim 7, characterized in that, The building energy consumption prediction method is implemented based on the DeST building energy consumption simulation software.
13. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the method of any one of claims 1 to 6.
14. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the method of any one of claims 1 to 6.
15. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the method of any one of claims 1 to 6.