Modularized temporary building energy consumption prediction method and system and medium

Abstract

The invention relates to the technical field of temporary building energy conservation, in particular to a modular temporary building energy consumption prediction method and system and a medium, and the method comprises the following steps: obtaining basic information of a target building, including a geographic position and an enclosure structure thermal insulation parameter; according to the geographic position, determining a thermal climate zone to which the geographic position belongs, and selecting a corresponding reference energy consumption calculation formula; calculating an energy consumption adjustment coefficient according to the local climate data and the building characteristic parameters; and calculating a predicted energy consumption value of the target building based on the reference energy consumption and the adjustment coefficient. According to the method, the energy consumption prediction model and the economical efficiency analysis model are established, so that the energy consumption of the modular temporary building can be quickly and accurately estimated, an optimization scheme is provided, the energy consumption and carbon emission of the temporary building are effectively reduced, economical efficiency is considered, and a scientific basis is provided for energy conservation and carbon reduction of a construction site.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of temporary building energy saving, in particular to a modular temporary building energy consumption prediction method, system and medium. BACKGROUND

[0002] Temporary buildings are widely used in engineering construction, emergency rescue and other scenes, but their energy consumption problems have not been paid enough attention. The existing temporary building system has the following defects: 1) Poor thermal insulation performance of the enclosure structure, insufficient heat insulation in summer leading to large air conditioning load, and insufficient heat preservation in winter leading to high heating energy consumption; 2) The thickness of the thermal insulation in the design process depends on experience, and lacks scientific calculation basis; 3) There is a lack of rapid energy consumption prediction tools for the characteristics of temporary buildings; 4) The economic analysis of energy saving measures is insufficient, and it is difficult to balance the energy saving effect and cost investment.

[0003] At present, the energy consumption simulation software (such as EnergyPlus, DeST, etc.) of permanent buildings is relatively mature, but these software need to establish a building model before energy consumption calculation can be carried out, which is complex to operate and has a long calculation period, and does not consider the structural simplification and modularization characteristics of temporary buildings, resulting in lower prediction accuracy compared with permanent buildings. The existing temporary building energy consumption estimation method is mostly based on empirical formula, which cannot accurately reflect the influence of different climate regions and thermal insulation measures, and is difficult to support energy saving optimization decision. SUMMARY

[0004] The application provides a modular temporary building energy consumption prediction method, system and medium, which solves the problems of low prediction accuracy, complex operation and lack of economic analysis of temporary building energy consumption in the prior art.

[0005] A modular temporary building energy consumption prediction method, comprising the following steps Obtaining the basic information of the target building, including the geographical location, the thermal insulation parameter of the enclosure structure; Determining the thermal climate division to which the geographical location belongs, and selecting the corresponding reference energy consumption calculation formula; According to the local climate data and the building characteristic parameters, the energy consumption adjustment coefficient is calculated; Based on the reference energy consumption and the adjustment coefficient, the predicted energy consumption value of the target building is calculated.

[0006] As a preferred embodiment of the modular temporary building energy consumption prediction method, the thermal climate division includes severe cold region, cold region, hot summer and cold winter region and hot summer and warm winter region, each division corresponds to a different reference energy consumption calculation formula, the formula is obtained by fitting a large amount of energy consumption simulation data, and the formula is a linear function of the thermal insulation thickness, specifically: Severe cold region: H= ; Cold region: H= ; Hot summer and cold winter region: H= ; Hot summer and warm winter region: H= ; wherein H is the reference energy consumption, is the insulation thickness of the outer wall, is the insulation thickness of the roof, is the insulation thickness of the ground.

[0007] As a preferred embodiment of the modular temporary building energy consumption prediction method of the present application, the calculation of the energy consumption adjustment coefficient uses the formula:

[0008] wherein, is the thermal bridge effect coefficient, 1.11 when there is no insulation, and 1.0 when there is insulation, and is the partition weight coefficient, determined according to the climate zoning, is the radiation cooling measure coefficient, with a value range of 0.68-1.0;

[0009]

[0010] wherein, is the local heating degree-day number, is the local cooling degree-day number, is the heating day base, is the air conditioning day base.

[0011] As a preferred embodiment of the modular temporary building energy consumption prediction method of the present application, it further includes the energy saving optimization step: providing multiple insulation thickness combination schemes; calculating the predicted energy consumption under each scheme; evaluating the comprehensive benefits of each scheme based on an economic analysis model; outputting the optimized recommendation scheme.

[0012] As a preferred embodiment of the modular temporary building energy consumption prediction method of the present application, the economic analysis model comprehensively considers economic benefits and environmental benefits, wherein:

[0013]

[0014] wherein, For the analysis of the years, For electricity price, and For the price of insulation materials, For carbon trading prices, and Energy consumption for the baseline and optimized schemes, respectively. , , and , , These are the baseline and optimized insulation thicknesses, respectively.

[0015] A modular temporary building energy consumption prediction system, including The data input module is used to receive building parameters input by the user; The calculation and analysis module is used to perform energy consumption prediction and economic analysis; The recommendation module is optimized to generate optimization solutions. The results output module is used to display the prediction results and analysis reports.

[0016] As a preferred embodiment of the modular temporary building energy consumption prediction method in this invention, it also includes a database module that stores basic data including climate data, material prices, and calculation formula parameters.

[0017] As a preferred embodiment of the modular temporary building energy consumption prediction method in this invention, the system is developed based on a B / S architecture and is accessed and used by users through a browser.

[0018] As a preferred embodiment of the modular temporary building energy consumption prediction method in this invention, the result output module provides various forms of output, including data tables, trend charts, and comparative analysis reports.

[0019] A computer-readable storage medium, wherein the program, when executed by a processor, implements the modular temporary building energy consumption prediction method.

[0020] The beneficial effects of this invention are: This invention establishes an energy consumption prediction model and an economic analysis model, which can quickly and accurately estimate the energy consumption of modular temporary buildings and provide optimization solutions to effectively reduce the energy consumption and carbon emissions of temporary buildings while taking into account economic efficiency, thus providing a scientific basis for energy conservation and carbon reduction at construction sites. Attached Figure Description

[0021] To more clearly illustrate the technical solutions in the embodiments of this application 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 this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0022] Figure 1 This is a flowchart illustrating the energy consumption prediction method according to an embodiment of this application; Detailed Implementation

[0023] To make the above-mentioned objectives, features, and advantages of this application more apparent and understandable, the specific embodiments of this application are described in detail below with reference to the accompanying drawings. Many specific details are set forth in the following description to provide a thorough understanding of this application. However, this application can be implemented in many other ways different from those described herein, and those skilled in the art can make similar modifications without departing from the spirit of this application. Therefore, this application is not limited to the specific embodiments disclosed below.

[0024] In the description of this application, it should be understood that the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", "clockwise", "counterclockwise", "axial", "radial", "circumferential", etc., indicating the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings, are only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of this application.

[0025] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "multiple" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0026] In this application, unless otherwise expressly specified and limited, the terms "installation," "connection," "joining," and "fixing," etc., should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components, unless otherwise expressly limited. Those skilled in the art can understand the specific meaning of the above terms in this application according to the specific circumstances.

[0027] In this application, unless otherwise expressly specified and limited, "above" or "below" the second feature can mean that the first feature is in direct contact with the second feature, or that the first feature is in indirect contact with the second feature through an intermediate medium. Furthermore, "above," "on top of," and "over" the second feature can mean that the first feature is directly above or diagonally above the second feature, or simply that the first feature is at a higher horizontal level than the second feature. "Below," "below," and "under" the second feature can mean that the first feature is directly below or diagonally below the second feature, or simply that the first feature is at a lower horizontal level than the second feature.

[0028] It should be noted that when an element is referred to as being "fixed to" or "set on" another element, it can be directly on the other element or there may be an intervening element. When an element is considered to be "connected to" another element, it can be directly connected to the other element or there may be an intervening element. The terms "vertical," "horizontal," "upper," "lower," "left," "right," and similar expressions used herein are for illustrative purposes only and do not represent the only possible implementation.

[0029] This embodiment provides a method for predicting the energy consumption of modular temporary buildings, used to quickly estimate the energy consumption of modular temporary buildings on construction sites, such as... Figure 1 As shown, it includes the following steps: Step 1: Obtain basic information about the target building First, users need to enter basic information about the target building, including: Geographical location: such as city name, used to determine thermal climate zones and local climate data.

[0030] Thermal insulation parameters for building envelope: including the thickness of external wall insulation, roof insulation, and ground insulation, all in millimeters.

[0031] Other optional parameters include whether the frame columns are insulated and whether radiant cooling measures are used.

[0032] Step 2: Determine the thermal climate zone and calculate the baseline energy consumption. Based on the building's geographical location, its corresponding thermal climate zone is determined. China is mainly divided into five zones: severe cold region, cold region, hot summer and cold winter region, hot summer and warm winter region, and temperate region. The temperate region is not considered in this embodiment. Each zone corresponds to a different baseline energy consumption calculation formula. The baseline energy consumption calculation formula is obtained by selecting a typical city in the climate zone, simulating the energy consumption of the roof, ground, and wall insulation thicknesses of its modular temporary buildings, and then fitting the data using SPSS. The linear function is as follows: Extremely cold regions: H= ; Cold regions: H= ; Hot summer and cold winter regions: H= ; Hot summer and warm winter regions: H= ; Where H is the baseline energy consumption (unit: kWh). For the insulation thickness of the exterior wall, For the insulation thickness of the roof, This refers to the thickness of the ground insulation. Ground insulation is only considered in extremely cold regions; other regions should use [specific thickness]. =0.

[0033] The insulation thickness should be within the recommended range as shown in Table 1 to ensure the insulation effect of the building envelope. Table 1. Range of thermal insulation values ​​for building envelopes in different thermal climate zones

[0034] Step 3: Calculate the energy consumption adjustment coefficient The energy consumption adjustment factor λ is used to correct the baseline energy consumption, reflecting the impact of local climate and building-specific measures. The calculation formula is:

[0035] in, The thermal bridging effect coefficient is 1.11 for frame beams and columns without insulation and 1.0 for those with insulation. and The partition weighting coefficients are determined according to Table 2. The value is the radiation cooling measure coefficient, ranging from 0.68 to 1.0. It is 1.0 when there is no measure and the lower value is used when there is a measure.

[0036] and The climate change coefficient is calculated as follows:

[0037]

[0038] in, This refers to the local heating degree-days (unit: °C·d). The local cooling degree days (unit: °C·d) can be obtained from its climate database. and The daily base figures for heating and air conditioning are shown in Table 2.

[0039] Table 2. Range of weighting coefficients, heating daily base number, and air conditioning daily base number for different thermal climate zones.

[0040] Step 4: Calculate the predicted energy consumption value The formula for calculating the annual air conditioning predicted energy consumption E of the target building is:

[0041] Where H is the baseline energy consumption calculated in step 2, and λ is the adjustment coefficient calculated in step 3.

[0042] Step 5: Energy Saving Optimization To optimize energy consumption, multiple insulation thickness combinations are provided, and the predicted energy consumption for each combination is calculated. Then, the overall benefits are evaluated based on an economic analysis model. Overall Benefits B: Includes economic benefits and environmental benefits ,Right now .

[0043] Economic benefits B:

[0044] Environmental benefits B Environment:

[0045] in, For the service life (in years). The local electricity price (RMB / kWh) The price is the unit price of rock wool (yuan / m³). Price per unit of glass wool (RMB / m³). The price is for carbon trading (RMB / ton). and The energy consumption (kWh) for the baseline and optimized schemes are respectively. , , and , , The reference and optimized insulation thicknesses are shown in mm.

[0046] This embodiment provides a modular temporary building energy consumption prediction system. The system is developed based on a B / S architecture and can be accessed by users through a browser. It mainly includes the following modules: Data input module: Used to receive building parameters input by the user, such as city, insulation thickness, thermal bridging measures, radiant cooling measures, etc. Manual input or selection from a database is supported.

[0047] The calculation and analysis module is used to perform energy consumption prediction and economic analysis, including: The thermal climate zone is automatically determined based on the input parameters.

[0048] Calculate the predicted energy consumption by calling the baseline energy consumption formula and the adjustment factor formula.

[0049] The benefits of different options are calculated based on an economic model.

[0050] Optimization Recommendation Module: Used to generate optimization solutions. The system has multiple built-in insulation thickness combinations and recommends the optimal solution based on a large amount of data simulation.

[0051] Results output module: Used to display prediction results and analysis reports, with output formats including data tables, trend charts, and comparative analysis reports.

[0052] Database module: Stores basic data, including climate data, material prices, calculation formula parameters, etc., and supports data updates and queries.

[0053] This embodiment also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the aforementioned modular temporary building energy consumption prediction method. Users can run the system through a device with this medium installed to perform energy consumption prediction and optimization.

[0054] Taking Tianjin as an example, the energy consumption prediction and optimization process is demonstrated: Basic requirements: The modular temporary building measures 6m × 3m × 3m, and the external wall insulation thickness is [not specified]. =75mm, roof insulation thickness =100mm, ground insulation thickness =0mm. The frame beams and columns are not insulated. =1.11), and the roof glass uses a radiative cooling film (c=0.89).

[0055] Energy consumption calculation: 1. Determine the thermal zoning: Tianjin belongs to the cold region, and the baseline energy consumption formula is adopted: H=

[0056] 2. Calculate the climate change coefficient: Retrieve Tianjin from the database =2743℃·d, =92℃·d,

[0057]

[0058] 3. Calculate the adjustment factor λ:

[0059] 4. Predicted energy consumption E:

[0060] Energy saving optimization Adjustment plan: External wall insulation thickness =120mm, roof insulation thickness =120mm, frame beams and columns are filled with thermal insulation (β=1.0), other conditions remain unchanged.

[0061] Recalculate energy consumption: H=

[0062]

[0063]

[0064] Benefit Analysis Assuming a service life of A = 5 years, the electricity price =0.85 yuan / kWh, rock wool unit price =47.53 yuan / m³, unit price of glass wool =18.1 yuan / m³, carbon trading price =91.8 yuan / ton.

[0065] Economic benefits B economy

[0066] Environmental benefits B Environment:

[0067] Overall Benefit B:

[0068] The optimized solution can save approximately 1044.85 yuan over 5 years and is recommended.

[0069] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0070] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims.

Claims

1. A method for predicting energy consumption of modular temporary buildings, characterized in that: Includes the following steps Obtain basic information about the target building, including its geographical location and the thermal insulation parameters of its building envelope; Determine the corresponding thermal climate zone based on the geographical location and select the corresponding benchmark energy consumption calculation formula; Calculate the energy consumption adjustment coefficient based on local climate data and building characteristic parameters; Based on the baseline energy consumption and adjustment coefficient, the predicted energy consumption of the target building is calculated.

2. The modular temporary building energy consumption prediction method according to claim 1, characterized in that, The thermal climate zones include severe cold regions, cold regions, hot summer and cold winter regions, and hot summer and warm winter regions. Each zone corresponds to a different baseline energy consumption calculation formula. These formulas are obtained by fitting a large amount of energy consumption simulation data and are in the form of a linear function of insulation thickness, specifically: Extremely cold regions: H= ; Cold regions: H= ; Hot summer and cold winter regions: H= ; Hot summer and warm winter regions: H= ; Where H is the baseline energy consumption. For the insulation thickness of the exterior wall, For the insulation thickness of the roof, This refers to the insulation thickness of the ground.

3. The modular temporary building energy consumption prediction method according to claim 1, characterized in that, The energy consumption adjustment coefficient is calculated using the following formula: in, This is the thermal bridge effect coefficient, taken as 1.11 without insulation and 1.0 with insulation. and These are the zoning weighting coefficients, determined according to the climate zoning. The coefficient of performance for radiative cooling measures ranges from 0.68 to 1.

0. in, This refers to the number of heating days in the local area. The number of local cooling days. As the base number for heating days, This is the daily base number for air conditioning.

4. The modular temporary building energy consumption prediction method according to claim 1, characterized in that, It also includes energy-saving optimization steps: Offers a variety of insulation thickness combinations; Calculate the predicted energy consumption for each scenario; The overall benefits of each option are evaluated based on an economic analysis model; Output optimization suggestions.

5. The modular temporary building energy consumption prediction method according to claim 4, characterized in that, The economic analysis model comprehensively considers both economic and environmental benefits, wherein: in, For the analysis of the years, For electricity price, and For the price of insulation materials, For carbon trading prices, and Energy consumption for the baseline and optimized schemes, respectively. , , and , , These are the baseline and optimized insulation thicknesses, respectively.

6. A modular temporary building energy consumption prediction system, characterized in that: include The data input module is used to receive building parameters input by the user; The calculation and analysis module is used to perform energy consumption prediction and economic analysis; The recommendation module is optimized to generate optimization solutions. The results output module is used to display the prediction results and analysis reports.

7. The modular temporary building energy consumption prediction method according to claim 6, characterized in that, It also includes a database module that stores basic data, including climate data, material prices, and calculation formula parameters.

8. The modular temporary building energy consumption prediction method according to claim 6, characterized in that, The system is developed based on a B / S architecture and is accessed and used by users through a browser.

9. The modular temporary building energy consumption prediction method according to claim 6, characterized in that, The results output module provides various output formats, including data tables, trend charts, and comparative analysis reports.

10. A computer-readable storage medium, characterized in that: When the program is executed by the processor, it implements the method as described in any one of claims 1-5.

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