Residential all-climate performance dynamic evaluation system and method based on full-scale scene

Abstract

The invention relates to the technical field of dwelling house performance evaluation, in particular to a dwelling house all-climate performance dynamic evaluation system and method based on a full-scale scene. A five-step closed-loop evaluation system of domestic climate data accurate docking-full-scale scene climate dynamic replication-comfortable temperature field double-model control-overall energy consumption real-time recording-component performance comparison evaluation is constructed, and the pain points of traditional component one-way detection, large simulation error and no overall evaluation are mainly solved. A real living scene is restored through a 1: 1 full-scale model, and in combination with domestic high-precision climate data and a dynamic climate duplicating technology, precise quantification of overall energy consumption and component performance of a house is realized, and a core technical support is provided for overall low energy consumption, component optimal matching and regional adaptation of a good house.

Description

Technical Field

[0001] This invention relates to the field of residential performance evaluation technology, and in particular to a dynamic evaluation system and method for residential all-weather performance based on full-scale scenarios. Background Technology

[0002] There is a core technological gap in the current assessment of residential energy consumption and climate adaptability: traditional industries lack systematic evaluation methods for the overall performance of residences, and can only conduct single-item performance tests on individual components such as doors, windows, and insulation boards, failing to reflect the overall energy consumption performance of the residence after component integration. In actual engineering, residential energy consumption assessment mainly relies on design code provisions and simulation software (such as PKPM, SWIFT, eQUEST, DeST, and Energy+) for calculation. However, simulation software has three major drawbacks: First, the calculation of multi-physics coupling such as building thermal and airflow organization is complex, and the software usually uses simplified equations, resulting in an energy consumption prediction error of ±10-30%; second, the database is outdated, usually updated only every few years, lacking performance data of new building materials and innovative components, making it impossible to accurately input parameters; third, it is based on ideal model assumptions, ignoring details such as gaps and component connections in actual construction, ultimately leading to significant deviations between simulation results and real residential energy consumption, making it difficult to support the design requirements of "low energy consumption and high adaptability" for good houses.

[0003] Meanwhile, traditional data evaluation of individual building components does not consider the impact of real-world residential spatial layout and dynamic climate changes on component performance, limiting the reference value of the evaluation results. Therefore, there is an urgent need for a closed-loop solution that relies on real-scale scenarios, connects with actual climate data, and achieves "overall evaluation + component quantification" to fill this industry gap.

[0004] Therefore, this case is brought. Summary of the Invention

[0005] The purpose of this invention is to provide a dynamic evaluation system and method for residential all-climate performance based on full-scale scenarios. Relying on a 1:1 full-scale residential model and connecting to a domestic climate database, it realizes a closed-loop evaluation of "regional climate - overall energy consumption - component performance". It is applicable to scenarios such as verification of overall residential energy consumption, regional adaptability design, and quantitative evaluation of the thermal insulation performance of building components.

[0006] To achieve the above objectives, the technical solution of the present invention is as follows:

[0007] A dynamic evaluation system for residential all-weather performance based on full-scale scenarios, comprising: The climate data integration module is used to acquire historical climate data based on the target region and the range of assessment dates, and generate a time series climate parameter table. A climate control module is configured around a 1:1 full-scale detachable residential model to dynamically reproduce the multidimensional outdoor climate environment based on the time series climate parameter table. A temperature field maintenance module is arranged inside the 1:1 full-scale detachable residential model for constant control of the indoor human comfort temperature field. The energy consumption recording module is used to collect the energy consumption data of the temperature field maintenance module in real time, synchronously record the timestamp and corresponding climate parameters, and preprocess the data. A component replacement and adaptation module is used for replacing the building components of the 1:1 full-scale detachable residential model. The module integration unit is used to couple the above modules to realize data conversion, climate replication, temperature field control, energy consumption recording, coupling analysis and component performance evaluation.

[0008] Furthermore, the climate data interface module calls the meteorological data center database through the API interface to extract historical climate data within the target area and evaluation date range, and transforms the historical climate data to generate a time series climate parameter table, with the target area accurate to the district / county level.

[0009] Furthermore, the transformation of the historical climate data includes the following process: determining the accuracy of environmental simulation data based on the minimum operating time of laboratory equipment; decomposing hourly data into controllable minute-level data through outlier removal, trend smoothing, and regional calibration; ensuring that the climate parameters match the actual climate of the target area with a degree of ≥97%; and reducing the delay in transforming historical climate data to ≤10 seconds.

[0010] Furthermore, the climate control module includes a temperature and humidity control unit, a wind speed control unit, a radiation control unit, and a control unit; the temperature control unit has a temperature control range of -46℃ to 60℃ with a temperature control accuracy of ±1℃, and a humidity control range of 30%-90%RH with a humidity control accuracy of ±5%; the wind speed control unit has a wind speed range of 0-50m / s with an adjustment accuracy of ±0.2m / s; the radiation control unit has a radiation intensity range of 0 to 1200W / m² with an error ≤3%.

[0011] Furthermore, the temperature field maintenance module includes no less than 300 K-type thermocouples, which are arranged in various functional areas of the 1:1 full-scale detachable residential model. The temperature measurement range of the K-type thermocouples is -50℃ to 200℃, with an accuracy of ±0.1℃.

[0012] Furthermore, the 1:1 full-scale detachable residential model has an area of ​​50-180㎡, including room partitions, door and window openings, kitchen and bathroom layouts, and enclosure structure. The detachable building components have pre-reserved disassembly and assembly interfaces, and the leakage rate of the model after disassembly and replacement of building components is ≤0.05m³ / (h·㎡).

[0013] Furthermore, the energy consumption recording module uses an electricity meter with an error level of ≤0.2 and a heat meter with an accuracy level of ≤0.3 to collect electricity and heat consumption data at a frequency of 1 time per minute, and stores the data in a distributed database according to the structure of "region-date-time-climate parameter-energy consumption".

[0014] Furthermore, the data preprocessing in the energy consumption recording module uses the 3σ criterion to remove abnormal energy consumption values ​​caused by abnormal fluctuations in equipment.

[0015] A dynamic evaluation method for residential all-climate performance based on full-scale scenarios using the above system includes the following steps: S1. Based on the target region and evaluation date range input by the user, retrieve the corresponding historical climate data from the meteorological data center and generate a time series climate parameter table; S2. Import the time series climate parameter table into the control unit of the climate control module. The control unit controls the temperature and humidity control unit, wind speed control unit and radiation control unit based on the PID adaptive algorithm to dynamically replicate the real climate of the target area in the 1:1 full-scale detachable residential model. S3. By collecting temperature data through K-type thermocouples and combining the PMV and PPD dual models of ISO 7730 standard, the variable frequency air conditioning and underfloor heating systems in the residential model are linked to maintain indoor comfort at PMV=0±0.2, PPD≤5%, and indoor temperature control accuracy ±0.5℃. S4. Collect energy consumption data of the temperature field maintenance module in real time, record timestamps and corresponding climate parameters synchronously, store them in a distributed database according to the structure of "region-date-time-climate parameter-energy consumption", and use the 3σ criterion to remove abnormal energy consumption values ​​caused by abnormal fluctuations in equipment. S5. Replace the target part, and maintain the same climate simulation scene and airtightness before and after the part replacement; Repeat steps S1 to S4, and collect energy consumption data corresponding to the new components under the same target area, the same evaluation date, and the same climate replication parameters. Based on the energy consumption difference before and after component replacement, quantify the impact of the components on the overall climate adaptability of the residence.

[0016] Furthermore, the quantization process in step S5 is as follows: Define the average energy consumption per unit area of ​​the old component as E0, and the average energy consumption per unit area of ​​the new component as E1; Energy consumption change rate: ; Improvement rate of heat transfer coefficient: ,in , These are the actual heat transfer coefficients of the original and new parts in full-scale scenarios, respectively, inferred from energy consumption data. , For the area of ​​the component, T in Indoor temperature, T out Where Q is the outdoor temperature, and Q is the total energy consumption; When ΔE>0 and ΔK>0, it indicates that the new component has better thermal insulation performance; conversely, it indicates that the new component has weaker thermal insulation performance than the old component.

[0017] The advantages of this invention are:

[0018] 1. This approach overcomes the limitations of traditional residential performance assessments, which emphasize simulation over actual measurement. Existing technologies primarily rely on energy consumption simulation software with simplified assumptions, making it difficult to reflect the overall performance of real buildings under complex climatic conditions. This solution uses a 1:1 full-scale residential model to realistically reproduce the building's spatial layout, envelope, and component integration. It dynamically replicates the actual climatic environment of the target region in a laboratory setting, ensuring that the assessment results highly match real-world residential energy consumption (matching degree ≥ 95%), significantly improving the scientific rigor and credibility of the assessment.

[0019] 2. A closed-loop evaluation system integrating "climate, overall energy consumption, and component performance" has been constructed: Traditional testing can only conduct isolated tests on individual components such as doors, windows, and insulation boards, failing to reflect their actual energy-saving effect on the entire building. This invention, through a standardized component replacement mechanism, quantifies the impact of different components on the overall energy consumption of the residence while strictly maintaining consistency with climate boundaries and airtightness. It achieves a leap from "component parameter compliance" to "system performance verification," providing data support for the selection of green building materials and the refined design of residences.

[0020] 3. The system connects to a meteorological data center, supports precise climate data access at the district and county levels, and combines the ISO 7730 standard PMV-PPD dual model to dynamically regulate the indoor temperature field, ensuring that all tests are conducted under uniform and comfortable conditions that conform to human perception, making the test results of different regions and different components comparable.

[0021] 4. For real estate companies, it can verify the actual energy consumption of residences in different climate zones in advance, optimize regional design schemes, and reduce energy consumption in later operation by 15%–25%; for component manufacturers, it can provide authoritative and authentic third-party performance verification services to promote the market promotion of high-quality products; for industry regulators, it fills the technical gap in dynamic evaluation of overall residential performance and helps the implementation of the national "high-quality housing" strategy. Attached Figure Description

[0022] Figure 1 This is an architecture diagram of a residential all-weather performance dynamic evaluation system based on full-scale scenarios, as shown in the embodiment. Figure 2 This is a schematic diagram of the dynamic evaluation process for residential all-weather performance based on a full-scale scenario in the embodiment. Figure 3 The diagram below shows the climate-energy coupling matching model in the example. Detailed Implementation

[0023] The present invention will be further described in detail below with reference to embodiments. It should be understood that the terms "upper", "lower", "front", "rear", "left", "right", "top", "bottom", "inner", "outer", etc., used in this document indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing the present invention 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. Therefore, they should not be construed as limiting the present invention.

[0024] This embodiment proposes a dynamic evaluation system for residential all-climate performance based on full-scale scenarios. It constructs a five-step closed-loop evaluation system: precise integration with domestic climate data, dynamic replication of full-scale scenario climate, dual-model control of comfort temperature field, real-time recording of overall energy consumption, and comparative evaluation of component performance. This system addresses the pain points of traditional methods such as "one-way component testing, large simulation errors, and lack of overall evaluation." By recreating real-world living scenarios using a 1:1 full-scale model and combining high-precision domestic climate data with dynamic climate replication technology, it achieves precise quantification of overall residential energy consumption and component performance, providing core technical support for achieving "low overall energy consumption + optimal component matching + regional adaptability" in good housing.

[0025] like Figure 1 As shown, this implementation plan system is based on "1:1 realistic restoration" as its core, and includes: The climate data integration module is used to acquire historical climate data based on the target region and the range of assessment dates, and generate a time series climate parameter table. A climate control module is configured around a 1:1 full-scale detachable residential model to dynamically reproduce the multidimensional outdoor climate environment based on the time series climate parameter table. A temperature field maintenance module is arranged inside the 1:1 full-scale detachable residential model for constant control of the indoor human comfort temperature field. The energy consumption recording module is used to collect the energy consumption data of the temperature field maintenance module in real time, synchronously record the timestamp and corresponding climate parameters, and preprocess the data. A component replacement and adaptation module is used for replacing the building components of the 1:1 full-scale detachable residential model. The module integration unit is used to couple the above modules to realize data conversion, climate replication, temperature field control, energy consumption recording, coupling analysis and component performance evaluation.

[0026] The specific configuration of this system is shown in the table below: Table 1. Specific Configuration Table of Residential All-Climate Performance Dynamic Evaluation System Based on Full-Scale Scene

[0027] The climate data interface module calls the meteorological data center database via API to extract historical climate data for the target region and within the evaluation date range. This historical climate data is then transformed to generate a time-series climate parameter table, with the target region accurate to the county level. The transformation of the historical climate data includes the following processes: determining the accuracy of the environmental simulation data based on the minimum operating time of the laboratory equipment; decomposing hourly data into controllable minute-level data through outlier removal, trend smoothing, and regional calibration; ensuring that the climate parameters match the actual climate of the target region with a degree of ≥97%; and ensuring that the delay in transforming the historical climate data is ≤10 seconds.

[0028] The climate control module includes a temperature and humidity control unit, a wind speed control unit, a radiation control unit, and a control unit. The temperature and humidity control unit has a temperature control range of -46℃ to 60℃ with a temperature control accuracy of ±1℃ and a humidity control range of 30%-90%RH with a humidity control accuracy of ±5%. The wind speed control unit has a wind speed range of 0-50m / s with an adjustment accuracy of ±0.2m / s. The radiation control unit has a radiation intensity range of 0 to 1200W / m² with an error of ≤3%.

[0029] The temperature field maintenance module includes no fewer than 300 K-type thermocouples, deployed in various functional areas of the 1:1 full-scale detachable residential model. The temperature measurement range of the K-type thermocouples is -50℃ to 200℃, with an accuracy of ±0.1℃. The 1:1 full-scale detachable residential model has an area of ​​50-180㎡, including room partitions, door and window openings, kitchen and bathroom layouts, and enclosure structures. Detachable building components have pre-reserved disassembly and assembly interfaces, and the model leakage rate after disassembly and replacement of building components is ≤0.05m³ / (h·㎡).

[0030] The energy consumption recording module uses electricity meters with an error level of ≤0.2 and heat meters with an accuracy level of ≤0.3 to collect electricity and heat consumption data at a frequency of 1 time per minute, and stores the data in a distributed database according to the structure of "region-date-time-climate parameter-energy consumption".

[0031] like Figure 2 and Figure 3 As shown, the evaluation process based on the above evaluation system includes the following steps.

[0032] Step S1: Automated conversion of regional information into climate parameters.

[0033] Input method: Users input the target area information (accurate to the district / county level, such as "XX City XX District") and the evaluation date range (extract the average coldest / hottest week of the past 10 years, or customize the time, such as November 1-7, 2025).

[0034] Data Integration: The software automatically calls the meteorological data center database of a meteorological bureau via API interface to extract hourly climate average data for the same period over the past 10 years for this region, including outdoor temperature (T). out (℃), relative humidity (RH) out ,%), wind speed (v, m / s), solar radiation intensity (G, W / m²).

[0035] The accuracy of environmental simulation data is determined based on the minimum operating time of laboratory equipment (5 minutes for temperature-controlled compressor units). Hourly data is decomposed into controllable 5-minute data through the "outlier removal - trend smoothing - regional calibration" method to ensure that the climate parameters match the actual climate of the target area by ≥97%.

[0036] Data output: Generate a "5-minute climate parameter table" (containing 12×24×n sets of data, where n is the number of assessment days, generally 7 days).

[0037] Step S2: Dynamically replicate the climate of the target area in a full-scale scene.

[0038] Parameter Import: The "5-minute climate parameter table" is automatically imported into the PLC controller, which then allocates control strategies according to its built-in PID control algorithm. The core logic of multi-parameter collaborative control (detailed explanation of the PID algorithm): Using the error between the "target climate parameter and the measured climate parameter" as input, the PID algorithm dynamically adjusts the output power of each control device to ensure that the climate parameters within the full-scale model are uniform and accurately track the target value. The following is the PID algorithm formula:

[0039] ① Error calculation: (Temperature error; the same applies to other parameters); In the formula, The target outdoor temperature. This is the actual outdoor temperature.

[0040] ② Proportional aspect: (Real-time response error);

[0041] ③ Points-based system: (Eliminate steady-state error);

[0042] ④ Differential component: (Suppressing overshoot oscillations);

[0043] ⑤ Total Output: (Equipment control signals); Parameter description: (proportion coefficient) (Integral coefficient) (Differential coefficients) are unitless parameters, based on the spatial characteristics of the full-scale model and the dynamic calibration of the thermal parameters of the building envelope, adapting to different climate scenarios (such as high temperature and high humidity, extreme cold) to ensure the stability of regulation; Multi-parameter linkage: Temperature, humidity, wind speed, and radiation parameters are controlled according to the "master-slave collaboration" logic. For example, when simulating a high-temperature and high-humidity climate, the temperature is first stabilized to the target value, and then the humidity is adjusted to avoid mutual interference between parameters.

[0044] Scene calibration: Real-world climate data is collected every five minutes via a distributed sensor array (covering the laboratory), compared with target parameters, and PID parameters are automatically corrected. Climate replication accuracy is ≥95%, and the deviation of climate parameters in each region within the full-scale model is ≤±1℃ (temperature), ±5%RH (humidity), and ±0.2m / s (wind speed).

[0045] Step S3: Constant control of the indoor human body comfort temperature field.

[0046] Comfort standard: In accordance with ISO 7730 standard, a dual model control is adopted using PMV (predicted average vote) and PPD (predicted percentage of dissatisfaction), with the goal of maintaining PMV=0±0.2 (corresponding to PPD≤5%, that is, more than 95% of the population feels comfortable). Core formula:

[0047] ① PMV formula: ; in: The value is the human metabolic rate (W / m2, taken as 108 when sitting). Work done on the human body (W / m2, take 0). The indoor water vapor partial pressure (Pa). The thermal resistivity c0 of clothing is taken as 0.5 for summer; 0.61 for spring and autumn; and 1.0 for winter. The surface temperature of the garment (°C). The mean radiation temperature is (°C). The convective heat transfer coefficient is (W / (m2·K)). Indoor air temperature (°C);

[0048] ② PPD Formula (Percentage of Comfort Based on PMV): .

[0049] Temperature measurement and control are linked: 300 K-type thermocouples are evenly distributed in the full-scale model of functional areas such as bedrooms, living rooms, kitchens and bathrooms to collect indoor air temperature data in real time. With mean radiation temperature The software simultaneously calculates PMV and PPD values; When PMV > 0.5 (PPD > 10%), the air conditioner cooling power is automatically increased; when PMV < -0.5 (PPD > 10%), the underfloor heating is activated or the air conditioner heating power is increased to ensure that PMV is stable at 0 ± 0.5 and indoor temperature fluctuation is ≤ ± 1.5℃. Interference elimination: Turn off irrelevant heat-generating devices (such as lamps) within the model, and only keep the temperature field maintenance module running to avoid additional energy consumption interfering with the overall evaluation results.

[0050] Step 4: Accurate recording of full-scale residential energy consumption data.

[0051] Monitoring scope: Only records the electricity / heat consumption data of the temperature field maintenance module (air conditioner, thermal compensator), directly reflecting the overall energy consumption demand of the full-scale residence under the target climate; Data Acquisition: High-precision smart meters (error ≤ 0.2) collect electricity consumption (unit: kWh) once per minute, synchronously record timestamps and corresponding climate parameters, and automatically upload the data to a distributed database, which is stored in categories of "region-date-time-climate parameters-energy consumption". Data preprocessing: using the "3σ criterion" Excluding abnormal energy consumption values ​​caused by abnormal equipment fluctuations, the accuracy of energy consumption data is ≥99%, and the error of energy consumption recording is ≤0.2 level.

[0052] Step S5: Quantitative assessment of the impact of components on the overall performance of the residence.

[0053] Climate-energy consumption data association storage: In step S4, the recorded "energy consumption data per unit area (kWh / (m²・h))" and the "5-minute climate parameter table" generated in step S1 are precisely bound by timestamp (timestamp error ≤ 1 second) to form a one-to-one correspondence dataset of "climate parameters-energy consumption". The dataset is then classified and stored in a distributed database (storage capacity ≥ 10TB) according to "target region-evaluation date-component type" to ensure that energy consumption data can be traced back to specific climate scenarios.

[0054] Component replacement and scenario consistency verification: Replace the target components (such as doors, windows, and wall insulation materials) according to standard procedures. After replacement: Seal the full-scale model with standardized clips and high-performance sealing strips, and test the airtightness (leakage rate ≤0.05m³ / (h・㎡)) to ensure consistency with the original. Calibrate thermocouples and temperature and humidity control equipment to verify the deviation of climate replication parameters (temperature ≤±1℃, humidity ≤±5% RH, wind speed ≤±0.2m / s) to ensure complete consistency with the climate scenario of the first evaluation (components are the only variable).

[0055] Energy consumption retest in the same scenario: Repeat steps S1 to S4. Under the same target area, the same evaluation date, and the same climate replication parameters, collect the corresponding dataset of "climate parameters - energy consumption" for the new component and record the average energy consumption per unit area of ​​the new component E1 (the energy consumption of the original component is E0).

[0056] Performance Quantification: Energy consumption change rate: ; Improvement rate of heat transfer coefficient: ; , The actual heat transfer coefficients of the original and new parts in full-scale scenarios are respectively derived from energy consumption data: , The area of ​​the component is (m²). (°C), T in Indoor temperature, T out Outdoor temperature Total energy consumption (kWh); Assessment Conclusion: , This indicates that the new component has better thermal insulation performance; the higher the value, the better the performance, with an evaluation error of ≤5%.

[0057] The table below lists the key parameters in the above evaluation process: Table 2. List of key parameters during the evaluation process

[0058] Compared to traditional solutions, this embodiment differs in the following ways: 1. Innovative full-scale scene reproduction: For the first time, a 1:1 full-scale residential model ranging from 50 to 180 square meters is used to completely replicate the layout of real rooms, component specifications, and spatial characteristics. This avoids the energy consumption and component performance evaluation errors caused by the "proportional calculation" of traditional scaled-down models. The evaluation results match the actual living scene with ≥95% accuracy. 2. Domestic Climate Precision Alignment Innovation: By connecting with the meteorological data center of a meteorological bureau, high-precision climate parameters can be automatically extracted at the district and county levels, solving the pain point of traditional simulation software that "climate data is general and deviates greatly from reality", and adapting to the assessment needs of various regions in China; 3. Innovative Climate Dynamic Replication: Based on the PID adaptive algorithm, combined with the dynamic calibration parameters of the full-scale model spatial characteristics, it achieves multi-parameter collaborative replication of temperature, humidity, wind speed, and radiation with an accuracy of ≥95%, highlighting the technical barriers of climate replication; 4. Overall Evaluation Closed-Loop Innovation: Breaking through the limitations of traditional "one-way component testing", a closed-loop evaluation system of "climate-overall energy consumption-component performance" is constructed to quantify the actual effect of component integration, rather than the isolated performance of a single component; 5. Innovative Dual-Model Control for Comfort: Integrating complete formulas for PMV and PPD, it achieves precise control of the human body's comfortable temperature field, ensuring that energy consumption assessments are based on unified comfort standards and that data comparability is enhanced.

[0059] For real estate companies, this solution allows for full-scale, comprehensive testing to verify the actual energy consumption of residences in different regions, optimizing regional designs (e.g., emphasizing thick insulation layers and sealed doors and windows in the north, and Low-E glass and rainproofing in the south), avoiding design flaws caused by simulation software errors, and reducing the energy consumption of the subsequent temperature field maintenance system by 15%-25%. For building component companies, it provides accurate third-party testing services based on "full-scale scenarios and real climates," quantifying the actual insulation effect after component integration, rather than the isolated performance of individual components, thus facilitating the promotion of high-quality components. For the industry, it fills the technological gap of traditional "one-way component testing and large simulation errors," establishing evaluation standards for the overall energy consumption and climate adaptability of residences, and promoting the transformation of good houses from "design compliance" to "actually low energy consumption and high comfort."

[0060] The following is a specific application of the above scheme, taking "evaluating the impact of different window and door systems (Low-E glass, ordinary insulated glass) on the overall performance of a residence" as an example, to explain in detail the evaluation and implementation process.

[0061] S1. Input region information: XX Province, XX City, XX District (district / county level address). The test time is selected from the typical low temperature period in winter in XX District (January 5th - January 11th). This period is the 7 consecutive days with the lowest average winter temperature in the past 10 years, which meets the requirements for winter insulation performance test.

[0062] Climate parameter conversion: Through the API interface of a meteorological data center of a certain meteorological bureau, hourly average climate data for the same period of the past 10 years within the assessment period of XX District were extracted. After preprocessing of "outlier removal - trend smoothing - regional calibration", a 5-minute parameter table was generated (outdoor temperature -2℃-8℃, daily average minimum temperature -1.5℃, daily average maximum temperature 6.2℃; relative humidity 75%-92%; wind speed 1.2m / s-3.5m / s; solar radiation intensity 20W / m²-280W / m²). The climate parameters matched the actual data with a degree of 98.2% and the data error was 2.1%.

[0063] S2. Full-Scale Scene Climate Replication: A 5-minute parameter table is imported into the PLC controller. Based on an 85㎡ full-scale residential model (3 bedrooms, 2 living rooms, 1 bathroom; exterior wall area 62㎡; door and window area 18㎡), PID parameters (Kp=2.8, Ki=0.6, Kd=0.3) are dynamically calibrated to drive 8 industrial-grade screw compressor units, variable frequency fan units, and an infrared radiation array to work collaboratively. Calibration is performed every 5 minutes via a distributed sensor array. Climate replication accuracy is 96.5%, with temperature deviation ≤±1℃, humidity deviation ≤±5%RH, and wind speed deviation ≤±0.2m / s in each area of ​​the model.

[0064] S3. Comfortable Temperature Field Maintenance: The underfloor heating and air conditioning heating systems are activated. Temperature data is collected in real time through 300 K-type thermocouples (accuracy ±0.1℃) installed indoors. Combined with the ISO 7730 standard PMV-PPD model for regulation, PMV=0±0.1 (PPD≤4.8%) is maintained, and the indoor temperature is finally stabilized at 21.5℃±0.3℃.

[0065] S4. Energy Consumption Recording: Using smart meters with an error of ≤0.2 and heat meters with an accuracy of ≤0.3, the overall energy consumption data corresponding to ordinary insulated glass is recorded at a frequency of 1 time / minute. After preprocessing according to the "3σ criterion", the data accuracy rate is 99.3%, and the average energy consumption per unit area of ​​ordinary insulated glass group is E0=0.12kWh / (m²·h).

[0066] S5. Component Replacement and Retesting: Following standard procedures, remove the ordinary insulated glass and replace it with Low-E glass. Seal the model using standardized clips and high-performance sealing strips. The leakage rate is measured to be 0.04 m³ / (h·m²). Repeat steps 1-5 to complete the retest in the same scenario, obtaining an average energy consumption per unit area of ​​0.09 kWh / (m²·h) for the Low-E glass assembly.

[0067] Performance evaluation: Calculate the rate of change in energy consumption: ; The actual heat transfer coefficient of doors and windows can be deduced from energy consumption data (formula: Where S = 18㎡ (door and window area). (Q is the total daily energy consumption); The actual heat transfer coefficient of ordinary insulated glass is K0'=2.9W / (m²·K) (3.6% deviation from the design value of 2.8W / (m²·K)); the actual heat transfer coefficient of Low-E glass is K1'=1.9W / (m²·K) (5.6% deviation from the design value of 1.8W / (m²·K)).

[0068] Heat transfer coefficient improvement rate (reflecting the improvement in the thermal insulation performance of doors and windows): ; Error calculation: ;in, (Climate parameter error) (Energy consumption recording error); (Calculation error); (Inconsistency error in the scene after component replacement); .

[0069] In summary, the final total assessment error is determined to be 4.25%.

[0070] In a full-scale winter scenario in XX District, XX City, the Low-E glass system reduced the overall energy consumption per unit area of ​​the residence by 25.0% compared to ordinary double-glazed glass systems, significantly improving winter insulation and energy efficiency; the actual heat transfer coefficient of doors and windows improved by 34.5%, meeting design expectations. The evaluation error throughout the entire process (including climate replication, measurement, and calculation errors) was ≤5% (4.25%), making the results reliable. In summary, the Low-E glass system significantly improves the overall winter performance (insulation and energy efficiency) of residences and is better suited to the winter climate needs of XX region.

[0071] The above embodiments are only used to explain the concept of the present invention, and are not intended to limit the protection of the present invention. Any non-substantial modifications made to the present invention using this concept should fall within the protection scope of the present invention.

Claims

1. A dynamic evaluation system for residential all-weather performance based on full-scale scenarios, characterized in that, include: The climate data integration module is used to acquire historical climate data based on the target region and the range of assessment dates, and generate a time series climate parameter table. A climate control module is configured around a 1:1 full-scale detachable residential model to dynamically reproduce the multidimensional outdoor climate environment based on the time series climate parameter table. A temperature field maintenance module is arranged inside the 1:1 full-scale detachable residential model for constant control of the indoor human comfort temperature field. The energy consumption recording module is used to collect the energy consumption data of the temperature field maintenance module in real time, synchronously record the timestamp and corresponding climate parameters, and preprocess the data. A component replacement and adaptation module is used for replacing the building components of the 1:1 full-scale detachable residential model. The module integration unit is used to couple the above modules to realize data conversion, climate replication, temperature field control, energy consumption recording, coupling analysis and component performance evaluation.

2. The residential all-weather performance dynamic evaluation system based on full-scale scenarios as described in claim 1, characterized in that, The climate data interface module calls the meteorological data center database through the API interface to extract historical climate data for the target area and within the evaluation date range, and transforms the historical climate data to generate a time series climate parameter table. The target area is accurate to the district / county level.

3. The residential all-weather performance dynamic evaluation system based on full-scale scenarios as described in claim 2, characterized in that, The transformation of the historical climate data includes the following process: determining the accuracy of environmental simulation data based on the minimum operating time of laboratory equipment; decomposing hourly data into controllable minute-level data through outlier removal, trend smoothing, and regional calibration; ensuring that the climate parameters match the actual climate of the target area with a degree of ≥97%; and reducing the delay in transforming historical climate data to ≤10 seconds.

4. The residential all-weather performance dynamic evaluation system based on full-scale scenarios as described in claim 1, characterized in that, The climate control module includes a temperature and humidity control unit, a wind speed control unit, a radiation control unit, and a control unit. The temperature and humidity control unit has a temperature control range of -46℃ to 60℃ with a temperature control accuracy of ±1℃, and a humidity control range of 30%-90%RH with a humidity control accuracy of ±5%. The wind speed control unit has a wind speed range of 0-50m / s with an adjustment accuracy of ±0.2m / s. The radiation control unit has a radiation intensity range of 0 to 1200W / m² with an error of ≤3%.

5. The residential all-weather performance dynamic evaluation system based on full-scale scenarios as described in claim 1, characterized in that, The temperature field maintenance module includes no fewer than 300 K-type thermocouples, which are arranged in various functional areas of the 1:1 full-scale detachable residential model. The temperature measurement range of the K-type thermocouples is -50℃ to 200℃, with an accuracy of ±0.1℃.

6. The residential all-weather performance dynamic evaluation system based on full-scale scenarios as described in claim 1, characterized in that, The 1:1 full-scale detachable residential model has an area of ​​50-180㎡, including room partitions, door and window openings, kitchen and bathroom layouts, and enclosure structure. The detachable building components have reserved disassembly and assembly interfaces, and the leakage rate of the model after the building components are disassembled and replaced is ≤0.05m³ / (h·㎡).

7. The residential all-weather performance dynamic evaluation system based on full-scale scenarios as described in claim 1, characterized in that, The energy consumption recording module uses an electricity meter with an error level of ≤0.2 and a heat meter with an accuracy level of ≤0.3 to collect electricity and heat consumption data at a frequency of 1 time per minute, and stores the data in a distributed database according to the structure of "region-date-time-climate parameter-energy consumption".

8. The residential all-weather performance dynamic evaluation system based on full-scale scenarios as described in claim 1, characterized in that, The data preprocessing in the energy consumption recording module uses the 3σ criterion to remove abnormal energy consumption values ​​caused by abnormal fluctuations in equipment.

9. A method for dynamic evaluation of residential all-climate performance based on a full-scale scenario using the system described in any one of claims 1 to 8, characterized in that, Includes the following steps: S1. Based on the target region and evaluation date range input by the user, retrieve the corresponding historical climate data from the meteorological data center and generate a time series climate parameter table; S2. Import the time series climate parameter table into the control unit of the climate control module. The control unit controls the temperature and humidity control unit, wind speed control unit and radiation control unit based on the PID adaptive algorithm to dynamically replicate the real climate of the target area in the 1:1 full-scale detachable residential model. S3. By collecting temperature data through K-type thermocouples and combining the PMV and PPD dual models of ISO 7730 standard, the variable frequency air conditioning and underfloor heating systems in the residential model are linked to maintain indoor comfort at PMV=0±0.2, PPD≤5%, and indoor temperature control accuracy ±0.5℃. S4. Collect energy consumption data of the temperature field maintenance module in real time, record timestamps and corresponding climate parameters synchronously, store them in a distributed database according to the structure of "region-date-time-climate parameter-energy consumption", and use the 3σ criterion to remove abnormal energy consumption values ​​caused by abnormal fluctuations in equipment. S5. Replace the target part, and maintain the same climate simulation scene and airtightness before and after the part replacement; Repeat steps S1 to S4, and collect energy consumption data corresponding to the new components under the same target area, the same evaluation date, and the same climate replication parameters. Based on the energy consumption difference before and after component replacement, quantify the impact of the components on the overall climate adaptability of the residence.

10. The method for dynamic evaluation of residential all-climate performance based on full-scale scenarios as described in claim 9, characterized in that, The quantization process in step S5 is as follows: Define the average energy consumption per unit area of ​​the old component as E0, and the average energy consumption per unit area of ​​the new component as E1; Energy consumption change rate: ; Improvement rate of heat transfer coefficient: ,in , These are the actual heat transfer coefficients of the original and new parts in full-scale scenarios, respectively, inferred from energy consumption data. , For the area of ​​the component, T in Indoor temperature, T out Where Q is the outdoor temperature, and Q is the total energy consumption; When ΔE>0 and ΔK>0, it indicates that the new component has better thermal insulation performance; Conversely, it indicates that the insulation performance of the new component is weaker than that of the old component.

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