A method for simulating energy consumption of a photo-thermal dual-response smart window
By calling EMS and plugins in Energy Plus and combining them with the simulation method of thermochromic glass, the problem that existing software cannot accurately simulate the energy consumption of light and heat dual-response smart windows has been solved, realizing more detailed energy consumption simulation and improving the accuracy and reliability of building energy consumption assessment.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- SOUTH CHINA UNIV OF TECH
- Filing Date
- 2026-05-22
- Publication Date
- 2026-06-19
Smart Images
Figure CN122242081A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of building energy consumption technology, and specifically discloses a method for simulating the energy consumption of a light and heat dual-response smart window. Background Technology
[0002] Smart windows are functional glass that can dynamically adjust their optical performance according to changes in the external environment or human commands. Their core function is to save energy, reduce consumption, and improve comfort and privacy: by intelligently regulating the sunlight and heat entering the room, they can save up to 80% of building heating and cooling energy consumption.
[0003] Currently, a unified and standardized methodology has not been established for energy consumption simulation research on smart windows with dual photothermal response characteristics. The mainstream approach in the industry typically involves secondary development based on traditional building energy simulation software platforms such as Energy Plus. This is done by writing customized modules or extending their functionality to define and simulate the complex optical control behavior of these smart windows. However, the traditional Energy Plus software itself has limitations. Its built-in simulation functions are mainly designed for materials with relatively simple response mechanisms, such as single thermochromic glass systems, and cannot accurately simulate the energy consumption of materials with complex response mechanisms.
[0004] Therefore, the present invention aims to provide a method for simulating energy consumption of a photothermal dual-response smart window to solve the above-mentioned problems. Summary of the Invention
[0005] The purpose of this invention is to provide a method for simulating the energy consumption of a photothermal dual-response smart window. Combining the simulation method of thermochromic glass, the method calls the energy management system (EMS) through an external plug-in to realize the output of dynamic simulation results based on static experimental data, thus solving the problem that it is impossible to perform accurate energy consumption simulation for materials with complex response mechanisms. The specific plan is as follows: A method for simulating energy consumption of a photothermal dual-response smart window, the method comprising the following steps: S1. Collect data on the smart window to be simulated and meteorological data; S2. Input the data from the smart window and meteorological data into Energy Plus. Through the external plug-in connected to Energy Plus, call EMS to read the outer surface temperature and incident solar radiation on the surface, and select the structure index. S3. Simulate thermal energy consumption and light energy consumption of the simulated smart window by using EMS and combining it with the constructed index. S4. Output the Energy Plus simulation results.
[0006] Furthermore, in step S3, the light energy consumption of the simulated smart window is simulated by measuring the radiation intensity.
[0007] The calculation method for the light energy consumption simulation is as follows:
[0008] in, The surface solar radiation intensity of the smart window is calculated by Energy Plus using meteorological data. The effective radiation intensity of the smart window can then be calculated using the above formula. :
[0009] in, , For 10 minutes, when At this point, during the fading stage, the response time constant is... For 60 minutes, when At this point, during the coloring phase, the response time constant is... It takes 30 minutes.
[0010] Furthermore, S3 calculates the outer surface temperature of the smart window using meteorological data via Energy Plus, and calculates the effective surface temperature of the smart window through thermal balance. Thermal energy consumption simulation was performed on the simulated smart window.
[0011] Furthermore, the Energy Plus, through and Determine the construction exponent for the next time step, and use the exponent from the previous time step. and As the surface incident solar radiation intensity and outer surface temperature at the next moment, EMS uses the tectonic index at the next moment and the previous moment's... and Dynamic simulation of thermal energy consumption and light energy consumption at a given moment in a smart window.
[0012] The present invention also provides a photothermal dual-response intelligent window energy consumption simulation system, the system comprising a data acquisition module, an energy consumption simulation module and an analog output module; The data acquisition module collects data from the smart window to be simulated and meteorological data. The energy consumption simulation module performs thermal energy consumption simulation and light energy consumption simulation on the smart window to be simulated based on the collected data of the smart window and meteorological data. The simulation output module outputs the dynamic simulation results of the energy consumption simulation module.
[0013] Furthermore, the energy consumption simulation module is connected to an external plug-in, which calls EMS to perform thermal energy consumption simulation and light energy consumption simulation of the smart window to be simulated.
[0014] Furthermore, the energy consumption simulation module calculates the outer surface temperature and incident solar radiation intensity of the smart window using Energy Plus and meteorological data, selects a construction index based on the outer surface temperature and incident solar radiation intensity, and then uses an external plugin to call EMS to calculate the effective surface temperature. and effective radiation intensity Perform thermal energy consumption and light energy consumption simulations for smart windows.
[0015] The Energy Plus is calculated by and Determine the construction exponent for the next moment, and then... and Using the intensity of incident solar radiation on the surface and the temperature of the outer surface as parameters, the light energy consumption and thermal energy consumption of the smart window at the next moment are simulated.
[0016] The present invention also provides an electronic device, the electronic device including a memory, a processor and a computer program stored in the memory and executable on the processor, the electronic device being used to perform the steps of the above-described photothermal dual-response smart window energy consumption simulation method.
[0017] Compared with existing technologies, the beneficial effects of this solution are: This invention uses an external plugin to call EMS to calculate the radiation intensity and effective surface temperature of a smart window, and performs light and heat response energy consumption simulation on the dual-response smart window. It realizes the output of dynamic simulation results based on static experimental data. At the same time, through this innovative simulation approach, it is possible to perform more detailed and accurate energy consumption simulation analysis on advanced materials with complex response mechanisms and variable behaviors, thereby significantly improving the accuracy and reliability of building energy-saving design and material performance evaluation. Attached Figure Description
[0018] Figure 1 This is a flowchart of the simulation method of the present invention.
[0019] Figure 2 This is a flowchart illustrating the simulation system workflow of the present invention.
[0020] Figure 3 This is a schematic diagram of typical urban energy consumption analysis according to the present invention, where a represents the annual cooling and heating energy consumption of different window types in five typical cities; b represents the monthly cooling and heating energy saving rate of PTSW window type relative to DWG window type under different climatic conditions; c represents the annual cooling and heating energy saving rate of TSW, PSW and PTSW window types relative to DWG window type; d represents the monthly cooling and heating energy consumption distribution curves of various window types in Riyadh and Copenhagen; and e represents the monthly cooling and heating energy consumption distribution curves of various window types in Guangzhou and Minneapolis.
[0021] Figure 4 This is a schematic diagram of daylighting analysis in typical cities according to the present invention, where a represents the annual effective daylight intensity (UDI) distribution of the DWG and PTSW systems, and the UDI in five typical cities. 300-3000 The relative improvement rate within the range; bi represents the UDI of the DWG and PTSW systems at 7 sensor points within four representative cities (e.g., Copenhagen in b and c). >3000 and UDI 300-3000 The spatial distribution; the solid line represents the average UDI value under the four building orientations, while the colored filled strips represent the fluctuation range between the maximum and minimum values.
[0022] Figure 5 This is a schematic diagram of the global energy consumption analysis of the present invention, where a represents the latitudinal distribution of cooling and heating energy-saving rates; b represents UDI. 300-3000 Latitude distribution of energy efficiency improvement rate; c represents the energy saving rate of cooling and heating and UDI. 300-3000 Correlation of energy efficiency improvement rate; d is the correlation between cooling and heating energy saving rate and absolute cooling / heating energy saving amount. Detailed Implementation
[0023] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.
[0024] Example 1: like Figures 1-2 As shown, the performance evaluation of photothermal dual-response glass (PTSW) in buildings was conducted using Energy Plus, a widely used building energy consumption simulation engine. Energy Plus directly utilizes the Energy Management System (EMS) and achieves high simulation accuracy; however, it does not have specific adaptations for photothermal dual-response smart windows. Therefore, this invention combines the simulation method of thermochromic glass with an external plugin (such as a Python plugin) to call the EMS, realizing a dynamic simulation of the photothermal dual-response of the smart window based on static experimental data.
[0025] The specific method is as follows: S1. Collect data on the smart window to be simulated. Also, collect relevant meteorological data for the past three days during the simulation, including solar radiation, air temperature and humidity, and wind speed, from a weather station. S2. Input the data collected from the smart window and meteorological data into Energy Plus. The data from the smart window is input into an IDF file, and the meteorological data is input into an EPW file. Through the Python plugin connected to Energy Plus, call EMS to read the outer surface temperature and the surface incident solar radiation, and select the structure index. S3. Perform thermal energy consumption and light energy consumption simulation of the simulated smart window using EMS and combined with the construction index; perform light energy consumption simulation of the simulated smart window using radiation intensity.
[0026] The calculation method for light energy consumption simulation is as follows:
[0027] in, The surface solar radiation intensity of the smart window is calculated by Energy Plus using meteorological data. The effective radiation intensity of the smart window can then be calculated using the above formula. :
[0028] in, , For 10 minutes, when At this point, during the fading stage, the response time constant is... For 60 minutes, when At this point, during the coloring phase, the response time constant is... The timeframe is 30 minutes. This recursive relationship allows us to describe the cumulative and hysteretic behavior of smart window light energy consumption response over a building simulation timescale without introducing complex photochemical reaction models.
[0029] The outer surface temperature of the smart window is calculated using Energy Plus and meteorological data, and the effective surface temperature of the smart window is calculated through thermal balance. Thermal energy consumption simulation was performed on the simulated smart window.
[0030] Energy Plus through and Determine the construction exponent for the next time step, and use the exponent from the previous time step. and As the surface incident solar radiation intensity and outer surface temperature at the next moment, EMS uses the tectonic index at the next moment and the previous moment's... and Simulation of thermal energy consumption and light energy consumption at a given moment in the simulation of a smart window.
[0031] S4. Output the dynamic simulation results of Energy Plus.
[0032] The present invention also provides a photothermal dual-response intelligent window energy consumption simulation system, the system comprising a data acquisition module, an energy consumption simulation module and an analog output module; The data acquisition module collects data from the smart window to be simulated, as well as meteorological data for the past three days during the simulation. The Energy Plus module of the energy consumption simulation module reads the outer surface temperature and incident solar radiation on the surface based on the collected data from the smart window and meteorological data, and selects the structure index. It then uses a Python plugin connected to the energy consumption simulation module to call EMS to calculate the effective surface temperature. and effective radiation intensity Perform thermal energy consumption and light energy consumption simulations for smart windows. Based on the calculated... and The surface incident solar radiation intensity and outer surface temperature at the next moment are used to determine the structural index at the next moment, and the thermal energy consumption and light energy consumption of the smart window at the next moment are simulated by EMS.
[0033] The simulation output module outputs the dynamic simulation results of the energy consumption simulation module.
[0034] The present invention also provides an electronic device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor. The electronic device is used to perform the steps of the above-described photothermal dual-response smart window energy consumption simulation method.
[0035] Example 2: This invention establishes a standardized office building energy consumption model to assess the applicability and energy-saving potential of PTSW (Potential Thermal Swing) globally. Considering the significant differences in solar radiation reception for windows with different orientations, the model includes typical office units in four independent thermal zones (east, south, west, and north), with a window-to-wall ratio of 0.42. To simulate the office environment in the middle floors of a large building, except for the exterior walls with windows, the other five building envelope surfaces (interior walls, floors, and ceilings) are set as adiabatic boundary conditions to eliminate heat transfer interference from adjacent areas. To ensure the validity of the simulation results in different climate zones globally, this invention references the standard construction defined in ASHRAE Standard 90.1-2019 Climate Zone 5 and adopts a general-purpose heavy-duty exterior wall construction that balances thermal insulation and heat preservation requirements. The specific construction and related parameters of this wall are shown in Table 1, with a total heat transfer coefficient of 0.48 W / (m²). 2•k) This design meets the insulation requirements of cold regions and also provides good thermal insulation in hot regions. The four types of windows used in the simulation employed the double-glazed glass construction shown in Table 2. The indoor side consisted of 5mm thick transparent glass with identical optical properties and a 3mm thick air gap. The outdoor side, depending on the window type, featured either 5mm thick transparent glass, a thermochromic glass layer, a photochromic glass layer, or a photothermal dual-response glass layer.
[0036] Table 1. Specific Structure of Packaging
[0037] Table 2 Window Construction
[0038] The simulated building load and output parameter settings are detailed in Table 3. To closely resemble the operating characteristics of a real office building, this invention sets daily operating schedules for personnel, lighting, equipment, and permeability.
[0039] Table 3 Analog Inputs and Outputs
[0040] 2. Experimental Results 2.1 Optical performance test results Test results show that PTSW exhibits stable dynamic regulation performance under different environmental conditions. By integrating spectral data in the 250-2500nm band, the photothermal regulation capability of this invention in four boundary states—ultraviolet, visible, and near-infrared—can be clearly observed. Specific parameters are shown in Tables 4 and 5.
[0041] Table 4. Combined spectral transmittance and reflectance of PTSW samples in the UV-Vis-IR band under different conditions.
[0042] Table 5. Overall spectral modulation capability of PTSW samples in the UV-Vis-IR band relative to the initial state (20℃ / fading) under different conditions.
[0043] 2.2 Outdoor Experiment Analysis and Model Validation The intelligent window energy consumption simulation method based on this invention calculates statistical indicators for different window systems (dual-glazed glass (DGW), thermochromic glass (TSW), photochromic glass (PSW), and photothermal dual-response glass (PTSW)) and compares the simulated data with measured data to determine whether the simulation method meets the accuracy requirements for building energy consumption simulation. Statistical evaluation indicators show that the simulation model has high reliability: the coefficients of determination for DGW, TSW, PSW, and PTSW are (…). The values were 0.913, 0.931, 0.929, and 0.925, respectively, and the corresponding root mean square error (RMSE) was controlled within 2℃.
[0044] Due to interference from strong radiation on thermocouples, the simulation results for the benchmark DGW group were generally lower than the measured results during the daytime. Other dynamic window samples, however, effectively blocked radiation under strong light, resulting in smaller errors. The simulation showed that the temperature drop of the PTSW was slightly lower than the measured value, reflecting that the current simulation method is relatively conservative in predicting the dynamic response of the PTSW. Overall, despite unavoidable biases, this simulation method successfully achieves the core function of the smart window's photothermal response, and its accuracy is sufficient to support subsequent performance evaluations in different climate zones and globally.
[0045] 2.3 Performance Simulation Results and Analysis 2.3.1 Energy consumption and daylighting of PTSW in cities in typical climate zones Building upon the preceding text, this invention analyzes the building energy consumption and daylighting performance of the five typical cities mentioned above. Firstly, regarding energy consumption... Figure 3 Figure a shows the total annual heating and cooling energy consumption of standard buildings equipped with different window systems (DWG, TSW, PSW, and PTSW) in various cities. The results indicate that PTSW demonstrates superior cooling energy consumption reduction capabilities compared to DWG in all typical cities. In the extremely dry and hot Riyadh and the humid and hot Singapore and Guangzhou, building energy consumption is absolutely dominated by cooling demand. PTSW reduces the annual cooling energy consumption in Riyadh and Singapore from 1051.1 MJ / m². 2 and 1342.8 MJ / m 2 Significantly reduced to 829.9 MJ / m 2 and 1183.5 MJ / m 2 In cold-climate cities where heating is the primary mode of energy consumption (such as Minneapolis), although the PTSW blocks some beneficial solar radiation in winter, leading to a slight increase in heating energy consumption (from 669.6 MJ / m²), 2 Increased to 677.1 MJ / m 2 However, the significant reduction in cooling energy consumption during the summer is sufficient to compensate for this heat loss, achieving a net energy saving benefit for the building throughout the year.
[0046] Figure 3 Figure 'c' shows the annual energy saving rate of each smart window system relative to DWG. It's important to note that the energy saving rate alone does not fully equate to the actual energy saving contribution. Combining the relative energy saving rate with the absolute energy saving provides a more accurate picture of the applicability of PTSW under different climatic conditions. For single-response TSW and PSW, their energy saving effects are limited, while PTSW, with its synergistic light and heat regulation mechanism, achieves optimal efficiency in all cities. Comparing data from Guangzhou and Singapore reveals that although Guangzhou's total annual energy saving rate of PTSW (13.24%) is slightly higher than Singapore's (11.87%), Singapore's high annual temperatures result in a large overall energy consumption base, and its total annual energy saving (159.3 MJ / m²) is significantly lower. 2 In fact, it far exceeds that of Guangzhou (112.9 MJ / m³). 2 In Riyadh, where the climate is most severe, PTSW not only achieved a maximum energy saving rate of 20.68%, but also ranked first among all cities in terms of total annual energy savings, far exceeding the single-response TSW (4.72%) and PSW (9.48%). Even in high-latitude regions such as Copenhagen and Minneapolis, PTSW maintained a total energy saving rate of approximately 10.00%, which confirms that the dual-response mechanism can effectively overcome the limitations of single-response smart windows.
[0047] From the perspective of monthly dynamic changes, such as Figure 3 As shown in b, in Riyadh and Singapore, PTSW maintained a positive energy saving rate throughout the year, with Riyadh achieving a total energy saving rate exceeding 30% during the mild winter months (January and December). For cities with distinct seasons (such as Minneapolis), PTSW also had a high total energy saving rate of 27.60% during the summer months (such as July), while experiencing a slight negative energy saving rate (-0.97%) during the cold month of January. However, combined with... Figure 3 The monthly total energy savings curves for d and e clearly show that, regardless of whether the city is in a high-latitude or low-latitude region, the high energy savings rate in summer corresponds to a significant reduction in peak cooling energy consumption. While high-latitude cities experience an increase in total energy consumption in winter, the difference in heating energy consumption caused by different types of windows is minimal, resulting in a negligible negative impact on overall energy consumption. This dynamic response characteristic of energy saving in summer and remaining stable in winter fully demonstrates the adaptive adjustment advantages of PTSW during transitional seasons and extreme climates.
[0048] While achieving good energy-saving effects, smart windows may sacrifice indoor lighting quality and visual comfort due to excessive shading. To address the potential reduction in visible light transmittance caused by dynamic dimming technology, this invention introduces Useful Daylight Intensity (UDI) as an evaluation system to preliminarily explore the dynamic optimization effect of PTSW on the indoor light environment.
[0049] Figure 4 Figure 'a' shows the distribution percentage of the average UDI for each orientation on standard simulated buildings in five typical city DWG and PTSW models, where UDI is divided into UDI... <300 (Insufficient illumination, artificial lighting needs to be turned on) UDI 300-3000 (Effective lighting area) and UDI >3000 (Dazzle risk exists) Three intervals. Statistical results show that, compared to DWG, PTSW effectively suppressed UDI in all tested cities. >3000 This significantly increases the time the interior is in a comfortable lighting range. Because the standard building model used has large windows and walls and lacks effective external shading, in Singapore and Riyadh where solar radiation is extremely strong, DWG's UDI... >3000 The proportions were as high as 51.61% and 43.64% respectively, indicating that indoor spaces face serious glare risks for more than half the year. After using PTSW, thanks to its rapid response under strong light and high temperatures, the glare duration (UDI) in both locations was significantly reduced. >3000 The proportions of light penetration decreased to 44.20% and 37.73% respectively; meanwhile, the excessive amount of blocked strong light was converted into comfortable light, making the effective daylight density (UDI) range in Singapore and Riyadh more comfortable. 300-3000 These figures represent relative increases of 15.32% and 10.37%, respectively. Even in two high-latitude, cold cities, PTSW did not result in excessive indoor darkness, and its UDI... <300 Only a slight increase occurred, while UDI 300-3000 However, it has steadily improved, proving that its dual-response mechanism can maintain high transparency under low light or low temperature conditions.
[0050] To further reveal the dynamic changes in lighting quality across spatial depth and building orientation. Figure 4 The biplot in the image details the spatial distribution of UDI (Universal Dimensioning Indicator) for different window systems in four representative cities. In high-latitude, cold cities (such as...),... Figure 4 In Copenhagen (as shown in b and c), due to the low solar altitude angle, direct sunlight, especially through south-facing windows, easily penetrates the interior. Data indicates that for DWG (Digital Viewing Window), the risk of glare is higher in the near-window area, and its UDI (Ultra-Intensity Discharge) is also higher. >3000 The average value is as high as 54.93%. Furthermore, the extreme fluctuation range affected by orientation is wide (from 33.51% to 78.14%). Even after using PTSW, although the differences between windows facing different orientations remain significant due to the inherent physical laws of building orientation, the glare time (UDI) in the near-window area is significantly reduced. >3000 The average value dropped to 48.62%, while its effective daylighting area (UDI) decreased. 300-3000 The average also increased from 36.95% to 42.83%.
[0051] For cities at low latitudes and with high solar radiation ( Figure 4 In the di section, the light distribution characteristics changed significantly with decreasing latitude. A horizontal comparison was made with Riyadh (…). Figure 4 d, e), Singapore ( Figure 4 f, g) and Guangzhou ( Figure 4 As can be seen from h and i in the data, glare problems become more severe in all directions as the solar altitude angle increases. These cities experience near-window glare time (UDI) when using DWG. >3000 The proportion of glare is even more severe, with average values as high as 82.39%, 92.53%, and 80.46%, respectively. Singapore, located near the equator, is particularly affected, with its four orientations exhibiting particularly high UDI (Ultra-Day Intensity Distance). >3000 The UDI reached a narrow high range of 87.09% to 96.96%. After using PTSW, the UDI in the near-window areas of Riyadh, Singapore, and Guangzhou... >3000 The average values dropped significantly to 75.24%, 87.77%, and 74.54%. Although glare is still unavoidable in near-window areas under extreme light conditions, PTSW does indeed reduce excessive visible light radiation to some extent and convert this intense light into effective daylight. For example, in the extremely glaring near-window areas of Singapore, PTSW reduces UDI... 300-3000 The average increased from 7.47% to 12.23%. In addition, in the middle and rear areas of the interior, all low-latitude cities maintained a stable average effective daylight level of over 65%.
[0052] 2.3.2 PTSW Global Energy Saving and Daylighting By analyzing simulation data and spatial interpolation maps from 1853 typical cities worldwide, this invention further determines the application prospects of the dual-response solar-thermal smart window under different geographical and climatic conditions, as well as the energy benefits brought by its dynamic regulation characteristics in the face of extreme high-temperature climates in the future. From a global spatial distribution perspective, the energy-saving and light-transmitting performance improvement of the PTSW exhibits significant spatial non-uniformity and altitude dependence. The peak area of the total annual heating and cooling load energy saving rate is not simply distributed along the equator, but is concentrated in the low-latitude tropical and subtropical regions, forming distinct performance highlands in specific terrains. Specifically, in addition to its excellent performance in traditional subtropical high-radiation areas such as the Middle East, North Africa, and the Australian outback, the PTSW also shows similar energy-saving effects in high-altitude areas such as the Yunnan-Guizhou Plateau, the Andes Mountains, and the Rocky Mountains. Taking Lhasa (29.7°N, 91.1°E) and Kunming (25.0°N, 102.7°E) as examples, although they are located in plateau climate zones with relatively low ambient temperatures throughout the year, thanks to their extremely high atmospheric transparency and strong shortwave radiation, their energy-saving improvement rates are as high as 20.91% and 20.89% respectively, significantly higher than the global average (14.90%). Regarding indoor lighting, the improvement rate of effective daylighting intensity (UDI) shows a more concentrated distribution pattern, with the core peak areas mainly along both sides of the equator and major plateau regions. For example, cities near the equator such as Turiazu (1.7°S, 45.4°W) in Brazil and Galera (1.8°N, 127.8°E) in Indonesia both have UDI improvement rates exceeding 10%. As latitude increases, the increase in UDI generally shows a downward trend, with data in high-latitude regions generally below 6.5%. This indicates that PTSW has great application potential in dealing with excessive indoor daylight caused by strong direct sunlight, and can significantly improve the quality of indoor light environment through dynamic filtering.
[0053] To further refine the impact of latitude on energy conservation and improved lighting performance, this invention conducts a sensitivity analysis combining data indicators and latitude and longitude. For example... Figure 5 As shown in Figure a, the energy-saving improvement rate exhibits a unique bimodal distribution pattern with latitude, with its peak mainly concentrated in the subtropical high-pressure control zone between 20° and 35° north and south latitude. For example, the Atacama Desert region of Chile (27.3°S, 70.8°W) boasts the highest energy-saving rate globally at 35.1%. This is primarily due to the region's extremely high atmospheric transparency and low cloud cover caused by descending airflow, resulting in extremely high shortwave radiation intensity received by the surface and very little annual precipitation, thus maximizing the photochromic and thermochromic effects of PTSW. In contrast, as... Figure 5As shown in b, the UDI enhancement rate exhibits a single-peak distribution centered on the equator. In the equator and low-latitude regions, PTSW demonstrates the best ability to reduce peak intensity for high-intensity direct sunlight throughout the year, effectively alleviating the problem of uneven indoor lighting caused by excessive radiation.
[0054] This invention further reveals, through correlation analysis, a certain synergistic effect rather than a fixed negative correlation between energy consumption and daylighting, as well as the conversion logic and reasons for the differences between energy saving rate and energy saving. Statistical results Figure 5 The data in Figure 'c' shows a significant positive correlation between the energy-saving improvement rate and the UDI improvement rate (R=0.482, p<0.001). The data points are concentrated in the high-performance quadrant of energy consumption and daylighting improvement, proving that the photothermal dual-response mechanism can overcome the problem of traditional static shading failing to balance energy saving and daylighting to a certain extent, achieving the performance of balancing indoor thermal comfort and indoor daylighting. Figure 5 As shown in d, after introducing the index of absolute energy saving, it was found that the value of energy saving and emission reduction in tropical regions was underestimated to some extent. Although the energy saving improvement rate and absolute energy saving showed a strong positive correlation overall (R=0.699, p<0.001), in the equatorial and tropical hot and humid climate zones ( Figure 5 (In the blue-green dotted area), the data exhibits a significant characteristic of low ratio but high total amount. Taking Lodwal (3.1°N, 35.6°E) and Christmas Island (2.0°N, 157.4°W) in Kenya as examples, although their energy-saving improvement rates are at a moderate level (16.1% and 17.0% respectively), due to the large total global cooling load in this region, the annual absolute energy savings generated by PTSW reach as high as 220 MJ / m 2 With 235 MJ / m 2 This demonstrates the wide range of applications of PTSW in low- to mid-latitude regions, exhibiting extremely high regulation efficiency in subtropical and plateau areas; while in tropical regions, it makes a significant contribution to the carbon emission reduction target throughout the building life cycle.
[0055] Example 3: This invention provides a method for simulating the energy consumption of a photothermal dual-response smart window. Through the Energy Plus Python plugin and EMS, a dynamic structural switching method for a temperature and humidity responsive smart window can be established. Meteorological data is collected, and the optical and thermal parameters under different temperature and humidity conditions are pre-written into an IDF file to generate corresponding material and structural objects. During the simulation, the Python plugin reads the outdoor air temperature, outdoor relative humidity, and the window's outer surface temperature at each time step, and switches the window structure in real time according to the current temperature and humidity conditions.
[0056] The temperature response is directly based on the external surface temperature of the window. As a control variable, the humidity response does not directly use the outdoor relative humidity from the meteorological file, but rather is first determined based on the outdoor air temperature. outdoor relative humidity Calculate the partial pressure of water vapor in air :
[0057]
[0058] Subsequently, the saturated vapor pressure at the window surface was calculated based on the temperature of the window's outer surface. :
[0059] This yields the equivalent surface relative humidity of the window:
[0060] This method can couple ambient humidity with window surface temperature, and more reasonably describe the moisture absorption and release driving force of humidity-responsive materials under actual window boundary conditions.
[0061] Considering the hygroscopic ... The dynamic humidity response state of the material is characterized. Its change process is represented by a first-order response model:
[0062] In the Energy Plus discrete time step, the above equation can be written in recursive form:
[0063]
[0064] in, The simulation time step is set to 10 minutes. Based on the asymmetry of the moisture absorption and desiccation processes, when... At that time, the material is in the moisture absorption stage, take =60min; when At that time, the material was in the dehumidification stage, and the following steps were taken: =30min.
[0065] At each time step, the program will and These are mapped to preset temperature and humidity levels, and the corresponding window configurations are invoked. The temperature levels set in this invention are 25℃, 30℃, 35℃, 40℃, 45℃, and 50℃, and the humidity levels are 10%, 20%, 30%, 40%, 50%, 60%, 65%, 70%, and 80%.
[0066] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Various modifications and variations can be made to the present invention by those skilled in the art. 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 simulating energy consumption of a photo-thermal dual-responsive smart window, characterized in that, The method includes the following steps: S1. Collect data on the smart window to be simulated and meteorological data; S2. Input the data from the smart window and meteorological data into Energy Plus. Through the external plug-in connected to Energy Plus, call the energy management system to read the outer surface temperature and the solar radiation incident on the surface, and select the structure index. S3. Simulate thermal energy consumption and light energy consumption of the simulated smart window by using an energy management system and combining it with a construction index. S4. Output the Energy Plus simulation results.
2. The energy consumption simulation method for a photothermal dual-response smart window according to claim 1, characterized in that, In step S3, the light energy consumption of the simulated smart window is simulated by measuring the radiation intensity.
3. The energy consumption simulation method for a photothermal dual-response smart window according to claim 2, characterized in that, The calculation method for the light energy consumption simulation is as follows: in, The surface solar radiation intensity of the smart window is calculated by Energy Plus using meteorological data. The effective radiation intensity of the smart window can then be calculated using the above formula. : in, , For 10 minutes, when At this point, during the fading stage, the response time constant is... For 60 minutes, when At this point, during the coloring phase, the response time constant is... It takes 30 minutes.
4. The energy consumption simulation method for a photothermal dual-response smart window according to claim 3, characterized in that, S3 calculates the outer surface temperature of the smart window using Energy Plus and meteorological data, and calculates the effective surface temperature of the smart window through thermal balance. Thermal energy consumption simulation was performed on the simulated smart window.
5. The energy consumption simulation method for a photothermal dual-response smart window according to claim 4, characterized in that, The Energy Plus, through and Determine the construction exponent for the next time step, and use the exponent from the previous time step. and As the surface incident solar radiation intensity and outer surface temperature at the next moment, the energy management system uses the structure index at the next moment and the previous moment's... and Dynamic simulation of thermal energy consumption and light energy consumption at a given moment in a smart window.
6. A photothermal dual-response intelligent window energy consumption simulation system, characterized in that, The system includes a data acquisition module, an energy consumption simulation module, and an analog output module; The data acquisition module collects data from the smart window to be simulated and meteorological data. The energy consumption simulation module performs thermal energy consumption simulation and light energy consumption simulation on the smart window to be simulated based on the collected data of the smart window and meteorological data. The simulation output module outputs the dynamic simulation results of the energy consumption simulation module.
7. The photothermal dual-response intelligent window energy consumption simulation system according to claim 6, characterized in that, The energy consumption simulation module is connected to an external plug-in, which calls the energy management system to simulate the thermal energy consumption and light energy consumption of the smart window to be simulated.
8. The photothermal dual-response intelligent window energy consumption simulation system according to claim 7, characterized in that, The energy consumption simulation module calculates the outer surface temperature and incident solar radiation intensity of the smart window using Energy Plus and meteorological data. It then selects a construction index based on these parameters and uses an external plugin to call the energy management system to calculate the effective surface temperature. and effective radiation intensity Perform thermal energy consumption and light energy consumption simulations for smart windows.
9. The photothermal dual-response intelligent window energy consumption simulation system according to claim 8, characterized in that, The Energy Plus is calculated by and Determine the construction exponent for the next moment, and then... and Using the intensity of incident solar radiation on the surface and the temperature of the outer surface as parameters, the light energy consumption and thermal energy consumption of the smart window at the next moment are simulated.
10. An electronic device, characterized in that, The electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. The electronic device is used to perform the steps of the photothermal dual-response smart window energy consumption simulation method as described in any one of claims 1 to 5.