A green building multi-objective optimization design method

Abstract

The application relates to the technical field of energy design construction, and particularly discloses a green building multi-target optimization design method. In a plurality of historical collection periods, the use peak time periods of a ground source heat pump and a solar water heating device in each historical collection period are respectively acquired, time period coincidence comparison is carried out, the shared peak time periods are determined, a switching strategy is switched on the basis of a designed temperature difference trigger, in each shared peak time period, state switching simulation testing is carried out on the ground source heat pump and the solar water heating device, the water temperature change type in each simulation testing stage is determined, the characteristics of water temperature fluctuation in different time periods are beneficially acquired, the energy supply of the ground source heat pump and the solar water heating device can be reasonably distributed, the demand characteristics of different user groups on water temperature in different time periods can be understood, water temperature fluctuation can be controlled within a reasonable range, the probability of system fault occurrence can be reduced, and the stability of hot water supply can be improved.

Description

Technical Field

[0001] This invention relates to the field of energy design technology, specifically to a multi-objective optimization design method for green buildings. Background Technology

[0002] In the field of green building, ground source heat pumps and solar water heating systems are widely used in hot water supply systems as important renewable energy utilization devices, aiming to reduce dependence on traditional energy sources and achieve energy conservation, emission reduction, and sustainable development goals. However, in actual operation, due to the different usage frequencies of the two at different times and their significant influence from environmental factors (such as solar energy being affected by solar radiation intensity and ground source heat pumps by soil temperature), frequent equipment status switching and drastic water temperature fluctuations occur during peak usage periods.

[0003] In existing technologies, ground source heat pumps and solar water heating systems often operate independently. However, when both systems are used during the same peak period, water temperature fluctuations become particularly prominent during state switching. This makes it difficult to control water temperature fluctuations within a reasonable range, increasing the probability of system failures and severely impacting the stability of hot water supply. Furthermore, users often experience sudden changes in water temperature, significantly reducing their user experience. Therefore, conducting state switching simulation tests on ground source heat pumps and solar water heating systems to determine the type of water temperature change in each simulation test phase is beneficial for obtaining the characteristics of water temperature fluctuations over different time periods. This allows for the rational allocation of energy supply between the ground source heat pump and solar water heating system, and also enables understanding the water temperature requirements of different user groups at different times.

[0004] Secondly, during water temperature fluctuations, it is difficult to accurately assess the risk of water outages, predict potential hot water supply interruptions in advance, and effectively optimize design methods to adjust equipment state switching parameters. Furthermore, it is impossible to flexibly adjust the switching range according to different users' habits and needs. Therefore, obtaining state switching optimization coefficients and optimizing the current switching water temperature range based on these coefficients when switching ground source heat pumps and solar water heating devices can effectively control water temperature fluctuations, ensure stable water temperatures for indoor water facilities, and allow for flexible adjustments based on different users' habits and needs, achieving efficient utilization of the hot water source. Summary of the Invention

[0005] The purpose of this invention is to provide a multi-objective optimization design method for green buildings to solve the problems mentioned above.

[0006] The objective of this invention can be achieved through the following technical solutions:

[0007] A multi-objective optimization design method for green buildings includes:

[0008] The peak usage periods of ground source heat pumps and solar water heating devices for each historical data collection period are obtained, and overlapping time periods are compared to determine the shared peak periods.

[0009] State switching simulation tests were conducted on ground source heat pumps and solar water heating devices during each peak shared period. During the state switching simulation test, a comprehensive analysis of the time and quantity dimensions of water temperature fluctuations was carried out to determine the water temperature fluctuation simulation stage.

[0010] Obtain the water temperature fluctuation range within each water temperature fluctuation simulation stage. While performing change stability analysis, obtain the water temperature fluctuation zone time corresponding to each water temperature fluctuation range and compare it with the water temperature fluctuation simulation zone time. Combine the results after change stability analysis with the results after comparison to assess whether it shows a high-risk water outage signal.

[0011] If it is a high-risk water outage signal, the state switching optimization coefficient is obtained. When switching the state of the ground source heat pump and solar water heating device, the current switching water temperature range is optimized based on the state switching optimization coefficient to determine the optimized design switching range.

[0012] As a further aspect of the present invention, the process for obtaining the shared peak time period is as follows:

[0013] Each historical data collection cycle is equally divided into several historical data collection periods. The usage frequency of ground source heat pumps and solar water heating devices in each historical data collection period is obtained and compared. The historical data collection period corresponding to the highest usage frequency of ground source heat pumps and the highest usage frequency of solar water heating devices are extracted as the geothermal peak period and the solar peak period, respectively.

[0014] The peak periods of geothermal energy and solar energy within each historical data collection cycle are sorted according to the time sequence within the cycle to obtain the peak time sequence of geothermal energy and solar energy.

[0015] By comparing the peak geothermal periods within the peak geothermal time series with the peak solar periods within the peak solar time series, the overlapping peak geothermal and peak solar periods are marked as shared peak periods.

[0016] As a further aspect of the present invention: a state switching simulation test is conducted on the ground source heat pump and solar water heating device during each shared peak period, the process of which is as follows:

[0017] Extract each simulated test phase within the shared peak period, and divide the simulated test phase equally to obtain several simulated test points. Obtain the water temperature value at each simulated test point, and substitute it into a two-dimensional coordinate system according to the obtained time series to construct a unit test water temperature change curve. Take the local test water temperature change curve between adjacent simulated test points as a unit water temperature change sub-curve to obtain multiple unit water temperature change sub-curves.

[0018] First rise decline Based on the extraction criteria, the reciprocating local curves are determined according to the sequential time of the unit water temperature change sub-curves on the two-dimensional coordinate system. The time interval between adjacent reciprocating local curves is obtained, and the time ratio is calculated with the duration of the shared peak period to output the reciprocating time interval ratio.

[0019] The proportion of the number of reciprocating local curves to the number of unit water temperature change sub-curves is obtained to get the reciprocating quantity ratio;

[0020] The ratio of the number of reciprocating movements to the reciprocating interval ratio is calculated, and the reciprocating change is output.

[0021] As a further aspect of the present invention, the process for determining the water temperature fluctuation simulation stage is as follows:

[0022] Within the reciprocating local curve, the amplitude between the unit water temperature change sub-curves with upward and downward trends is obtained to obtain the unit reciprocating amplitude. The standard deviation of all unit reciprocating amplitudes is calculated and the standard deviation of the reciprocating amplitude is output. The reciprocating change value is summed with the standard deviation of the reciprocating amplitude and the type identification value is output.

[0023] If the type identification value is greater than the type identification threshold, it will be displayed as a water temperature fluctuation signal, and the simulation test stage that displays as a water temperature fluctuation signal will be marked as the water temperature fluctuation simulation stage.

[0024] As a further aspect of the present invention: the method for obtaining the water temperature fluctuation range during the water temperature fluctuation simulation stage is as follows:

[0025] Within each water temperature fluctuation simulation phase, the water temperature values ​​at all simulated test points are compared, and the maximum and minimum water temperature values ​​are extracted as the water temperature fluctuation range.

[0026] As a further aspect of the present invention: when extracting the water temperature fluctuation zone corresponding to the water temperature fluctuation simulation stage, a comparison is performed with the water temperature fluctuation simulation zone, the process of which is as follows:

[0027] The difference between the maximum and minimum water temperature values ​​within the water temperature fluctuation range is calculated and then compared with the rated water temperature value to obtain the water temperature fluctuation range value. The standard deviation of the water temperature fluctuation range value corresponding to all water temperature fluctuation simulation stages is calculated, and the stable value of the water temperature fluctuation range is output.

[0028] The time between the start and end simulation test points in each water temperature fluctuation simulation phase is obtained as the water temperature fluctuation zone time. The ratio of this time to the corresponding water temperature fluctuation simulation zone time is calculated, and the zone time overlap ratio is output.

[0029] The water temperature fluctuation range stability value is summed with the time overlap ratio to output the water outage risk value.

[0030] As a further aspect of the present invention, the process for assessing the degree of water outage risk is as follows:

[0031] If the water outage risk value is greater than the water outage risk threshold, it will be displayed as a high-risk water outage signal;

[0032] If the water outage risk value is less than or equal to the water outage risk threshold, it is displayed as a low-risk water outage signal.

[0033] As a further aspect of the present invention: the method for obtaining the state switching optimization coefficient is as follows:

[0034] The difference between the water outage risk value and the water outage risk threshold is calculated, and the ratio of the difference to the water outage risk threshold is used to calculate the state switching optimization coefficient.

[0035] As a further aspect of the present invention, the method for optimizing the current switching water temperature range is as follows:

[0036] Input the state switching optimization coefficient into the minimum switching water temperature value within the current switching water temperature range to obtain the minimum switching water temperature value after optimization design;

[0037] Input the state switching optimization coefficient into the maximum switching water temperature value within the current switching water temperature range to obtain the maximum switching water temperature value after optimization design.

[0038] As a further aspect of the present invention, the process for determining the optimized switching interval is as follows:

[0039] Based on the minimum and maximum switching water temperatures after optimization, an optimized switching range is constructed.

[0040] The beneficial effects of this invention are:

[0041] Within multiple historical data collection periods, the peak usage times of ground source heat pumps and solar water heating devices are acquired for each historical data collection period. The overlapping time periods are compared to determine the shared peak periods. Based on the designed temperature difference triggering-based switching strategy, state switching simulation tests are conducted on ground source heat pumps and solar water heating devices during each shared peak period to determine the type of water temperature change in each simulation test phase. This is beneficial for obtaining the characteristics of water temperature fluctuations in different time periods, thereby rationally allocating the energy supply of ground source heat pumps and solar water heating devices. Moreover, it is possible to understand the water temperature demand characteristics of different user groups at different time periods, so that water temperature fluctuations are controlled within a reasonable range, reducing water temperature fluctuations, lowering the probability of system failures, and improving the stability of hot water supply.

[0042] This invention performs stability analysis on the water temperature fluctuation range corresponding to each water temperature fluctuation simulation stage. When extracting the water temperature fluctuation zone corresponding to the simulation stage, it compares it with the simulated water temperature fluctuation zone to assess the risk of water outage. If a high-risk water outage signal is detected, a state switching optimization coefficient is obtained. When switching the state of the ground source heat pump and solar water heating device, the current switching water temperature range is optimized based on the state switching optimization coefficient. This optimized switching range effectively controls water temperature fluctuations, ensuring a stable water temperature for indoor water facilities. When users use hot water, it avoids sudden temperature changes due to equipment state switching. Furthermore, the optimized switching range can be flexibly adjusted according to different users' habits and needs. For users with high water temperature requirements, the switching range can be appropriately narrowed to ensure water temperature stability; for users less sensitive to water temperature changes, the range can be appropriately widened to achieve efficient energy utilization. Attached Figure Description

[0043] The invention will now be further described with reference to the accompanying drawings.

[0044] Figure 1 This is a flowchart of the steps of a multi-objective optimization design method for green buildings according to the present invention;

[0045] Figure 2 This is a schematic diagram illustrating the judgment of a multi-objective optimization design method for green buildings in this invention. Detailed Implementation

[0046] 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. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0047] Example 1

[0048] Please see Figure 1 - Figure 2 As shown in the figure, a multi-objective optimization design method for green buildings according to an embodiment of the present invention includes the following steps:

[0049] Step 1: Within multiple historical data collection periods, obtain the peak usage times of ground source heat pumps and solar water heating devices for each historical data collection period, and compare the overlapping time periods to determine the shared peak time periods;

[0050] In some embodiments, each historical collection period is equally divided into several historical collection time periods, wherein the duration of each historical collection time period is equal.

[0051] The frequency of ground source heat pump usage is obtained for each historical data collection period, and the frequency is compared. The historical data collection period corresponding to the highest frequency of ground source heat pump usage is extracted as the peak geothermal period.

[0052] It should be noted that if, when comparing the magnitudes, the frequency of use of ground source heat pumps is at its highest in two or more historical data collection periods, then the corresponding historical data collection periods are all geothermal peak periods.

[0053] Similarly, the usage frequency of solar water heating devices in each historical data collection period is obtained and compared. The highest usage frequency of solar water heating devices is extracted as the peak solar energy period.

[0054] The peak periods of geothermal energy and solar energy within each historical data collection cycle are sorted according to the time sequence within the cycle to obtain the peak time sequence of geothermal energy and solar energy.

[0055] By comparing the peak geothermal periods in the peak geothermal time series with the peak solar energy periods in the peak solar energy time series, the overlapping geothermal peak periods and solar energy peak periods in the time dimension are marked as shared peak periods.

[0056] Geothermal peak periods and solar peak periods that do not overlap in time dimension will be marked as non-co-peak periods;

[0057] Understandably, the significance of obtaining information on shared peak hours lies in two aspects: From the perspective of equipment energy efficiency, identifying and screening shared peak hours facilitates the storage of excess solar energy during off-peak hours, providing data support for improving the spatiotemporal transfer and utilization of solar energy during shared peak hours; from the perspective of building indoor comfort, since shared peak hours reflect the concentrated use of ground source heat pumps and solar water heating devices by users within the building, monitoring and analyzing shared peak hours allows for advance preparation and optimized design of equipment operation, avoiding equipment failures or operational instability during peak demand periods, and ensuring that ground source heat pumps and solar water heating devices can stably serve users within the building during shared peak hours.

[0058] Step 2: During each peak shared period, conduct state switching simulation tests on the ground source heat pump and solar water heating device to determine the type of water temperature change in each simulation test phase, thus obtaining the water temperature fluctuation simulation phase.

[0059] It should be noted that the designed temperature difference trigger works as follows: by monitoring key temperature parameters related to hot water supply within the building in real time, when the actual temperature difference reaches or exceeds the set threshold, the system automatically triggers a state switch between the ground source heat pump and the solar water heating device. For example, during peak hours, if the outlet water temperature of the solar collector is high and the temperature difference with the outlet water temperature of the ground source heat pump is large, the system can switch to the solar-dominated heating mode; if the temperature difference is small or solar energy resources are insufficient, the system switches to the ground source heat pump auxiliary or dominant heating mode.

[0060] However, when using the designed temperature difference trigger, the water temperature in the storage tank will fluctuate rapidly due to the frequent switching of water points during peak hours (such as multiple people taking showers continuously). If the temperature sensor responds late, there may be a gap between the solar water heater stopping and the ground source heat pump starting, or worse, both may start briefly at the same time, causing the instantaneous water supply to exceed the pipeline design flow, resulting in a sudden drop in water pressure and the phenomenon of water flow interruption at the water point.

[0061] Among them, the water temperature change type is either a water temperature fluctuation signal or a water temperature stability signal;

[0062] In some embodiments, each simulated test phase within a shared peak period is extracted, and the simulated test phase is divided equally to obtain several simulated test points, wherein the duration between adjacent simulated test points is equal;

[0063] The water temperature value at each simulated test point is obtained, and the obtained time series is substituted into a two-dimensional coordinate system to construct the unit test water temperature change curve.

[0064] On the unit test water temperature change curve, the local test water temperature change curve between adjacent simulated test points is taken as a unit water temperature change sub-curve, and multiple unit water temperature change sub-curves are obtained.

[0065] First rise decline Based on the extraction criteria, the reciprocating local curves are extracted from multiple unit water temperature change sub-curves according to the sequential time of the unit water temperature change sub-curves on the two-dimensional coordinate system.

[0066] The reciprocating local curve consists of three continuous unit water temperature change sub-curves, and has the first trough coordinate point, the first peak coordinate point, the second trough coordinate point, and the second peak coordinate point. It includes the unit water temperature change sub-curve with the first upward trend, the unit water temperature change sub-curve with the downward trend, and the unit water temperature change sub-curve with the last upward trend.

[0067] To further clarify, there are two peak coordinate points and two trough coordinate points on the reciprocating local curve. The first trough coordinate point is the starting point of the unit water temperature change sub-curve with the first upward trend, and the first peak coordinate point is the ending point of the unit water temperature change sub-curve with the first upward trend, or it can be the starting point of the unit water temperature change sub-curve with the downward trend (that is, the unit water temperature change sub-curves with the upward and downward trends are continuous in the two-dimensional coordinate system). The second trough coordinate point is the ending point of the unit water temperature change sub-curve with the downward trend, and the second peak coordinate point is the unit water temperature change sub-curve with the last upward trend.

[0068] Obtain the time interval between adjacent reciprocating local curves, calculate the time ratio with the duration of the shared peak period, and output the reciprocating time interval ratio.

[0069] The proportion of the number of reciprocating local curves to the number of unit water temperature change sub-curves is obtained to get the reciprocating quantity ratio;

[0070] The ratio of the number of reciprocating movements to the reciprocating interval ratio is calculated, and the reciprocating change value is output.

[0071] It is understandable that the meaning represented by the reciprocating change value is a combination of the frequency and concentration of reciprocating fluctuations. On the one hand, the reciprocating quantity ratio reflects the proportion of water temperature changes with reciprocating fluctuation patterns in the entire constructed water temperature change curve, which to some extent reflects the concentration of such reciprocating fluctuations in the water temperature change during the entire peak period. On the other hand, the reciprocating interval ratio reflects the frequency of water temperature reciprocating fluctuations (i.e., the specific fluctuation pattern of rising-falling-rising) during the shared peak period.

[0072] Within the reciprocating local curve, the amplitude between the unit water temperature change sub-curves with upward and downward trends is obtained to obtain the unit reciprocating amplitude;

[0073] Specifically, the amplitude of the first peak and valley is obtained by subtracting the Y coordinate of the first trough coordinate point from the Y coordinate of the first peak coordinate point and taking the absolute value.

[0074] Similarly, the amplitude of the first peak and trough is obtained by subtracting the Y coordinate of the first peak and the Y coordinate of the second trough and taking the absolute value.

[0075] The amplitude of the second peak and valley is obtained by subtracting the Y-coordinate of the second trough coordinate point from the Y-coordinate of the second peak coordinate point and taking the absolute value.

[0076] The summation and averaging of the first peak-valley amplitude, the first peak-valley amplitude, and the second peak-valley amplitude are used to calculate and output the unit reciprocating amplitude.

[0077] Calculate the standard deviation of the reciprocating amplitude of all units and output the standard deviation of the reciprocating amplitude.

[0078] The sum of the cyclic change value and the standard deviation of the cyclic amplitude is used to output the type identification value;

[0079] If the type identification value is greater than the type identification threshold, it means that during the simulation test phase, the water temperature fluctuations are not only frequent and concentrated, but also the water temperature fluctuation amplitudes in different repetitive phases are quite different, and the overall water temperature is relatively volatile, which is displayed as a water temperature fluctuation signal. The simulation test phase that displays the water temperature fluctuation signal is marked as the water temperature fluctuation simulation phase.

[0080] If the type identification value is less than or equal to the type identification threshold, it means that during the simulation test phase, the water temperature fluctuations are not only infrequent and scattered, but also the difference in the amplitude of water temperature fluctuations in different repetitive phases is small, and the overall water temperature is relatively stable, which is displayed as a stable water temperature signal.

[0081] It is understandable that the type identification value represents the following: It comprehensively considers the frequency, concentration, and stability of water temperature fluctuations. In terms of heat source utilization, frequent switching causes both ground source heat pumps and solar water heating systems to operate inefficiently, increasing energy consumption. Therefore, analyzing the type identification value allows us to understand the characteristics of water temperature fluctuations over different time periods, thus enabling the rational allocation of energy supply to ground source heat pumps and solar water heating systems. In terms of system stability, drastic water temperature fluctuations can lead to frequent thermal expansion and contraction of pipes, accelerating pipe aging and potentially affecting the normal operation of equipment and shortening its lifespan. Therefore, by using the type identification value, we can reduce water temperature fluctuations, lower the probability of system failures, and improve the stability of hot water supply. In terms of building user experience, different types of users have different needs for water temperature stability and variation range. For example, the elderly are more sensitive to water temperature changes and require more stable water temperatures. Analyzing the type identification value allows us to understand the water temperature needs of different user groups at different times, keeping water temperature fluctuations within a reasonable range, providing users with a more comfortable water environment, and improving the overall quality of green buildings.

[0082] Example 2

[0083] Please see Figure 1 - Figure 2 As shown in the figure, a multi-objective optimization design method for green buildings according to an embodiment of the present invention includes the following steps:

[0084] Step 3: While performing a stability analysis on the water temperature fluctuation range corresponding to each water temperature fluctuation simulation stage, extract the water temperature fluctuation area corresponding to the water temperature fluctuation simulation stage and compare it with the water temperature fluctuation simulation area time to assess the degree of water outage risk.

[0085] In some embodiments, the water temperature fluctuation range during the water temperature fluctuation simulation phase is obtained as follows:

[0086] Within each water temperature fluctuation simulation phase, the water temperature values ​​at all simulated test points are compared, and the maximum and minimum water temperature values ​​are extracted as the water temperature fluctuation range.

[0087] The difference between the maximum and minimum water temperature values ​​within the water temperature fluctuation range is calculated, and then the ratio is taken to the rated water temperature value to obtain the water temperature fluctuation range value.

[0088] It should be noted that the rated water temperature value is set by designers in this field during the design process;

[0089] The standard deviation of the water temperature fluctuation range values ​​corresponding to all water temperature fluctuation simulation stages is calculated, and the stable value of the water temperature fluctuation range is output.

[0090] The process of overlap comparison is as follows:

[0091] The time between the start and end simulation test points in each water temperature fluctuation simulation phase is obtained as the water temperature fluctuation zone time. The ratio of this time to the corresponding water temperature fluctuation simulation zone time is calculated, and the zone time overlap ratio is output.

[0092] The water temperature fluctuation range stability value is summed with the time zone overlap ratio to output the water outage risk value.

[0093] It should be noted that the meaning of the water outage risk value is: it combines the stability of the water temperature fluctuation range and the duration of the water temperature fluctuation. On the one hand, the stability value of the water temperature fluctuation range reflects the dispersion of the water temperature fluctuation amplitude in different water temperature fluctuation simulation stages. On the other hand, the overlap ratio of time zones reflects the degree of overlap between the period of large water temperature fluctuation (water temperature fluctuation time zone) and the period of state switching of the ground source heat pump and solar water heating device (state switching time zone). Since the equipment start-up, shutdown and energy conversion during the state switching process between the simulated ground source heat pump and solar water heating device are inherently prone to affecting the water temperature, combining the stability value of the water temperature fluctuation range and the time zone ratio improves the accuracy of capturing the key factors and periods that lead to water outage risk.

[0094] Specifically, since the stability of water temperature is closely related to energy consumption, by analyzing the water outage risk value, we can understand the stability of water temperature fluctuations in different water temperature fluctuation simulation stages. Moreover, designers can understand the energy demand characteristics of the water system at different stages and optimize the design of energy allocation strategies based on the water outage risk value. In high-risk stages, we can make energy reserves in advance or optimize the design of energy supply methods to improve energy utilization efficiency.

[0095] Secondly, in the process of green building design, it is necessary to consider that sudden changes in water temperature in scenarios such as showering and hand washing may cause discomfort to users. By assessing the risk of water outage and optimizing the water system design, it is possible to ensure that indoor water facilities provide a stable water temperature and improve user comfort. In addition, reasonable time scheduling (reflected by the time zone overlap ratio) can also avoid unnecessary water treatment or heating when users do not need to use water, reducing interference with the indoor environment.

[0096] The comparison between the water outage risk value and the water outage risk threshold is as follows:

[0097] If the water outage risk value is greater than the water outage risk threshold, it indicates that the water temperature fluctuates significantly between different water temperature fluctuation simulation stages and the water temperature fluctuation lasts for a long time, which is a high-risk water outage signal.

[0098] If the water outage risk value is less than or equal to the water outage risk threshold, it indicates that the water temperature fluctuation difference between different water temperature fluctuation simulation stages is small and the water temperature fluctuation duration is short, which shows a low-risk water outage signal.

[0099] Step 4: If it is a high-risk water outage signal, obtain the state switching optimization coefficient. When switching the state of the ground source heat pump and solar water heating device, optimize the current switching water temperature range based on the state switching optimization coefficient to determine the optimized design switching range.

[0100] In some embodiments, the difference between the water outage risk value and the water outage risk threshold is calculated, and the ratio of the difference to the water outage risk threshold is calculated to output the state switching optimization coefficient.

[0101] Input the state switching optimization coefficient into the minimum switching water temperature value within the current switching water temperature range to obtain the minimum switching water temperature value after optimization. ;

[0102] Specifically, ,in, This is represented as the state transition optimization coefficient. This is the minimum switching water temperature value within the current switching water temperature range;

[0103] Input the state switching optimization coefficient into the maximum switching water temperature value within the current switching water temperature range to obtain the maximum switching water temperature value after optimization design. ;

[0104] Specifically, ,in, This is represented as the state transition optimization coefficient. This represents the maximum switching water temperature value within the current switching water temperature range.

[0105] Based on the minimum switching water temperature value after optimization design Maximum switching water temperature value after optimization design Construct an optimized design switching range;

[0106] The specific solution in this embodiment is as follows: While performing stability analysis on the water temperature fluctuation range corresponding to each water temperature fluctuation simulation stage, the water temperature fluctuation area corresponding to the water temperature fluctuation simulation stage is extracted and compared with the water temperature fluctuation simulation area to assess the risk of water outage. If a high-risk water outage signal is detected, a state switching optimization coefficient is obtained. When switching the state of the ground source heat pump and solar water heating device, the current switching water temperature range is optimized based on the state switching optimization coefficient. This optimized switching range effectively controls water temperature fluctuations, ensuring a stable water temperature for indoor water facilities. When users use hot water, it avoids sudden changes in water temperature due to equipment state switching. Furthermore, the optimized switching range can be flexibly adjusted according to different users' habits and needs. For users with high water temperature requirements, the switching range can be appropriately narrowed to ensure water temperature stability; for users less sensitive to water temperature changes, the range can be appropriately expanded to achieve efficient energy utilization.

[0107] The foregoing has provided a detailed description of one embodiment of the present invention, but this description is merely a preferred embodiment and should not be construed as limiting the scope of the invention. All equivalent variations and modifications made within the scope of the present invention should still fall within the scope of the present invention.

Claims

1. A multi-objective optimization design method for green buildings, characterized in that: include: The peak usage periods of ground source heat pumps and solar water heating devices for each historical data collection period are obtained, and overlapping time periods are compared to determine the shared peak periods. State switching simulation tests were conducted on ground source heat pumps and solar water heating devices during each peak shared period. During the state switching simulation test, a comprehensive analysis of the time and quantity dimensions of water temperature fluctuations was carried out to determine the water temperature fluctuation simulation stage. Obtain the water temperature fluctuation range within each water temperature fluctuation simulation stage. While performing change stability analysis, obtain the water temperature fluctuation zone time corresponding to each water temperature fluctuation range and compare it with the water temperature fluctuation simulation zone time. Combine the results after change stability analysis with the results after comparison to assess whether it shows a high-risk water outage signal. The method for obtaining the water temperature fluctuation range during the water temperature fluctuation simulation phase is as follows: Within each water temperature fluctuation simulation phase, the water temperature values ​​of all simulated test points are compared, and the maximum and minimum water temperature values ​​are extracted as the water temperature fluctuation range. When obtaining the water temperature fluctuation zone corresponding to each water temperature fluctuation range, it is compared with the water temperature fluctuation simulation zone. The process is as follows: The difference between the maximum and minimum water temperature values ​​within the water temperature fluctuation range is calculated and then compared with the rated water temperature value to obtain the water temperature fluctuation range value. The standard deviation of the water temperature fluctuation range value corresponding to all water temperature fluctuation simulation stages is calculated, and the stable value of the water temperature fluctuation range is output. The time between the start and end simulation test points in each water temperature fluctuation simulation phase is obtained as the water temperature fluctuation simulation zone time. The ratio of the water temperature fluctuation zone time corresponding to the water temperature fluctuation interval to the corresponding water temperature fluctuation simulation zone time is calculated, and the zone time overlap ratio is output. The water temperature fluctuation range stability value is summed with the time zone overlap ratio to output the water outage risk value. If it is a high-risk water outage signal, the state switching optimization coefficient is obtained. When switching the state of the ground source heat pump and solar water heating device, the current switching water temperature range is optimized based on the state switching optimization coefficient to determine the optimized design switching range.

2. The green building multi-objective optimization design method according to claim 1, characterized in that: The process for obtaining shared peak hours is as follows: Each historical data collection cycle is equally divided into several historical data collection periods. The usage frequency of ground source heat pumps and solar water heating devices in each historical data collection period is obtained and compared. The historical data collection period corresponding to the highest usage frequency of ground source heat pumps and the highest usage frequency of solar water heating devices are extracted as the geothermal peak period and the solar peak period, respectively. The peak periods of geothermal energy and solar energy within each historical data collection cycle are sorted according to the time sequence within the cycle to obtain the peak time sequence of geothermal energy and solar energy. By comparing the peak geothermal periods within the peak geothermal time series with the peak solar periods within the peak solar time series, the overlapping peak geothermal and peak solar periods are marked as shared peak periods.

3. The green building multi-objective optimization design method according to claim 1, characterized in that: The state switching simulation test of the ground source heat pump and solar water heating system during each peak shared period was carried out as follows: Extract each simulated test phase within the shared peak period, and divide the simulated test phase equally to obtain several simulated test points. Obtain the water temperature value at each simulated test point, and substitute it into a two-dimensional coordinate system according to the obtained time series to construct a unit test water temperature change curve. Take the local test water temperature change curve between adjacent simulated test points as a unit water temperature change sub-curve to obtain multiple unit water temperature change sub-curves. First rise decline Based on the extraction criteria, the reciprocating local curves are determined according to the sequential time of the unit water temperature change sub-curves on the two-dimensional coordinate system. The time interval between adjacent reciprocating local curves is obtained, and the time ratio is calculated with the duration of the shared peak period to output the reciprocating time interval ratio. The proportion of the number of reciprocating local curves to the number of unit water temperature change sub-curves is obtained to get the reciprocating quantity ratio; The ratio of the number of reciprocating movements to the reciprocating interval ratio is calculated, and the reciprocating change value is output.

4. The green building multi-objective optimization design method according to claim 3, characterized in that: The process for determining the water temperature fluctuation simulation stage is as follows: Within the reciprocating local curve, the amplitude between the unit water temperature change sub-curves with upward and downward trends is obtained to obtain the unit reciprocating amplitude. The standard deviation of all unit reciprocating amplitudes is calculated and the standard deviation of the reciprocating amplitude is output. The reciprocating change value is summed with the standard deviation of the reciprocating amplitude and the type identification value is output. If the type identification value is greater than the type identification threshold, it will be displayed as a water temperature fluctuation signal, and the simulation test stage that displays as a water temperature fluctuation signal will be marked as the water temperature fluctuation simulation stage.

5. The multi-objective optimization design method for green buildings according to claim 1, characterized in that: The process for assessing the risk level of a water outage is as follows: If the water outage risk value is greater than the water outage risk threshold, it will be displayed as a high-risk water outage signal; If the water outage risk value is less than or equal to the water outage risk threshold, it is displayed as a low-risk water outage signal.

6. The multi-objective optimization design method for green buildings according to claim 1, characterized in that: The optimization coefficient for state transition is obtained as follows: The difference between the water outage risk value and the water outage risk threshold is calculated, and the ratio of the difference to the water outage risk threshold is used to calculate the state switching optimization coefficient.

7. The multi-objective optimization design method for green buildings according to claim 1, characterized in that: The method for optimizing the current water temperature switching range is as follows: Input the state switching optimization coefficient into the minimum switching water temperature value within the current switching water temperature range to obtain the minimum switching water temperature value after optimization design; Input the state switching optimization coefficient into the maximum switching water temperature value within the current switching water temperature range to obtain the maximum switching water temperature value after optimization design.

8. The multi-objective optimization design method for green buildings according to claim 1, characterized in that: The process for determining the optimized design switching range is as follows: Based on the minimum and maximum switching water temperatures after optimization, an optimized switching range is constructed.

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