Central air conditioning system host water outlet temperature and cold and hot water pump frequency coupling operation method

Abstract

The application discloses a kind of central air conditioning system outlet water temperature and cold hot water pump frequency coupling operation method, including S1, collection basic data;S2, based on the basic data collected analysis central air conditioning cold and heat load influencing factor;S3, based on the cold and heat load influencing factor analyzed, constructs decision regression tree model;S4, according to the decision regression tree model constructed, central air conditioning system is carried out cooling and heating condition division;S5, according to the condition category divided to central air conditioning operation data is screened, determines the optimal host outlet water temperature and cold hot water pump frequency under each condition category as corresponding condition optimal setting parameter, realizes coupling operation.(1) the operation control method provided by the application simultaneously to host outlet water temperature and cold hot water pump frequency optimization, improve the energy-saving potential of central air conditioning system;The method of the application starts from actual engineering data, optimizes water temperature and water pump frequency while ensuring indoor comfort requirements, ensure the feasibility of the method.

Description

Technical Field

[0001] This invention belongs to the field of air conditioning cooling and heating load optimization technology, specifically relating to a method for coupled operation of the main unit outlet water temperature and the frequency of the hot and cold water pumps in a central air conditioning system. Background Technology

[0002] The construction industry has developed rapidly, but at the same time, building energy consumption has increased dramatically. Therefore, how to reduce the energy consumption of air conditioning systems and improve the operational efficiency of air conditioning systems and buildings has become an urgent problem to be solved in current HVAC engineering.

[0003] As the core equipment of central air conditioning, the air conditioning system main unit and the chilled and hot water pump are often selected by designers based on the maximum cooling and heating load of the building design. As a result, the system operates at partial load for 80% of the actual operation, causing a great deal of energy waste.

[0004] Therefore, most scholars have focused on the aforementioned two devices to improve the energy efficiency of central air conditioning systems and building operations. However, most studies use software simulation for analysis, constructing air conditioning system models and solving for the main unit's outlet water temperature or the frequency of hot and cold water pumps based on established objective functions and constraints. However, in actual engineering projects, the coupling between various devices is complex, and indoor occupants have varying energy consumption habits, leading to unsatisfactory modeling results. This easily overlooks changes in the indoor environment, resulting in insufficient thermal comfort. Furthermore, the aforementioned methods only solve for single variables, lacking optimization of the combined outlet water temperature and pump frequency, resulting in limited energy-saving potential. To address these issues, this invention proposes a coupled operation strategy for the main unit's outlet water temperature and the frequency of hot and cold water pumps in a central air conditioning system. Summary of the Invention

[0005] To address the aforementioned shortcomings in existing technologies, the central air conditioning system main unit outlet water temperature and chilled / hot water pump frequency coupling operation method provided by this invention starts from the cooling and heating load demand, classifies the air conditioning operation conditions in winter and summer, and selects the optimal main unit outlet water temperature and chilled / hot water pump frequency, thus solving the problems of insufficient comfort and unclear energy-saving potential in existing adjustment methods.

[0006] To achieve the aforementioned objectives, the present invention employs the following technical solution: a method for coupled operation of the main unit outlet water temperature and the frequency of the hot and cold water pumps in a central air conditioning system, comprising the following steps:

[0007] S1. Collect basic data;

[0008] S2. Analyze the factors affecting the cooling and heating load of central air conditioning based on the collected basic data;

[0009] S3. Based on the analysis of factors affecting heating and cooling loads, construct a decision regression tree model;

[0010] S4. Based on the constructed decision regression tree model, classify the central air conditioning system into cooling and heating operating conditions;

[0011] S5. The central air conditioning operation data is filtered according to the divided operating condition categories, and then the optimal main unit outlet water temperature and hot and cold water pump frequency under each operating condition category are determined as the optimal setting parameters for the corresponding operating condition to achieve coupled operation.

[0012] Furthermore, in step S1, the basic data includes central air conditioning system operation data, outdoor meteorological parameter data, and indoor temperature and humidity fluctuation data; wherein, the central air conditioning operation data includes the unit's set outlet water temperature, the set frequency of the hot and cold water pumps, the supply and return water temperatures of the hot and cold water system, and the hot and cold water flow rates; the outdoor meteorological parameter data includes outdoor dry-bulb temperature, outdoor relative humidity, and solar radiation illuminance.

[0013] For the basic data collected, data that does not meet the requirements for human comfort is removed based on the feedback data from staff regarding the indoor environment.

[0014] Further, step S2 specifically includes:

[0015] S21. Calculate the cooling and heating load of the central air conditioning system based on the collected central air conditioning system operation data;

[0016] S22. Calculate the correlation coefficient between the collected outdoor meteorological parameter data and the cooling and heating load;

[0017] S23. Determine the influencing factors of cooling and heating loads based on the calculated correlation coefficients.

[0018] Furthermore, in step S2, when analyzing the factors affecting the cooling and heating load of the central air conditioning system, based on the already determined factors affecting the cooling and heating load, load data of the central air conditioning system for 1 to 2 weeks are selected. When the load data shows attenuation and periodic changes, the time series of working hours and working days are added to the factors affecting the cooling and heating load.

[0019] Furthermore, in step S3, a decision regression tree model is constructed using the influencing factor X of heating and cooling load as the input variable and the heating and cooling load Y as the output parameter.

[0020] The specific methods for constructing the decision regression tree model include:

[0021] By partitioning the input space composed of input variables, the optimal splitting variable is found, and the input space is further divided into two subspaces. The splitting variable is searched sequentially, and each subspace is divided until the sum of squared residuals corresponding to the splitting point of the splitting variable in each subspace is minimized, thus completing the construction of the decision regression tree model. Wherein, when the sum of squared residuals corresponding to the splitting point of the splitting variable in each subspace is minimized, the splitting variable is the optimal splitting variable for each subspace.

[0022] Furthermore, the constructed decision regression tree model The expression is:

[0023]

[0024] In the formula, M The number of subspaces divided by the input space consisting of the input variables. M Each subspace is represented as , ... , For each subspace, there exists a fixed output value. For subspace;

[0025] The decision regression tree model, through N The criterion of minimizing the value determines the quality of the decision regression tree model, and its expression is:

[0026]

[0027] In the formula, for The Middle i One input variable sample, for The corresponding output; in the subspace Optimal output value For all input data samples in this subspace Corresponding output The mean, i.e. .

[0028] Furthermore, the first Input variables and its value As the splitting variable and splitting point, it corresponds to the two subspaces divided. and They are respectively:

[0029]

[0030]

[0031] The sum of squared residuals P corresponding to the split point is:

[0032]

[0033]

[0034] .

[0035] Furthermore, in step S4, the cooling and heating load of the central air conditioning system is used as the standard for dividing the operating conditions. Each cooling and heating load has a corresponding working time, working date, and outdoor dry-bulb temperature.

[0036] Further, step S5 specifically includes:

[0037] For the central air conditioning operation data under each type of operating condition, the maximum system COP is determined by using the system COP as the target, and the corresponding main unit set outlet water temperature and chilled / hot water pump frequency are used as the optimal setting parameters under the corresponding operating condition to achieve coupled operation.

[0038] Furthermore, the formula for calculating the system COP is:

[0039]

[0040] In the formula, For air conditioning system COP, For heating and cooling loads, This refers to the total energy consumption of the air conditioning system equipment, including the main unit, chilled and hot water pumps, and air conditioning terminals.

[0041] The beneficial effects of this invention are as follows:

[0042] (1) The operation control method provided by the present invention optimizes both the outlet water temperature of the main unit and the frequency of the hot and cold water pumps, thereby improving the energy-saving potential of the central air conditioning system;

[0043] (2) The method of the present invention starts from actual engineering data, optimizes water temperature and water pump frequency while ensuring indoor comfort requirements, thus ensuring the feasibility of the method. Attached Figure Description

[0044] Figure 1 The flowchart of the coupling operation method of the main unit outlet water temperature and the frequency of the hot and cold water pumps in the central air conditioning system provided by the present invention is shown.

[0045] Figure 2 The present invention provides information on air conditioning load changes.

[0046] Figure 3 An example of the initial classification result provided by the algorithm of this invention.

[0047] Figure 4This is an example of the screening results for a certain category under winter heating conditions provided by the present invention.

[0048] Figure 5 The present invention provides the COP distribution for different operating conditions of summer cooling.

[0049] Figure 6 The present invention provides the COP distribution for different operating conditions of winter heating.

[0050] Figure 7 This invention provides a comparison of the energy consumption of the air conditioning system before and after optimization for summer operating conditions.

[0051] Figure 8 The indoor environment conditions after summer operation are optimized according to the present invention.

[0052] Figure 9 This invention provides a comparison of the energy consumption of the air conditioning system before and after optimization for winter operating conditions.

[0053] Figure 10 The indoor environment conditions after winter operation optimization provided by this invention. Detailed Implementation

[0054] The specific embodiments of the present invention are described below to enable those skilled in the art to understand the present invention. However, it should be understood that the present invention is not limited to the scope of the specific embodiments. For those skilled in the art, various changes are obvious as long as they are within the spirit and scope of the present invention as defined and determined by the appended claims. All inventions utilizing the concept of the present invention are protected.

[0055] Example 1:

[0056] This invention provides a method for coupled operation of the main unit outlet water temperature and the frequency of the hot and cold water pumps in a central air conditioning system, such as... Figure 1 As shown, it includes the following steps:

[0057] S1. Collect basic data;

[0058] S2. Analyze the factors affecting the cooling and heating load of central air conditioning based on the collected basic data;

[0059] S3. Based on the analysis of factors affecting heating and cooling loads, construct a decision regression tree model;

[0060] S4. Based on the constructed decision regression tree model, classify the central air conditioning system into cooling and heating operating conditions;

[0061] S5. The central air conditioning operation data is filtered according to the divided operating condition categories, and then the optimal main unit outlet water temperature and hot and cold water pump frequency under each operating condition category are determined as the optimal setting parameters for the corresponding operating condition to achieve coupled operation.

[0062] In step S1 of this embodiment of the invention, basic data is collected through a comprehensive combination and debugging experiment of the outlet water temperature and water pump frequency based on an existing experimental platform. This data includes central air conditioning system operation data, outdoor meteorological parameter data, and indoor temperature and humidity fluctuation data. The central air conditioning operation data includes the unit's set outlet water temperature, the set frequency of the hot and cold water pumps, the supply and return water temperatures of the hot and cold water system, and the hot and cold water flow rates. The outdoor meteorological parameter data includes outdoor dry-bulb temperature, outdoor relative humidity, and solar radiation illuminance, and the hourly average values ​​of these data are calculated. For the collected basic data, data that does not meet human comfort requirements are removed based on feedback from staff regarding the indoor environment. Specifically, indoor temperature and humidity fluctuation data can reflect indoor comfort conditions, and during the experiment, data sets of chilled water outlet temperature and water pump frequency combinations that do not meet comfort requirements are removed.

[0063] Step S2 in this embodiment of the invention is specifically as follows:

[0064] S21. Calculate the cooling and heating load of the central air conditioning system based on the collected central air conditioning system operation data;

[0065] S22. Calculate the correlation coefficient between the collected outdoor meteorological parameter data and the cooling and heating load;

[0066] S23. Determine the influencing factors of cooling and heating loads based on the calculated correlation coefficients.

[0067] In step S21 of this embodiment, the formula for calculating the heating and cooling load is:

[0068]

[0069] In the formula, Q For heating and cooling loads, kW ; c The specific heat capacity of hot and cold water, kJ / ( kg K ); T For the temperature difference between hot and cold water supply and return water, K .

[0070] In step S22 of the embodiment, the correlation coefficient is calculated. The formula is:

[0071]

[0072] In the formula, The number of data sets. For each influencing factor, for The average value, For heating and cooling loads, for The average value can be used to determine the correlation between each factor and the load through Table 1. Therefore, in step S23 of this embodiment, factors with a correlation coefficient greater than 0.6 are selected as factors affecting the heating and cooling loads.

[0073] Table 1: Judgment of the degree of correlation between variables

[0074]

[0075] In step S2 of this embodiment of the invention, due to the fluctuations in indoor occupancy, the heat storage capacity of the building itself, and the strong stagnant properties of the water system, the heating and cooling loads exhibit attenuation and periodic fluctuations at different times. This indicates that the load is also related to the time series, such as... Figure 2 As shown, however, the above characteristics cannot be directly calculated and judged using the correlation coefficient formula. Therefore, when analyzing the influencing factors of the cooling and heating load of the central air conditioning system, based on the already determined influencing factors, load data of the central air conditioning system for 1-2 weeks is selected. When the load data shows attenuation and periodic changes, the time series of working hours and working days are added to the influencing factors of cooling and heating load; for example, the two time series items of working hours (e.g., 9:00-17:00) and working days (e.g., Monday-Friday) are directly used as the influencing factors of cooling and heating load.

[0076] In step S3 of this embodiment of the invention, a decision regression tree model is constructed using the influencing factor X of heating and cooling load as the input variable and the heating and cooling load Y as the output parameter; for a given input parameter and output parameters Then the dataset D can be represented as: ;

[0077] The construction of the decision regression tree model includes: dividing the input space composed of input variables, finding the optimal splitting variable, and then dividing the input space into two subspaces; sequentially finding the splitting variable and dividing each subspace until the sum of squared residuals of the splitting variable corresponding to the splitting point in each subspace is minimized, thus completing the construction of the decision regression tree model; wherein, when the sum of squared residuals of the splitting variable corresponding to the splitting point in each subspace is minimized, the splitting variable is the optimal splitting variable for each subspace;

[0078] In this embodiment, the constructed decision regression tree model The expression is:

[0079]

[0080] In the formula, M The number of subspaces divided by the input space consisting of the input variables.M Each subspace is represented as , ... , For each subspace, there exists a fixed output value. For subspace;

[0081] Decision regression tree model through N The criterion of minimizing the value determines the quality of the decision regression tree model, and its expression is:

[0082]

[0083] In the formula, for The Middle i One input variable sample, for The corresponding output; in the subspace Optimal output value For all input data samples in this subspace Corresponding output The mean, i.e. .

[0084] In the above decision regression tree model, the first... Input variables and its value As the splitting variable and splitting point, it corresponds to the two subspaces divided. and They are respectively:

[0085]

[0086]

[0087] The sum of squared residuals P corresponding to the split point is:

[0088]

[0089]

[0090] .

[0091] In this embodiment, based on the above analysis of factors affecting heating and cooling loads, the main relevant factors are... Input parameters include outdoor dry-bulb temperature, outdoor relative humidity, solar irradiance, working hours (replace 9:00-17:00 with values ​​9-17), and working day (replace Monday-Friday with values ​​1-5), as well as heating and cooling loads. As output parameters, a decision regression tree model was built using WEKA data analysis software.

[0092] In step S4 of this embodiment of the invention, the cooling and heating load of the central air conditioning system is used as the standard for dividing the working conditions. Each cooling and heating load has a corresponding working time, working date and outdoor dry bulb temperature.

[0093] Specifically, the initial results obtained based on the aforementioned decision regression tree model are in the form of a tree diagram, such as... Figure 3 As shown, for ease of observation, the results in the figure are categorized and represented according to the following text, for example:

[0094] Time: 9:00-17:00, weekdays: Monday-Tuesday, outdoor dry bulb temperature: <10℃, load: 400kW;

[0095] Time: 9:00-17:00, weekdays: Monday-Tuesday, outdoor dry bulb temperature: ≥10℃, load: 300kW;

[0096] Time: 9:00-17:00, weekdays: Wednesday-Friday, outdoor dry bulb temperature: <10℃, load: 350kW;

[0097] Operating hours: 9:00-17:00, weekdays: Wednesday-Friday, outdoor dry bulb temperature: <10℃, load: 250kW.

[0098] Step S5 of this embodiment of the invention is specifically as follows:

[0099] For the central air conditioning operation data under each type of operating condition, the maximum system COP is determined by using the system COP as the target, and the corresponding main unit set outlet water temperature and chilled / hot water pump frequency are used as the optimal setting parameters under the corresponding operating condition to achieve coupled operation.

[0100] The system COP calculation formula in this embodiment is:

[0101]

[0102] In the formula, COP for air conditioning systems; For heating and cooling loads, ; This represents the total energy consumption of the air conditioning system equipment. This includes air conditioning system equipment such as main unit, hot and cold water pumps, and air conditioning terminals.

[0103] In this embodiment of the invention, the screening results of a certain category under winter heating conditions in actual engineering are used as an example for illustration, such as... Figure 4As shown, it can be found that the maximum COP of the system under this classification condition is 2.82, and the corresponding outlet water temperature and pump frequency are 38℃ and 35Hz, respectively. Therefore, this value is the optimal operating parameter for the host and the pump.

[0104] Example 2:

[0105] This invention provides an example of coupled operation control of a central air conditioning system based on the method in Example 1:

[0106] This case study is an office building in Chengdu, Sichuan Province, with an area of ​​approximately 15,000 square meters, which uses an air source heat pump system for cooling and heating.

[0107] (1) The outlet water temperature and pump frequency of the air conditioning system were tested. The set outlet water temperature of the air conditioning system unit, the set frequency of the cold and hot water pump, the supply and return water temperature of the cold and hot water system, the cold and hot water flow rate, the outdoor dry bulb temperature, and the outdoor relative humidity were collected. Data were recorded at 5-minute intervals, the hourly average value of the data was calculated, the building's cooling and heating load was calculated, and data that did not meet the indoor comfort requirements were removed. The test time was mainly concentrated in the cooling and heating seasons of 2021-2023.

[0108] (2) The main influencing factors of the load were analyzed. According to the Pearson correlation coefficient calculation formula, the outdoor dry-bulb temperature and outdoor relative humidity had the strongest correlation with the load, which were 0.83 and 0.7 respectively. Therefore, the main influencing factors selected were outdoor dry-bulb temperature, outdoor relative humidity, working time and working day date.

[0109] (3) Using the above-mentioned main influencing factors as input parameters and cooling and heating loads as output parameters, a decision tree regression tree (CART) model was established to classify the operating conditions of the air conditioning system. Summer operating conditions were divided into 6 categories, and winter operating conditions were divided into 7 categories. The specific classification results are shown in Tables 2 and 3. It can be seen that the final classification results are independent of outdoor relative humidity and working hours. This is because there is a strong correlation between outdoor temperature and outdoor relative humidity, with a correlation coefficient of 0.88. Moreover, the cooling and heating loads do not fluctuate significantly throughout the day during working hours. Therefore, the algorithm only solves for the impact of outdoor temperature and working day date on the load.

[0110] Table 3: Classification Results of Air Conditioning System Cooling Operation Conditions in Summer

[0111]

[0112] Table 4: Classification Results of Winter Heating Operation Conditions for Air Conditioning Systems

[0113]

[0114] (4) Using system COP as a metric, the COP of winter and summer air conditioning systems was statistically screened based on the above classification results. The distribution of COP for each category is as follows: Figure 1 , 2 As shown, the optimal outlet water temperature of the main unit and the frequency of the hot and cold water pumps were optimized, and the results are shown in Tables 4 and 5.

[0115] Table 4. Optimal outlet water temperature and pump frequency for different summer cooling operating conditions.

[0116]

[0117] Table 5. Optimal outlet water temperature and pump frequency for different operating conditions in winter heating.

[0118]

[0119] Validity analysis:

[0120] To verify the operational performance of the central air conditioning system before and after optimization in winter and summer, the following content compares and analyzes the total energy consumption of the system and the indoor temperature and humidity on each floor of the building.

[0121] (1) The energy consumption data for the summer cooling operation was compared using energy consumption data from 16 July 2021 to 31 July 2022, a total of 15 days. The comparison results are as follows: Figure 7 As shown, the total energy consumption of the air conditioning system was significantly reduced after the debugging, saving approximately 30% of electricity during that period; at the same time, according to Figure 8 Based on the daily average indoor temperature and humidity on each floor, the indoor temperature remained below 27℃ and the humidity remained below 70% during the test period, meeting the design standards of the "Code for Design of Heating, Ventilation and Air Conditioning of Civil Buildings" and the requirements for indoor human thermal comfort. The optimization of the central air conditioning water system has achieved significant results.

[0122] (2) The energy consumption data for the winter heating operation were compared using energy consumption data from January 11, 2021 to January 21, 2022 (a total of 10 days). The comparison results are as follows. Figure 9 As shown, the total energy consumption of the air conditioning system was significantly reduced after debugging, with a reduction of approximately 38.9% during this period. According to the design standard "Code for Design of Heating, Ventilation and Air Conditioning of Civil Buildings," the indoor temperature should not be lower than 18℃ under Class II thermal comfort requirements during winter heating, but there are no specific requirements for indoor relative humidity. Figure 10 Based on the average daily temperature and humidity on each floor, the indoor temperature of each room on each floor was above 18℃ during the test period, which meets the design standards and the requirements for indoor thermal comfort.

[0123] Specific embodiments have been used to illustrate the principles and implementation methods of this invention. The descriptions of the embodiments above are only for the purpose of helping to understand the method and core ideas of this invention. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this invention. Therefore, the content of this specification should not be construed as a limitation of this invention.

[0124] Those skilled in the art will recognize that the embodiments described herein are intended to help the reader understand the principles of the invention, and should be understood that the scope of protection of the invention is not limited to such specific statements and embodiments. Those skilled in the art can make various other specific modifications and combinations based on the technical teachings disclosed in this invention without departing from the spirit of the invention, and these modifications and combinations are still within the scope of protection of this invention.

Claims

1. A method for coupled operation of the main unit outlet water temperature and the frequency of the hot and cold water pumps in a central air conditioning system, characterized in that, Includes the following steps: S1. Collect basic data; S2. Analyze the factors affecting the cooling and heating load of central air conditioning based on the collected basic data; S3. Based on the analysis of factors affecting heating and cooling loads, construct a decision regression tree model; S4. Based on the constructed decision regression tree model, classify the central air conditioning system into cooling and heating operating conditions; S5. The central air conditioning operation data is filtered according to the divided operating condition categories, and then the optimal main unit outlet water temperature and hot and cold water pump frequency under each operating condition category are determined as the optimal setting parameters for the corresponding operating condition to achieve coupled operation. In step S3, a decision regression tree model is constructed using the influencing factor X of heating and cooling load as the input variable and the heating and cooling load Y as the output parameter. The construction of the decision regression tree model includes: dividing the input space composed of input variables, finding the optimal splitting variable, and then dividing the input space into two subspaces; sequentially finding the splitting variable and dividing each subspace until the sum of squared residuals of the splitting variable corresponding to the splitting point in each subspace is minimized, thus completing the construction of the decision regression tree model; wherein, when the sum of squared residuals of the splitting variable corresponding to the splitting point in each subspace is minimized, the splitting variable is the optimal splitting variable for each subspace; Constructed decision regression tree model The expression is: In the formula, M The number of subspaces divided by the input space consisting of the input variables. M Each subspace is represented as , ... , For each subspace, there exists a fixed output value. For subspace; The decision regression tree model, through N The criterion of minimizing the value determines the quality of the decision regression tree model, and its expression is: In the formula, for The Middle i One input variable sample, for The corresponding output; in the subspace Optimal output value For all input data samples in this subspace Corresponding output The mean, i.e. ; The first Input variables and its value As the splitting variable and splitting point, it corresponds to the two subspaces divided. and They are respectively: The sum of squared residuals P corresponding to the split point is: 。 2. The method for coupled operation of the main unit outlet water temperature and the frequency of the hot and cold water pumps in a central air conditioning system according to claim 1, characterized in that, In step S1, the basic data includes central air conditioning system operation data, outdoor meteorological parameter data, and indoor temperature and humidity fluctuation data; wherein, the central air conditioning operation data includes the unit's set outlet water temperature, the set frequency of the hot and cold water pumps, the supply and return water temperatures of the hot and cold water system, and the hot and cold water flow rate; the outdoor meteorological parameter data includes outdoor dry-bulb temperature, outdoor relative humidity, and solar radiation illuminance. For the basic data collected, data that does not meet the requirements for human comfort is removed based on the feedback data from staff regarding the indoor environment.

3. The method for coupled operation of the main unit outlet water temperature and the frequency of the hot and cold water pumps in a central air conditioning system according to claim 2, characterized in that, Step S2 specifically involves: S21. Calculate the cooling and heating load of the central air conditioning system based on the collected central air conditioning system operation data; S22. Calculate the correlation coefficient between the collected outdoor meteorological parameter data and the cooling and heating load; S23. Determine the influencing factors of cooling and heating loads based on the calculated correlation coefficients.

4. The method for coupled operation of the main unit outlet water temperature and the frequency of the hot and cold water pumps in a central air conditioning system according to claim 3, characterized in that, In step S2, when analyzing the factors affecting the cooling and heating load of the central air conditioning system, based on the already determined factors affecting the cooling and heating load, load data of the central air conditioning system for 1 to 2 weeks is selected. When there are periodic changes in the load data, the time series of working time is added to the factors affecting the cooling and heating load.

5. The method for coupled operation of the main unit outlet water temperature and the frequency of the hot and cold water pumps in a central air conditioning system according to claim 2, characterized in that, In step S4, the cooling and heating load of the central air conditioning system is used as the standard for dividing the operating conditions. Each cooling and heating load has a corresponding working time and outdoor dry-bulb temperature.

6. The method for coupled operation of the main unit outlet water temperature and the frequency of the hot and cold water pumps in a central air conditioning system according to claim 2, characterized in that, Step S5 specifically involves: For the central air conditioning operation data under each type of operating condition, the maximum system COP is determined by using the system COP as the target, and the corresponding main unit set outlet water temperature and chilled / hot water pump frequency are used as the optimal setting parameters under the corresponding operating condition to achieve coupled operation.

7. The method for coupled operation of the main unit outlet water temperature and the frequency of the hot and cold water pumps in a central air conditioning system according to claim 6, characterized in that, The formula for calculating the system's COP is: In the formula, For air conditioning system COP, For heating and cooling loads, This refers to the total energy consumption of the air conditioning system equipment, including the main unit, chilled and hot water pumps, and air conditioning terminals.

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