Start-stop control method for air-conditioning cooling station units considering measurement uncertainty

Abstract

The present application relates to a method for controlling the start and stop of air-conditioning and cooling station units taking into account measurement uncertainty. The present application is applicable to the fields of building energy conservation and intelligent building control technology. The technical solution includes: obtaining the sensor deviation and measurement noise of each measurement parameter with uncertainty in the air-conditioning and cooling station, and calculating the measurement error distribution of the sensor corresponding to each measurement parameter; based on the measurement value of each sensor at the current control moment and the measurement error distribution of each sensor, a plurality of possible true value scenarios are generated by the adaptive Monte Carlo method, and the possible true value scenarios include the possible true values ​​of each measurement parameter; based on the possible true value scenarios, combined with the mathematical expectation form of the measurement parameters, the optimal start and stop strategy of each chiller of the air-conditioning and cooling station at the current control moment is solved and calculated; the mathematical expectation form of the measurement parameters is to express the start and stop control tasks of each chiller of the air-conditioning and cooling station using robust optimization theory.

Description

Technical Field

[0001] The present invention relates to a method for controlling the start and stop of an air-conditioning refrigeration unit taking into account measurement uncertainty, and is applicable to the technical fields of building energy conservation and intelligent building control. Background Art

[0002] During the operation of public buildings, central air conditioning systems consume approximately 50%-60% of their energy, exceeding the total energy consumption of systems like lighting and elevators, making them the largest energy consumer during the building's operation. Chillers are the primary energy consumer in central air conditioning systems. Improving the operating efficiency of chiller groups is crucial for reducing overall central air conditioning system energy consumption and offers a powerful entry point for achieving energy conservation and low-carbon development in public buildings. Controlling chiller groups through rational start-stop strategies, thereby reducing system operating energy consumption while meeting the demands of various terminal loads, is a research hotspot in this field.

[0003] The implementation of start-stop control methods for air conditioning cooling plants relies on the accurate measurement of key operating parameters. Due to factors such as sensor bias and measurement noise, measurement uncertainty is unavoidable during the operation of actual central air conditioning systems, often resulting in a certain deviation between the input and actual operating parameter values. However, existing optimization control methods for central air conditioning systems rarely consider the impact of measurement uncertainty on optimization results. This not only affects system efficiency and end-user thermal comfort, but in severe cases, can even affect the system's safe and stable operation. Summary of the Invention

[0004] The technical problem to be solved by the present invention is: in view of the above-mentioned problems, a method for starting and stopping air-conditioning refrigeration units is provided that takes measurement uncertainty into consideration.

[0005] The technical solution adopted by the present invention is: a method for controlling the start and stop of an air-conditioning refrigeration unit taking into account measurement uncertainty, characterized by comprising:

[0006] Obtain the sensor bias and measurement noise of each measurement parameter with uncertainty in the air conditioning and cooling station, and calculate the measurement error distribution of the sensor corresponding to each measurement parameter;

[0007] Based on the measurement value of each sensor at the current control moment and the measurement error distribution of each sensor, a plurality of possible true value scenarios are generated by the adaptive Monte Carlo method, and the possible true value scenarios include the possible true value of each measurement parameter;

[0008] Based on possible truth value scenarios and the mathematical expectation form of the measured parameters, the optimal start and stop strategy for each chiller in the air-conditioning cooling station at the current control time is calculated.

[0009] The mathematical expectation form of the measurement parameters is used to express the start and stop control tasks of each chiller in the air-conditioning cooling station using robust optimization theory.

[0010] The start-stop control task includes an objective function, a chiller cooling capacity constraint, and a cooling capacity conservation constraint. The objective function is to minimize the total energy consumption of the chiller group in the air-conditioning cold station.

[0011] The mathematical expectation form of the measurement parameters includes:

[0012]

[0013] st0.3·α chi,i Q chi,i,rd ≤Q chi,i ≤α chi,i Q chi,i,rd ,i=1,...,N chi

[0014]

[0015] Where J is the total energy consumption of the chiller group in the air-conditioning cooling station; N chi is the number of chillers; P chi,i is the energy consumption function of the i-th chiller; α chi,i is the start / stop status of the i-th chiller, 0 means shutdown, 1 means startup; Q chi,i is the cooling capacity of the i-th chiller; Q chi,i,rd represents the rated cooling capacity of the i-th chiller; N s is the number of possible truth value scenarios; is the possible true value of the terminal cooling load under the sth possible true value scenario, which is calculated based on the possible true values ​​of each measured parameter under the sth scenario; τ represents the degree to which the optimization result is allowed to violate the cooling conservation constraint.

[0016] The step of obtaining sensor deviation and measurement noise of each measurement parameter of the air conditioning cooling station and calculating the measurement error distribution of the sensor corresponding to each measurement parameter includes:

[0017]

[0018] Among them, e j is the measurement error distribution of the sensor corresponding to the jth measurement parameter; μ j is the sensor bias of the sensor corresponding to the jth measurement parameter; are the measurement noises of the sensors corresponding to the j-th measurement parameters.

[0019] The method generates multiple possible true value scenarios based on the measurement values ​​of each sensor at the current control moment and the measurement error distribution of each sensor through the adaptive Monte Carlo method, including:

[0020] Get the measurement value of each sensor at the current control moment;

[0021] Perform multiple random samplings from the measurement error distribution corresponding to each measurement parameter to generate a set of possible true values ​​of each measurement parameter;

[0022] Determine whether all possible truth value sets have met the convergence conditions. If not, continue sampling to generate possible truth value sets.

[0023] The convergence conditions include:

[0024]

[0025] in, is the mean vector of each measurement parameter in the i-th possible true value set; is the variance of each measurement parameter in the i-th possible true value set; STD is the standard deviation calculation function; h is the number of all possible true value sets at present; ε1 and ε2 are the corresponding thresholds.

[0026] The measurement parameters include chilled water flow rate, chilled water supply temperature and chilled water return temperature.

[0027] An air-conditioning refrigeration plant start-stop control system taking into account measurement uncertainty, characterized by comprising:

[0028] The error calculation module is used to obtain the sensor deviation and measurement noise of each measurement parameter with uncertainty in the air conditioning and cooling station, and calculate the measurement error distribution of the sensor corresponding to each measurement parameter;

[0029] A scenario generation module is used to generate multiple possible true value scenarios based on the measurement values ​​of each sensor at the current control moment and the measurement error distribution of each sensor through an adaptive Monte Carlo method. The possible true value scenarios include possible true values ​​of each measurement parameter;

[0030] The start-stop optimization module is used to solve and calculate the optimal start-stop strategy for each chiller in the air-conditioning cooling station at the current control time based on possible true value scenarios and the mathematical expectation form of the measurement parameters;

[0031] The mathematical expectation form of the measurement parameters is used to express the start and stop control tasks of each chiller in the air-conditioning cooling station using robust optimization theory.

[0032] A storage medium stores a computer program that can be executed by a processor, characterized in that when the computer program is executed, the steps of the air-conditioning refrigeration station start-stop control method considering measurement uncertainty are implemented.

[0033] An electronic device has a memory and a processor, wherein the memory stores a computer program that can be executed by the processor, and is characterized in that when the computer program is executed, the steps of the air-conditioning refrigeration station start-stop control method considering measurement uncertainty are implemented.

[0034] The beneficial effects of the present invention are as follows: the present invention uses adaptive Monte Carlo simulation to generate a series of possible true value scenarios of uncertainty parameters, thereby simulating and estimating the distribution law of measurement errors. The present invention uses robust optimization theory to comprehensively consider various possible true value scenarios of uncertainty parameters, and calculates the optimal control scheme under measurement uncertainty conditions from the perspective of mathematical expectation, thereby improving the robustness of the control scheme. The start-stop control method for air-conditioning refrigeration units considering measurement uncertainty proposed by the present invention can effectively reduce the impact of measurement uncertainty on the start-stop strategy, not only ensuring the safe and reliable operation of the refrigeration group under measurement uncertainty conditions, but also improving the operating efficiency of the system.

[0035] The proposed start-stop control method, which accounts for measurement uncertainty, improves the operating efficiency of chiller clusters in air conditioning plants even when sensor measurements are inaccurate. By simulating and estimating the possible true values ​​of sensor measurements, this method mitigates the impact of measurement uncertainty on start-stop strategies and reduces energy waste caused by sensor measurement deviations. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 Flowchart of an embodiment of the present invention.

[0037] Figure 2 Schematic diagram of an air-conditioning cold station in an embodiment of the present invention.

[0038] Figure 3 are the actual values ​​of the terminal cooling load under the two working conditions in the embodiment of the present invention.

[0039] Figure 4 The results of the terminal cooling load satisfaction in the embodiment of the present invention are compared.

[0040] Figure 5 The figure shows the comparison of the cooling machine operation and the total energy consumption of the system in the embodiment of the present invention. DETAILED DESCRIPTION

[0041] Example 1: Figure 1 As shown, this embodiment is a method for controlling the start and stop of an air-conditioning refrigeration unit taking into account measurement uncertainty, which specifically includes the following steps:

[0042] S1. Obtain the sensor bias and measurement noise of each measurement parameter of the air-conditioning cooling station that has uncertainty and is associated with the terminal cooling load, and calculate the measurement error distribution of the sensor corresponding to each measurement parameter.

[0043] In this embodiment, there are three uncertain measurement parameters, including chilled water flow, chilled water supply temperature, and chilled water return temperature. The measurement error distribution of the sensor corresponding to each measurement parameter is shown in the following formula:

[0044]

[0045] Where e1, e2, and e3 are the measurement error distributions of the chilled water flow rate, chilled water supply temperature, and chilled water return temperature, respectively; μ1, μ2, and μ3 are the sensor deviations of the chilled water flow rate, chilled water supply temperature, and chilled water return temperature, respectively. The sensor deviations are determined by a calibration method, which introduces a set of redundant reference sensors with higher accuracy. By comparing the measurement differences of the same physical quantity between the sensor to be calibrated and the reference sensors, the sensor deviations are calculated. The measurement noise corresponding to the chilled water flow rate, chilled water supply temperature and chilled water return temperature, respectively, is determined by the following formula:

[0046]

[0047] Among them, E1, E2, and E3 are the sensor accuracy indicated in the sensor manual corresponding to the chilled water flow, chilled water supply temperature, and chilled water return temperature, respectively.

[0048] S2. Based on the measurement value of each sensor at the current control moment and the measurement error distribution of each sensor, a plurality of possible true value scenarios are generated by an adaptive Monte Carlo method. The possible true value scenarios include possible true values ​​of each measurement parameter.

[0049] S2-1. Set the number of measurement parameters N msr =3 and the number of samples in a single possible true value set N batch =10.

[0050] S2-2, obtain the actual measurement value θ of the sensor corresponding to each measurement parameter at the control moment msr,j .

[0051] S2-3. Perform multiple random samplings from the measurement error distribution corresponding to each measurement parameter to generate a set of possible true values ​​of each measurement parameter.

[0052] From the measurement error distribution e j Random sampling is performed and the possible true value of each measurement parameter is determined according to the following formula:

[0053] θPT,j =θ msr,j -e j

[0054] Among them, θ PT,j is the possible true value of the jth measurement parameter.

[0055] The above sampling is repeated N times batch times, generating a set of possible truth values, as shown below:

[0056]

[0057] in, is the possible true value set of the hth measurement parameter, and a single set contains N batch = 10 possible truth value scenarios.

[0058] S2-4: Determine whether all possible true value sets currently collected meet the convergence condition. If so, the sampling process ends and proceeds to step S3. If not, return to step S2-3 and perform sampling again.

[0059] In this embodiment, the convergence judgment condition is that the following two equations are satisfied simultaneously:

[0060]

[0061] in, is the mean vector of each measurement parameter in the i-th possible true value set; is the variance of each measurement parameter in the i-th possible true value set; STD is the standard deviation calculation function; h is the number of all possible true value sets at present; ε1 and ε2 are the corresponding thresholds.

[0062] S3. Based on possible true value scenarios and combined with the mathematical expectation form of the measurement parameters, the optimal start and stop strategy for each chiller in the air-conditioning cooling station at the current control time is calculated.

[0063] In this embodiment, the mathematical expectation form of the measurement parameters is to express the start-stop control tasks of each chiller in the air-conditioning cold station using robust optimization theory. The start-stop control tasks of each chiller in the air-conditioning cold station include objective functions, chiller cooling capacity constraints, and cooling capacity conservation constraints.

[0064] In this embodiment, the objective function is to minimize the total energy consumption of the chiller group of the air-conditioning cold station; the chiller cooling capacity constraint is that the cooling capacity of the chiller during operation should be greater than or equal to 30% of the rated cooling capacity; the cooling capacity conservation constraint is that the sum of the cooling capacity of the chiller group should be equal to the total cooling load demand of the terminal.

[0065] In this example, the start and stop control tasks of each chiller in the air-conditioning cooling station are as follows:

[0066]

[0067] st0.3·α chi,i Q chi,i,rd ≤Q chi,i ≤α chi,i Q chi,i,rd ,i=1,...,N chi

[0068]

[0069] Where J is the total energy consumption of the chiller group in the air-conditioning cooling station, N chi is the number of chillers, P chi,i is the energy consumption function of the i-th chiller, α chi,i is the start / stop status of the i-th chiller, 0 means shutdown, 1 means start, Q chi,i is the cooling capacity of the i-th chiller, Q cl is the total cooling load demand at the terminal, Q chi,i,rd represents the rated cooling capacity of the i-th chiller.

[0070] In this embodiment, the total cooling load demand Q cl It is calculated based on the chilled water flow, chilled water supply temperature and chilled water return temperature, as shown in the following formula:

[0071] Q cl =c p m chw (T chwr -T chws )

[0072] Among them, c p is the specific heat capacity of water at constant pressure; T chwr and T chws Respectively represent the return water temperature and supply water temperature of the chilled water main, m chw Represents the chilled water main flow. These three parameters are determined based on the possible true values ​​of each measured parameter in each possible true value scenario.

[0073] Energy consumption function P of the chiller chi As shown below:

[0074]

[0075] Among them, COP rd is the rated performance coefficient of the chiller, and a1, a2, and a3 are the unknown coefficients of the chiller load rate.

[0076] In this embodiment, the robust optimization theory is used to express the original start-stop control task as a mathematical expectation form of the measurement parameters, as shown in the following formula:

[0077]

[0078] st0.3·α chi,i Q chi,i,rd ≤Q chi,i ≤α chi,i Q chi,i,rd ,i=1,...,N chi

[0079]

[0080] Among them, N s is the number of possible truth value scenarios, is the possible true value of the terminal cooling load under the sth scenario, which is calculated from the possible true values ​​of the chilled water flow, chilled water supply temperature, and chilled water return temperature generated in step S2. τ represents the degree to which the optimization result is allowed to violate the cooling conservation constraint.

[0081] In this example, the degree to which the optimization result is allowed to violate the cold conservation constraint is determined by the following formula:

[0082]

[0083] Among them, χ is the probability that the decision maker allows the optimization result to violate the cold conservation constraint, The maximum deviation allowed for the optimization result to violate the cold conservation constraint.

[0084] In this example, based on the above formula, the optimal start and stop strategy of each chiller in the air-conditioning cooling station at the control time is calculated. In this example, the air-conditioning cooling station has 5 chillers (see Figure 2 ), its optimal start-stop strategy includes [α chi,1 ,α chi,2 ,α chi,3 ,α chi,4 ,α chi,5 ] and output it to the actuator to execute the control action.

[0085] The following is a specific example to illustrate the following: The main configuration of the air conditioning cold station in this embodiment is as follows: the rated cooling capacity of the chiller is 1600kW, the rated COP is 5.71, the rated power of the chilled water pump is 27kW, the rated flow rate is 288m 3 / h, the rated power of the cooling water pump is 40kW and the rated flow rate is 324m 3 / h, the rated heat exchange of the cooling tower is 2194kW, the rated power of the fan is 22kW, and the rated flow rate of the fan is 4200m 3 / min.

[0086] Two measurement uncertainty conditions are set for performance verification: In condition 1, the terminal cooling load measurement value calculated based on the chilled water flow rate, chilled water supply temperature, and chilled water return temperature is always less than its true value. In condition 2, the terminal cooling load measurement value calculated based on the chilled water flow rate, chilled water supply temperature, and chilled water return temperature is always greater than its true value. The measurement uncertainty settings of each measurement parameter under the two conditions are shown in the following table. The true value of the terminal cooling load under the two conditions is as follows: Figure 3 As shown. χ is set to 5%, The load was set to 100 kW. A traditional deterministic start-stop control method was used for performance comparison. This method directly uses the measured value of the terminal cooling load as the input for the start-stop control task. The control interval for all three control methods was set to 10 minutes.

[0087]

[0088] Comparison of the results of terminal cooling load satisfaction under different control methods in working condition 1 Figure 4 As shown. Among them, the load dissatisfaction rate indicates the proportion of the cooling load demand that cannot be met during the test period to the total cooling load demand, and the load dissatisfaction duration indicates the duration during which the total cooling capacity provided by the chiller is less than the terminal cooling load demand. Since the cooling load measurement value in working condition one is always less than the actual cooling load value, the traditional deterministic control method underestimates the cooling load demand at the terminal, resulting in insufficient number of chillers turned on in some time periods. Its load dissatisfaction rate during the working condition one test period was close to 5%, and there was a time when the load dissatisfaction rate exceeded 10% for nearly 40 hours. In contrast, the supply of cooling load was significantly improved after adopting the method of the present invention, with a load dissatisfaction rate of only 0.13% and a load dissatisfaction duration of only 0.5 hours, which are respectively reduced by 97.3% and 98.7% compared with the deterministic control method.

[0089] Comparison of the operating conditions of the chiller group and the total energy consumption of the air conditioning cooling station under different control methods in working condition 2 Figure 5 As shown. Because the measured cooling load value in operating condition 2 is always greater than the actual cooling load value, the traditional deterministic control method overestimates the cooling load demand at the terminal, resulting in too many chillers being turned on in some time periods, with an average of 3.7 units turned on, resulting in a large waste of energy. In contrast, the method of the present invention can avoid the increase in energy consumption caused by turning on too many chillers as much as possible by estimating the uncertainty of the cooling load. The average number of chillers turned on is 3.5, and the operating energy consumption is reduced by 2.4%. At the same time, the cooling load demand at the terminal can be met during the entire test condition.

[0090] It can be seen that the present invention can reduce the impact of measurement uncertainty on the start-stop strategy of the cooling unit group of the air-conditioning cooling station, on the one hand, ensuring the safety and reliability of the operation of the air-conditioning cooling station, and on the other hand, can improve the operating efficiency of the air-conditioning cooling station.

[0091] Example 2: This example is an air-conditioning refrigeration station start-stop control system that takes measurement uncertainty into account, including: an error calculation module, a scenario generation module, a start-stop optimization module, etc.

[0092] In this example, the error calculation module is used to obtain the sensor bias and measurement noise of each uncertain measurement parameter of the air conditioning cooling station, and calculate the measurement error distribution of the sensor corresponding to each measurement parameter.

[0093] In this embodiment, the scenario generation module is used to generate multiple possible true value scenarios based on the measurement values ​​of each sensor at the current control moment and the measurement error distribution of each sensor through an adaptive Monte Carlo method. The possible true value scenarios include possible true values ​​of each measurement parameter.

[0094] In this embodiment, the start-stop optimization module is used to solve and calculate the optimal start-stop strategy for each chiller in the air-conditioning cold station at the current control moment based on possible true value scenarios and combined with the mathematical expectation form of the measurement parameters.

[0095] In this embodiment, the mathematical expectation form of the measurement parameters is to express the start-stop control tasks of each chiller in the air-conditioning cooling station using the robust optimization theory.

[0096] Example 3: This example is a storage medium on which a computer program that can be executed by a processor is stored. When the computer program is executed, the steps of the air-conditioning refrigeration station start-stop control method considering measurement uncertainty in Example 1 are implemented.

[0097] Example 4: An electronic device having a memory and a processor, wherein the memory stores a computer program that can be executed by the processor, and when the computer program is executed, the steps of the air-conditioning refrigeration station start-stop control method considering measurement uncertainty in Example 1 are implemented.

[0098] The embodiment described above is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Persons skilled in the art may make various changes and modifications without departing from the spirit and scope of the present invention. Therefore, any technical solution obtained by equivalent substitution or equivalent transformation falls within the scope of protection of the present invention.

Claims

1. A method for controlling the start and stop of an air-conditioning cooling station unit taking into account measurement uncertainty, characterized in that: include: Obtain the sensor bias and measurement noise of each measurement parameter with uncertainty in the air conditioning and cooling station, and calculate the measurement error distribution of the sensor corresponding to each measurement parameter; Based on the measurement value of each sensor at the current control moment and the measurement error distribution of each sensor, a plurality of possible true value scenarios are generated by the adaptive Monte Carlo method, and the possible true value scenarios include the possible true value of each measurement parameter; Based on possible truth value scenarios and the mathematical expectation form of the measured parameters, the optimal start and stop strategy for each chiller in the air-conditioning cooling station at the current control time is calculated. The mathematical expectation form of the measurement parameters is to express the start and stop control tasks of each chiller in the air-conditioning cooling station using robust optimization theory; The start-stop control task includes an objective function, a chiller cooling capacity constraint, and a cooling capacity conservation constraint. The objective function is to minimize the total energy consumption of the chiller group in the air-conditioning cooling station. The mathematical expectation form of the measurement parameters includes: ； ； ； in, is the total energy consumption of the chiller group in the air-conditioning cooling station; N chi is the number of chillers; P chi,i is the energy consumption function of the i-th chiller; α chi,i is the start / stop status of the i-th chiller, 0 means shutdown, 1 means startup; Q chi,i is the cooling capacity of the i-th chiller; represents the rated cooling capacity of the i-th chiller; N s is the number of possible truth value scenarios; is the possible true value of the terminal cooling load under the sth possible true value scenario, which is calculated based on the possible true values ​​of each measured parameter under the sth scenario; Indicates the degree to which the optimization result is allowed to violate the cold conservation constraint.

2. The air-conditioning refrigeration unit start-stop control method considering measurement uncertainty according to claim 1, characterized in that: The step of obtaining sensor deviation and measurement noise of each measurement parameter of the air conditioning cooling station and calculating the measurement error distribution of the sensor corresponding to each measurement parameter includes: ； in, is the measurement error distribution of the sensor corresponding to the jth measurement parameter; is the sensor bias of the sensor corresponding to the jth measurement parameter; are the measurement noises of the sensors corresponding to the j-th measurement parameters.

3. The method for controlling the start and stop of an air-conditioning refrigeration unit considering measurement uncertainty according to claim 1, characterized in that: The method of generating multiple possible true value scenarios based on the measurement value of each sensor at the current control moment and the measurement error distribution of each sensor by an adaptive Monte Carlo method includes: obtaining the measurement value of each sensor at the current control moment; Perform multiple random samplings from the measurement error distribution corresponding to each measurement parameter to generate a set of possible true values ​​of each measurement parameter; Determine whether all possible truth value sets have met the convergence conditions. If not, continue sampling to generate possible truth value sets.

4. The method for controlling the start and stop of an air-conditioning refrigeration unit considering measurement uncertainty according to claim 3, characterized in that: The convergence conditions include: ； ； in, is the mean vector of each measurement parameter in the i-th possible true value set; is the variance of each measurement parameter in the i-th possible true value set; STD is the standard deviation calculation function; h is the number of all possible true value sets at present; and is the corresponding threshold.

5. The method for controlling the start and stop of an air-conditioning refrigeration unit taking into account measurement uncertainty according to claim 1, characterized in that: The measurement parameters include chilled water flow rate, chilled water supply temperature and chilled water return temperature.

6. An air conditioning refrigeration unit start-stop control system considering measurement uncertainty, characterized in that: include: The error calculation module is used to obtain the sensor deviation and measurement noise of each measurement parameter with uncertainty in the air conditioning and cooling station, and calculate the measurement error distribution of the sensor corresponding to each measurement parameter; A scenario generation module is used to generate multiple possible true value scenarios based on the measurement values ​​of each sensor at the current control moment and the measurement error distribution of each sensor through an adaptive Monte Carlo method. The possible true value scenarios include possible true values ​​of each measurement parameter; The start-stop optimization module is used to solve and calculate the optimal start-stop strategy for each chiller in the air-conditioning cooling station at the current control time based on possible true value scenarios and the mathematical expectation form of the measurement parameters; The mathematical expectation form of the measurement parameters is to express the start and stop control tasks of each chiller in the air-conditioning cooling station using robust optimization theory; The start-stop control task includes an objective function, a chiller cooling capacity constraint, and a cooling capacity conservation constraint. The objective function is to minimize the total energy consumption of the chiller group in the air-conditioning cooling station. The mathematical expectation form of the measurement parameters includes: ； ； ； in, is the total energy consumption of the chiller group in the air-conditioning cooling station; N chi is the number of chillers; P chi,i is the energy consumption function of the i-th chiller; α chi,i is the start / stop status of the i-th chiller, 0 means shutdown, 1 means startup; Q chi,i is the cooling capacity of the i-th chiller; represents the rated cooling capacity of the i-th chiller; N s is the number of possible truth value scenarios; is the possible true value of the terminal cooling load under the sth possible true value scenario, which is calculated based on the possible true values ​​of each measured parameter under the sth scenario; Indicates the degree to which the optimization result is allowed to violate the cold conservation constraint.

7. A storage medium having stored thereon a computer program executable by a processor, characterized in that: When the computer program is executed, the steps of the air-conditioning refrigeration station start-stop control method considering measurement uncertainty described in any one of claims 1 to 5 are implemented.

8. An electronic device comprising a memory and a processor, wherein the memory stores a computer program executable by the processor, wherein: When the computer program is executed, the steps of the air-conditioning refrigeration station start-stop control method considering measurement uncertainty described in any one of claims 1 to 5 are implemented.

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