Air conditioner cold station unit start-stop control method considering measurement uncertainty

Abstract

The invention relates to an air conditioner cold station unit start-stop control method considering measurement uncertainty. The method is suitable for the technical field of building energy saving and intelligent building control. According to the technical scheme, the method comprises the following steps: acquiring the sensor deviation and measurement noise of each measurement parameter with uncertainty of the air conditioner cold station, and calculating the measurement error distribution of the sensor corresponding to each measurement parameter; based on the measurement value of each sensor and the measurement error distribution of each sensor at the current control moment, generating a plurality of possible truth value scenes through an adaptive Monte Carlo method, the possible truth value scenes comprising possible truth values of each measurement parameter; based on the possible truth value scene, in combination with a mathematical expectation form about measurement parameters, solving and calculating to obtain an optimal start-stop strategy of each water chilling unit of the air conditioner cold station at the current control moment; the mathematical expectation form about the measurement parameters is to express the start-stop control task of each water chilling unit of the air conditioner cold station by using a robust optimization theory.

Description

Technical Field

[0001] The invention relates to a start-stop control method for air-conditioning refrigeration station units 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, the energy consumption of central air-conditioning systems accounts for about 50%-60%, exceeding the total energy consumption of lighting, elevators and other systems, and is the largest energy consumer in the operation stage of public buildings. Among them, the chiller is the main energy-consuming equipment in the central air-conditioning system. Improving the operating efficiency of the chiller group is crucial to reducing the overall energy consumption of the central air-conditioning system, and is a powerful entry point for achieving energy conservation and low carbon in public buildings. Controlling the chiller group through a reasonable start-stop strategy to reduce the system operation energy consumption while meeting the requirements of each terminal load is a research hotspot in this field.

[0003] The implementation of the start-stop control method of air-conditioning cooling station units depends on the accurate measurement of key operating parameters. Due to sensor deviation and measurement noise, measurement uncertainty is inevitable in the actual operation of central air-conditioning systems, and there is often a certain deviation between the input value and the true value of the operating parameters. However, the existing central air-conditioning system optimization control method rarely considers the impact of measurement uncertainty on the optimization results, which not only affects the system's operating efficiency and the thermal comfort of end users, but may even affect the safety and stable operation of the system in serious cases. 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 station units taking into account measurement uncertainty is provided.

[0005] The technical solution adopted by the present invention is: a method for starting and stopping air conditioning refrigeration units considering measurement uncertainty, characterized in that it includes:

[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 an adaptive Monte Carlo method, and the possible true value scenarios include possible true values ​​of each measurement parameter;

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

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

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

[0011] The mathematical expectation form of the measured 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 cold 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 cold 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 measured values ​​of each sensor at the current control moment and the measurement error distribution of each sensor by using an adaptive Monte Carlo method, including:

[0020] Get the measured 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 possible true value set of each measurement parameter;

[0022] Determine whether all possible truth value sets have satisfied the convergence condition. 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 current possible true value sets; 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 considering measurement uncertainty, characterized in that it includes:

[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 refrigeration station, and calculate the measurement error distribution of the sensor corresponding to each measurement parameter;

[0029] A scenario generation module, used to generate multiple possible true value scenarios through an adaptive Monte Carlo method based on the measurement values ​​of each sensor at the current control moment and the measurement error distribution of each sensor, wherein 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 cold station at the current control time based on possible true value scenarios and combined with the mathematical expectation form of the measurement parameters;

[0031] The mathematical expectation form of the measurement parameters is to describe the start and stop control tasks of each chiller in the air-conditioning cold station using the 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 unit 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 unit 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, which can not only ensure the safe and reliable operation of the refrigeration group under measurement uncertainty, but also improve the operating efficiency of the system.

[0035] The start-stop control method considering measurement uncertainty proposed in the present invention improves the operating efficiency of the refrigeration group of the air-conditioning refrigeration station under the condition of inaccurate sensor measurement. By simulating and estimating the possible true value of the sensor measurement process, the method reduces the impact of measurement uncertainty on the start-stop strategy and can reduce energy waste caused by sensor measurement deviation. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 is a flow chart 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 is the actual value of the terminal cooling load under 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 is a comparison of the cooling machine operation and the total energy consumption of the system in the embodiments of the present invention. DETAILED DESCRIPTION

[0041] Example 1: Figure 1 As shown, this embodiment is a method for starting and stopping air conditioning refrigeration units taking into account measurement uncertainty, which specifically includes the following steps:

[0042] S1. Obtain the sensor deviation 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] Among them, 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 deviation is determined by the calibration method, that is, a set of redundant reference sensors with higher accuracy is introduced, and the sensor deviation is calculated by comparing the measurement difference of the sensor to be calibrated and the reference sensor on the same physical quantity; 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 accuracies 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 values ​​of each sensor and the measurement error distribution of each sensor at the current control moment, 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 possible true value set of each measurement parameter.

[0052] From the measurement error distribution e j Random sampling is performed in the , 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 have met the convergence condition. If the convergence condition is met, the sampling process ends and enters step S3. If the convergence condition is not met, return to step S2-3 and sample again.

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

[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 current possible true value sets; ε1 and ε2 are the corresponding thresholds.

[0062] S3. Based on possible truth 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 cold station at the current control time is calculated and solved.

[0063] In this embodiment, the mathematical expectation form of the measurement parameters is to use robust optimization theory to express the start and stop control tasks of each chiller in the air-conditioning cold station. The start and 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 cooling capacity constraint of the chiller 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 at the terminal.

[0065] In this example, the start and stop control tasks of each chiller in the air-conditioning cold 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 cold 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 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 terminal cooling load demand Q cl It is calculated by 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 types of parameters are determined based on the possible true values ​​of each measured parameter under each possible true value scenario.

[0073] Energy consumption function P of 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 in 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 that allows the optimization result to violate the cold conservation constraint.

[0084] In this example, the optimization solution is performed based on the above formula to calculate the optimal start and stop strategy of each chiller in the air conditioning cold station at the control time. In this example, the air conditioning cold station has 5 chillers (see Figure 2 ), the 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 for verification and explanation: 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, 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, 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 shown in Figure 3 As shown. χ is set to 5%, Set to 100kW. Set the traditional deterministic start-stop control method for performance comparison. This method directly uses the measured value of the terminal cooling load as the input of the start-stop control task. The control interval of the three control methods is set to 10 minutes.

[0087]

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

[0089] The operation status of the chiller group and the total energy consumption of the air conditioning cooling station under different control methods in working condition 2 are compared. Figure 5 As shown. Since the measured value of the cold load in condition 2 is always greater than the true value of the cold load, the traditional deterministic control method overestimates the cold load demand at the terminal, resulting in too many chillers being turned on in some time periods, with an average of 3.7 units started, 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 cold load. The average number of chillers started is 3.5, and the operating energy consumption is reduced by 2.4%. At the same time, the cold 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 machine group of the air-conditioning cooling station, which on the one hand ensures the safety and reliability of the operation of the air-conditioning cooling station, and on the other hand can improve the operation efficiency of the air-conditioning cooling station.

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

[0092] In this example, the error calculation module is used to obtain the sensor deviation 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 through an adaptive Monte Carlo method based on the measurement values ​​of each sensor at the current control moment and the measurement error distribution of each sensor. 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 time 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 describe the start and stop control tasks of each chiller in the air-conditioning cold station using the robust optimization theory.

[0096] Embodiment 3: This embodiment 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 cold station unit start-stop control method considering measurement uncertainty in Embodiment 1 are implemented.

[0097] Embodiment 4: An electronic device comprises a memory and a processor, wherein the memory stores a computer program executable 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 Embodiment 1 are implemented.

[0098] The above-described embodiment is only a preferred solution of the present invention, but it is not intended to limit the present invention. A person skilled in the relevant technical field may make various changes and modifications without departing from the spirit and scope of the present invention. Therefore, any technical solution obtained by equivalent replacement or equivalent transformation falls within the protection scope of the present invention.

Claims

1. A method for starting and stopping air conditioning refrigeration units considering 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 an adaptive Monte Carlo method, and the possible true value scenarios include possible true values ​​of each measurement parameter; Based on possible truth value scenarios and combined with the mathematical expectation form of the measured parameters, the optimal start and stop strategy of each chiller in the air-conditioning cold station at the current control time is calculated; The mathematical expectation form of the measurement parameters is to describe the start and stop control tasks of each chiller in the air-conditioning cold station using the robust optimization theory.

2. The method for starting and stopping air conditioning refrigeration units considering measurement uncertainty according to claim 1, characterized in that: The start-stop control task includes an objective function, a chiller capacity constraint and a chiller conservation constraint, wherein the objective function is to minimize the total energy consumption of the chiller group of the air-conditioning cold station.

3. The method for starting and stopping air conditioning refrigeration units considering measurement uncertainty according to claim 2, characterized in that: The mathematical expectation form of the measured parameters includes: s.t.0.3·α chi,i ·Q chi,i,rd ≤Q chi,i ≤α chi,i ·Q chi,i,rd ,i=1,...,N chi Where J is the total energy consumption of the chiller group in the air-conditioning cold 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.

4. The method for starting and stopping air conditioning refrigeration units 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 cold station and calculating the measurement error distribution of the sensor corresponding to each measurement parameter includes: 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.

5. The method for starting and stopping air conditioning refrigeration units considering measurement uncertainty according to claim 1, characterized in that: The method generates multiple possible true value scenarios based on the measured values ​​of each sensor at the current control moment and the measurement error distribution of each sensor by using an adaptive Monte Carlo method, including: Get the measured 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 possible true value set of each measurement parameter; Determine whether all possible truth value sets have satisfied the convergence condition. If not, continue sampling to generate possible truth value sets.

6. The method for starting and stopping air conditioning refrigeration units considering measurement uncertainty according to claim 5, 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 current possible true value sets; ε1 and ε2 are the corresponding thresholds.

7. The method for starting and stopping air conditioning refrigeration units considering 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.

8. 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 refrigeration station, and calculate the measurement error distribution of the sensor corresponding to each measurement parameter; A scenario generation module, used to generate multiple possible true value scenarios through an adaptive Monte Carlo method based on the measurement values ​​of each sensor at the current control moment and the measurement error distribution of each sensor, wherein 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 cold station at the current control time based on possible true value scenarios and combined with the mathematical expectation form of the measurement parameters; The mathematical expectation form of the measurement parameters is to describe the start and stop control tasks of each chiller in the air-conditioning cold station using the robust optimization theory.

9. 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 unit start-stop control method considering measurement uncertainty as described in any one of claims 1 to 7 are implemented.

10. 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 unit start-stop control method considering measurement uncertainty as described in any one of claims 1 to 7 are implemented.

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