Robust optimization control method considering measurement uncertainty for air conditioner cooling water system

Abstract

The invention relates to an air conditioner cooling water system robust optimization 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 steps of obtaining sensor deviation and measurement noise of each measurement parameter with uncertainty in the air conditioner cooling water system, and calculating measurement error distribution of the sensor corresponding to each measurement parameter; generating a plurality of possible truth value scenes based on the measurement value and measurement error distribution; based on the possible truth value scene, the cooling water outlet temperature of each water chilling unit of the air conditioner cooling station at the current control moment is solved and calculated in combination with a mathematical expectation form about measurement parameters; the mathematical expectation form about the measurement parameters is to express an optimization control task of the air conditioner cooling water system by using a robust optimization theory; the optimization control task of the air conditioner cooling water system comprises an objective function, heat discharge constraint and energy conservation constraint of an optimization control problem, and the objective function is the minimum total energy consumption of the air conditioner cooling water system.

Description

Technical Field

[0001] The invention relates to a robust optimization control method for an air conditioning cooling water system 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 cooling water system is an important part of the central air-conditioning system. Improving the operating efficiency of the air-conditioning cooling water system 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. The research hotspot in this field is to regulate the air-conditioning cooling water system through reasonable optimization control methods, thereby reducing the system operation energy consumption while meeting the requirements of each terminal load.

[0003] The implementation of the optimization control method of the air conditioning cooling water system 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 the air conditioning cooling water system, and there is often a certain deviation between the input value and the true value of the operating parameter. However, the existing optimization control method of the air conditioning cooling water system rarely considers the impact of measurement uncertainty on the optimization results, which not only affects the operating efficiency of the system, 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 robust optimization control method for an air-conditioning cooling water system taking into account measurement uncertainty is provided.

[0005] The technical solution adopted by the present invention is: a robust optimization control method for an air conditioning cooling water system considering measurement uncertainty, characterized in that it includes:

[0006] Obtain the sensor deviation and measurement noise of each measurement parameter with uncertainty in the air conditioning cooling water system, 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 the possible true value scenario and combined with the mathematical expectation form of the measured parameters, the cooling water outlet temperature 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 express the optimization control task of the air conditioning cooling water system using the robust optimization theory;

[0010] The optimization control task of the air-conditioning cooling water system includes the objective function of the optimization control problem, the heat rejection constraint and the energy conservation constraint, and the objective function is to minimize the total energy consumption of the air-conditioning cooling water system.

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

[0012]

[0013] Where J is the total energy consumption of the air conditioning cooling water system; N s is the number of possible truth value scenarios; N chi 、N cwp and N ct Respectively represent the number of chillers, cooling water pumps and cooling towers in the air conditioning cooling water system; P chi,i , P cwp,i and P ct,i They represent the energy consumption functions of the i-th chiller, cooling water pump and cooling tower respectively; T cwr,i and T cws,i are the cooling water outlet temperature and return water temperature of the i-th chiller, respectively; m cw,i is the cooling water flow rate flowing through the i-th cooling water pump; and RH s are the possible true values ​​of outdoor dry-bulb temperature and relative humidity under the sth possible true value scenario; Q cl is the terminal cooling load; Q ct,i is the heat rejection of the i-th cooling tower; c p is the specific heat of water at constant pressure.

[0014] The step of obtaining sensor deviation and measurement noise of each measurement parameter with uncertainty in the air conditioning cooling water system and calculating the measurement error distribution of the sensor corresponding to each measurement parameter includes:

[0015]

[0016] 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.

[0017] Based on the measurement values ​​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, including:

[0018] Get the measured value of each sensor at the current control moment;

[0019] 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;

[0020] Determine whether all possible truth value sets have satisfied the convergence condition. If not, continue sampling to generate possible truth value sets.

[0021] The convergence conditions include:

[0022]

[0023] 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.

[0024] The measured parameters include outdoor dry bulb temperature and outdoor relative humidity.

[0025] A robust optimization control system for an air conditioning cooling water system taking into account measurement uncertainty, characterized in that it includes:

[0026] The error calculation module is used to obtain the sensor deviation and measurement noise of each measurement parameter with uncertainty in the air conditioning cooling water system, and calculate the measurement error distribution of the sensor corresponding to each measurement parameter;

[0027] 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;

[0028] The system optimization module is used to solve and calculate the cooling water outlet temperature of 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;

[0029] The optimization control task of the air-conditioning cooling water system includes the objective function of the optimization control problem, the heat rejection constraint and the energy conservation constraint, and the objective function is to minimize the total energy consumption of the air-conditioning cooling water system.

[0030] 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 robust optimization control method for an air-conditioning cooling water system taking into account measurement uncertainty are implemented.

[0031] 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 robust optimization control method for an air-conditioning cooling water system taking into account measurement uncertainty are implemented.

[0032] 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. The robust optimization control method for air-conditioning cooling water system considering measurement uncertainty proposed by the present invention can effectively reduce the influence of measurement uncertainty on optimization results, and can not only ensure the safe and reliable operation of the air-conditioning cooling water system under measurement uncertainty environment, but also improve the operation efficiency of the system.

[0033] The robust optimization control method considering measurement uncertainty proposed in the present invention improves the operating efficiency of the air conditioning cooling water system under the condition of inaccurate sensor measurement. The method reduces the impact of measurement uncertainty on the optimal control scheme by simulating and estimating the possible true value of the sensor measurement process, and can reduce the energy waste caused by sensor measurement deviation. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 is a flow chart of an embodiment of the present invention.

[0035] Figure 2 Schematic diagram of an air conditioning cooling water system in an embodiment of the present invention.

[0036] Figure 3 1 and 2 are the measured values ​​of outdoor dry-bulb temperature and relative humidity under two working conditions in the embodiment of the present invention.

[0037] Figure 4 Result comparison of cooling water return temperature in the embodiment of the present invention.

[0038] Figure 5 The results of the system operation energy consumption in the embodiment of the present invention are compared. DETAILED DESCRIPTION

[0039] Example 1: Figure 1 As shown, this embodiment is a robust optimization control method for an air conditioning cooling water system considering measurement uncertainty, which specifically includes the following steps, including:

[0040] S1. Obtain the sensor deviation and measurement noise of each measurement parameter in the air-conditioning cooling water system that has uncertainty and is associated with the heat rejection of the cooling tower in the system, and calculate the measurement error distribution of the sensor corresponding to each measurement parameter.

[0041] In this embodiment, there are two uncertain measurement parameters, including outdoor dry-bulb temperature and outdoor relative humidity. The measurement error distribution of the sensor corresponding to each measurement parameter is shown in the following formula:

[0042]

[0043] Among them, e1 and e2 are the measurement error distributions of outdoor dry-bulb temperature and outdoor relative humidity respectively; μ1 and μ2 are the sensor deviations of outdoor dry-bulb temperature and outdoor relative humidity 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 outdoor dry-bulb temperature and outdoor relative humidity, respectively, is determined by the following equations:

[0044]

[0045] Among them, E1 and E2 are the sensor accuracy indicated in the sensor manual corresponding to the outdoor dry-bulb temperature and outdoor relative humidity respectively.

[0046] 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.

[0047] S2-1. Set the number of measurement parameters N msr = 2 and the number of samples in a single possible true value set N batch =10.

[0048] S2-2, obtain the actual measurement value θ of the sensor corresponding to each measurement parameter at the control moment msr,j .

[0049] 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.

[0050] 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:

[0051] θ PT,j =θ msr,j -e j

[0052] Among them, θ PT,j is the possible true value of the jth measurement parameter.

[0053] The above sampling is repeated N timesbatch times, generating a set of possible truth values, as shown below:

[0054]

[0055] in, is the possible true value set of the hth measurement parameter, and a single set contains N batch = 10 possible truth value scenarios.

[0056] 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.

[0057] In this embodiment, the convergence judgment condition is that the following two equations are satisfied at the same time:

[0058]

[0059] 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.

[0060] S3. Based on the possible true value scenario and in combination with the mathematical expectation form of the measurement parameters, the cooling water outlet temperature of each chiller of the air-conditioning cold station at the current control moment is calculated and solved.

[0061] In this embodiment, the mathematical expectation form of the measurement parameters is to express the optimization control task of the air-conditioning cooling water system using the robust optimization theory. The optimization control task of the air-conditioning cooling water system includes the objective function of the optimization control problem, the heat rejection constraint and the energy conservation constraint.

[0062] In this embodiment, the objective function is to minimize the total energy consumption of the air conditioning cooling water system (chiller, cooling water pump and cooling tower); the heat rejection constraint is that the total heat rejection of the cooling tower should be equal to the sum of the total operating energy consumption of the chiller and the terminal cooling load.

[0063] The optimization control task of the air conditioning cooling water system in this example is shown in the following formula:

[0064]

[0065]

[0066] Q ct,i (T db ,RH)=c p mcw,i (T cws,i -T cwr,i ),i=1,...,N ct

[0067] Among them, J is the total energy consumption of the air conditioning cooling water system, N chi 、N cwp and N ct Respectively represent the number of chillers, cooling water pumps and cooling towers in the air conditioning cooling water system, P chi,i , P cwp,i and P ct,i Respectively represent the energy consumption functions of the i-th chiller, cooling water pump and cooling tower, T cwr,i and T cws,i are the cooling water outlet temperature and return water temperature of the i-th chiller, respectively, m cw,i is the cooling water flow rate flowing through the i-th cooling water pump, T db and RH represent the outdoor dry bulb temperature and relative humidity respectively (the two most important measurement parameters for air conditioning cooling water system), Q cl is the terminal cooling load, Q ct,i is the heat rejection of the i-th cooling tower, c p is the specific heat of water at constant pressure.

[0068] In this embodiment, the energy consumption functions of the chiller, cooling water pump and cooling tower are respectively as follows:

[0069]

[0070] Among them, Q nom is the rated cooling capacity of the chiller, COP nom is the rated performance coefficient of the chiller, Q chi is the actual cooling capacity of the chiller, T chw,out is the chilled water supply temperature of the chiller, T cw,in is the cooling water return temperature of the chiller, a1-a3, b1-b6 are the undetermined coefficients of the chiller, η cwp is the pump efficiency, SG is the specific gravity of the conveyed fluid (take SG = 1), m cwp is the cooling water pump flow rate, f cwp is the operating frequency of the cooling water pump, f rated is the rated operating frequency of the water pump and fan, h1-h3 is the coefficient of the cooling water pump to be determined, P ct,rated is the rated power of the cooling tower fan, f ct is the operating frequency of the cooling tower fan, and d1-d4 are the undetermined coefficients of the cooling tower fan.

[0071] The heat rejection function of the cooling tower in this embodiment is as follows:

[0072]

[0073] Among them, m ct,air is the air volume of the cooling tower fan, m ct,cw,in is the cooling water inlet flow rate of the cooling tower, T ct,cw,in is the cooling water inlet temperature of the cooling tower, c1-c3 is the heat transfer coefficient of the cooling tower, T wb is the outdoor wet-bulb temperature, which is calculated from the outdoor dry-bulb temperature and outdoor relative humidity.

[0074] In this embodiment, the robust optimization theory is used to express the optimization control task of the air conditioning cooling water system as a mathematical expectation form of the outdoor dry-bulb temperature and the outdoor relative humidity, as shown in the following formula:

[0075]

[0076] Among them, N s is the number of possible truth value scenarios, and RH s are the possible true values ​​of the outdoor dry-bulb temperature and relative humidity under the sth possible true value scenario, respectively, obtained from the possible true value set generated by step S2, and τ represents the degree to which the robust optimization method is allowed to violate the constraints during optimization, which is determined by the following formula:

[0077]

[0078] Among them, χ is the probability that the decision maker allows the robust optimization method to violate the constraints when searching for the best solution. is the maximum deviation allowed for violating the constraint.

[0079] In this embodiment, the optimization solution is performed based on the above formula to calculate a set of optimal optimization variables at the control time. In this example, the air conditioning cold station has 5 chillers, 5 fixed-frequency chilled water pumps, 5 variable-frequency cooling water pumps and 5 cooling towers (see Figure 2 ), the optimal optimization variable is the cooling water outlet temperature of each chiller [T cwr,1 ,T cwr,2 ,T cwr,3 ,T cwr,4 ,T cwr,5 ] and output it to the actuator to execute the control action.

[0080] The following is a specific example for verification and explanation: The air conditioning cooling water system in this embodiment is mainly configured 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, and 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.

[0081] Two measurement uncertainty conditions are set for performance verification: In condition 1, the measured value of the outdoor wet-bulb temperature calculated based on the outdoor dry-bulb temperature and relative humidity is always less than its true value, and the measurement error distribution of the outdoor dry-bulb temperature is N (-1.5, 0.4 2 ), the unit is ℃, the measurement error distribution of relative humidity is N(-4.5,1.9 2 ), in %. In condition 2, the measured value of the outdoor wet-bulb temperature is always greater than its true value, and the measurement error distribution of the outdoor dry-bulb temperature is N(1.5,0.4 2 ), the measurement error distribution of relative humidity is N(4.5,1.9 2 ). The measured values ​​of outdoor dry-bulb temperature and relative humidity under the two working conditions are as follows Figure 3 As shown. χ is set to 5%, Set to 100kW.

[0082] Two other control methods were set up for performance comparison: Method 1 is a traditional deterministic control method, which directly uses the measured values ​​of outdoor dry-bulb temperature and relative humidity as inputs for the optimization control problem without considering the existence of measurement uncertainty. Method 2 is an ideal control method, which uses the actual values ​​of outdoor dry-bulb temperature and relative humidity as inputs to evaluate the degree of deviation between the optimization results obtained by different control methods under measurement uncertainty and the ideal results. The control interval of the three control methods is set to 10 minutes.

[0083] The results of cooling water return temperature under different control methods in working condition 1 are as follows: Figure 4 As shown. Since the measured value of the outdoor wet-bulb temperature in working condition 1 is always lower than its true value, the traditional deterministic control method overestimates the heat removal capacity of the cooling tower, resulting in the cooling water return temperature being always high, and the time exceeding 33°C (the upper limit of the cooling water return temperature recommended in the design specification manual) accounts for as much as 84.1%. In contrast, the time of the cooling water return temperature exceeding 33°C in the method of the present invention accounts for only 0.3%, which is close to the level of the ideal control method.

[0084] The results of the system energy consumption under different control methods in working condition 2 are compared. Figure 5 As shown. Since the measured value of the outdoor wet-bulb temperature in working condition 2 is always greater than its true value, the traditional deterministic control method underestimates the heat removal capacity of the cooling tower, resulting in an increase in the operating frequency of the cooling tower fan and an increase in the operating energy consumption of the system. In contrast, the operating energy consumption of the method of the present invention decreased by 4.5%, and the deviation from the ideal control method was less than 0.1%.

[0085] It can be seen that the present invention can reduce the influence of measurement uncertainty on the optimization results of the traditional control method of the air-conditioning cooling water system, on the one hand, ensuring the safety and reliability of the operation process of the air-conditioning cooling water system, and on the other hand, improving the operation efficiency of the air-conditioning cooling water system.

[0086] Embodiment 2: This embodiment is a robust optimization control system for an air-conditioning cooling water system taking into account measurement uncertainty, including: an error calculation module, a scenario generation module, a system optimization module, etc.

[0087] In this embodiment, the error calculation module is used to obtain the sensor deviation and measurement noise of each measurement parameter with uncertainty in the air-conditioning cooling water system, and calculate the measurement error distribution of the sensor corresponding to each measurement parameter.

[0088] In this example, 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.

[0089] The system optimization module in this embodiment is used to solve and calculate the cooling water outlet temperature of each chiller of the air-conditioning cold station at the current control moment based on possible true value scenarios and in combination with the mathematical expectation form of the measurement parameters.

[0090] In this embodiment, the optimization control task of the air conditioning cooling water system includes the objective function of the optimization control problem, the heat rejection constraint and the energy conservation constraint, and the objective function is to minimize the total energy consumption of the air conditioning cooling water system.

[0091] 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 robust optimization control method of the air-conditioning cooling water system considering measurement uncertainty in Embodiment 1 are implemented.

[0092] 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 robust optimization control method of the air-conditioning cooling water system considering the measurement uncertainty in Embodiment 1 are implemented.

[0093] 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 robust optimization control method for an air conditioning cooling water system considering measurement uncertainty, characterized in that: include: Obtain the sensor deviation and measurement noise of each measurement parameter with uncertainty in the air conditioning cooling water system, 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 the possible true value scenario and combined with the mathematical expectation form of the measured parameters, the cooling water outlet temperature 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 express the optimization control task of the air conditioning cooling water system using the robust optimization theory; The optimization control task of the air-conditioning cooling water system includes the objective function of the optimization control problem, the heat rejection constraint and the energy conservation constraint, and the objective function is to minimize the total energy consumption of the air-conditioning cooling water system.

2. The robust optimization control method for air conditioning cooling water system considering measurement uncertainty according to claim 1 is characterized in that: The mathematical expectation form of the measured parameters includes: Where J is the total energy consumption of the air conditioning cooling water system; N s is the number of possible truth value scenarios; N chi 、N cwp and N ct Respectively represent the number of chillers, cooling water pumps and cooling towers in the air conditioning cooling water system; P chi,i , P cwp,i and P ct,i They represent the energy consumption functions of the i-th chiller, cooling water pump and cooling tower respectively; T cwr,i and T cws,i are the cooling water outlet temperature and return water temperature of the i-th chiller, respectively; m cw,i is the cooling water flow rate flowing through the i-th cooling water pump; and RH s are the possible true values ​​of outdoor dry-bulb temperature and relative humidity under the sth possible true value scenario; Q cl is the terminal cooling load; Q ct,i is the heat rejection of the i-th cooling tower; c p is the specific heat of water at constant pressure.

3. The robust optimization control method for air conditioning cooling water system considering measurement uncertainty according to claim 1 is characterized in that: The step of obtaining sensor deviation and measurement noise of each measurement parameter with uncertainty in the air conditioning cooling water system 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.

4. The robust optimization control method for air conditioning cooling water system considering measurement uncertainty according to claim 1, characterized in that: Based on the measurement values ​​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, 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.

5. The robust optimization control method for air conditioning cooling water system considering measurement uncertainty according to claim 4 is 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.

6. The robust optimization control method for air conditioning cooling water system considering measurement uncertainty according to claim 1, characterized in that: The measured parameters include outdoor dry bulb temperature and outdoor relative humidity.

7. A robust optimization control system for air conditioning cooling water 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 cooling water system, 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 system optimization module is used to solve and calculate the cooling water outlet temperature of 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 optimization control task of the air-conditioning cooling water system includes the objective function of the optimization control problem, the heat rejection constraint and the energy conservation constraint, and the objective function is to minimize the total energy consumption of the air-conditioning cooling water system.

8. 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 robust optimization control method for an air conditioning cooling water system taking into account measurement uncertainty as described in any one of claims 1 to 6 are implemented.

9. 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 robust optimization control method for an air conditioning cooling water system taking into account measurement uncertainty as described in any one of claims 1 to 6 are implemented.

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