A method for real-time simulation and optimization of energy efficiency in a central air conditioning computer room system

Abstract

A real-time simulation and optimization method for the energy efficiency of a central air conditioning equipment room system includes: S1: Real-time acquisition of operating data for various conditions of the central air conditioning equipment room system, selecting historical non-abnormal data as background data for fitting mathematical relationship models between variables; S2: Establishing energy consumption models and correlation models for each operating condition, including at least chiller units, air conditioning water pumps, cooling water pumps, and cooling towers, and fitting the models using the background data to obtain fitting coefficients for each model; S3: Establishing the energy consumption correlation relationship between the coupled models to construct an energy efficiency simulation system; Based on the energy efficiency simulation system, calculating the system simulation results under the current environmental conditions and control strategy; S4: Using an optimization algorithm to repeatedly change the control strategy and obtain simulation results for evaluation, finally outputting the optimal control strategy. This invention achieves low-error prediction of equipment room energy efficiency through a simulation system with high equipment correlation, and optimizes the control strategy by combining an optimization algorithm, ultimately achieving real-time and efficient operation of the equipment room throughout the year.

Description

Technical Field

[0001] This invention relates to the field of energy consumption simulation and optimization control technology for large central air conditioning room systems in public buildings, and in particular to a real-time simulation and optimization method for the energy efficiency of central air conditioning room systems. Background Technology

[0002] In public buildings, central air conditioning equipment rooms account for 40% of energy consumption. Most equipment room control systems lack systematic optimization capabilities, with each device operating under independent closed-loop control. Real-time full-condition simulation optimization is an effective way to achieve systematic optimization of equipment rooms. Accurate full-condition energy consumption models for each device are the foundation for system simulation and reducing overall errors. Reasonable correlation between models is also a prerequisite for the feasibility of system optimization. These are areas where existing technologies fall short.

[0003] CN116697530A discloses a high-efficiency adaptive energy-saving control system and method for computer rooms based on operating condition prediction. Although it provides a method to collect data on the operating conditions of terminals, various equipment in the computer room, air conditioning and cooling pipes, analyze and predict the demand of the unit and various auxiliary equipment, and decompose this demand to each equipment to optimize system control in order to reduce energy consumption and improve system efficiency, it lacks a model for quantitatively solving its demand and a specific linkage idea between the demand of each equipment. It cannot implement precise optimization and global optimization and achieve high-efficiency and low-consumption operation throughout the year.

[0004] CN114165854A discloses an intelligent optimization control method based on a dynamic simulation platform for central air conditioning systems. It provides quantitative solution models for various types of equipment, builds an energy consumption simulation system by connecting the various models, and optimizes an efficient control strategy using a coded genetic algorithm. However, each model has defects in key factors or indirect correlations, resulting in a large overall prediction error. Furthermore, it does not consider integrating the impact of outdoor weather on system energy efficiency into the simulation system, making it impossible to achieve accurate and stable optimization control and ultimately achieve efficient operation throughout the year.

[0005] CN119538570A discloses a data-driven method and apparatus for simulating the energy consumption of an air conditioning system. This method determines whether the system energy consumption is abnormal by establishing an energy consumption prediction model and collecting real-time data for prediction. It can realize a system energy consumption prediction scheme based on the equipment control state. However, this scheme cannot predict the mutual influence of each prediction model under changes in control state, and therefore lacks the feasibility of simulation optimization. Summary of the Invention

[0006] The purpose of this invention is to solve at least one of the above-mentioned technical problems by providing a real-time simulation and optimization method for the energy efficiency of a central air conditioning computer room system. The method achieves low-error prediction of computer room energy efficiency through a simulation system with high equipment correlation, and optimizes the control strategy by combining optimization algorithms, ultimately achieving real-time and efficient operation of the computer room throughout the year.

[0007] The technical solution of this invention is:

[0008] A method for real-time energy efficiency simulation and optimization of a central air conditioning computer room system includes the following steps:

[0009] S1: Real-time acquisition of operating data of the central air conditioning system in the computer room, real-time analysis and verification of the data, storage of non-abnormal data after verification; and selection of historical non-abnormal data as background data for fitting the mathematical relationship model between variables;

[0010] S2: Establish an energy consumption model that includes at least a chiller unit, an air conditioning water pump, a cooling water pump, and a cooling tower, as well as a correlation model for each operating condition, and use the background data to fit the model to obtain the fitting coefficients of each model.

[0011] S3: Integrate the energy consumption models in S2, establish the energy consumption correlation between the coupled models, and construct an energy efficiency simulation system; based on the energy efficiency simulation system, load real-time indoor and outdoor environmental parameters as fixed input conditions, and set control strategy-related variables as adjustable inputs to calculate the system simulation results under the current environmental conditions and control strategy.

[0012] S4: The optimal control strategy is finally output by repeatedly changing the control strategy and obtaining simulation results using the optimization algorithm.

[0013] Furthermore, in S2, the method for establishing the energy consumption model of the chiller unit includes: establishing the energy consumption function of the chiller unit by taking the total heat brought into the evaporator by the air conditioning water and the pressure difference between the condenser and the evaporator as two main input variables, obtaining the energy consumption model expression of the chiller unit with fitting coefficients, and using the background data to perform fitting to obtain the fitting coefficients;

[0014] or,

[0015] The method for establishing the energy consumption model of the air conditioning water pump includes: establishing a total energy consumption function of the air conditioning water pump with the number of air conditioning water pumps operating simultaneously and the frequency of a single air conditioning water pump as two main input variables; and establishing an air conditioning water flow function with the total energy consumption of the air conditioning water pump, the number of air conditioning water pumps operating simultaneously, and the pressure difference between the outlet and inlet of the air conditioning water pump as three main input variables, obtaining the total energy consumption model expression and the air conditioning water flow model expression with fitting coefficients, and using the background data to perform fitting to obtain the fitting coefficients;

[0016] or,

[0017] The method for establishing the energy consumption model of the cooling water pump includes: establishing a total energy consumption function of the cooling water pump with the number of cooling water pumps operating simultaneously and the frequency of a single cooling water pump as two main input variables; and establishing a cooling water flow rate function with the total energy consumption of the cooling water pump, the number of cooling water pumps operating simultaneously, and the pressure difference between the outlet and inlet of the cooling water pump as three main input variables, obtaining the total energy consumption model expression and the cooling water flow rate model expression of the cooling water pump with fitting coefficients, and using the background data to perform fitting to obtain the fitting coefficients;

[0018] or,

[0019] The method for establishing the energy consumption model of the cooling tower includes: establishing the total air volume function and the total energy consumption function of the cooling tower by using the frequency of a single fan and the number of cooling towers in operation as two main input variables; establishing the cooling load function of the cooling tower by using the total heat carried into the evaporator by the air conditioning water as a main input variable; and establishing the cooling tower outlet water temperature function by using the outdoor wet-bulb temperature, cooling load, total fan air volume, the difference between outdoor dry-bulb temperature and wet-bulb temperature, and the difference between outdoor dry-bulb temperature and the average temperature of the inlet and outlet water of the cooling tower as main input variables; obtaining the total energy consumption model expression of the cooling tower with fitting coefficients, and fitting it using the background data to obtain the fitting coefficients; and also obtaining the model expressions for the total fan air volume and the cooling tower outlet water temperature.

[0020] Furthermore, in S2, the associated model for each operating condition includes the total energy consumption model of the computer room system, and the expression for the total energy consumption W0 of the computer room is as follows:

[0021] W0 = W WaterChiller +W KTpump +W LQpump +W LQtower

[0022] In the formula, W WaterChiller The total energy consumption of the chiller unit, W KTpump For the total energy consumption of the air conditioning water pump, W LQpump W represents the total energy consumption of the cooling water pump. LQtower This represents the total energy consumption of the cooling tower.

[0023] Furthermore, the energy consumption function of the chiller unit is: W WaterChiler =f(Q) KT ,P Δ )

[0024] In the formula, W WaterChiller Q represents the total energy consumption of the chiller unit. KT P represents the total heat carried into the evaporator by the air conditioning water. Δ The pressure difference between the refrigerant intake and exhaust of the compressor;

[0025] Since the total heat carried into the evaporator by the air conditioning water is equal to the heat exchange of the air conditioning water, and the pressure difference between the compressor's intake and exhaust of refrigerant is equal to the pressure difference between the condenser and the evaporator, then:

[0026] Q KT =C Water ·(T KTin -T KTout )·F KTpump

[0027] P Δ =P LNQ -P ZFQ

[0028] In the above formula, C Water T is the specific heat capacity of water. KTin The inlet water temperature of the air conditioning water before it enters the evaporator; T KTout The outlet temperature of the air conditioning water after exiting the evaporator; F KTpump P is the air conditioning water flow rate; LNQ P is the absolute pressure of the high-pressure refrigerant inside the condenser. ZFQ The absolute pressure of the low-pressure refrigerant inside the evaporator; where:

[0029] P ZFQ =B1·T KTout

[0030]

[0031] In the formula, B1 and C1 are both comprehensive heat transfer coefficients, obtained by fitting the background data; T LQout T is the outlet temperature of the cooling water after exiting the condenser. LQin The inlet temperature of the cooling water before it enters the condenser;

[0032] The energy consumption model expression for the chiller unit is as follows:

[0033] W WaterChiller =A1·Q KT +A2·Q KT 2 +A3·Q KT ·P Δ +A4·Q KT +A5·Q KT 2 +A6

[0034] In the above formula, A1 to A6 are fitting coefficients, which are obtained by fitting the chiller unit energy consumption model expression based on multiple non-abnormal background data recorded in S1 at the same time.

[0035] Furthermore, the total energy consumption function of the air conditioning water pump is: W KTpump =f(N) KTpumpHZ Set )

[0036] In the formula, W KTpump Total energy consumption of air conditioning water pump; N KTpump Number of air conditioning water pumps that can be turned on simultaneously; HZ Set This refers to the frequency of a single air conditioner water pump, and the frequency of the water pump is the same for each air conditioner when it is turned on.

[0037] Set separate model expressions for each air conditioner water pump in operation:

[0038]

[0039] In the formula: n is the number of air conditioning water pumps in operation; D 1_1 ~D n_2 The fitting coefficients are obtained by fitting multiple non-abnormal background data recorded in S1 into the above independent model expressions.

[0040] The air conditioning water flow function is: F KTpump =f(W KTpump N KTpump H KToutin )

[0041] In the formula, H KToutin The pressure difference between the outlet and inlet of the air conditioning water pump;

[0042] Set separate model expressions for each air conditioner water pump in operation:

[0043]

[0044] In the formula: n is the number of water pumps in operation; E 1_1 ~E n_5 The fitting coefficients are obtained by fitting multiple non-anomaly background data points recorded in S1 at the same time into the above independent model expressions; where:

[0045]

[0046] In the formula: F 1_1 ~F 24_2 The fitting coefficients are obtained by fitting multiple non-abnormal background data recorded in S1 at the same time into the above expression.

[0047] or,

[0048] The total energy consumption function of the cooling water pump is: W LQpump =f(N) LQpump HZ SHt )

[0049] In the formula, W LQpump Total energy consumption of cooling water pump; NLQpump Number of cooling water pumps operating simultaneously; HZ SHt This refers to the frequency of a single cooling water pump, and each cooling water pump operates at the same frequency.

[0050] Set independent model expressions for each cooling water pump in operation:

[0051]

[0052] In the formula: n is the number of water pumps in operation; G 1_1 ~G n_2 The fitting coefficients are obtained by fitting multiple non-abnormal background data recorded in S1 into the above independent model expressions.

[0053] The cooling water flow rate function is: F LQpump =f(W LQpump N LQpump H LQoutin )

[0054] In the formula, H LQoutin This refers to the pressure difference between the outlet and inlet of the cooling water pump.

[0055] Set independent model expressions for each cooling water pump in operation:

[0056]

[0057] In the formula: n is the number of cooling water pumps in operation; H 1_1 ~H n_5 The fitting coefficients are obtained by fitting multiple non-anomaly background data points recorded in S1 at the same time into the above independent model expressions; where:

[0058]

[0059] In the formula: I 1_1 ~I n_2 The fitting coefficients are obtained by fitting multiple non-abnormal background data recorded in S1 into the above expression.

[0060] Furthermore, the total air volume function of the cooling tower fan and the total energy consumption function of the cooling tower are respectively:

[0061] FL LQtower =f(HZ) LQtower N LLQtower )

[0062] W LQtower =f(HZ) LQtower N LQtower )

[0063] In the formula, FLLQtower W represents the total air volume of the cooling tower. LQtower For the total energy consumption of the cooling tower, HZ LQtower For the frequency of a single wind turbine, N LQtower This represents the number of cooling towers that are turned on; the specific expression is:

[0064] FL LQtower =c·N LQtower ·HZ LQtower

[0065]

[0066] In the formula, c is a constant, and J1 to J2 are fitting coefficients, which are obtained by fitting multiple non-abnormal background data recorded in S1 in the above formula.

[0067] Cooling tower heat dissipation Q LQtower The heat brought into the chiller unit by the air conditioning water is then represented by the following cooling load model and expression:

[0068] Q LQtower =f(Q) KT )

[0069] Q LQtower =(K1·Q KT +K2)

[0070] In the formula: Q KT The total heat carried into the evaporator by the air conditioning water; K1~K2 are fitting coefficients, which are obtained by fitting multiple non-abnormal background data recorded in S1 into the above formula;

[0071] The cooling tower outlet water temperature function is:

[0072] T LQin =f(T) Air_w Q LQtower ,FL LQtower T Air_d -T LQavg T Air_d -T Air_w , (T LQout -T LQwaterin )·Q LQtower )

[0073] In the formula: T LQin T represents the outlet water temperature of the cooling tower. Air_w The outdoor atmospheric wet-bulb temperature; T Air_d The outdoor dry-bulb temperature; T LQavg T represents the average temperature of the cooling water heat exchange. LQwaterin The temperature of the water added to the cooling water tank; where:

[0074]

[0075] In the formula, T LQout L1 represents the inlet water temperature of the cooling tower; L1 to L4 are the fitting coefficients, which are obtained by fitting multiple non-abnormal background data recorded in S1 into the above formula.

[0076] Furthermore, S1 specifically includes the following steps:

[0077] Various operating condition sensors are deployed inside and outside the computer room, and communication links are established between each operating condition sensor and the host computer. The host computer performs real-time analysis and verification of the received data. Based on the pre-set hierarchical restrictions of each received data and its combination, the data without anomalies after verification is written into the corresponding structure table of the relational database on the host computer to achieve orderly data storage. Historical non-abnormal data is selected as background data for fitting the mathematical relationship model between variables.

[0078] Furthermore, S2 also includes a building air conditioning load model, with the building load function being: Q KT =f(T) Air_d RH Air Lux Air )

[0079] In the formula, Q KT This is the building load at this time, that is, the total heat brought into the evaporator by the air conditioning water; T Air_d Outdoor dry-bulb temperature; RH Air Atmospheric humidity; Lux Air This represents the current outdoor illuminance; the specific expression is as follows:

[0080] Q KT =M1·T Air_d +M2·T Air_d 2 +M3·T Air_d ·RH Air +M4·RH Air +M5·Lux Air

[0081] In the formula: M1 to M5 are fitting coefficients, which are obtained by fitting multiple non-abnormal background data recorded in S1 in the above expression;

[0082] The average heat exchange temperature function of air conditioning water supply and return is:

[0083] In the formula, T KTavg T represents the average heat exchange temperature of the air conditioning water supply and return water. inner The dry-bulb temperature (RH) of a representative area of ​​the building's interior. innerT represents the air humidity in a representative indoor area. Air_d Outdoor dry-bulb temperature; RH Air For atmospheric humidity; the specific expression is as follows: T KTavg =N1·T inner +N2·RH inner +N3·T Air_d +N4·RH Air +N5

[0084] In the formula: N1 to N5 are fitting coefficients, which are obtained by fitting multiple non-abnormal background data recorded in S1 at the same time into the above expression.

[0085] Furthermore, in S3, the method for establishing the energy efficiency simulation system includes the following steps:

[0086] S3-1: Set input quantities, including environmental quantification and control strategy groups;

[0087] The environmental quantification is as follows:

[0088] In the above quantitative data, T Air_w The outdoor atmospheric wet-bulb temperature, T Air_d The outdoor dry-bulb temperature, RH Air Atmospheric humidity; Lux Air T represents the current outdoor illuminance. inner For indoor dry-bulb temperature requirements, RH inner The required humidity level for indoor use; Time day For the current week number, Time hour This represents the current hour; where T is the current hour. Air_w and RH Air From the sensor, T inner and RH inner Based on human experience or set by month; T LQwaterin Temperature of the soft water inside the cooling water replenishment tank;

[0089] The control strategy group is as follows:

[0090] Where, N KTpump Number of air conditioning water pumps turned on, HZ Set This refers to the frequency of a single air conditioner water pump, and the frequency of each air conditioner water pump is the same when it is turned on; N LQpump The number of cooling water pumps in operation, HZ SHt N represents the frequency of a single cooling water pump, and all cooling water pumps operate at the same frequency; LQtower The number of cooling towers in operation, HZ LQtower This refers to the frequency of a single cooling tower fan, and the frequency of each operating cooling tower fan is the same.

[0091] S3-2: Correlation Calculation: Input the environmental quantitative and control strategy group, perform correlation point calculations, establish energy consumption relationships between various models, and finally obtain the total energy consumption (W) of the chiller unit. WaterChiller Total energy consumption of air conditioning water pump (W) KTpump Total energy consumption of cooling water pump (W) LQpump Total energy consumption of cooling tower (W) LQtower Therefore, the total energy consumption W0 and system efficiency S0 of the computer room system are obtained as follows:

[0092] W0 = W WaterChiller +W KTpump +W LQpump +W LQtower

[0093]

[0094] In the formula, Q KT This refers to the total heat carried into the evaporator by the air conditioning water.

[0095] Furthermore, in S4, the real-time optimization method includes the following steps:

[0096] S4-1: Set the optimization objective: Take the energy efficiency of the data center system as the objective, set the optimization variable as the data center system efficiency S0, and the optimization direction as the positive direction;

[0097] S4-2: Set constraints: The simulation process monitors the optimization nodes at all times. If the operating conditions of each node do not meet the constraints, it is judged as invalid optimization. Constraint group 1 is the number of available devices and the variable frequency of each device. Constraint group 2 is the operating conditions range for safe operation of the device.

[0098] Constraint group 1 is as follows:

[0099]

[0100] Where: N KTpump N KTpump_Max HZ Set These represent the number of air conditioning water pumps in operation, the number of available air conditioning water pumps, and the frequency of the uniformly set air conditioning water pump input, respectively; N LQpump N LQpump_Max HZ SHt These represent the number of cooling water pumps in operation, the number of cooling water pumps available, and the frequency of the uniformly set cooling water pump input, respectively; N LQtower N LQtower_Max HZ LQtower These represent the number of cooling towers to be activated, the number of cooling towers available, and the frequency of the unified cooling tower settings, respectively.

[0101] Constraint group 2 is as follows:

[0102]

[0103] In the formula, T KTout P is the outlet temperature of the air conditioning water after exiting the evaporator. LNQ P is the absolute pressure of the high-pressure refrigerant inside the condenser. ZFQ T is the absolute pressure of the low-pressure refrigerant inside the evaporator. Air_w The outdoor atmospheric wet-bulb temperature; T LQin F represents the outlet water temperature of the cooling tower. KTpump F represents the air conditioning water flow rate. LQpump This refers to the cooling water flow rate;

[0104] S4-3: Iterative optimization is performed using an optimization algorithm, with the variables of constraint 1 as optimization terms, and the optimal control strategy is output.

[0105] The beneficial effects of this invention are:

[0106] (1) Non-abnormal data is obtained through real-time data collection and analysis as background data, which can be used to fit the energy consumption model of each device to obtain fitting coefficients. On the one hand, the real-time data can reflect the current operating status and environmental conditions of the device, and the fitting coefficients can dynamically correct the model deviation and avoid long-term prediction inaccuracy caused by device aging or operating condition deviation. On the other hand, it can ensure the purity of the training set of the input model and reduce the pollution of the fitting results by noise.

[0107] (2) By establishing energy consumption models for chillers, air conditioning pumps, cooling water pumps and cooling towers, as well as correlation models for various operating conditions, precise optimization and global optimization can be achieved, and efficient and low-consumption operation can be achieved throughout the year; and the impact of outdoor weather on system energy efficiency can be integrated into the simulation system to achieve precise and stable optimization control and ultimately efficient operation throughout the year.

[0108] (3) In the energy consumption model of chiller units, the pressure difference between the condenser and the evaporator is used as a more direct factor affecting the energy consumption of the compressor. Compared with the existing model which uses the air conditioning and cooling water temperature as indirect factors, the model is more accurate and more conducive to judging the performance changes of the unit equipment.

[0109] (4) By adding friction resistance factors to the models of air conditioning water pumps and cooling water pumps, the long-term accuracy is greatly improved compared to directly solving the water flow rate by water pump frequency, speed or power. Furthermore, by removing the end-effect factors in the flow model, the abnormal performance of the water pump can be accurately judged. It can be used not only for fault diagnosis but also for judging performance degradation. It can also serve as a basis for cooling tower cleaning and water pump equipment maintenance.

[0110] (5) The energy efficiency simulation system established by this invention not only has higher accuracy compared with historical data, but also, through the correlation of energy consumption between devices in this simulation model architecture, it can still have the advantage of low error between simulation optimization results and implementation results when the actual simulation optimization and control are implemented in the case of new control strategy group.

[0111] (6) This invention improves the system operating efficiency through the entire process, and ultimately reduces the annual energy consumption of public buildings; it solves the problem that the existing simulation system has inaccurate equipment models, which leads to large overall energy efficiency prediction errors, and solves the problem that the simulation architecture lacks internal correlation and each equipment seeks optimization independently, which leads to the optimization strategy being incomplete and further prevents the system from operating efficiently. Detailed Implementation

[0112] The present invention will be further described in detail below with reference to specific embodiments.

[0113] In this embodiment, the process equipment of the central air conditioning room system mainly includes: a chiller unit, a cooling tower, an air conditioning water circulation pump (hereinafter referred to as the air conditioning water pump), a cooling water circulation pump (hereinafter referred to as the cooling water pump), and a cooling water makeup device; the chiller unit includes a compressor, an evaporator, and a condenser; the inner side of the evaporator is low-pressure refrigerant, and the outer side is medium-temperature air conditioning water; the inner side of the condenser is high-pressure refrigerant, and the outer side is medium-temperature cooling water;

[0114] The process flow of the central air conditioning room system described in this embodiment is divided into three heat cycles:

[0115] 1. Air conditioning water circulation: The fan coil unit with automatic air volume control in the building transfers heat from the indoor air to the air conditioning water and raises the temperature of the air conditioning water. Driven by the air conditioning water pump, the heated air conditioning water is sent back to the outside of the evaporator of the chiller unit in the machine room through the air conditioning return water pipe, where it transfers heat to the low-pressure refrigerant to cool the air conditioning water. After exiting the evaporator, it returns to the fan coil unit through the air conditioning water supply pipe to achieve air conditioning water circulation.

[0116] 2. Refrigerant Circulation: The low-pressure refrigerant gains heat from the air conditioning water in the evaporator. After being pressurized by the compressor, it becomes high-temperature, high-pressure refrigerant and is sent to the condenser. It transfers heat to the medium-temperature cooling water outside the condenser and then passes through the expansion valve to become low-temperature, low-pressure refrigerant again, completing the refrigerant circulation.

[0117] 3. Cooling water circulation: The cooling water that has gained heat in the evaporator enters the cooling tower under the push of the cooling water pump. In the cooling tower, the heat of the cooling water is carried away by surface evaporation, turning it into low-temperature cooling water. Under the action of the cooling water pump, it re-enters the unit to complete the cooling water circulation. During this process, a cooling water makeup pipe is installed on the pipeline to replenish the cooling water.

[0118] The overall solution described in this embodiment is based on the above-mentioned process equipment and flow, and specifically includes the following steps:

[0119] S1: Real-time data acquisition and analysis record:

[0120] After the sensors are deployed, a stable and reliable communication link is established between the sensors and the host computer through corresponding communication modules. The host computer uses specially developed data acquisition software to perform real-time parsing and verification of the received data. Data without anomalies is written into the corresponding structure table of the relational database mounted on the host computer. The application characteristics collected, analyzed, and recorded are as follows:

[0121] S1.1: Data verification characteristics:

[0122] The hierarchical limits for each received data and its combination are pre-defined. For example, for air conditioning water supply temperature sensor data, the amplitude limit is set to 3-30; if it exceeds this range, the data is considered abnormal. Corresponding abnormality judgments are configured for other sensor data.

[0123] S1.2: Characteristics of the database table structure of the host computer:

[0124] The table structure is set up with timestamps as the primary key and index key, and the index type is set to UNIQUE. In conjunction with various sensor data fields, a timer is used to insert data row by row into the corresponding time-series data table in the relational database on the host computer at the same interval, so as to achieve orderly storage of data, so as to facilitate subsequent data query, data cleaning and other operations based on time series.

[0125] S1.3: Data application-oriented characteristics:

[0126] Historical non-anomaly data is selected as background data for fitting a mathematical model of the relationship between variables. Real-time data is selected as the input source for the simulation system; if the data is missing or anomaly-prone, real-time optimization is paused.

[0127] S2: Establish relevant models for each equipment system.

[0128] The background data obtained from S1 is applied to the energy consumption models of each device. By fitting the energy consumption models of the chiller, air conditioning water pump, cooling water pump, and cooling tower respectively, as well as the associated models, they are finally combined into a real-time energy efficiency model of the central air conditioning room system.

[0129] S2.1: Relevant Models for Chiller Units

[0130] The energy consumption from the chiller unit is the most significant source of energy consumption in the entire system. Its main energy consumption is used to power the refrigerant compressor. The compressor draws low-pressure refrigerant from the evaporator, pressurizes it into high-pressure refrigerant, and sends it to the condenser. The amount of refrigerant drawn in is affected by the total heat carried into the evaporator by the air conditioning water returning from the terminal. Since all compressors share the same evaporator and condenser, the refrigerant pressure difference is the same. Therefore, the chiller unit's energy consumption is related to the average pressure difference between the high and low-pressure refrigerants and the heat from the air conditioning water. Thus, the chiller unit's energy consumption W can be obtained. WaterChiller function.

[0131] W WaterChiller =f(Q) KT P Δ (1)

[0132] In the above formula: W WaterChiller Q represents the total power of all compressors in the chiller unit, which is also the total energy consumption of the chiller unit; KT P represents the total heat carried into the evaporator by the air conditioning water. Δ The pressure difference between the refrigerant intake and exhaust of the compressor.

[0133] The total heat carried into the evaporator by the air conditioning water is equal to the heat exchange of the air conditioning water. The pressure difference between the compressor's intake and exhaust of the refrigerant is equal to the pressure difference between the condenser and the evaporator. Therefore:

[0134] Q KT =C Water ·(T KTin -T KTout )F KTpump (2)

[0135] P Δ =R LNQ -P ZFQ (3)

[0136] In the above formula, C Water T is the specific heat capacity of water. KTin The temperature of the air conditioning water inlet pipe section outside the unit before it enters the evaporator is referred to as the air conditioning water inlet temperature; T KTout The temperature of the air conditioning water outlet is the temperature of the external air conditioning pipe section after the air conditioning water exits the evaporator; it is also known as the air conditioning water outlet temperature. KTpump The total circulating volume of the air conditioning water, hereinafter referred to as air conditioning water flow rate; P LNQ P is the absolute pressure of the high-pressure refrigerant inside the condenser. ZFQ This is the absolute pressure of the low-pressure refrigerant inside the evaporator.

[0137] The final energy consumption model expression for the chiller unit is as follows:

[0138] W WaterChille =A1·Q KT +A2·Q KT2 +A3·Q KT ·P Δ +A4·Q KT +A5·Q KT 2 +A6 (4)

[0139] In the above formula, A1 to A6 are fitting coefficients. Based on the multiple non-abnormal background data recorded in S1 at the same time, the fitting is performed in formula (4), and the six fitting coefficients A1 to A6 can be obtained. Then, in the simulation environment, the total heat Q of the air conditioning water brought into the evaporator by the air conditioner is required by formula (4). KT The pressure difference P between the compressor intake and exhaust of refrigerant Δ This allows us to obtain the energy consumption prediction results for the chiller unit. The above formula uses the pressure difference between the condenser and evaporator as a more direct factor affecting the compressor's energy consumption. Compared with existing models that use air conditioning and cooling water temperatures as indirect factors, the model has higher accuracy and is more suitable as a basis for judging changes in the unit's equipment performance.

[0140] The evaporator pressure is controlled by the compressor load, which in turn is affected by the air conditioning water outlet temperature. Therefore, the absolute pressure model of the low-pressure refrigerant inside the evaporator can be expressed as follows:

[0141] P ZFQ =f(T) KTout (5)

[0142] The dynamic equilibrium between the low-pressure refrigerant and the outlet water temperature of the air conditioning water in the evaporator is mainly affected by the heat exchange area and heat transfer coefficient. Therefore, the specific expression for the absolute pressure model of the low-pressure refrigerant in the evaporator is as follows:

[0143] P ZFQ =B1·T KTout (6)

[0144] The above formula B1 is the comprehensive heat transfer coefficient. Based on the multiple non-abnormal background data recorded in S1 at the same time, the fitting coefficient B1 can be obtained by fitting in formula (6).

[0145] The absolute pressure of the high-pressure refrigerant in the condenser under dynamic equilibrium is related to the heat transfer area, heat transfer coefficient, and average heat transfer temperature of the cooling water heat exchange tubes outside the condenser. Therefore, the model for the absolute pressure of the high-pressure refrigerant inside the condenser is as follows:

[0146] P LNQ =f(T) LQout (7)

[0147] The absolute pressure of the high-pressure refrigerant inside the condenser has a linear relationship with the average heat exchange temperature, and its specific model expression is as follows:

[0148]

[0149] In the above formula: T LQout T represents the temperature of the cooling water exiting the condenser from the external cooling pipe section of the unit, i.e., the cooling water outlet temperature after exiting the condenser. LQin C1 is the temperature of the external cooling pipe section of the unit before the cooling water enters the condenser, which is also the inlet temperature of the cooling water before entering the condenser; C1 is the comprehensive heat transfer coefficient. Based on the multiple non-abnormal background data recorded in S1 at the same time, the fitting coefficient C1 can be obtained by fitting in formula (8).

[0150] S2.2: Air Conditioning Water Pump Related Model

[0151] The total energy consumption of air conditioning water pumps is affected by the number of pumps operating simultaneously and the frequency regulation of their independent inverters at the upper end of the circuit. Each inverter is set to the same regulation frequency. The model is as follows:

[0152] W KTpump =f(N) KTpump HZ Set (9)

[0153] In the above formula, W KTpump Total energy consumption of air conditioning water pump; N KTpump Number of air conditioning water pumps that can be turned on simultaneously; HZ Set This refers to the frequency of a single air conditioner water pump, and the frequency of each air conditioner water pump is the same when it is turned on.

[0154] Set independent model expressions for each number of water pumps in operation:

[0155]

[0156] In the above formula: n is the number of air conditioning water pumps in operation; D 1_1 ~D n_2 The fitting coefficients are obtained by fitting multiple non-anomaly background data recorded at the same time in S1 using formula (10). 1_1 ~D n_2 .

[0157] The air conditioning water pump overcomes the resistance along the air conditioning pipes, enabling the circulation of air conditioning water between the building's terminals and the chiller units in the central air conditioning room, thus transferring heat from the building's indoor air to the chiller units. The air conditioning water pump's flow rate can be adjusted by changing the frequency and motor load. Therefore, the air conditioning water flow rate F... KTpump Related to pipe friction resistance and air conditioning water pump load:

[0158] F KTpump =f(W KTpump N KTpump H KToutin (11)

[0159] In the above formula, W KTpump Total energy consumption of air conditioning water pump; N KTpump Number of air conditioning water pumps that can be turned on simultaneously; H KToutin This refers to the pressure difference between the outlet and inlet of the air conditioning water pump.

[0160] Set independent model expressions for each number of water pumps in operation:

[0161]

[0162] In the above formula: n is the number of water pumps in operation; E 1_1 ~E n_5 The fitting coefficients are obtained by fitting multiple non-abnormal background data recorded at the same time in S1 into formula (12). 1_1 ~E n_5 .

[0163] H KToutin The pressure difference between the outlet and inlet of the air conditioning water pump, resulting from the frictional resistance overcome during the air conditioning water circulation process, is typically in the range of W. KTpump It remains stable when W is constant. KTpump Changes occur, and the proportion of changes in the internal business types of public buildings, which vary with the 24-hour rhythm, affects W. KTpump With H KToutin Relationship, H KToutin The function is as follows:

[0164]

[0165] Based on theoretical analysis and actual data, H KToutin With W KTpump The relationship is linear, and the specific expression is as follows:

[0166]

[0167] In the above formula: F 1_1 ~F 24_2 The fitting coefficients are obtained by fitting multiple non-abnormal background data recorded at the same time in S1 into formula (14). 1_1 ~F 24_2 .

[0168] Compared to directly calculating the air conditioning water flow rate by using the air conditioning water pump frequency, speed, or power, this embodiment incorporates friction loss factors, resulting in higher long-term accuracy. Furthermore, by removing end-point influencing factors from the flow model, it can accurately identify abnormal equipment performance. This can be used not only for equipment fault diagnosis but also for assessing performance degradation, serving as a basis for equipment maintenance.

[0169] S2.3: Cooling water pump related models

[0170] The total energy consumption of the cooling water pumps is affected by the number of pumps operating simultaneously and the frequency regulation of the independent frequency converters at the upper end of their circuits. Each frequency converter is set to the same regulation frequency. The model is as follows:

[0171] W LQpump =f(N) LQpump HZ SHt (15)

[0172] In the above formula, W LQpump Total energy consumption of cooling water pump; N LQpump Number of cooling water pumps operating simultaneously; HZ SHt This refers to the frequency of a single cooling water pump, and each cooling water pump operates at the same frequency.

[0173] Set independent model expressions for each number of water pumps in operation:

[0174]

[0175] In the above formula: n is the number of water pumps in operation; G 1_1 ~G n_2 The fitting coefficients are obtained by fitting multiple non-anomaly background data recorded at the same time in S1 using formula (16). 1_1 ~G n_2 .

[0176] The cooling water pump pushes cooling water to overcome the friction resistance along the cooling pipes, enabling the cooling water to circulate between the building's end and the chiller unit in the central cooling room, carrying heat from the building's indoor air to the chiller unit. The cooling water flow rate can be adjusted by regulating the pump's frequency and changing the motor load. Therefore, the cooling water flow rate F... LQpump Related to pipe friction resistance and cooling water pump load:

[0177] F LQpump =f(W LQpump N LQpump H LQoutin (17)

[0178] In the above formula, W LQpump Total energy consumption of cooling water pump; N LQpump Number of cooling water pumps operating simultaneously; H LQoutin This represents the pressure difference between the outlet and inlet of the cooling water pump.

[0179] Set independent model expressions for each number of water pumps in operation:

[0180]

[0181] In the above formula: n is the number of cooling water pumps in operation; H 1_1 ~H n_5 The fitting coefficient H can be obtained by fitting multiple non-abnormal background data recorded at the same time in S1 using formula (18). 1_1 ~H n_5 .

[0182] H LQoutin The pressure difference between the outlet and inlet of the cooling water pump, resulting from the frictional resistance overcome during the cooling water circulation process, is typically in the range of W. LQpump It remains stable when W is constant. LQpump Changes occur, affecting W LQpump With H LQoutin The main factor affecting the relationship is the number of cooling towers in operation, N. LQtower H decreases as H increases. LQoutin The function is as follows:

[0183] H LQoutin =f(W LQpump N LQtower (19)

[0184] Based on theoretical analysis and actual data, H LQoutin With W LQpump The relationship is linear, and the specific expression is as follows:

[0185]

[0186] In the above formula: I 1_1 ~I n_2 The fitting coefficients are obtained by fitting multiple non-abnormal background data recorded at the same time in S1 using formula (20). 1_1 ~I n_2 .

[0187] Compared to directly calculating the cooling water flow rate by the frequency, speed, or power of the cooling water pump, this embodiment, by incorporating friction resistance, achieves higher long-term accuracy. Furthermore, by removing end-point influencing factors from the flow model, it can accurately identify abnormal cooling water pump performance. This can be used not only for fault diagnosis but also for assessing performance degradation, and simultaneously serve as a basis for cooling tower cleaning and cooling water pump equipment maintenance.

[0188] S2.4: Cooling Tower Related Models

[0189] The main energy consumption of a cooling tower comes from the useful power consumption of the fan motor. The total airflow of the fan and the total energy consumption of the cooling tower are the sum of the airflow and power consumption of each cooling tower at its corresponding frequency and speed, respectively. The functions for the total airflow of the cooling tower fan and the total energy consumption of the cooling tower are as follows:

[0190] FL LQtower =f(HZ) LQtower N LQtower ) (twenty one)

[0191] W LQtower =f(HZ) LQtower N LQtower ) (twenty two)

[0192] In the above formula, FL LQtower W represents the total air volume of the cooling tower. LQtower For the total energy consumption of the cooling tower, HZ LQtower For the frequency of a single wind turbine, N LQtower Let FL represent the number of cooling towers that are turned on. Assuming the fan frequency remains consistent when the cooling towers are turned on, then... LQtower and W LQtower The specific expression is as follows:

[0193] FL LQtower =c·N LQtower ·HZ LQtower (twenty three)

[0194]

[0195] In the above formula, c is a certain constant, and J1~J2 are fitting coefficients. Based on the multiple non-abnormal background data recorded in S1 at the same time, the fitting coefficients J1~J2 can be obtained by fitting in formula (24).

[0196] Cooling tower heat dissipation Q LQtower The heat brought into the chiller unit by the air conditioning water is then represented by the following cooling load model and expression:

[0197] Q LQtower =f(Q) KT (25)

[0198] Q LQtower =(K1·Q KT +K2) (26)

[0199] In the above formula: Q KT The total heat carried into the evaporator by the air conditioning water can be obtained from formula (2); K1~K2 are fitting coefficients. Based on the multiple non-abnormal background data recorded in S1 at the same time, the fitting coefficients K1~K2 can be obtained by fitting in formula (26).

[0200] The outlet water temperature of the cooling tower is a key indicator affecting the energy consumption of the computer room system and a crucial link between the cooling water system and the chiller unit. Cooling towers reduce cooling water temperature primarily through three methods: evaporative cooling, heat exchange with air, and replenishment with water from the cooling water makeup tank. The first two methods of heat exchange between cooling water and air can be approximated as two processes: a heat and mass exchange between the cooling water and saturated, humid air at a wet-bulb temperature, and a heat and mass exchange between the saturated, humid air and the outdoor atmosphere at a dry-bulb temperature.

[0201] Based on the above analysis, the cooling tower outlet water temperature is affected by the outdoor wet-bulb temperature, cooling load, fan output air volume, the difference between the outdoor dry-bulb temperature and wet-bulb temperature, and the difference between the outdoor dry-bulb temperature and the average temperature of the cooling tower inlet and outlet water. The relevant functions are as follows:

[0202] T LQin =f(T) Air_w Q LQtwer FL LQtower ,T Air_d -T LQawg T Air_d -T Air_w , (T LQout -T LQwaterin )Q LQtower (27)

[0203] In the above formula: T LQin Q is the temperature of the cooling water exiting the cooling tower (referred to as the cooling tower outlet temperature); LQtower FL represents the total heat dissipation of cooling water within the cooling tower. LQtower T represents the total air volume of the cooling tower. Air_w The outdoor atmospheric wet-bulb temperature is the wet-bulb temperature value obtained from an air temperature and humidity sensor near the cooling inlet; T Air_d The outdoor atmospheric dry-bulb temperature is the dry-bulb temperature value obtained from an air temperature and humidity sensor near the cooling inlet; T LQavg T represents the average temperature of the cooling water heat exchange. LQwaterin This refers to the replenishment water temperature in the cooling water tank. The average inlet and outlet water temperatures of the cooling tower are T. LQavg and cooling tower inlet water temperature T LQout The expression is as follows:

[0204]

[0205] In the above formula, T LQout The temperature of the cooling water before it enters the cooling tower (referred to as the cooling tower inlet water temperature).

[0206] In T LQin Among the relevant variables, those related to Q LQtower T Air_d -T LQavg T Air_d-T Air_w 、(T LQout -T LQwaterin )·Q LQtower Both are directly proportional to FL. LQtower The relationship is inverse, T Air_w Using this as the baseline value, and combining it with formula (23), the specific function model expression is as follows:

[0207]

[0208] In the above formula: L1~L4 are fitting coefficients. Based on the multiple non-abnormal background data recorded in S1 at the same time, the fitting coefficients L1~L4 can be obtained by fitting in formula (30).

[0209] In applying the model to calculate T LQin When formula (29) has a self-nested structure, iterative calculation is required. The iterative expression is as follows:

[0210]

[0211] In the above formula: Let n be the initial value and n be the number of iterations. The result is the technical outcome after n iterations, and the third equation is the termination condition for the iteration, ε. LQinIteration Set it to 0.1.

[0212] S2.5: Total Energy Consumption Model of Computer Room System

[0213] The energy-consuming equipment in the computer room comes from the aforementioned chiller units, air conditioning water pumps, cooling water pumps, and cooling towers. The total energy consumption W0 of the computer room is expressed as follows:

[0214] W0 = W WaterChiller +W KTpump +W LQpump +W LQtower (32)

[0215] S2.6: Building Air Conditioning Load Related Model

[0216] The load on public buildings is primarily influenced by weather conditions, and also varies regularly with the week and 24-hour cycle; therefore, the function is as follows:

[0217] Q KT =f(T) Air_d RH Air Lux Air (33)

[0218] In the above formula, Q KT This is the building load at this time, that is, the total heat brought into the evaporator by the air conditioning water; T Air_d Outdoor dry-bulb temperature; RHAir Atmospheric humidity; Lux Air This represents the current outdoor illuminance. The specific expression is as follows:

[0219] Q KT =M1·T Air_d +M2·T Air_d 2 +M3·T Air_d ·RH Air +M4·RH Air +M5·Lux Air (34)

[0220] In the above formula: M1~M5 are fitting coefficients. Based on the multiple non-abnormal background data recorded in S1 at the same time, the fitting coefficients M1~M5 can be obtained by fitting in formula (34).

[0221] To ensure thermal balance between indoor and outdoor environments in public buildings and to meet corresponding indoor temperature and humidity requirements, a corresponding average heat exchange temperature must be provided on the hot water side of the building's air conditioning fan coil units. Therefore, the average heat exchange temperature function for the air conditioning water supply and return is as follows:

[0222]

[0223] In the above formula, T KTavg T represents the average heat exchange temperature of the air conditioning water supply and return water. inner The dry-bulb temperature (RH) of a representative area of ​​the building's interior. inner T represents the air humidity in a representative indoor area. Air_d Outdoor dry-bulb temperature; RH Air This refers to atmospheric humidity. The specific expression is as follows:

[0224] T KTavg =N1·T inner +N2·RH inner +N3·T Air_d +N4·RH Air +N5 (36)

[0225] In the above formula: N1~N5 are fitting coefficients. Based on the multiple non-abnormal background data recorded in S1 at the same time, the fitting coefficients N1~N5 can be obtained by fitting in formula (36).

[0226] S3: Establishment of the simulation system

[0227] The simulation optimization of the central air conditioning room is based on a simulation system established by associating the above models. The establishment steps are as follows:

[0228] S3.1: Set input quantity

[0229] The simulation system inputs described in this embodiment are divided into environmental quantification and control strategy groups.

[0230] The environmental quantification is as follows:

[0231]

[0232] The above quantitative values: T Air_w The outdoor atmospheric wet-bulb temperature, T Air_d The outdoor dry-bulb temperature, RH Air Atmospheric humidity; Lux Air T represents the current outdoor illuminance. inner For indoor dry-bulb temperature requirements, RH inner The required humidity level for indoor use; Time day For the current week number, Time hour This represents the current hour; where T is the current hour. Air_w and RH Air From the sensor, T inner and RH inner Based on human experience or set by month; T LQwaterin The temperature of the soft water inside the cooling water replenishment tank.

[0233] The control strategy groups are as follows:

[0234]

[0235] Where: N KTpump Number of air conditioning water pumps turned on, HZ Set This refers to the frequency of a single air conditioner water pump, and the frequency of each air conditioner water pump is the same when it is turned on; N LQpump The number of cooling water pumps in operation, HZ SHt N represents the frequency of a single cooling water pump, and all cooling water pumps operate at the same frequency; LQtower The number of cooling towers in operation, HZ LQtower This refers to the frequency of a single cooling tower fan, and all cooling tower fans operate at the same frequency.

[0236] S3.2: Association Calculation

[0237] To calculate the outlet water temperature of the chiller unit, after inputting the quantifications of formulas (37) and (38) in S3.1, Q is obtained through the air conditioning load model established in S2.6. KT And T is obtained through the average heat transfer temperature model of air conditioning water. KTavg And the air conditioning water flow model F established in S2.2 KTpump Finally, the air conditioning water outlet temperature T was obtained. KTout The expression is:

[0238]

[0239] To calculate the average heat exchange temperature of the chiller unit's cooling water, input the quantitative values ​​in S3.1 and the above-mentioned Q. KT The calculation results, Q, are obtained through the cooling tower heat dissipation model established in S2.4. LQtower T is obtained by using the cooling tower outlet water temperature established in S2.4. LQin The cooling water flow rate F is obtained through the cooling water flow rate model established in S2.3. LQpump The final average heat exchange temperature T of the chiller unit's cooling water LQavg The expression is:

[0240]

[0241] To calculate the pressure difference between the high-pressure refrigerant inside the condenser and the low-pressure refrigerant inside the evaporator of the chiller unit, input the above T. KTout T LQavg The calculation results show that the absolute pressure P of the low-pressure refrigerant in the evaporator can be obtained through the absolute pressure model of the high-pressure refrigerant in S2.1. ZFQ The absolute pressure P of the high-pressure refrigerant inside the condenser can be obtained using the absolute pressure model of the high-pressure refrigerant inside the condenser established in S2.1. LNQ .

[0242] Through the above-mentioned correlation point calculation steps, this invention ultimately achieves the quantitative correlation of air conditioning water circulation, cooling water circulation, and refrigerant circulation, and through the following system energy consumption calculation, it ultimately achieves the correlation of energy consumption and strategies among various devices in the computer room, and finally realizes the feasibility of system simulation optimization.

[0243] S3.3: System Energy Consumption Simulation

[0244] To calculate the energy consumption of the chiller unit, input the quantitative value in S3.1 and the above-mentioned Q. KT The calculation results, W, can be obtained through the chiller unit energy consumption model established in S2.1. WaterChiller The energy consumption model of the air conditioning water pump established in S2.2 can be used to obtain W. KTpump The energy consumption W of the cooling water pump can be obtained through the cooling water pump energy consumption model established in S2.3. LQpump The energy consumption W of the cooling tower can be obtained through the cooling tower energy consumption model established in S2.4. LQtower The final energy consumption W0 and system efficiency S0 of the central air conditioning system in the public building are as follows:

[0245] W0 = W WaterChiller +W KTpump +W LQpump +W LQtower (41)

[0246]

[0247] This invention, through the above steps, involves: S1 deploying various operating condition sensors inside and outside the computer room, establishing communication between each sensor and the host computer, and analyzing and recording data in real time into a feature database; S2 establishing relevant models for each device and fitting them using background data that has passed secondary verification; and S3 establishing energy consumption relationships between the models through simulation methods. Ultimately, this allows for accurate simulation of the computer room system's energy consumption, as well as simulation optimization by modifying the input strategy group of the simulation model.

[0248] To verify the accuracy of the simulation system of this invention, the total energy consumption (W) of the computer room system at a certain moment is obtained from the relational database. 0_real_i Based on the readings from various sensors and control strategies, the energy consumption (W) of the simulation system is calculated using actual data. 0_simulation_i In comparison, the goodness of fit is evaluated using statistical indicators commonly used to measure it, such as Rfit. 2 The calculation formula is as follows:

[0249]

[0250] Simulations were performed at n time points using the method of this invention to obtain R. 2 With an accuracy exceeding 0.95, the energy consumption simulation accuracy of the public building central air conditioning equipment room system of this invention is higher than that of existing technologies. Not only does it exhibit higher accuracy when compared with historical data, but the correlation of energy consumption between devices within this simulation model architecture also ensures that subsequent actual simulation optimization and implementation control, under new control strategy groups, still maintains the advantage of low error between simulation optimization results and actual implementation results.

[0251] S4: Real-time optimization method:

[0252] Based on the aforementioned simulation system, quantitative data representing the real-time indoor and outdoor environment are obtained from the input quantities. The variables within the input quantities that serve as the control strategy are then changed, and the system simulation results under the current indoor and outdoor environment and control strategy are calculated. An optimization algorithm is used to repeatedly change the control strategy and obtain and evaluate the simulation results, ultimately obtaining the most efficient control strategy at this point. This enables the data center to operate efficiently at this time, further achieving high system efficiency and low energy consumption throughout the year.

[0253] S4.1: Set the optimization target

[0254] The simulation system in this embodiment is adapted to various optimization algorithms (such as differential evolution algorithm, genetic algorithm, bat algorithm, gray wolf optimization algorithm, whale optimization algorithm, etc.). In the optimization process, the goal is to achieve efficient and energy-saving operation of the computer room system. The optimization variable is set as the efficiency S0 of the computer room system, and the optimization direction is positive.

[0255] S4.2: Set constraints

[0256] The simulation process continuously monitors the optimization nodes. Nodes whose operating conditions do not meet the constraints are deemed invalid optimizations. Constraint group 1 specifies the number of available devices and the variable frequency for each device, while constraint group 2 specifies the operating conditions within which the equipment can operate safely.

[0257] Constraint group 1 is as follows:

[0258]

[0259] In the above formula: N KTpump N KTpump_Max HZ Set These represent the number of air conditioning water pumps in operation, the number of available air conditioning water pumps, and the frequency of the uniformly set air conditioning water pump input, respectively; N LQpump N LQpump_Max HZ SHt These represent the number of cooling water pumps in operation, the number of cooling water pumps available, and the frequency of the uniformly set cooling water pump input, respectively; N LQtower N LQtower_Max HZ LQtower These represent the number of cooling towers to be activated, the number of cooling towers available, and the frequency of the unified cooling tower settings, respectively.

[0260] Constraint group 2 is as follows:

[0261]

[0262] S4.3: Optimization Algorithm

[0263] This embodiment can utilize iterative optimization methods such as genetic algorithms, particle swarm optimization, and gradient descent, all using the variable in constraint 1 as the optimization term. Since these algorithms are existing technologies, they will not be described in detail here.

[0264] In summary, this invention aims to reduce energy consumption in public buildings. It acquires real-time and background data through real-time data collection and analysis; establishes energy consumption models for equipment such as chillers, air conditioning pumps, cooling water pumps, and cooling towers, as well as correlation models for various operating conditions of the computer room system and building air conditioning load. These models are then fitted using background data to obtain characteristic coefficients. These models are then correlated to construct a real-time energy efficiency simulation system, enabling correlated calculations and low-error simulation of system energy efficiency. Finally, with the energy efficiency of the computer room system as the target, optimization variables and constraints are set, and real-time optimization is achieved using various optimization algorithms (such as genetic algorithms). This invention improves system operating efficiency through the above steps, ultimately reducing the annual energy consumption of public buildings. This invention solves the problem of inaccurate equipment models in existing simulation systems, leading to large overall energy efficiency prediction errors, and also addresses the problem of a lack of internal correlation in the simulation architecture and independent optimization of each device, resulting in incomplete optimization strategies and ultimately hindering efficient system operation.

[0265] Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of this application.

Claims

1. A method for real-time simulation and optimization of energy efficiency in a central air conditioning system, characterized in that, Includes the following steps: S1: Real-time acquisition of operating data of the central air conditioning system in the computer room, real-time analysis and verification of the data, and storage of non-abnormal data after verification; Historical non-anomaly data was selected as background data for fitting a mathematical relationship model between variables; S2: Establish energy consumption models for chiller units, air conditioning water pumps, cooling water pumps, and cooling towers, as well as correlation models for each operating condition, and use the background data to fit the models to obtain the fitting coefficients of each model. The method for establishing the energy consumption model of the chiller unit includes: using the total heat carried into the evaporator by the air conditioning water and the pressure difference between the condenser and the evaporator as two input variables to establish the energy consumption function of the chiller unit, obtaining the energy consumption model expression of the chiller unit with fitting coefficients, and using the background data to perform fitting to obtain the fitting coefficients; or, The method for establishing the energy consumption model of the air conditioning water pump includes: establishing a total energy consumption function of the air conditioning water pump by taking the number of air conditioning water pumps operating simultaneously and the frequency of a single air conditioning water pump as two input variables; and establishing an air conditioning water flow function by taking the total energy consumption of the air conditioning water pump, the number of air conditioning water pumps operating simultaneously, and the pressure difference between the outlet and inlet of the air conditioning water pump as three input variables, obtaining the total energy consumption model expression and the air conditioning water flow model expression with fitting coefficients, and using the background data to perform fitting to obtain the fitting coefficients; or, The method for establishing the energy consumption model of the cooling water pump includes: establishing a total energy consumption function of the cooling water pump by taking the number of cooling water pumps running simultaneously and the frequency of a single cooling water pump as two input variables; and establishing a cooling water flow rate function by taking the total energy consumption of the cooling water pump, the number of cooling water pumps running simultaneously, and the pressure difference between the outlet and inlet of the cooling water pump as three input variables, obtaining the total energy consumption model expression and the cooling water flow rate model expression with fitting coefficients, and using the background data to perform fitting to obtain the fitting coefficients; or, The method for establishing the energy consumption model of the cooling tower includes: establishing the total air volume function and the total energy consumption function of the cooling tower by using the frequency of a single fan and the number of cooling towers in operation as two input variables; establishing the cooling load function of the cooling tower by using the total heat carried into the evaporator by the air conditioning water as an input variable; and establishing the cooling tower outlet water temperature function by using the outdoor wet-bulb temperature, cooling load, total fan air volume, the difference between outdoor dry-bulb temperature and wet-bulb temperature, and the difference between outdoor dry-bulb temperature and the average temperature of the inlet and outlet water of the cooling tower as input variables; obtaining the total energy consumption model expression of the cooling tower with fitting coefficients, and fitting it using the background data to obtain the fitting coefficients; and also obtaining the model expressions for the total fan air volume and the cooling tower outlet water temperature. S3: Integrate the energy consumption models in S2, establish the energy consumption correlation between the coupled models, and construct an energy efficiency simulation system; based on the energy efficiency simulation system, load real-time indoor and outdoor environmental parameters as fixed input conditions, and set control strategy-related variables as adjustable inputs to calculate the system simulation results under the current environmental conditions and control strategy. S4: The optimal control strategy is finally output by repeatedly changing the control strategy and obtaining simulation results using the optimization algorithm.

2. The method for real-time energy efficiency simulation and optimization of a central air conditioning room system according to claim 1, characterized in that, In S2, the associated models for each operating condition include the total energy consumption model of the computer room system, and the total energy consumption of the computer room. The expression is as follows: In the formula, This represents the total energy consumption of the chiller unit. This refers to the total energy consumption of the air conditioning water pump. The total energy consumption of the cooling water pump. This represents the total energy consumption of the cooling tower.

3. The method for real-time energy efficiency simulation and optimization of a central air conditioning room system according to claim 1, characterized in that, The energy consumption function of the chiller unit is: ; In the formula, This represents the total energy consumption of the chiller unit. The total heat carried into the evaporator by the air conditioning water. The pressure difference between the refrigerant intake and exhaust of the compressor; Since the total heat carried into the evaporator by the air conditioning water is equal to the heat exchange of the air conditioning water, and the pressure difference between the compressor's intake and exhaust of refrigerant is equal to the pressure difference between the condenser and the evaporator, then: In the above formula, This is the specific heat capacity of water; The inlet temperature of the air conditioning water before it enters the evaporator; The outlet temperature of the air conditioning water after exiting the evaporator; This refers to the water flow rate for air conditioning. This refers to the absolute pressure of the high-pressure refrigerant inside the condenser. The absolute pressure of the low-pressure refrigerant inside the evaporator; where: ； ； In the formula, and All are comprehensive heat transfer coefficients, obtained by fitting the background data; This refers to the outlet temperature of the cooling water after exiting the condenser. The inlet temperature of the cooling water before it enters the condenser; The energy consumption model expression for the chiller unit is as follows: ； In the above formula, ~ The fitting coefficients are obtained by fitting multiple non-abnormal background data recorded in S1 at the same time into the energy consumption model expression of the chiller unit.

4. The method for real-time energy efficiency simulation and optimization of a central air conditioning room system according to claim 1, characterized in that, The total energy consumption function of the air conditioning water pump is: ; In the formula, Total energy consumption of the air conditioning water pump; The number of air conditioning water pumps that can be turned on simultaneously; This refers to the frequency of a single air conditioner water pump, and the frequency of the water pump is the same for each air conditioner when it is turned on. Set separate model expressions for each air conditioner water pump in operation: In the formula: Number of air conditioning water pumps to be turned on; ~ The fitting coefficients are obtained by fitting multiple non-abnormal background data recorded in S1 into the above independent model expressions. The air conditioning water flow function is: ; In the formula, The pressure difference between the outlet and inlet of the air conditioning water pump; Set separate model expressions for each air conditioner water pump in operation: In the formula: The number of water pumps in operation; ~ The fitting coefficients are obtained by fitting multiple non-anomaly background data points recorded in S1 at the same time into the above independent model expressions; where: In the formula: ~ The fitting coefficients are obtained by fitting multiple non-abnormal background data recorded in S1 at the same time into the above expression. or, The total energy consumption function of the cooling water pump is: ; In the formula, Total energy consumption of cooling water pump; The number of cooling water pumps that can be turned on simultaneously; This refers to the frequency of a single cooling water pump, and each cooling water pump operates at the same frequency. Set independent model expressions for each cooling water pump in operation: In the formula: The number of water pumps in operation; ~ The fitting coefficients are obtained by fitting multiple non-abnormal background data recorded in S1 into the above independent model expressions. The cooling water flow rate function is: ; In the formula, This refers to the pressure difference between the outlet and inlet of the cooling water pump. Set independent model expressions for each cooling water pump in operation: In the formula: Number of cooling water pumps in operation; ~ The fitting coefficients are obtained by fitting multiple non-anomaly background data points recorded in S1 at the same time into the above independent model expressions; where: In the formula: ~ The fitting coefficients are obtained by fitting multiple non-abnormal background data recorded in S1 at the same time into the above expression.

5. The method for real-time energy efficiency simulation and optimization of a central air conditioning room system according to claim 1, characterized in that, The total air volume function of the cooling tower fan and the total energy consumption function of the cooling tower are respectively: In the formula, This represents the total air volume of the cooling tower. The total energy consumption of the cooling tower For the frequency of a single fan, This represents the number of cooling towers that are turned on; the specific expression is: In the formula, c is a certain constant. ~ The fitting coefficients are obtained by fitting multiple non-abnormal background data recorded in S1 at the same time into the above formula. Cooling tower heat dissipation The heat brought into the chiller unit by the air conditioning water is then represented by the following cooling load model and expression: In the formula: The total heat carried into the evaporator by the air conditioning water; ~ The fitting coefficients are obtained by fitting multiple non-abnormal background data recorded in S1 at the same time into the above formula. The cooling tower outlet water temperature function is: In the formula: This refers to the outlet water temperature of the cooling tower. The outdoor atmospheric wet-bulb temperature; This refers to the outdoor dry-bulb temperature. This refers to the average temperature of the cooling water heat exchange. The temperature of the water added to the cooling water tank; where: ； ； ； In the formula, This refers to the inlet water temperature of the cooling tower. ~ The fitting coefficients are obtained by fitting multiple non-abnormal background data recorded in S1 at the same time into the above formula.

6. The method for real-time energy efficiency simulation and optimization of a central air conditioning room system according to claim 1, characterized in that, S1 specifically includes the following steps: Various operating condition sensors are deployed inside and outside the computer room, and communication links are established between each operating condition sensor and the host computer. The host computer performs real-time analysis and verification of the received data. Based on the pre-set hierarchical restrictions of each received data and its combination, the data without anomalies after verification is written into the corresponding structure table of the relational database on the host computer to achieve orderly data storage. Historical non-abnormal data is selected as background data for fitting the mathematical relationship model between variables.

7. The method for real-time energy efficiency simulation and optimization of a central air conditioning room system according to claim 3, characterized in that, S2 also includes a building air conditioning load model, with the building load function as follows: ; In the formula, This is the building load at this time, that is, the total heat brought into the evaporator by the air conditioning water; This refers to the outdoor dry-bulb temperature. Atmospheric humidity; This represents the current outdoor illuminance. The specific expression is as follows: In the formula: ~ The fitting coefficients are obtained by fitting multiple non-abnormal background data recorded in S1 at the same time into the above expression. The average heat exchange temperature function of air conditioning water supply and return is: ; In the formula, The average heat exchange temperature of the air conditioning water supply and return water. The dry-bulb temperature of air in a representative area of ​​the building's interior; The humidity level of a representative indoor area. This refers to the outdoor dry-bulb temperature. This refers to atmospheric humidity; the specific expression is as follows: In the formula: ~ The fitting coefficients are obtained by fitting multiple non-abnormal background data recorded in S1 at the same time into the above expression.

8. The method for real-time energy efficiency simulation and optimization of a central air conditioning room system according to any one of claims 1 to 7, characterized in that, In S3, the method for establishing the energy efficiency simulation system includes the following steps: S3-1: Set input quantities, including environmental quantification and control strategy groups; The environmental quantification is as follows: In the above quantitative data, The outdoor atmospheric wet-bulb temperature. The outdoor dry-bulb temperature. Atmospheric humidity; This represents the current outdoor illuminance. For indoor dry-bulb temperature requirements, The required humidity level for indoor use; This is the current week number. This represents the current hour; where and From the sensor, and Settings based on human experience or set by month; Temperature of the soft water inside the cooling water replenishment tank; The control strategy group is as follows: in, The number of air conditioning water pumps to be turned on. This refers to the frequency of a single air conditioner water pump, and the frequency of the water pump is the same for each air conditioner when it is turned on. The number of cooling water pumps in operation. This refers to the frequency of a single cooling water pump, and each cooling water pump operates at the same frequency. The number of cooling towers to be turned on. This refers to the frequency of a single cooling tower fan, and the frequency of each operating cooling tower fan is the same. S3-2: Correlation Calculation: Input the environmental quantitative and control strategy group, perform correlation point calculations, establish energy consumption relationships between various models, and finally obtain the total energy consumption of the chiller unit. Total energy consumption of air conditioning water pump Total energy consumption of cooling water pump Total energy consumption of cooling tower ; and thus ultimately obtain the total energy consumption of the data center system. System efficiency as follows: ； ； In the formula, This refers to the total heat carried into the evaporator by the air conditioning water.

9. The method for real-time energy efficiency simulation and optimization of a central air conditioning room system according to claim 8, characterized in that, In S4, the real-time optimization method includes the following steps: S4-1: Set the optimization objective: With the energy efficiency of the data center system as the objective, set the optimization variable as the data center system efficiency. The optimization direction is positive; S4-2: Set constraints: The simulation process monitors the optimization nodes at all times. If the operating conditions of each node do not meet the constraints, it is judged as invalid optimization. Constraint group 1 is the number of available devices and the variable frequency of each device. Constraint group 2 is the operating conditions range for safe operation of the device. Constraint group 1 is as follows: In the formula: , , These represent the number of air conditioning water pumps in operation, the number of air conditioning water pumps available, and the frequency of the unified setting for air conditioning water pump input, respectively. , , These represent the number of cooling water pumps in operation, the number of cooling water pumps available, and the frequency of the unified setting for the cooling water pump input, respectively. , , These represent the number of cooling towers to be activated, the number of cooling towers available, and the frequency of the unified cooling tower settings, respectively. Constraint group 2 is as follows: ； In the formula, The outlet temperature of the air conditioning water after exiting the evaporator; This refers to the absolute pressure of the high-pressure refrigerant inside the condenser. This is the absolute pressure of the low-pressure refrigerant inside the evaporator; The outdoor atmospheric wet-bulb temperature; This refers to the outlet water temperature of the cooling tower. This refers to the water flow rate for air conditioning. This refers to the cooling water flow rate; S4-3: Iterative optimization is performed using an optimization algorithm, with the variables of constraint 1 as optimization terms, and the optimal control strategy is output.

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