A semi-empirical performance prediction method and device for medium-temperature chillers

Abstract

The present invention discloses a semi-empirical performance prediction method and device for medium-temperature chillers, belonging to the technical field of air-conditioning systems, comprising: S1: identifying key factors affecting COP in measured data and performing quantitative analysis on the key factors; S2: constructing a medium-temperature chiller performance prediction model; S3: inputting the key factors into the medium-temperature chiller performance prediction model for processing and outputting prediction results. In response to the problems that the coefficients of existing chiller energy consumption models vary with operating conditions, require fitting with measured historical data, are limited in applicability, and do not cover medium-temperature chillers with chilled water temperatures between 9°C and 15°C, based on the re-identification of the equation form, the nonlinear relationship between key variables is analyzed to simplify a more efficient and practical equation form, thereby more accurately describing the variation pattern of COP and significantly improving the prediction accuracy under medium-temperature operating conditions.

Description

Technical Field

[0001] The present invention relates to the technical field of air-conditioning systems, and in particular to a semi-empirical performance prediction method and device for a medium-temperature chiller. Background Art

[0002] In public buildings with centralized cooling systems, air conditioning systems are the primary energy consumer, accounting for over 50% of the building's total energy consumption. Chilled water centralized air conditioning systems are particularly energy-intensive, with approximately 50% to 60% of the electrical load consumed by the chillers. Therefore, the operating efficiency of chillers directly impacts the energy consumption and operating costs of the air conditioning system. Research on energy consumption models for equipment in centralized air conditioning systems is a key component of energy-saving and optimized operation. Chillers contribute significantly to energy consumption, making their energy consumption models a primary target for this research.

[0003] Existing chiller energy consumption models fall into three main categories: white-box models, gray-box models, and black-box models. White-box models are based on physical principles and unit structural parameters, constructed through relationships between physical mechanisms such as heat transfer and mass transfer. While highly accurate, they are complex to model, rely on equipment information, and are only applicable to the established energy consumption model. Energy consumption models developed using the white-box modeling principle include those based on parameter lumping, using theoretical analysis and formula derivation to establish chiller power models, including evaporator, condenser, compressor, and expansion valve models. Gray-box models combine physical principles with data-driven approaches, utilizing partially known equipment parameters and historical data for modeling, thus balancing the requirements for accuracy and complexity. Energy consumption models developed using the gray-box modeling principle include the DOE-2 model, which models chiller performance and includes three performance curves and 15 undetermined coefficients. Black-box models rely entirely on historical data, require a large amount of high-quality data, and lack physical interpretability. The energy consumption model established using the black box model principle has a support vector regression machine parameter optimization algorithm based on the chaotic particle swarm optimization algorithm (CPSO) to establish a chiller energy consumption prediction model.

[0004] As the cooling source for centralized air conditioning systems, chillers provide cooling to terminal units by maintaining the chilled water supply temperature at a set value. Mathematical models are widely used to calculate the coefficient of performance (COP) of chillers in energy consumption forecasting and operational management to simplify applications and improve efficiency. The main parameters influencing the COP are the chilled water supply temperature, the cooling water inlet temperature, and the load factor (PLR). However, existing mathematical models for predicting chiller energy consumption have the following two major problems in practical applications:

[0005] First, the model coefficients are not fixed and need to be fitted with measured historical data, which limits its applicability.

[0006] The coefficients of existing models often change with operating conditions, making them incapable of being directly generalized to different operating conditions. For example, the quadratic curve function model proposed by the American Society of Heating, Refrigerating, and Air-Conditioning Engineers (ASHRAE) can describe the relationship between COP and PLR, but it does not incorporate the effects of chilled water supply temperature and cooling water inlet temperature, causing the model coefficients to vary with temperature. Similarly, the GN model requires recalculation of its equation coefficients when the cooling capacity and cooling water inlet temperature change. This non-fixed coefficient characteristic makes the model calculation cumbersome and difficult to meet practical application requirements. Furthermore, in actual operation, frequent changes in operating conditions require constant adjustment of the model coefficients, increasing computational complexity and errors. Similarly, when faced with a newly built refrigeration room, it is impossible to establish an energy consumption model without actual measured data. The actual measured data of the unit must be run first. During this period, without targeted optimization and control, additional energy consumption losses often occur.

[0007] Second, it has not adapted to the expansion of the chilled water supply temperature range.

[0008] With the rapid development of medium-temperature chillers (with chilled water supply temperatures between 9 and 15°C), existing energy consumption models were designed and calculated only for conventional chillers (with a typical temperature range of 7°C supply water and 12°C return water). These models failed to consider the impact of higher supply water temperatures, significantly reducing the accuracy of energy consumption predictions for medium-temperature chillers and failing to meet the demands of new technology applications. This limitation not only restricted the model's applicability but also hindered the widespread adoption of new technologies. Summary of the Invention

[0009] The purpose of the present invention is to overcome the shortcomings of the prior art, such as the non-fixed model coefficients, the need for fitting with measured historical data, limited applicability, and the inability to adapt to the expansion of the chilled water supply temperature range, and to provide a semi-empirical performance prediction method and equipment for medium-temperature chillers.

[0010] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0011] A semi-empirical performance prediction method for a medium-temperature chiller comprises the following steps:

[0012] S1: Identify the key factors affecting COP in the measured data and perform quantitative analysis on the key factors;

[0013] S2: Build a performance prediction model for medium-temperature chillers;

[0014] S3: Input the key factors into the medium-temperature chiller performance prediction model for processing and then output prediction results.

[0015] As a preferred solution of the present invention, step S1 includes: using principal component analysis to identify key factors affecting COP in the measured data, and performing quantitative analysis on the key factors, wherein the key factors include: chilled water outlet temperature, cooling water inlet temperature and load rate.

[0016] As a preferred embodiment of the present invention, the chilled water outlet temperature is in the range of 9 to 15° C., and has a significant linear relationship with the COP, and the fitting formula is:

[0017] y=d*x+e

[0018] The cooling water inlet temperature is in the range of 14 to 32° C., and the load rate is in the range of 30 to 100%, which has a nonlinear quadratic relationship with the COP. The fitting formula is:

[0019] y=d*x 2 +e*x+f

[0020] Where x is the cooling water inlet temperature; y is the COP; d, e, and f are fitting coefficients.

[0021] As a preferred embodiment of the present invention, in the case of historical measured data, the semi-empirical performance prediction method further includes the following steps before step S1:

[0022] S0: Collecting historical operating measured data of medium-temperature chillers with different cooling capacities and preprocessing the measured data;

[0023] The measured data in step S1 is pre-processed measured data.

[0024] As a preferred embodiment of the present invention, step S2 of constructing a performance prediction model for a medium-temperature chiller includes: selecting medium-temperature chillers with different cooling capacities, studying the relationship between the chilled water outlet temperature, the cooling water inlet temperature, and the COP at different load rates, and after preliminary analysis and fitting of the data, finding that when the inverse of the COP, EIR, is used as the dependent variable, at the same load rate, EIR has an obvious, approximate linear relationship with the chilled water outlet temperature and the cooling water inlet temperature.

[0025] As a preferred embodiment of the present invention, the reciprocal EIR of the coefficient of performance of the medium-temperature chiller is fitted as a function of the chilled water outlet temperature and the cooling water inlet temperature:

[0026] EIR=a*x1+b*x2+c

[0027] Where x1 is the chilled water outlet temperature, x2 is the cooling water inlet temperature, and a, b, and c are functions of the load rate.

[0028] As a preferred solution of the present invention, the equations of different load rates are fitted to obtain the value table of a, b and c at different load rates. The value table is studied and analyzed to obtain a nonlinear fourth power relationship between a and the load rate. After fitting R 2 =0.98; b has a nonlinear fourth power relationship with the load rate. 2 =0.99; c has a nonlinear fourth power relationship with the load rate. 2 =0.99.

[0029] As a preferred solution of the present invention, in the absence of historical measured data, the medium-temperature chiller performance prediction model described in step S3 is:

[0030]

[0031] Among them, EIR is the reciprocal of the coefficient of performance COP of the chiller; EIR * is the reciprocal of the coefficient of performance of the chiller under rated conditions; x1 is the chilled water outlet temperature, x2 is the cooling water inlet temperature, a1, b1, c1 are a, b, c and EIR in the formula EIR=a*x1+b*x2+c * ratio.

[0032] On the other hand, an electronic device is disclosed, comprising at least one processor and a memory communicatively connected to the at least one processor; the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute any of the semi-empirical performance prediction methods for medium-temperature chillers described above.

[0033] Compared with the prior art, the present invention has the following advantages:

[0034] In order to address the problems that the coefficients of the existing chiller energy consumption model vary with operating conditions, require fitting with measured historical data, have limited applicability, and do not cover medium-temperature chillers (chilled water temperature 9-15°C), based on the re-identification of the equation form, the nonlinear relationship of key variables is analyzed to simplify the equation form to a more efficient and practical form, thereby more accurately describing the change law of COP and greatly improving the prediction accuracy under medium-temperature conditions.

[0035] The actual operation data verification shows that when there is historical data, the single prediction error of the medium temperature chiller performance prediction model does not exceed 7%; when there is no historical data, the verification error of other chillers with different cooling capacities does not exceed 15%, which is significantly better than traditional models (such as DOE-2 model, R 2=0.8464, maximum error 36.8%), and is applicable to medium-temperature chillers with various cooling capacities such as 200RT, 600RT, and 1000RT, demonstrating wide applicability and versatility.

[0036] The present invention successfully solves the shortcomings of existing models in predicting energy consumption of medium-temperature chillers by considering the availability of historical data, fixing model coefficients, expanding the water temperature range, and improving prediction accuracy. It provides a scientific basis and technical support for the optimized operation and energy conservation and consumption reduction of chillers, and has important engineering application value.

[0037] Furthermore, this invention simplifies the traditional complex equation form by analyzing and fitting data from medium-temperature operating conditions, reducing computational complexity and improving efficiency. Actual operating data has verified that the prediction error of this patent significantly outperforms traditional models, with the maximum error for a single unit prediction not exceeding 7%. This patent demonstrates broad applicability, providing important technical support for the optimized operation, energy efficiency evaluation, and energy conservation and consumption reduction of medium-temperature chillers, and possesses significant application value and engineering significance. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Various other advantages and benefits will become apparent to those skilled in the art upon reading the detailed description of the preferred embodiment below. The accompanying drawings are for illustration purposes only and are not to be considered as limiting the present invention. Throughout the drawings, the same reference numerals are used to denote the same components. In the drawings:

[0039] Figure 1 This is a flow chart of a semi-empirical performance prediction method for a medium-temperature chiller according to Example 1 of the present invention;

[0040] Figure 2 A graph showing the relationship between chilled water outlet temperature and COP in a semi-empirical performance prediction method for a medium-temperature chiller according to Example 1 of the present invention;

[0041] Figure 3 A graph showing the relationship between the cooling water inlet temperature and the COP of a semi-empirical performance prediction method for a medium-temperature chiller according to Example 1 of the present invention;

[0042] Figure 4 A graph showing the load rate and COP of a semi-empirical performance prediction method for a medium-temperature chiller according to Example 1 of the present invention;

[0043] Figure 5 This is a structural block diagram of an electronic device described in Example 5 of the present invention. DETAILED DESCRIPTION

[0044] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, not all of the embodiments. The components of the embodiments of the present invention generally described and shown in the drawings herein can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making creative work are within the scope of protection of the present invention.

[0045] It should be noted that similar numbers and letters represent similar items in the following figures. Therefore, once an item is defined in one figure, it does not need to be further defined or explained in subsequent figures. At the same time, in the description of the present invention, the terms "first" and "second" are used only to distinguish the description and should not be understood as indicating or implying relative importance, or implying any actual relationship or order between these entities or operations. In addition, the terms "connected" and "connected" can refer to direct connection between components or indirect connection through other components.

[0046] Implementation Column 1

[0047] A semi-empirical performance prediction method for medium temperature chillers, such as Figure 1 As shown, the following steps are included:

[0048] S1: Identify the key factors affecting COP in the data and perform quantitative analysis on the key factors;

[0049] The data in step S1 include: chilled water outlet temperature, cooling water inlet temperature and unit load rate;

[0050] S2: Build a performance prediction model for medium-temperature chillers;

[0051] Specifically, step S2 includes: using principal component analysis (PCA) to identify key factors affecting the coefficient of performance (COP) of the medium-temperature chiller, and performing quantitative analysis on the key factors (chilled water outlet temperature, cooling water inlet temperature, and load rate):

[0052] like Figure 2 As shown in the figure, the chilled water outlet temperature is in the range of 9 to 15°C and has a significant linear relationship with COP. The fitting formula is y = d*x + e, R 2 All above 0.98;

[0053] like Figure 3As shown, the cooling water inlet temperature: in the range of 14 ~ 32 ℃, it has a significant nonlinear quadratic relationship with COP, and the fitting formula is y = d * x 2 +e*x+f,R 2 All above 0.95;

[0054] like Figure 4 As shown, the load rate: in the range of 30% to 100%, it has a significant nonlinear quadratic relationship with COP. Its coefficient is affected by the chilled water outlet temperature and the cooling water inlet temperature. The fitting formula is y=d*x 2 +e*x+f,R 2 All above 0.95; R 2 It is an indicator used to evaluate the model's fit to the actual data and does not play a role within the model. 2 The range is (0,1). The larger the value, the more accurate the function reflects the law of the object. The calculation formula is:

[0055] R 2 =1-SSE / SST

[0056]

[0057] Where i ranges from 1 to n, yi is the actual value, is the predicted value, is the average value of yi;

[0058] S3: Input the key factors into the medium-temperature chiller performance prediction model for processing and then output prediction results.

[0059] Implementation Column 2

[0060] This embodiment is a specific embodiment of the semi-empirical performance prediction method for a medium-temperature chiller described in Example 1 when historical measured data is available;

[0061] The semi-empirical performance prediction method further includes, before step S1:

[0062] S0: Collecting historical operating measured data of medium-temperature chillers with different cooling capacities and preprocessing the measured data;

[0063] Specifically, we first collect historical operating data of medium-temperature chillers with different cooling capacities (200RT, 600RT, and 1000RT) (a general energy consumption model is established using a medium-temperature chiller with a cooling capacity of 600RT, and is verified using chillers with a cooling capacity of 200RT and 1000RT), including the chilled water outlet temperature (9-15°C), the cooling water inlet temperature (13-32°C), the unit load rate (30-100%), etc.

[0064] The data in step S1 are pre-processed measured data.

[0065] The construction of the medium-temperature chiller performance prediction model described in step S3 specifically includes: first, selecting a medium-temperature chiller with a cooling capacity of 600RT, and studying the relationship between the chilled water outlet temperature, the cooling water inlet temperature and COP at different load rates of 30% to 100%. Here, x1 is the chilled water outlet temperature, x2 is the cooling water inlet temperature, and x3 is the load rate. After preliminary analysis and fitting of the data, it was found that when using the inverse of COP EIR as the dependent variable, EIR has an obvious approximate linear relationship with the chilled water outlet temperature and the cooling water inlet temperature at the same load rate, as shown in the following formula (1-1). And R of formula (1-1) at different load rates of 30% to 100% 2 The values ​​are all between 0.98 and 0.99, which are highly explanatory.

[0066] EIR=a*x1+b*x2+c (1-1)

[0067] That is, the reciprocal EIR of COP is fitted as a function of the chilled water outlet temperature and the cooling water inlet temperature. The coefficients a, b, and c have different values ​​at different load rates, that is, a, b, and c are functions of the load rate. Then, the equation is fitted when the load rate is 30-100%, and different fitting equations are obtained, where the values ​​of a, b, and c are shown in Table 1. The R of the fitting equation from 30-100% is 2 The values ​​are all between 0.98 and 0.99, which is a very high accuracy.

[0068]

[0069]

[0070] Table 1600RT medium temperature chiller unit 30 ~ 100% load factor a, b, c value

[0071] Based on the energy consumption model of 600RT medium temperature chiller, in order to directly apply it in actual use, without changing the equation coefficients with the load rate, a unified COP fitting equation is obtained. The variation law of the coefficients a, b, and c with the load rate is studied respectively. Then, a large amount of data analysis is carried out, and it is found that there is a nonlinear fourth power relationship between a and the load rate. After fitting R 2 =0.98; b has a nonlinear fourth power relationship with the load rate. 2 =0.99; c and load rate have a nonlinear fourth power relationship. 2 =0.99. The fitting equations of a, b, and c are shown in equations (1-2) to (1-4), respectively.

[0072]

[0073] Among them, x3 is the load rate;

[0074] Similarly, the same method is used to calculate the coefficients a, b, and c of the medium-temperature chillers with a cooling capacity of 1000RT and 200RT at a load rate of 30% to 100%, as well as the calculation of the fitting formula, as shown in Tables 2 and 3. The coefficients a, b, and c are shown in (1-5) to (1-10). When calculating the relationship between the load rate and a, b, and c, its R 2 In the further verification of the error of a single 1000RT medium temperature chiller, the R 2 =0.981, the maximum error reached 20.99%. Similarly, when verifying the error of a single 200RT medium-temperature chiller, the R of its EIR prediction formula 2 =0.972, with a maximum error of 21.93%. In the prediction formula for the EIR of the 600RT medium-temperature chiller, the maximum error between the actual value and the prediction formula is within 6% within the range of 30% to 100%.

[0075] During the error analysis process, it was found that the maximum error of the prediction formula of the EIR of these two medium-temperature chillers was concentrated in the load rate range of 30% to 40%. The efficient operating range of medium-temperature chillers is generally 60% to 80% load rate. In order to reduce energy consumption, the medium-temperature chillers operate at a small proportion of 30% to 40% load rate. In summary, the range of medium-temperature chillers' load rate was reduced and re-verified. When the medium-temperature chillers were at a load rate of 40% to 100%, the error of the EIR prediction formula of the 1000RT medium-temperature chiller was verified. 2 =0.99, the maximum error is 6.79%. Similarly, when verifying the error of a single medium-temperature 200RT chiller, the R of its EIR prediction formula is 2 =0.972, with a maximum error of 6.34%. This indicates that the EIR prediction formula for 1000RT and 200RT medium-temperature chillers is also accurate at load rates of 40% to 100%.

[0076]

[0077] Table 21000RT medium temperature chiller unit 30 ~ 100% load factor a, b, c value

[0078]

[0079] Among them, x3 is the load rate;

[0080]

[0081] Table 3200RT medium temperature chiller unit 30 ~ 100% load factor a, b, c value

[0082]

[0083]

[0084] Among them, x3 is the load factor.

[0085] Example 3

[0086] This embodiment is a specific embodiment of the semi-empirical performance prediction method for a medium-temperature chiller described in Example 1 when there is no historical measured data;

[0087] When establishing a single energy consumption model for a medium-temperature chiller, measured historical data is required. However, in a refrigeration room that has just been put into use, the lack of measured historical data conflicts with the need to obtain an energy consumption model for the medium-temperature chiller for optimized energy-saving control. Therefore, a general energy consumption prediction model is established based on the 600RT medium-temperature chiller. When there is no measured data, the energy consumption of the medium-temperature chiller is calculated to optimize energy-saving control. After the functions of the coefficients a, b, and c and the load rate are accurately fitted, the degree of fitting of the formula is good for a chiller with a cooling capacity of 600RT. The R 2 The value is 0.98, and the maximum error does not exceed 6%. In order to consider its universality and applicability to chillers of other capacities, the formula form of the ratio of EIR to the predicted characteristic EIR of the chiller is selected and verified as the final model. After analysis and verification, the EIR of the unit is selected as the rated operating EIR when the chilled water outlet temperature is 9℃, the cooling water inlet temperature is 28℃, and the unit load rate is 70%. * , then the performance prediction model of the medium-temperature chiller without historical data is shown in formula (1-11).

[0088]

[0089] Where, EIR is the reciprocal of the chiller's coefficient of performance (COP); EIR * is the reciprocal of the performance coefficient of the chiller under rated conditions; x1 is the chilled water outlet temperature of the chiller (°C); x2 is the cooling water inlet temperature of the chiller (°C); a1 is the product of formula (1-2) and EIR * The ratio of b1 is the ratio of EIR to formula (1-3). * The ratio of c1 to EIR is (1-4). * Ratio of EIR * 600 ——EIR of a chiller with a cooling capacity of 600RT * , the value is 0.127.

[0090] In summary, there are two types of energy consumption prediction models for medium-temperature chillers: one is when there is historical operating data, formula (1-1) is selected, and the coefficients a, b, and c are fitted to make predictions, which solves the shortcoming that conventional chiller energy consumption prediction models cannot predict medium-temperature conditions; the other is when there is no historical operating data, the general medium-temperature chiller energy consumption prediction model of formula (1-11) is selected to directly calculate the energy consumption of the chiller.

[0091] Example 4

[0092] This embodiment is a specific verification and optimization method for the models of Examples 2 and 3;

[0093] The DOE-2 model is widely used in the energy consumption prediction of conventional chillers. Before verifying the universality of formula (1-11), the DOE-2 model is first fitted and verified to observe the accuracy of the existing chiller energy consumption prediction model.

[0094] First, the DOE-2 model is used for establishment and verification, and then fitting is performed. The DOE-2 model uses three performance curves to characterize the performance of the unit. The DOE-2 chiller energy consumption model is obtained as follows:

[0095]

[0096] Where x1 is the chilled water outlet temperature of the chiller; x2 is the cooling water inlet temperature of the chiller; and PLR is the load factor.

[0097] During validation, the model's R 2 Value: 0.8464, the maximum error is 36.8%, the overall fitting accuracy is low, the error is large, and the COP of the medium-temperature chiller cannot be accurately predicted.

[0098] Formula (1-1) is a medium-temperature chiller energy consumption prediction model that requires historical measured data. After verification, when the medium-temperature chiller is at a load rate of 40% to 100%, the EIR prediction formula R is used to predict the error of the performance coefficient of a single 1000RT medium-temperature chiller. 2 =0.99, the maximum error is 6.79%. Similarly, when verifying the error prediction of a single medium-temperature 200RT chiller, the R of the EIR prediction formula is 2 =0.972, and the maximum error is 6.34%.

[0099] The following verification of the energy consumption prediction model of medium-temperature chillers does not require historical measured data for Equation (1-11). When using a 200RT chiller to verify Equation (1-11), the R 2 Value: 0.9626, the maximum error is 13%. When the 1000RT chiller is used to verify the formula (1-11), the R 2The value is 0.9126, and the maximum error is 14%, which shows that the fitted equation has strong versatility.

[0100] In summary, the medium-temperature chiller energy consumption prediction model effectively solves the existing problems that the conventional chiller energy consumption model cannot be applied to medium-temperature working conditions, and that the chiller energy consumption prediction model cannot be directly used for medium-temperature chillers in newly built refrigeration rooms.

[0101] Implementation 5

[0102] like Figure 5 As shown, an electronic device includes at least one processor and a memory communicatively connected to the at least one processor; the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the semi-empirical performance prediction method for a medium-temperature chiller described in the aforementioned embodiment. The input and output interfaces may include a display, a keyboard, a mouse, and a USB interface for inputting and outputting data; and the power supply is used to provide power to the electronic device.

[0103] Those skilled in the art will understand that all or part of the steps of implementing the above-mentioned method embodiment can be completed by hardware related to program instructions, and the aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it executes the steps of the above-mentioned method embodiment; and the aforementioned storage medium includes: mobile storage devices, read-only memories (ROM), magnetic disks or optical disks, and other media that can store program codes.

[0104] When the above-mentioned integrated unit of the present invention is implemented in the form of a software functional unit and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiment of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium and includes a number of instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as mobile storage devices, ROMs, magnetic disks, or optical disks.

[0105] It should also be noted that the various specific technical features described in the above specific embodiments can be combined in any appropriate manner without contradiction. In order to avoid unnecessary repetition, the present disclosure will not further describe various possible combinations.

[0106] In addition, the various embodiments of the present disclosure may be arbitrarily combined, and as long as they do not violate the concept of the present disclosure, they should also be regarded as the contents disclosed by the present disclosure.

Claims

1. A semi-empirical performance prediction method for a medium-temperature chiller, characterized in that: The following steps are involved: S1: Identify key factors affecting COP in measured data and perform quantitative analysis on the key factors, wherein COP is the coefficient of performance of the chiller; Step S1 includes: using principal component analysis to identify key factors affecting COP in the measured data, and performing quantitative analysis on the key factors, wherein the key factors include: chilled water outlet temperature, cooling water inlet temperature, and unit load rate; The chilled water outlet temperature is in the range of 9-15°C and has a significant linear relationship with the COP. The fitting formula is: The cooling water inlet temperature is in the range of 14-32°C and the load rate is in the range of 30-100%, which has a nonlinear quadratic relationship with the COP. The fitting formula is: Where x2 is the cooling water inlet temperature; y is the COP, d, e, and f are fitting coefficients; S2: Build a performance prediction model for medium-temperature chillers; S3: Input the key factors into the medium-temperature chiller performance prediction model for processing and then output prediction results.

2. A semi-empirical performance prediction method for a medium-temperature chiller according to claim 1, characterized in that: In the case of historical measured data, the semi-empirical performance prediction method further includes the following steps before step S1: S0: Collecting historical operating measured data of medium-temperature chillers with different cooling capacities and preprocessing the measured data; The measured data in step S1 is pre-processed measured data.

3. A semi-empirical performance prediction method for a medium-temperature chiller according to claim 2, characterized in that: The construction of the medium-temperature chiller performance prediction model described in step S2 includes: selecting medium-temperature chillers with different cooling capacities, and studying the relationship between the chilled water outlet temperature, the cooling water inlet temperature, and the COP at different load rates. After preliminary analysis and fitting of the data, it was found that when the inverse of the COP, EIR, was used as the dependent variable, there was an obvious, approximately linear relationship between EIR and the chilled water outlet temperature and the cooling water inlet temperature at the same load rate.

4. A semi-empirical performance prediction method for a medium-temperature chiller according to claim 3, characterized in that: The reciprocal EIR of the chiller coefficient of performance is fitted as a function of the chilled water outlet temperature and the cooling water inlet temperature: Among them, x1 is the chilled water outlet temperature, x2 is the cooling water inlet temperature, a, b, c are functions of the load rate, and a, b, c change with the load rate.

5. A semi-empirical performance prediction method for a medium-temperature chiller according to claim 4, characterized in that: Equations for different load rates were fitted to obtain a table of values ​​for a, b, and c at different load rates. Analysis of the table revealed that a had a nonlinear fourth-power relationship with the load rate, with an R² of 0.98 after fitting; b had a nonlinear fourth-power relationship with the load rate, with an R² of 0.99 after fitting; and c had a nonlinear fourth-power relationship with the load rate, with an R² of 0.99 after fitting. R² is an indicator used to evaluate the fit of the model to the actual data and does not play a role within the model. The range of R² is (0,1). The larger the value, the more it proves that the function accurately reflects the law of the object.

6. A semi-empirical performance prediction method for a medium-temperature chiller according to claim 1, characterized in that: In the absence of historical measured data, the medium-temperature chiller performance prediction model described in step S3 is: Among them, EIR is the reciprocal of the coefficient of performance COP of the chiller; EIR * is the reciprocal of the coefficient of performance of the chiller under rated conditions; x1 is the chilled water outlet temperature, x2 is the cooling water inlet temperature, a1, b1, c1 are a, b, c and EIR in the formula EIR=a*x1+b*x2+c * ratio.

7. An electronic device, characterized in that: It includes at least one processor and a memory communicatively connected to the at least one processor; the memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the semi-empirical performance prediction method for a medium-temperature chiller according to any one of claims 1 to 6.

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