A COP prediction method for operating system of air-conditioning and refrigeration room

Abstract

The invention relates to the technical field of air conditioning energy consumption prediction, and specifically discloses an air conditioning energy consumption prediction method for COP calculation of an air conditioning refrigeration room operation system. The method comprises: acquiring a system operation temperature data set, comparing and obtaining an optimized cooling water inlet temperature and a chilled water outlet temperature of a refrigeration unit, comparing and obtaining a COP curve corresponding to the optimized cooling water inlet temperature and the chilled water outlet temperature of the refrigeration unit, comparing and obtaining a target building's hourly predicted wet-bulb temperature on the current date, obtaining an hourly predicted COP value of the room based on the obtained target building's hourly predicted wet-bulb temperature on the current date and the COP curve, and comprehensively analyzing and obtaining an hourly predicted room energy consumption. The invention solves the problem that traditional energy consumption prediction methods are often based on statistical models, lack consideration of the dynamic characteristics of the room air conditioning system, and lack dynamic response capabilities to real-time data, and is helpful to better guide energy management practices and achieve more sustainable energy management and control.

Description

Technical Field

[0001] The invention relates to the technical field of air conditioning energy consumption prediction, and in particular to a COP prediction method for an air conditioning refrigeration room operation system. Background Art

[0002] COP (Coefficient of Performance) is an important indicator used to measure the performance of cooling, heating and air-conditioning systems. With the rapid development of information technology, the energy consumption of air-conditioning rooms has become a focus of attention. There is a huge energy exchange during the operation of the air-conditioning host. The cooling efficiency of the same air-conditioning at different load rates at different temperatures is completely different, resulting in different energy consumption. The air-conditioning system in the room accounts for a large proportion of the energy consumption, so how to effectively predict and control the energy consumption of the air-conditioning system has become an important issue.

[0003] For example, the invention patent with the publication number CN117091240A discloses a method for predicting energy consumption and adjusting energy saving of central air conditioners based on laboratory environmental data, establishes a COP prediction model based on the LSTM time series model; obtains the power consumption according to the COP prediction model; finally, changes the environmental conditions to obtain the COP value, and calculates the parameter setting when the total power is the lowest according to the accumulation of the COP curve, so as to adjust the parameters of the air conditioner host. The invention explores the air conditioning energy consumption model of laboratory air conditioners at different temperatures for energy consumption prediction, so as to prepare the corresponding energy supply work in advance and provide the basis for modifying parameters for the automatic control system, so as to achieve the purpose of energy saving.

[0004] Nowadays, there are still some shortcomings in the research on the COP prediction method of an air-conditioning refrigeration room operation system. Specifically, the traditional energy consumption prediction method is often based on statistical models, lacks consideration of the dynamic characteristics of the room air-conditioning system, and lacks the ability to dynamically respond to real-time data. It is often difficult to accurately capture the changes and fluctuations of the air-conditioning system in actual operation, resulting in low accuracy of energy consumption prediction results. The lack of dynamic response capability to real-time data makes it difficult to timely reflect changes in the environment and load. In actual operation, the COP prediction results may deviate from the actual value, affecting energy efficiency management. Traditional methods have limitations in data utilization and cannot make full use of the massive data collected in real time for accurate prediction and optimization, resulting in the potential for energy efficiency optimization not being fully tapped. Traditional energy consumption prediction methods mainly rely on experience and rules, lack intelligent and automated analysis and optimization capabilities, and cannot achieve intelligent control and optimization management. Summary of the invention

[0005] In view of the deficiencies in the prior art, the present invention provides a COP prediction method for an air-conditioning refrigeration room operating system, which can effectively solve the problems involved in the above-mentioned background technology.

[0006] To achieve the above objectives, the present invention is implemented by the following technical solutions: a COP prediction method for an air-conditioning refrigeration room operation system, comprising the following steps: obtaining a building indoor environment temperature data set, comparing and determining the optimized cooling water inlet temperature and the chilled water outlet temperature of the refrigeration unit based on the obtained building indoor environment temperature data set; comparing and obtaining the COP curve corresponding to the optimized cooling water inlet temperature and the chilled water outlet temperature of the refrigeration unit based on the optimized cooling water inlet temperature and the chilled water outlet temperature of the refrigeration unit; obtaining a target building basic information data set, comprehensively analyzing and obtaining the target building basic information characteristic value based on the obtained target building basic information data set, obtaining the current date of the target building, combining the target building basic information characteristic value, comparing and obtaining the building most similar to the target building; obtaining the building history data set most similar to the target building, comparing and obtaining the target building current date predicted hourly wet-bulb temperature based on the obtained building history data set most similar to the target building; obtaining the target building current date predicted hourly wet-bulb temperature and the COP curve based on the obtained target building current date predicted hourly wet-bulb temperature and the COP curve, obtaining the hourly predicted COP value of the room; obtaining the actual wet-bulb temperature and combining the hourly predicted COP value of the room, comprehensively analyzing and obtaining the hourly predicted energy consumption of the room.

[0007] As a further method, based on the acquired system operating temperature data set, the optimized cooling water inlet temperature and the chilled water outlet temperature of the refrigeration unit are determined by comparison. The specific analysis process is: obtain the system operating temperature data set, the system operating temperature data set specifically includes the indoor return air temperature of the air conditioner, the indoor supply air temperature difference of the air conditioner, the chilled water outlet temperature of the refrigeration unit, and the chilled water supply and return temperature difference of the refrigeration unit; based on the acquired system operating temperature data set, a comprehensive analysis is performed to obtain the system operating temperature characteristic value, and the system operating temperature characteristic value is used as the analysis basis for comparing the optimized cooling water inlet temperature and the chilled water outlet temperature of the refrigeration unit; compare the system operating temperature characteristic value with the optimized cooling water inlet temperature and the chilled water outlet temperature of the refrigeration unit corresponding to the system operating temperature characteristic values ​​of each building stored in the database, and obtain the optimized cooling water inlet temperature and the chilled water outlet temperature of the refrigeration unit corresponding to the system operating temperature characteristic value of the indoor working condition of the computer room.

[0008] As a further method, the system operating temperature characteristic value, the specific analysis process is:

[0009] ;

[0010] In the formula, is the system operating temperature characteristic value, is the indoor return air temperature of the air conditioner, The indoor air supply temperature difference of the air conditioner is is the chilled water outlet temperature of the refrigeration unit, The temperature difference between the supply and return water of the refrigeration unit. is the compensation factor for the set indoor return air temperature of the air conditioner. is the compensation factor for the set indoor air supply temperature difference of the air conditioner. is the compensation factor for the chilled water outlet temperature of the set refrigeration unit, It is the compensation factor for the set temperature difference between the chilled water supply and return water of the refrigeration unit.

[0011] As a further method, based on the optimized cooling water inlet temperature and the chilled water outlet temperature of the refrigeration unit, the COP curves corresponding to the optimized cooling water inlet temperature and the chilled water outlet temperature of the refrigeration unit are compared. The specific analysis process is: the optimized cooling water inlet temperature and the chilled water outlet temperature of the refrigeration unit are stored as designated tags; the designated tag is compared with the COP curves corresponding to the designated tags stored in the database to obtain the COP curve corresponding to the designated tag.

[0012] As a further method, a building most similar to the target building is obtained by comparison, and the specific analysis process is: obtain a basic information data set of the target building, the basic information data set of the target building specifically includes the target building's floor area, the target building's indoor volume, and the target building's highest height; based on the obtained basic information data set of the target building, a comprehensive analysis is performed to obtain a basic information characteristic value of the target building, and the basic information characteristic value of the target building is used as an analysis basis for obtaining the building most similar to the target building by comparison; obtain the current date of the target building, and store the current date of the target building and the basic information characteristic value of the target building as a designated label; compare the designated label with the building most similar to the target building corresponding to each designated label stored in the database, and obtain the building most similar to the target building corresponding to the designated label.

[0013] As a further method, the basic information characteristic value of the target building, the specific analysis process is:

[0014] ;

[0015] In the formula, is the basic information characteristic value of the target building, is the target building area, is the indoor volume of the target building, is the maximum height of the target building, is the compensation factor for the target building area, is the compensation factor for the indoor volume of the target building, is the compensation factor for the maximum height of the target building, and e is a natural constant.

[0016] As a further method, the historical data set of the building most similar to the target building specifically includes the historical average light intensity of the building most similar to the target building, the historical average ambient temperature of the building most similar to the target building, and the historical hourly power load of the building most similar to the target building.

[0017] As a further method, based on the acquired historical data set of buildings most similar to the target building, the predicted hourly wet-bulb temperature of the target building on the current date is obtained by comparison. The specific analysis process is: based on the acquired historical data set of buildings most similar to the target building, a comprehensive analysis is performed to obtain the historical characteristic values ​​of the building most similar to the target building, and the historical characteristic values ​​of the building most similar to the target building are used as the analysis basis for comparing and obtaining the predicted hourly wet-bulb temperature of the target building on the current date; the historical characteristic values ​​of the building most similar to the target building are compared with the predicted hourly wet-bulb temperatures of the target building on the current date corresponding to the historical characteristic values ​​of the building most similar to the target building stored in the database, so as to obtain the predicted hourly wet-bulb temperature of the target building on the current date corresponding to the historical characteristic values ​​of the building most similar to the target building.

[0018] As a further method, based on the obtained target building's current hourly predicted wet-bulb temperature and COP curve, the hourly predicted COP value of the computer room is obtained. The specific analysis process is: based on the polynomial regression model, the historical hourly wet-bulb temperature of the target building is fitted with the total load rate to obtain a polynomial function of the wet-bulb temperature and the total load rate, and the target building's current hourly predicted wet-bulb temperature corresponding to the target building's current hourly predicted total load rate is obtained; based on the COP curve, the hourly predicted COP value of the computer room corresponding to the target building's current hourly predicted total load rate is obtained.

[0019] As a further method, the actual wet-bulb temperature is obtained and combined with the hourly predicted COP value of the computer room, and the hourly predicted energy consumption of the computer room is obtained through comprehensive analysis. The specific analysis process is as follows: based on the polynomial regression model, a polynomial function of the predicted hourly wet-bulb temperature of the target building on the current date and the hourly predicted COP value of the computer room is established, marked as the polynomial function of wet-bulb temperature and COP value; the actual wet-bulb temperature is obtained, and the total load rate of the computer room is obtained based on the polynomial function of wet-bulb temperature and total load rate; the COP value of the computer room is obtained based on the polynomial function of wet-bulb temperature and COP value; the total cooling capacity of the chiller and its supporting equipment is obtained; based on the hourly energy consumption calculation formula of the computer room, the hourly energy consumption of the computer room is calculated, and the calculation formula of the hourly energy consumption of the refrigeration computer room is:

[0020] ;

[0021] In the formula, is the hourly energy consumption of the refrigeration room, is the total cooling capacity of the chiller and its supporting equipment, is the total load rate of the computer room, is the COP value of the computer room.

[0022] Compared with the prior art, the embodiments of the present invention have at least the following advantages or beneficial effects:

[0023] (1) The present invention provides a COP prediction method for the operation system of an air-conditioning refrigeration room, obtains a system operation temperature data set and obtains a COP curve based on the optimized cooling water inlet temperature and the chilled water outlet temperature of the refrigeration unit, so as to achieve accurate optimization of energy utilization, ensure that the system operates in the best working state, and minimize energy consumption. By combining the basic information data set of the target building and the characteristic values ​​obtained by analysis, the building most similar to the target building is obtained by comparison, and personalized energy efficiency improvement suggestions can be provided for the target building. The hourly wet-bulb temperature is predicted based on the historical data set of similar buildings, so as to better guide energy management practices. By continuously collecting, analyzing and comparing data, the energy management strategy of the room can be continuously improved, the system operation efficiency can be optimized, the energy utilization efficiency can be improved, and more sustainable energy management and control can be achieved.

[0024] (2) The present invention obtains COP curves corresponding to the optimized cooling water inlet temperature and the chilled water outlet temperature of the refrigeration unit by comparing the optimized cooling water inlet temperature and the chilled water outlet temperature of the refrigeration unit. Since the COP curve can reflect the energy efficiency performance under different operating parameters, comparing the COP curve can help determine the optimal cooling water inlet temperature and the chilled water outlet temperature of the refrigeration unit to achieve the best performance of the air-conditioning system. By comparing the COP curves, the best operating parameters under different conditions can be found, thereby saving energy consumption, reducing operating costs, and improving the energy utilization efficiency of the computer room air-conditioning system. Continuously comparing the COP curves can continuously improve the optimization strategy of the cooling water inlet temperature and the chilled water outlet temperature of the refrigeration unit, thereby achieving continuous improvement and optimization of the operating performance of the central air-conditioning system.

[0025] (3) The present invention obtains the hourly predicted COP value of the computer room by predicting the hourly wet-bulb temperature and COP curve based on the current date of the target building, so as to better understand the energy efficiency performance of the air-conditioning system under different working conditions, which is helpful for the computer room manager to optimize energy utilization and reduce energy consumption costs. Knowing the hourly COP value of the computer room can help the manager adjust the operation mode of the air-conditioning system at the appropriate time to save energy and reduce energy consumption. Predicting the hourly COP value of the computer room helps to optimize the operation plan of the central air-conditioning system, improve the operation efficiency and stability of the system, reduce the risk of failure, and enhance the reliability of the refrigeration room. In addition, the manager can manage and monitor energy consumption more accurately, find problems in time and take measures to ensure the optimal efficiency of energy utilization in the computer room. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] The present invention is further described using the accompanying drawings, but the embodiments in the accompanying drawings do not constitute any limitation to the present invention. A person skilled in the art can obtain other drawings based on the following drawings without creative work.

[0027] Figure 1 It is a schematic diagram of the method flow of the present invention.

[0028] Figure 2 This is an image of the hourly energy consumption of the computer room changing with the total load rate of the computer room. DETAILED DESCRIPTION

[0029] 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 part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0030] Reference Figure 1 As shown, the present invention provides an air conditioning energy consumption prediction method for COP calculation of a central air-conditioning refrigeration room, comprising: obtaining an indoor operating temperature data set of the room, and based on the obtained indoor operating temperature data set of the room, comparing the optimized cooling water inlet temperature and the chilled water outlet temperature of the refrigeration unit.

[0031] Specifically, based on the obtained indoor operating temperature data set of the computer room, the optimized cooling water inlet temperature and the chilled water outlet temperature of the refrigeration unit are compared. The specific analysis process is: obtain the system operation temperature data set, and the system operation temperature data set specifically includes the indoor return air temperature of the air conditioner, the indoor supply air temperature difference of the air conditioner, the chilled water outlet temperature of the refrigeration unit, and the chilled water supply and return temperature difference of the refrigeration unit. The indoor return air temperature of the air conditioner refers to the temperature of the indoor air collected in the air-conditioning system and sent back to the air-conditioning system. It is used to measure the temperature of the indoor air and to judge the cooling or heating effect of the air-conditioning system. The indoor return air temperature of the air conditioner is measured by the sensor in the indoor air-conditioning system. The indoor supply air temperature difference of the air conditioner refers to the difference between the temperature of the air sent into the room by the air-conditioning system and the indoor target temperature. It is used to measure the regulation of the supply air temperature in the air-conditioning system and is usually used to judge the operating efficiency and performance of the air-conditioning system. The temperature of the air supply outlet and the indoor target temperature are measured by the sensor in the indoor air-conditioning system, and the temperature difference between the two is calculated. The chilled water outlet temperature of the refrigeration unit refers to the temperature of the air supplied to the cooling unit from the cooling system. The temperature of the chilled water of the equipment is used to measure the temperature of the water in the cooling system and to adjust the working state of the cooling system. The outlet temperature of the chilled water is measured by the sensor in the cooling system. The supply and return temperature difference of the chilled water of the refrigeration unit refers to the difference between the outlet temperature of the chilled water of the refrigeration unit of the chiller in the refrigeration system and the return temperature of the chilled water. It is used to measure the temperature difference of the chilled water in the refrigeration system and to judge the operating efficiency and performance of the refrigeration system. The supply temperature and return temperature of the chilled water are measured by the sensor in the refrigeration system, and the temperature difference between the two is calculated. Based on the acquired system operating temperature data set, the system operating temperature characteristic value is comprehensively analyzed to obtain the system operating temperature characteristic value, which is used as the analysis basis for comparing the optimized cooling water inlet temperature and the chilled water outlet temperature of the refrigeration unit. The system operating temperature characteristic value is compared with the optimized cooling water inlet temperature and the chilled water outlet temperature of the refrigeration unit corresponding to each system operating temperature characteristic value stored in the database, and the optimized cooling water inlet temperature and the chilled water outlet temperature of the refrigeration unit corresponding to the system operating temperature characteristic value of the indoor working condition of the computer room are obtained.

[0032] It needs to be explained that the above-mentioned air-conditioning indoor return air temperature directly affects the size of the air-conditioning indoor supply air temperature difference. If the air-conditioning indoor return air temperature is high, the air-conditioning system needs to invest more cooling capacity to reach the set target temperature, which will increase the air-conditioning indoor supply air temperature difference. The air-conditioning indoor supply air temperature difference directly depends on the set value of the chilled water outlet temperature of the refrigeration unit in the refrigeration system. If the chilled water outlet temperature of the refrigeration unit is low, the air-conditioning system supply air temperature difference may increase accordingly to meet the indoor target temperature needs. Increasing the chilled water supply temperature and the cooling water inlet temperature set in the refrigeration system are beneficial to improving the refrigeration energy efficiency.

[0033] It needs to be explained that the above-mentioned adjustment of the cooling water inlet temperature and the chilled water outlet temperature of the refrigeration unit according to the actual system operating temperature characteristic value can more accurately control the operating conditions of the central air-conditioning system and its cooling system, so that the system can minimize energy consumption while meeting the needs of the computer room, and improve the energy efficiency performance of the system. Optimizing the cooling water inlet temperature and the chilled water outlet temperature of the refrigeration unit can improve the efficiency of system operation, ensure that the system can operate under the best conditions, improve the refrigeration effect and the working stability of the equipment, and optimize the cooling water inlet temperature and the chilled water outlet temperature of the refrigeration unit to reduce the system operation energy consumption, reduce the power consumption and loss of the equipment, reduce the maintenance cost and increase the life of the equipment. Optimizing the cooling water inlet temperature and the chilled water outlet temperature of the refrigeration unit can make the temperature control of the central air-conditioning system of the refrigeration room more stable and accurate, improve the indoor air quality and comfort of the building, and ensure that the equipment and personnel work in a suitable environment.

[0034] Furthermore, the system operating temperature characteristic value, the specific analysis process is:

[0035] ;

[0036] In the formula, is the system operating temperature characteristic value, is the indoor return air temperature of the air conditioner, The air supply temperature difference for indoor air conditioning in buildings, is the chilled water outlet temperature of the refrigeration unit, The temperature difference between the supply and return water of the chilled water of the refrigeration unit. is the compensation factor for the set indoor return air temperature of the air conditioner. is the compensation factor for the set indoor air supply temperature difference of the air conditioner. is the compensation factor for the chilled water outlet temperature of the set refrigeration unit, It is the compensation factor for the set temperature difference between the chilled water supply and return water of the refrigeration unit.

[0037] It should be explained that the above system operating temperature characteristic value is calculated by the indoor return air temperature of the air conditioner, the indoor supply air temperature difference of the air conditioner, the chilled water outlet temperature of the refrigeration unit, and the chilled water supply and return temperature difference of the refrigeration unit. The indoor return air temperature of the air conditioner, the indoor supply air temperature difference of the air conditioner, the chilled water outlet temperature of the refrigeration unit, and the chilled water supply and return temperature difference of the refrigeration unit are normalized. Taking into account multiple factors such as the indoor return air temperature of the air conditioner, the indoor supply air temperature difference of the air conditioner, the chilled water outlet temperature of the refrigeration unit, and the chilled water supply and return temperature difference of the refrigeration unit, the operating characteristics of the refrigeration system can be more comprehensively evaluated to help understand the overall operating status of the refrigeration room. By considering multiple parameters, the actual operating conditions of the refrigeration room are more accurately reflected, which is helpful to accurately evaluate the temperature conditions and cooling effects of the refrigeration room. The calculation of the system operating temperature characteristic value can help optimize the operation of the refrigeration system in the room, improve cooling efficiency, and reduce energy consumption, thereby achieving energy saving. Monitoring and analyzing the changing trend of the system operating temperature characteristic value can promptly discover abnormal conditions in the refrigeration system or air-conditioning equipment. , early warning of failure risks, ensuring the stability and reliability of the computer room operation, the calculation of the system operation temperature characteristic value can visualize the data, which is convenient for building managers and operation and maintenance personnel to intuitively understand the temperature status of the computer room, and make adjustments and improvements based on the data. The set compensation factors of the air-conditioning indoor return air temperature, air-conditioning indoor supply air temperature difference, refrigeration unit chilled water outlet temperature, and refrigeration unit chilled water supply and return water temperature difference are obtained from the database. According to the historical data, a mapping set of the historically measured air-conditioning indoor return air temperature, air-conditioning indoor supply air temperature difference, refrigeration unit chilled water outlet temperature, refrigeration unit chilled water supply and return water temperature difference and the air-conditioning indoor return air temperature, air-conditioning indoor supply air temperature difference, refrigeration unit chilled water outlet temperature, and refrigeration unit chilled water supply and return water temperature difference compensation factors is established to obtain the current air-conditioning indoor return air temperature, air-conditioning indoor supply air temperature difference, refrigeration unit chilled water outlet temperature, and refrigeration unit chilled water supply and return water temperature difference corresponding to the air-conditioning indoor return air temperature, air-conditioning indoor supply air temperature difference, refrigeration unit chilled water outlet temperature, and refrigeration unit chilled water supply and return water temperature difference compensation factors.

[0038] Based on the optimized cooling water inlet temperature and the chilled water outlet temperature of the refrigeration unit, the COP curves corresponding to the optimized cooling water inlet temperature and the chilled water outlet temperature of the refrigeration unit are obtained by comparison.

[0039] Specifically, based on the optimized cooling water inlet temperature and the chilled water outlet temperature of the refrigeration unit, the COP curves corresponding to the optimized cooling water inlet temperature and the chilled water outlet temperature of the refrigeration unit are obtained by comparison. The specific analysis process is: the optimized cooling water inlet temperature and the chilled water outlet temperature of the refrigeration unit are stored as designated tags; the designated tag is compared with the COP curves corresponding to the designated tags stored in the database to obtain the COP curve corresponding to the designated tag.

[0040] It should be explained that the labels of the optimized cooling water inlet temperature and the chilled water outlet temperature of the refrigeration unit are stored and compared with the COP curve in the database to obtain an accurate COP curve. Therefore, a COP prediction method for an air-conditioning refrigeration room operation system can ensure accurate prediction of energy consumption, provide a reliable basis for the energy management of the system, compare and analyze the COP curve, understand the energy consumption performance of the system under different working conditions, and make corresponding optimization measures for different working conditions to improve the overall energy efficiency performance of the refrigeration system. It can also find energy consumption peaks and energy-saving potentials, make targeted energy-saving adjustments, reduce energy consumption in system operation, and achieve the goals of energy conservation and environmental protection and emission reduction. Monitoring and analyzing the COP curve can help discover problems and unstable factors in system operation, and make timely adjustments and improvements, thereby improving the stability and reliability of the system.

[0041] It should be explained that the above-mentioned COP curves corresponding to the optimized cooling water inlet temperature and the chilled water outlet temperature of the refrigeration unit are obtained by comparing the optimized cooling water inlet temperature and the chilled water outlet temperature of the refrigeration unit. Since the COP curve can reflect the energy efficiency performance under different operating parameters, comparing the COP curve can help determine the optimal cooling water inlet temperature and the chilled water outlet temperature setting of the refrigeration unit to achieve the best performance of the air-conditioning system. By comparing the COP curves, the best operating parameters under different conditions can be found, thereby saving energy consumption, reducing operating costs, and improving the energy utilization efficiency of the computer room air-conditioning system. Continuously comparing the COP curves can continuously improve the optimization strategy of the cooling water inlet temperature and the chilled water outlet temperature of the refrigeration unit, so as to achieve continuous improvement and optimization of the operating performance of the central air-conditioning system.

[0042] Obtain a target building basic information dataset, based on the obtained target building basic information dataset, obtain the target building basic information feature value through comprehensive analysis, obtain the current date of the target building, and combine the target building basic information feature value to obtain the building most similar to the target building through comparison.

[0043] Specifically, the building most similar to the target building is obtained by comparison. The specific analysis process is as follows: obtain a data set of basic information of the target building, which specifically includes the floor area of ​​the target building, the indoor volume of the target building, and the highest height of the target building. The floor area of ​​the target building refers to the ground space area occupied by the entire building, usually in square meters or square feet. The indoor volume of the target building represents the total volume inside the building, usually in cubic meters or cubic feet. The indoor volume reflects the size of the space inside the building. The highest height of the target building represents the vertical height of the highest point of the building from the ground, usually in meters or feet. Laser rangefinders, measuring instruments, etc. can be used to measure the building on the spot to obtain accurate building size and height data; based on the obtained data set of basic information of the target building, a comprehensive analysis is performed to obtain the basic information characteristic value of the target building, and the basic information characteristic value of the target building is used as the analysis basis for comparing and obtaining the building most similar to the target building; obtain the current date of the target building, and store the current date of the target building and the basic information characteristic value of the target building as a designated label; compare the designated label with the buildings most similar to the target building corresponding to each designated label stored in the database to obtain the building most similar to the target building corresponding to the designated label.

[0044] It needs to be explained that the above-mentioned floor area reflects the size of the space occupied by the building on the ground, while the indoor volume reflects the size of the available space inside the building. The indoor volume of the building will increase with the increase of the floor area. The indoor volume is closely related to the maximum height. The indoor volume of the building is limited by the height of the building. The indoor volume will increase with the increase of the height of the building. There is also a certain relationship between the floor area and the maximum height of the building. The higher the maximum height of the building, the larger the floor area may be.

[0045] It needs to be explained that the above-mentioned energy consumption forecasting using historical data similar to the target building can more accurately infer the energy consumption of the target building. By comparing the historical data of the target building and similar buildings, patterns and trends can be discovered, thereby obtaining more reliable forecasting results. Energy consumption forecasting based on historical data of similar buildings can save time and cost. The historical data of similar buildings already exists and is complete, and can be used directly for forecasting, avoiding the need to collect and organize large amounts of data and improving forecasting efficiency. Historical data can help understand the changing patterns of building energy consumption. By comparing with the historical data of similar buildings, we can better grasp the changing trends and fluctuation ranges of energy consumption forecasts, thereby improving the stability of forecasting results. Energy consumption forecasting based on historical data of similar buildings can help formulate energy-saving optimization strategies, guide the implementation of effective energy-saving measures, and reduce energy consumption costs. Using historical data of similar buildings to forecast energy consumption can provide strong decision-making support.

[0046] Furthermore, the specific analysis process of the basic information characteristic value of the target building is as follows:

[0047] ;

[0048] In the formula, is the basic information characteristic value of the target building, is the target building area, is the indoor volume of the target building, is the maximum height of the target building, is the compensation factor for the target building area, is the compensation factor for the indoor volume of the target building, is the compensation factor for the maximum height of the target building, and e is a natural constant.

[0049] It needs to be explained that the above-mentioned characteristic values ​​of the basic information of the target building are calculated through the target building's floor area, the target building's indoor volume and the target building's maximum height. The target building's floor area, the target building's indoor volume and the target building's maximum height are normalized. The target building's floor area, the target building's indoor volume and the target building's maximum height are important features for describing the building. The characteristic values ​​of the building's floor area, indoor volume and height will affect the building's energy consumption level. By calculating these characteristic values, energy analysis can be carried out to evaluate the building's energy consumption, and provide a reference for building energy-saving design and improvement. The compensation factors of the target building's floor area, the target building's indoor volume and the target building's maximum height are obtained from the database. A mapping set of the historically measured target building's floor area, the target building's indoor volume and the target building's maximum height and the compensation factors of the target building's floor area, the target building's indoor volume and the target building's maximum height are established based on historical data to obtain the compensation factors of the target building's floor area, the target building's indoor volume and the target building's maximum height corresponding to the current target building's floor area, the target building's indoor volume and the target building's maximum height.

[0050] A historical data set of a building that is most similar to the target building is obtained, and based on the obtained historical data set of a building that is most similar to the target building, the predicted hourly wet-bulb temperature of the target building on the current date is obtained by comparison.

[0051] Specifically, the historical data set of the building most similar to the target building includes the historical average light intensity of the building most similar to the target building, the historical average ambient temperature of the building most similar to the target building, and the historical hourly power load of the building most similar to the target building. The historical average light intensity of the building most similar to the target building indicates the average light intensity level of the building most similar to the target building in the historical records. Light intensity refers to the amount of light per unit area, usually expressed in lux, which can affect the lighting demand and heat load inside the building. The average light intensity can be monitored and recorded in real time by light sensors or light meters installed in the building. The historical average ambient temperature of the building most similar to the target building represents the average ambient temperature level in the historical records of the building most similar to the target building. The ambient temperature refers to the air temperature inside the building, which has an important impact on the load and energy consumption of the air-conditioning system. The average ambient temperature can be monitored and recorded in real time by temperature sensors or meteorological station equipment installed in the building. The historical hourly power load of the building most similar to the target building represents the total power load per hour in the historical records of the building most similar to the target building, that is, the total electric energy consumed. The historical power load data can help understand the energy consumption pattern, peak load situation and energy-saving potential of the building. The historical total power load per hour can be collected and recorded in real time by the power load monitoring equipment inside the building.

[0052] In a specific embodiment, when the light intensity is high, less artificial lighting may be required inside the building, thereby reducing the electrical load of the lighting equipment. When the ambient temperature rises, the air-conditioning system often needs to consume more electricity to maintain a comfortable temperature inside the building, resulting in an increase in the total electrical load. The combined changes in light intensity and ambient temperature will directly affect the energy consumption pattern inside the building. How to reasonably manage light, temperature and electricity usage will directly affect the total electrical load.

[0053] Specifically, based on the acquired historical data set of buildings most similar to the target building, the predicted hourly wet-bulb temperature of the target building on the current date is obtained by comparison. The specific analysis process is: based on the acquired historical data set of buildings most similar to the target building, a comprehensive analysis is performed to obtain the historical characteristic values ​​of the building most similar to the target building, and the historical characteristic values ​​of the building most similar to the target building are used as the analysis basis for comparing and obtaining the predicted hourly wet-bulb temperature of the target building on the current date; the historical characteristic values ​​of the building most similar to the target building are compared with the predicted hourly wet-bulb temperatures of the target building on the current date corresponding to the historical characteristic values ​​of the building most similar to the target building stored in the database, so as to obtain the predicted hourly wet-bulb temperature of the target building on the current date corresponding to the historical characteristic values ​​of the building most similar to the target building.

[0054] In a specific embodiment, using the historical data set of buildings that are most similar to the target building for comparison and analysis can more accurately predict the wet-bulb temperature of the target building every hour on the current date, because there may be certain patterns and associations between similar buildings, and this comparison can better capture these associations and improve the accuracy of prediction. By comparing and analyzing the historical data set of buildings that are most similar to the target building, the time cost of data collection and processing can be reduced. Compared with collecting data and building models from scratch, using existing similar building data can make predictive analysis faster, saving time and cost, and can provide a reference for the prediction of the wet-bulb temperature of the target building. Such an analysis process can provide building managers or environmental controllers with more convincing and reliable prediction results, helping them make more informed decisions. Accurately predicting the wet-bulb temperature of the target building every hour can help optimize building energy management, better understand the changing trend of wet-bulb temperature, and take corresponding measures to save energy and improve energy efficiency, thereby reducing energy consumption and reducing environmental impact.

[0055] It should be explained that the specific analysis process of the historical characteristic values ​​of the building most similar to the target building is as follows:

[0056] ;

[0057] In the formula, is the historical characteristic value of the building most similar to the target building, is the historical average light intensity of the building most similar to the target building, is the historical average ambient temperature of the building most similar to the target building, is the historical hourly power load of the building most similar to the target building, is the compensation factor for the historical average light intensity of the building that is most similar to the target building. is the compensation factor for the historical average ambient temperature of the building that is most similar to the target building. The compensation factor for the historical hourly power load of the building that is most similar to the target building.

[0058] It should be explained that the historical characteristic values ​​of the building most similar to the target building are calculated through the historical average light intensity of the building most similar to the target building, the historical average ambient temperature of the building most similar to the target building, and the historical hourly power load of the building most similar to the target building. The historical average light intensity of the building most similar to the target building, the historical average ambient temperature of the building most similar to the target building, and the historical hourly power load of the building most similar to the target building are normalized. By comparing the historical data of the building most similar to the target building, such as the average light intensity, the average ambient temperature and the total hourly power load, the energy consumption of the target building can be analyzed and evaluated, which is helpful to discover potential energy-saving optimization opportunities and reduce energy consumption costs. According to the historical hourly total power load data, the power consumption of the building can be analyzed, the peak power consumption hours and power consumption trends can be understood, which is helpful to reasonably formulate power consumption plans and management strategies, and can provide a reference basis for the wet-bulb temperature prediction of the target building, and provide more convincing and reliable prediction results. The target building is set to be most similar to the target building. The compensation factors of the historical average light intensity of similar buildings, the historical average ambient temperature of the building most similar to the target building, and the historical hourly power consumption load of the building most similar to the target building are obtained from a database, and a mapping set of the historically measured historical average light intensity of the building most similar to the target building, the historical average ambient temperature of the building most similar to the target building, and the historical hourly power consumption load of the building most similar to the target building and the historical average light intensity of the building most similar to the target building, the historical average ambient temperature of the building most similar to the target building, and the historical hourly power consumption load of the building most similar to the target building is established based on the historical data, so as to obtain the compensation factors of the historical average light intensity of the building most similar to the target building, the historical average ambient temperature of the building most similar to the target building, and the historical hourly power consumption load of the building most similar to the target building corresponding to the current historical average light intensity of the building most similar to the target building, the historical average ambient temperature of the building most similar to the target building, and the historical hourly power consumption load of the building most similar to the target building.

[0059] Based on the obtained target building's current date predicted hourly wet-bulb temperature and COP curve, the predicted COP value of the computer room per hour is obtained.

[0060] Specifically, based on the obtained target building's current hourly predicted wet-bulb temperature and COP curve, the hourly predicted COP value of the computer room is obtained. The specific analysis process is: based on the polynomial regression model, the historical hourly wet-bulb temperature of the target building is fitted with the total load rate to obtain a polynomial function of the wet-bulb temperature and the total load rate, and the total load rate of the target building predicted hourly on the current date corresponding to the target building's current hourly predicted wet-bulb temperature is obtained; based on the COP curve, the hourly predicted COP value of the computer room corresponding to the total load rate of the target building predicted hourly on the current date is obtained.

[0061] In a specific embodiment, the polynomial regression model is a form of regression analysis, which models the relationship between the independent variable x and the dependent variable y as an n-order polynomial, fits the wet-bulb temperature with the total load rate, and combines it with the COP curve to more accurately predict the hourly COP value of the computer room. The influence of the wet-bulb temperature and the load rate on the COP is taken into account, which improves the accuracy of energy efficiency prediction. Accurately predicting the hourly COP value of the computer room helps to optimize energy utilization. Buildings can adjust equipment operating parameters or energy strategies according to the hourly COP value to maximize energy efficiency and reduce energy waste. Predicting the hourly COP value of the computer room can monitor and evaluate the performance of the equipment, and timely discover abnormal changes in the COP value or inefficient operation, which helps to take measures for maintenance and optimization to ensure that the equipment is in the best operating state. Accurately predicting the hourly COP value of the computer room allows building managers to plan energy use more effectively and reduce energy costs. Reasonable use of COP prediction results can help enterprises save energy costs and improve economic benefits. Based on the predicted COP value of the computer room, building managers can formulate more reasonable operation strategies and maintenance plans to avoid excessive equipment operation, save energy consumption, extend equipment life, and improve system reliability and stability.

[0062] It should be explained that the above-mentioned prediction of the hourly wet-bulb temperature and COP curve based on the current date of the target building can obtain the hourly predicted COP value of the computer room, which can better understand the energy efficiency performance of the air-conditioning system under different working conditions, and help computer room managers optimize energy utilization and reduce energy consumption costs. Understanding the hourly COP value of the computer room can help managers adjust the operating mode of the air-conditioning system at the right time to save energy and reduce energy consumption. Predicting the hourly COP value of the computer room helps to optimize the operation plan of the central air-conditioning system, improve the operating efficiency and stability of the system, reduce the risk of failure, and improve the reliability of the refrigeration room. Managers can manage and monitor energy consumption more accurately, discover problems in time and take measures to ensure the optimal efficiency of energy utilization in the computer room.

[0063] The actual wet-bulb temperature is obtained and combined with the hourly predicted COP value of the computer room, and the hourly predicted energy consumption of the computer room is obtained through comprehensive analysis.

[0064] Specifically, the actual wet-bulb temperature is obtained and combined with the hourly predicted COP value of the computer room, and the hourly predicted energy consumption of the computer room is obtained through comprehensive analysis. The specific analysis process is as follows: based on the polynomial regression model, a polynomial function of the predicted hourly wet-bulb temperature of the target building on the current date and the predicted COP value of the computer room is established, marked as the polynomial function of wet-bulb temperature and COP value; the actual wet-bulb temperature is obtained. The wet-bulb temperature can be measured by using a special wet-bulb thermometer. Based on the polynomial function of wet-bulb temperature and total load rate, the total load rate of the computer room is obtained; based on the polynomial function of wet-bulb temperature and COP value, the COP value of the computer room is obtained; the total cooling capacity of the chiller and its supporting equipment is obtained. The heat meter is a device for measuring heat transfer, which can be installed at the inlet and outlet of the cooling medium. By measuring the data of the heat meter, the heat of the cooling medium can be calculated, and then the cooling capacity of the chiller can be calculated; based on the hourly energy consumption calculation formula of the computer room, the hourly energy consumption of the computer room is calculated. The calculation formula for the hourly energy consumption of the computer room is:

[0065] ;

[0066] In the formula, is the energy consumption of the computer room per hour, is the total cooling capacity of the chiller and its supporting equipment, is the total load rate of the computer room, is the COP value of the computer room.

[0067] In a specific embodiment, the hourly energy consumption of the computer room is calculated by combining the total cooling capacity of the chiller and its supporting equipment, the total load rate of the computer room, and the COP value of the computer room, so that a more accurate hourly energy consumption of the computer room can be obtained, which can more comprehensively reflect the actual energy consumption of the computer room and help building managers to accurately predict and manage energy costs. Calculating energy consumption by combining the cooling capacity of the chiller, the load rate of the computer room and the COP value can help building managers better understand the energy efficiency of the computer room. According to the hourly energy consumption data, the equipment operation strategy can be adjusted in time, the equipment performance can be optimized, the energy utilization efficiency can be improved, and energy costs can be saved at the same time. The hourly energy consumption of the computer room can be calculated using the cooling capacity of the refrigeration unit, the total load rate of the computer room and the COP value, which can realize real-time energy consumption monitoring and adjustment. By monitoring the energy consumption data, managers can promptly discover abnormal energy consumption or equipment operation problems, and adjust the equipment operation parameters in time to improve equipment efficiency and reduce energy consumption. It also helps building managers to make more scientific energy management decisions. According to the actual data, specific energy-saving measures can be formulated for different situations, and the operation strategy can be optimized to achieve more efficient energy management. Accurate calculation of the hourly energy consumption of the computer room can help building managers find the direction of optimizing energy utilization, thereby reducing the hourly energy consumption cost, optimizing equipment operation, and improving energy utilization efficiency, which can help buildings save operating costs.

[0068] In a specific embodiment, the actual wet-bulb temperature and the predicted COP value are combined to obtain the hourly energy consumption of the computer room based on the polynomial regression model, which can provide a more accurate energy consumption forecast, help building managers formulate more effective energy use strategies, avoid energy waste and optimize equipment operation. The actual wet-bulb temperature is obtained in real time, and the hourly energy consumption of the computer room is calculated. Real-time monitoring and adjustment of energy use can be carried out. According to the actual data and the predicted results, timely adjustments can be made to improve energy utilization efficiency and reduce unnecessary energy consumption. The hourly energy consumption of the computer room can be calculated to evaluate equipment performance. By comparing the actual energy consumption and the predicted energy consumption, potential problems in equipment operation can be discovered, and corresponding measures can be taken to optimize and improve equipment performance and efficiency. Combining the actual wet-bulb temperature, the predicted COP value and the energy consumption of the computer room together can help optimize energy management. Building managers can formulate more scientific energy management strategies based on these data to achieve optimal energy utilization efficiency and reduce energy costs. Accurately predicting the hourly energy consumption of the computer room helps save energy costs, optimize energy use and equipment operation, and buildings can achieve more efficient energy management, reduce energy waste and reduce energy expenses.

[0069] It should be explained that the above provides a COP prediction method for the operation system of an air-conditioning refrigeration room, obtains the system operation temperature data set and obtains the COP curve based on the optimized cooling water inlet temperature and the chilled water outlet temperature of the refrigeration unit, so as to achieve accurate optimization of energy utilization, ensure that the system operates in the best working state, and minimize energy consumption. By combining the basic information data set of the target building and the characteristic values ​​obtained by analysis, the building most similar to the target building is compared, and personalized energy efficiency improvement suggestions can be provided for the target building. The hourly wet-bulb temperature is predicted based on the historical data set of similar buildings, so as to better guide energy management practices. By continuously collecting, analyzing and comparing data, the energy management strategy of the computer room can be continuously improved, the system operation efficiency can be optimized, the energy utilization efficiency can be improved, and more sustainable energy management and control can be achieved.

[0070] like Figure 2 As shown in the figure, it is an image of the hourly energy consumption of the computer room as the total load rate of the computer room changes. The x-axis represents the total load rate of the computer room, and the y-axis represents the hourly energy consumption of the computer room. It can help to intuitively understand how the total load rate of the computer room affects the hourly energy consumption of the computer room. The greater the total load rate of the computer room, the greater the hourly energy consumption of the computer room, indicating that the hourly energy consumption of the computer room is greater. As the total load rate of the computer room increases, the impact of the total load rate of the computer room on the hourly energy consumption of the computer room remains unchanged. The total cooling capacity of the chiller and its supporting equipment is set to 200 unchanged, the COP value of the computer room remains unchanged at 5, and only the size of the total load rate of the computer room is changed. The example values ​​of the total load rate of the computer room are as follows:

[0071] Table 1: Example values ​​of the total load factor of the computer room in the hourly energy consumption of the computer room

[0072]

[0073] It should be noted that the larger the COP value of the computer room, the less energy consumption of the computer room per hour. As the COP value of the computer room increases, the impact of the COP value of the computer room on the energy consumption of the computer room per hour gradually decreases. The total cooling capacity of the chiller and its supporting equipment is set to 200, the total load rate of the computer room is set to 80%, and only the size of the COP value of the computer room is changed. The example values ​​of the COP value of the computer room are as follows:

[0074] Table 2: Example values ​​of the COP value of the computer room in the hourly energy consumption of the computer room

[0075]

[0076] The above contents are merely examples and explanations of the structure of the present invention. The technicians in this technical field may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the structure of the invention or exceed the scope defined by the claims, they should all fall within the protection scope of the present invention.

Claims

1. A method for predicting the COP of an air-conditioning and refrigeration room operating system, characterized in that: The following steps are involved: Acquire a building indoor environment temperature data set, and compare and determine an optimized cooling water inlet temperature and a chilled water outlet temperature of a refrigeration unit based on the acquired building indoor environment temperature data set; Based on the optimized cooling water inlet temperature and the chilled water outlet temperature of the refrigeration unit, a COP curve corresponding to the optimized cooling water inlet temperature and the chilled water outlet temperature of the refrigeration unit is obtained by comparison; Obtain a target building basic information data set, obtain a target building basic information feature value through comprehensive analysis based on the obtained target building basic information data set, obtain the current date of the target building, and compare and obtain a building that is most similar to the target building in combination with the target building basic information feature value; Obtain a historical data set of a building that is most similar to the target building, and compare and obtain the predicted hourly wet-bulb temperature of the target building on the current date based on the acquired historical data set of the building that is most similar to the target building; Based on the obtained target building's current date predicted hourly wet-bulb temperature and COP curve, the predicted COP value of the computer room per hour is obtained; The actual wet-bulb temperature is obtained and combined with the predicted COP value of the computer room every hour, and the predicted energy consumption of the computer room is obtained through comprehensive analysis; The wet-bulb temperature and COP curve of the target building are predicted every hour on the current date to obtain the predicted COP value of the computer room every hour. The specific analysis process is as follows: Based on the polynomial regression model, the historical hourly wet-bulb temperature of the target building is fitted with the total load rate to obtain the polynomial function of the wet-bulb temperature and the total load rate, and the total load rate of the target building predicted hourly on the current date corresponding to the predicted hourly wet-bulb temperature of the target building on the current date is obtained; Based on the COP curve, the predicted COP value of the computer room per hour corresponding to the predicted total load rate per hour of the target building on the current date is obtained; The actual wet-bulb temperature is obtained and combined with the predicted COP value of the computer room every hour, and the predicted energy consumption of the computer room every hour is obtained through comprehensive analysis. The specific analysis process is as follows: Based on the polynomial regression model, a polynomial function of the predicted hourly wet-bulb temperature of the target building on the current date and the predicted COP value of the computer room per hour is established, which is marked as the polynomial function of wet-bulb temperature and COP value; Obtain the actual wet-bulb temperature, and obtain the total load rate of the computer room based on the polynomial function of the wet-bulb temperature and the total load rate; Based on the polynomial function of wet bulb temperature and COP value, the COP value of the computer room is obtained; Obtain the total refrigeration capacity of the chiller and its supporting equipment; Based on the hourly energy consumption calculation formula of the computer room, the hourly energy consumption of the computer room is calculated. The hourly energy consumption calculation formula of the computer room is: ； In the formula, is the energy consumption of the computer room per hour, is the total cooling capacity of the chiller and its supporting equipment, is the total load rate of the computer room, is the COP value of the computer room.

2. According to claim 1, a COP prediction method for an air-conditioning refrigeration room operating system is characterized in that: Based on the acquired building indoor environment temperature data set, the optimized cooling water inlet temperature and the chilled water outlet temperature of the refrigeration unit are compared and determined. The specific analysis process is as follows: Obtain the system operating temperature data set, which specifically includes the indoor return air temperature of the air conditioner, the indoor supply air temperature difference of the air conditioner, the chilled water outlet temperature of the refrigeration unit, and the chilled water supply and return temperature difference of the refrigeration unit; Based on the acquired system operating temperature data set, a comprehensive analysis is performed to obtain the building system operating temperature characteristic value, which is used as the analysis basis for comparing the optimized cooling water inlet temperature and the chilled water outlet temperature of the refrigeration unit; The system operating temperature characteristic value is compared with the optimized cooling water inlet temperature and the chilled water outlet temperature of the refrigeration unit corresponding to each system operating temperature characteristic value stored in the database to obtain the optimized cooling water inlet temperature and the chilled water outlet temperature of the refrigeration unit corresponding to the system operating temperature characteristic value.

3. The COP prediction method for an air-conditioning refrigeration room operating system according to claim 2 is characterized in that: The system operating temperature characteristic value, the specific analysis process is: ； In the formula, is the system operating temperature characteristic value, is the indoor return air temperature of the air conditioner, The indoor air supply temperature difference of the air conditioner is is the chilled water outlet temperature of the refrigeration unit, The temperature difference between the supply and return water of the refrigeration unit. is the compensation factor for the set indoor return air temperature of the air conditioner. is the compensation factor for the set indoor air supply temperature difference of the air conditioner. is the compensation factor for the chilled water outlet temperature of the set refrigeration unit, It is the compensation factor for the set temperature difference between the chilled water supply and return water of the refrigeration unit.

4. According to claim 1, a COP prediction method for an air-conditioning refrigeration room operating system is characterized in that: Based on the optimized cooling water inlet temperature and the chilled water outlet temperature of the refrigeration unit, the COP curve corresponding to the optimized cooling water inlet temperature and the chilled water outlet temperature of the refrigeration unit is obtained by comparison. The specific analysis process is as follows: The optimized cooling water inlet temperature and the chilled water outlet temperature of the refrigeration unit are stored as designated tags; The designated tag is compared with the COP curves corresponding to the designated tags stored in the database to obtain the COP curve corresponding to the designated tag.

5. The method for predicting COP of an air-conditioning and refrigeration room operating system according to claim 1, characterized in that: The comparison obtains the building most similar to the target building, and the specific analysis process is as follows: Obtaining a target building basic information dataset, wherein the target building basic information dataset specifically includes the target building floor area, the target building indoor volume, and the target building maximum height; Based on the acquired target building basic information data set, a comprehensive analysis is performed to obtain the target building basic information characteristic value, and the target building basic information characteristic value is used as the analysis basis for obtaining the building most similar to the target building by comparison; Get the current date of the target building, and store the current date of the target building and the basic information feature value of the target building as a specified label; The designated tag is compared with the building most similar to the target building corresponding to each designated tag stored in the database to obtain the building most similar to the target building corresponding to the designated tag.

6. The method for predicting COP of an air-conditioning and refrigeration room operating system according to claim 5, characterized in that: The specific analysis process of the basic information characteristic value of the target building is as follows: ； In the formula, is the basic information characteristic value of the target building, is the target building area, is the indoor volume of the target building, is the maximum height of the target building, is the compensation factor for the target building area, is the compensation factor for the indoor volume of the target building, is the compensation factor for the maximum height of the target building, and e is a natural constant.

7. The COP prediction method for an air-conditioning and refrigeration room operating system according to claim 1, characterized in that: The historical data set of the building most similar to the target building specifically includes the historical average light intensity of the building most similar to the target building, the historical average ambient temperature of the building most similar to the target building, and the historical hourly power load of the building most similar to the target building.

8. The method for predicting COP of an air-conditioning and refrigeration room operating system according to claim 7, characterized in that: The historical data set of buildings most similar to the target building is obtained, and the predicted hourly wet-bulb temperature of the target building on the current date is obtained by comparison. The specific analysis process is as follows: Based on the acquired historical data set of buildings most similar to the target building, a comprehensive analysis is performed to obtain the historical characteristic values ​​of the buildings most similar to the target building, and the historical characteristic values ​​of the buildings most similar to the target building are used as the analysis basis for obtaining the predicted hourly wet-bulb temperature of the target building on the current date; The historical characteristic value of the building most similar to the target building is compared with the wet-bulb temperature predicted every hour on the current date of the target building corresponding to each historical characteristic value of the building most similar to the target building stored in the database to obtain the wet-bulb temperature predicted every hour on the current date of the target building corresponding to the historical characteristic value of the building most similar to the target building.

Images