CN115081220A - Adjusting method and system for high-energy-efficiency central air-conditioning system - Google Patents

Adjusting method and system for high-energy-efficiency central air-conditioning system Download PDF

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CN115081220A
CN115081220A CN202210738275.XA CN202210738275A CN115081220A CN 115081220 A CN115081220 A CN 115081220A CN 202210738275 A CN202210738275 A CN 202210738275A CN 115081220 A CN115081220 A CN 115081220A
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data
conditioning system
central air
temperature
weather station
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刘洪涛
陈绪高
陈�峰
曹红军
何永深
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Guangdong Zhongke Guangnianshuzhi Technology Co ltd
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Guangdong Zhongke Guangnianshuzhi Technology Co ltd
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F24—HEATING; RANGES; VENTILATING
    • F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
    • F24F11/00—Control or safety arrangements
    • F24F11/62—Control or safety arrangements characterised by the type of control or by internal processing, e.g. using fuzzy logic, adaptive control or estimation of values
    • F24F11/63—Electronic processing
    • F24F11/64—Electronic processing using pre-stored data
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F24—HEATING; RANGES; VENTILATING
    • F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
    • F24F11/00—Control or safety arrangements
    • F24F11/70—Control systems characterised by their outputs; Constructional details thereof
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/21—Design, administration or maintenance of databases
    • G06F16/211—Schema design and management
    • G06F16/212—Schema design and management with details for data modelling support
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/25—Integrating or interfacing systems involving database management systems
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
    • G06F2119/08—Thermal analysis or thermal optimisation
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
    • Y02P80/00—Climate change mitigation technologies for sector-wide applications
    • Y02P80/10—Efficient use of energy, e.g. using compressed air or pressurized fluid as energy carrier

Abstract

The invention relates to the field of air conditioner regulating systems, and particularly discloses a regulating method and a regulating system of an energy-efficient central air conditioning system, wherein the regulating method comprises the steps of data acquisition, database establishment, algorithm model establishment, result acquisition and regulating equipment; and establishing a total energy consumption algorithm model related to the energy efficiency relation of the central air-conditioning system according to the operation data of the central air-conditioning system, and obtaining an optimal solution of the overall energy efficiency of the system based on the total energy consumption algorithm model so as to operate system equipment, so that the energy consumed by the central air-conditioning system in operation is the lowest, the energy consumption is reduced, and the energy is saved.

Description

Method and system for adjusting high-energy-efficiency central air conditioning system
Technical Field
The invention relates to the field of air conditioner adjusting systems, in particular to a central air conditioner adjusting method and system.
Background
At present, the regulation of the existing central air-conditioning system is mainly controlled manually, operators with professional knowledge and responsible centers can carry out proper regulation in the running process of the central air-conditioning, part of air-conditioning regulating systems are provided with pid feedback regulation, corresponding chilled water temperature feedback regulation is carried out according to outdoor temperature change, but part of the central air-conditioning regulating systems directly carry out central air-conditioning running according to temperature parameters set by factories after delivery, and the operators only carry out start-stop operation of equipment; the common disadvantages of these air conditioning systems are that the energy efficiency of the central air conditioning system equipment is in low-efficiency operation in most cases, or the energy supply is greater than the actual demand, even if equipment adjustment is performed, partial energy waste is reduced, but the comfort level cannot be matched in time, and the hysteresis of the system energy supply exists.
The technical problem to be solved by the application is as follows: how to solve the problem of low equipment energy efficiency in the central air-conditioning system and realize the optimal operation of the whole equipment energy efficiency of the central air-conditioning system.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention aims to provide an energy-efficient central air-conditioning system adjusting method and an energy-efficient central air-conditioning system adjusting system.
The technical scheme adopted by the invention is as follows: an energy-efficient central air conditioning system adjusting method comprises the following steps:
data acquisition: the control host collects and stores the operation data of the central air-conditioning system;
establishing a database: the control host computer establishes a database of function models about the operation of the equipment;
establishing an algorithm model: calling a function model of a database and establishing an algorithm model related to the total energy consumption of the central air-conditioning system in unit time;
and (3) obtaining a result: obtaining the highest efficiency result of the central air-conditioning system and the flow result of each device in the central air-conditioning system based on the total energy consumption algorithm model;
adjusting equipment: the control host machine adjusts the flow of each device in the central air-conditioning system.
The control host establishes a special total energy consumption algorithm model belonging to the central air-conditioning system according to actual operation data in the central air-conditioning system and a database, so that the highest efficiency result of the central air-conditioning system and the flow rate result of each device in the central air-conditioning system are calculated, and the control host adjusts the flow rate of each device in the central air-conditioning system according to the obtained result, so as to achieve the purpose of high energy efficiency operation of the central air-conditioning system.
In some embodiments, the total energy consumption algorithm model is: q Indoor use ＝K·Q Refrigeration system F (η) is obtained from F (x) General assembly )]＝F[f(η 1 +η 2 +η 3 +η 4 )]；
Wherein Q Indoor use For cooling loads in rooms, Q Refrigeration system The main machine refrigerating capacity of the air conditioning system, X is the total power consumption of the system, and K is the coefficient relation between the refrigerating load called from the database and the main machine refrigerating capacity of the air conditioning system; because the total power consumption of the system is related to the energy efficiency of the air conditioning system host, the cooling pump, the refrigerating pump and the cooling tower, the database is called and the relationship between the indoor cooling efficiency and the flow of single equipment in unit time is established, wherein eta is 1 For the energy efficiency relationship of the main machine of the air conditioning system, eta 2 For cooling pump energy efficiency relations, η 3 For the energy efficiency relationship of the refrigerating pump, eta 4 For the energy efficiency relationship of the cooling tower, η General assembly And F is a functional relation between the total energy efficiency relation and the total power consumption of the system.
At the same flow rate of each equipment, eta is reached General assembly Maximum to obtain the minimum value of X, and control the host according to eta General assembly And controlling each device in the central air-conditioning system to realize corresponding flow.
Because the equipment connection mode, model and service life of each central air conditioner are differentAnd preliminarily establishing an operation algorithm model of each device of the central air-conditioning system according to the function relation of the calling database by collecting operation data of each device as required. Because the central air conditioner mainly adjusts the indoor temperature, the cold load Q Indoor use The heat to be discharged for the indoor temperature reduction to the specified temperature is discharged due to the refrigerating capacity Q of the main machine of the air conditioning system Refrigeration There will be a partial loss in the process and thus Q Refrigeration system And Q Indoor use Certain coefficient relation exists between the two; refrigerating capacity Q of main machine of air conditioning system Refrigeration system The total power consumption X of the system is also lost in the running process and energy loss exists between the total power consumption X and the heat energy, so Q Refrigeration And a certain functional relation exists between the energy efficiency relation of each device, and the energy efficiency relation of each device can be obtained by setting the relation between the cooling efficiency and the flow of a single device because the flow of each device is consistent in the operation process of the central air conditioner, and calculating the energy efficiency relation of each device in Q according to the energy efficiency relation of each device Indoor In the same case, η General assembly And the maximum power consumption X of the system is minimum, so that the energy efficiency optimization operation of the whole equipment of the central air-conditioning system is realized.
In some embodiments, the total energy consumption algorithm model comprises:
η 1 ＝f 1 (t for supplying to 、t Go back to 、G Freezing )；
η 2 ＝f 2 (G Cooling )；
η 3 ＝f 3 (G Freezing )；
η 4 ＝f 4 (T For supplying to 、T Go back to 、G Cooling down )；
Wherein t is For supplying to Temperature of supply of chilled water, t Go back to Is the return water temperature of the chilled water, T For supplying to Temperature of water supplied to the cooling water, T Chinese character hui For cooling water return temperature, G Freezing For chilled water flow, G Cooling down For cooling water flow, call database to get f 1 、f 2 、f 3 、f 4 ，f 1 Is eta 1 And t For supplying to 、t Go back to 、G Freezing Functional relationship between; f. of 2 Is eta 2 And G Cooling down Functional relationship between; f. of 3 Is eta 3 And G Freezing Functional relationship between f 4 Is eta 4 And T For supplying to 、T Go back to 、G Cooling Functional relationship between; the control host controls the temperature and flow rate corresponding to each device.
Because the energy consumption of the air conditioning system host, the cooling pump, the refrigerating pump and the cooling tower is related to the temperature or the flow of the cooling water or the chilled water passing through the air conditioning system host, the energy efficiency relationship of the air conditioning system host, the cooling pump, the refrigerating pump and the cooling tower has a certain functional relationship with the temperature or the flow of the cooling water or the chilled water passing through the air conditioning system host, and the functional relationship between each device and the temperature or the flow is established for further obtaining the more exact energy efficiency relationship of each device.
In some embodiments, the algorithmic model further comprises a time lag model, the time lag model being:
ΔT＝h·f h1 (Δt)；
wherein, the delta T is the indoor temperature rise difference, the delta T is the host temperature difference, the h is the set temperature time, and the database is called to obtain f h1 ，f h1 Is Δ T as a function of Δ T with respect to h.
Because the central air-conditioning system and the building have certain time lag, namely after the central air-conditioning system is started, the temperature reduction process is that the temperature of the building is reduced to be consistent with the indoor temperature, then the indoor air temperature is integrally reduced, different buildings show different heat consumptions, namely, the time lag of the temperature reduction is different, the relationship between the temperature difference delta T of the main machine and the set temperature time h required for reaching the indoor temperature rise delta T is established, the set temperature time h required for reaching different indoor temperature rise delta T is calculated by statistics, thereby calculating the heat consumption of the building, further acquiring the unique data about the performance of the individual central air-conditioning system, and the system can help the operator to directly know the time required for reaching the set temperature when the central air-conditioning system is started, thereby reducing the hysteresis of the system energy supply.
In some embodiments, the data collection further comprises collecting outdoor weather station data and local weather station data, the database comprises a weather data function model, the algorithm model comprises an outdoor data interference model, the outdoor data interference model comprises:
A＝f temperature 1 (a)；
Wherein A is the data of the outdoor weather station at the moment, a is the data of the local weather station at the moment, and f is obtained by calling the database Temperature 1 ，f Temperature 1 The data A of the outdoor weather station at the moment and the data a of the local weather station at the moment are in a functional relationship;
setting predicted data B of one hour in the future of the local weather station and predicted data C of two hours in the future of the local weather station, and predicting data B of one hour in the future and data C of two hours in the future of the outdoor weather station based on A ═ f (a); recording actual outdoor weather station data B1 and local weather station data B1 after one hour, verifying whether B is consistent with B1, establishing B1-F (B1), and predicting data C1 of the outdoor weather station after one hour in the future according to B1-F (B1);
recording the actual outdoor weather station data C2 and the local weather station data C2 after two hours, and verifying if C, C1 and C2 are consistent.
The method comprises the steps of establishing an outdoor weather station, comparing data of the outdoor weather station with data of a local weather station, further pre-judging the temperature outside a building in order to establish the change of the outdoor temperature of the building provided with a central air-conditioning system, wherein the temperature outside the building is pre-judged, the temperature and the flow of a cooling tower can be adjusted in advance by pre-judging the temperature outside the building, meanwhile, a control host is helped to set the indoor temperature more suitable for the temperature of a human body, the indoor temperature and the running state of equipment are dynamically adjusted, and the outdoor temperature which can be accurately pre-judged for a long time according to the local weather station can be obtained by establishing the temperature relation of each time interval.
In some embodiments, the system further comprises data simulation, the data simulation is matched with simulation data obtained according to the total energy consumption algorithm model, the simulation data is matched with actual operation data of the central air-conditioning system, and the matching rate is higher than 90% and can be put into use. In order to ensure that a well-established individual central air-conditioning regulating system is well established, the simulation data obtained by a total energy consumption algorithm model established based on the database and the equipment operation data is compared with the actual equipment operation data, and the matching rate is higher than 90% before the system can be put into use.
In some embodiments, the cold load is calculated by the control host according to the temperature difference between the indoor temperature and the specific target indoor temperature or according to the temperature difference between the indoor temperature and the proper human body temperature matching the outdoor temperature. The cold load is the heat of the temperature difference between the indoor temperature and the set temperature, and the set temperature is acquired by two modes, wherein the specific setting is manually carried out, and the control host is matched and set according to the outdoor temperature, so that two different modes are set for operators to use in a humanized and intelligent mode.
A central air conditioning adjusting system applies any one of the energy-efficient central air conditioning system adjusting methods, a control host comprises a data acquisition module, a network communication module, a self-adaptive algorithm module and a storage module, the control host is in communication connection with the central air conditioning system, the storage module comprises a database and a storage server used for storing running data of the central air conditioning system, and the self-adaptive algorithm module is used for calling a function model of the database and establishing a total energy consumption algorithm model. The control host collects the operation data of each device through the network communication module and the data acquisition module, stores the operation data into the storage module, calls the device operation data and functions from the storage module, and calculates the personalized algorithm model of the individual central air-conditioning system through the self-adaptive algorithm module, wherein the modules supplement each other and complement each other in cooperation.
The invention has the beneficial effects that:
according to the energy-efficient central air-conditioning system adjusting method, the optimal solution of the overall energy efficiency of the system is established for the differentiated central air-conditioning system to operate the system equipment, so that the energy consumed by each equipment in the system is the lowest, the energy consumption is reduced, the energy is saved, and the energy-saving and environment-friendly effects advocated by the state are realized.
Drawings
FIG. 1 is a schematic diagram of the connection between the modules of the control host and the central air conditioning system according to the present invention;
FIG. 2 is a schematic diagram showing the relationship between the indoor cooling efficiency and the flow rates of the air conditioning system main unit, the cooling pump, the refrigerating pump and the cooling tower in unit time;
FIG. 3 is a schematic diagram of the relationship between the indoor cooling efficiency and the flow rates of the air conditioning system main unit, the cooling pump, the freezing pump and the cooling tower in unit time;
fig. 4 is a schematic diagram of matching and comparing simulation data with actual data.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The invention provides a technical scheme that:
a high-energy-efficiency central air-conditioning system adjusting method and system thereof, includes a control host and a central air-conditioning system, please refer to fig. 1, wherein the control host includes a data acquisition module, a network communication module, a self-adaptive algorithm module and a storage module, the storage module includes a database for storing function models related to equipment operation and a storage server for storing operation data of each equipment of the central air-conditioning system, the control host collects the operation data of each equipment of the central air-conditioning system through the network communication module and the data acquisition module, stores the operation data of each equipment into the storage module, calls the operation data and functions of each equipment of the central air-conditioning system from the storage module, and calculates the personalized algorithm model related to the individual central air-conditioning system through the self-adaptive algorithm module, and the implementation method is as follows:
1. and (4) sorting, confirming and combing according to the equipment condition of the on-site central air-conditioning system to obtain equipment (including read-only data and controllable data) needing data acquisition, and confirming the running time and running habit of the central air-conditioning system.
2. Acquiring data of central air-conditioning system equipment and additionally arranging sensor equipment influencing operating factors of the central air-conditioning system; the central air-conditioning system mainly influences efficiency except the performance of the central air-conditioning system, the temperature of a host correspondingly influences the supply and return water temperature of chilled water of a system pipeline, the frequency of a freezing pump and a cooling pump correspondingly influences the flow and pressure of the pipeline, and the frequency number of cooling towers corresponds to the heat dissipation capacity of the cooling water; and collecting the data, and performing remote communication connection on each device.
3. All relevant factor equipment (including main equipment and sensors) of the central air-conditioning system are in communication connection with the control host, data acquisition and integration are carried out through a communication protocol and plc, data transmission is carried out through a wireless network, and all operation data of the central air-conditioning system are stored in a storage server.
4. Customizing and adjusting the operation algorithm of the central air-conditioning system according to the operation requirement of the central air-conditioning system; and the control host calls the function model of the database and then establishes an algorithm model related to the total energy consumption of the central air-conditioning system in unit time by combining the operation data of the central air-conditioning system in the storage server.
5. Referring to fig. 4, the control host inputs data at an early stage to perform total energy consumption algorithm model simulation to obtain simulation data; and extracting actual collected data according to the total energy consumption algorithm model, calculating to obtain actual data, comparing the simulated data with the actual data, and enabling the matching rate to exceed more than 90% to be actually put into use.
6. And the control host obtains the highest performance result of the central air-conditioning system and the flow rate result of each device in the central air-conditioning system according to the total energy consumption algorithm model.
7. And the control host further adjusts the flow of each device in the central air-conditioning system according to the result.
The total energy consumption algorithm model is as follows: q Indoor use ＝K·Q Refrigeration system F (eta) is obtained General assembly )]＝F[f(η 1 +η 2 +η 3 +η 4 )]；
Wherein Q Indoor use For cooling indoorsCold load, Q Refrigeration system The main machine refrigerating capacity of the air conditioning system, X is the total power consumption of the system, and K is the coefficient relation between the refrigerating load called from the database and the main machine refrigerating capacity of the air conditioning system; referring to fig. 2 and 3, since the total power consumption of the system is related to the energy efficiency of the air conditioning system host, the cooling pump, the refrigeration pump and the cooling tower, the database is called and the relationship between the cooling efficiency in the room and the flow rate of the single device in unit time, η 1 For the energy efficiency relationship of the main machine of the air conditioning system, eta 2 For cooling pump energy efficiency relations, η 3 For the energy efficiency relationship of the refrigerating pump, eta 4 For the energy efficiency relationship of the cooling tower, η General assembly And F is a functional relation between the total energy efficiency relation and the total power consumption of the system.
At the same flow rate of each equipment, eta is reached General assembly Maximum to obtain the minimum value of X, and control the host according to eta General (1) And controlling the flow corresponding to each device.
The cold load is calculated by the control host according to the temperature difference between the indoor temperature and the set specific target indoor temperature or according to the temperature difference between the indoor temperature and the proper human body temperature matched with the outdoor temperature.
Further acquiring a more specific algorithm model and better controlling the energy efficiency of each device, namely a total energy consumption algorithm model eta 1 ＝f 1 (t For supplying to 、t Go back to 、G Freezing )；
η 2 ＝f 2 (G Cooling down )；
η 3 ＝f 3 (G Freezing )；
η 4 ＝f 4 (T For supplying to 、T Go back to 、G Cooling )；
Wherein t is For supplying to Temperature of supply of chilled water, t Go back to Is the return water temperature of chilled water, T For supplying to Temperature of water supplied to the cooling water, T Go back to For cooling water return temperature, G Freezing For chilled water flow, G Cooling down For cooling water flow, call database to get f 1 、f 2 、f 3 、f 4 ，f 1 Is eta 1 And t For supplying to 、t Go back to 、G Freezing Functional relationship between; f. of 2 Is eta 2 And G Cooling down Functional relationship between; f. of 3 Is eta 3 And G Freezing Functional relationship between f 4 Is eta 4 And T For supplying to 、T Go back to 、G Cooling down Functional relationship between; the control host controls the temperature and flow rate corresponding to each device of the central air-conditioning system.
The control host controls the temperature and flow rate corresponding to each device of the central air-conditioning system.
In order to further reduce the hysteresis of energy supply and understand the heat consumption of the building, the algorithm model further comprises a time lag model, wherein the time lag model is as follows:
ΔT＝h·f h1 (Δt)；
wherein, the delta T is the indoor temperature rise difference, the delta T is the host temperature difference, the h is the set temperature time, and the database is called to obtain f h1 ，f h1 Is a function of Δ T and Δ T with respect to h, where Δ T ═ T Go back to -t For supplying to 。
In order to further determine the appropriate indoor temperature and adjust the heat dissipation efficiency of the cooling tower, an outdoor weather station of a building where the central air conditioner is located is established, a storage server collects data of the outdoor weather station and data of a local weather station, a database comprises a weather data function model, an algorithm model comprises an outdoor data interference model, and an outdoor data interference model:
A＝f temperature 1 (a)；
Wherein A is the data of the outdoor weather station at the moment, a is the data of the local weather station at the moment, and f is obtained by calling the database Temperature 1 ，f Temperature 1 The data A of the outdoor weather station at the moment and the data a of the local weather station at the moment are in a functional relationship;
setting predicted data B of one hour in the future of the local weather station and predicted data C of two hours in the future of the local weather station, and predicting data B of one hour in the future and data C of two hours in the future of the outdoor weather station based on A ═ f (a); recording actual outdoor weather station data B1 and local weather station data B1 after one hour, verifying whether B is consistent with B1, establishing B1-F (B1), and predicting data C1 of the outdoor weather station after one hour in the future according to B1-F (B1);
recording the actual outdoor weather station data C2 and the local weather station data C2 after two hours, and verifying if C, C1 and C2 are consistent.
If the matching degree of the data B and the data B1 is more than 90%, namely A ═ f (a), the outdoor meteorological data of the building in the future 1 hour can be predicted according to the local meteorological station; if the data matching degree of C and C2 is more than 90%, namely A ═ f (a) can predict building outdoor meteorological data for at least 2 hours according to a local meteorological station;
if the data matching degree of B and B1 is below 90%, the data matching degree of C1 and C2 is above 90%, namely B1 ═ F (B1) can predict the outdoor weather data of the building in 1 hour in the future according to the local weather station; if the matching degree of the data of the B and the B1 is below 90 percent, and the matching degree of the data of the C1 and the C2 is below 90 percent, no effective function can predict the outdoor future meteorological data temporarily, namely, the relationship between the data of the outdoor meteorological station at the moment and the data of the local meteorological station at the moment is recalculated.
Finally, it should be noted that: although the present invention has been described in detail with reference to the foregoing embodiments, it will be apparent to those skilled in the art that changes may be made in the embodiments and/or equivalents thereof without departing from the spirit and scope of the invention. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims (8)

1. A method for adjusting a high-energy-efficiency central air conditioning system is characterized by comprising the following steps:
data acquisition: the control host collects and stores the operation data of the central air-conditioning system;
establishing a database: the control host computer establishes a database of function models about equipment operation;
establishing an algorithm model: calling a function model of a database and establishing an algorithm model related to the total energy consumption of the central air-conditioning system in unit time;
and (3) obtaining a result: obtaining the highest efficiency result of the central air-conditioning system and the flow result of each device in the central air-conditioning system based on the total energy consumption algorithm model;
adjusting equipment: the control host machine adjusts the flow of each device in the central air-conditioning system.
2. The method as claimed in claim 1, wherein the total energy consumption algorithm model is: q Indoor use ＝K·Q Refrigeration system F (eta) is obtained General assembly )]＝F[f(η 1 +η 2 +η 3 +η 4 )]；
Wherein Q Indoor use For cooling loads in rooms, Q Refrigeration system The main machine refrigerating capacity of the air conditioning system, X is the total power consumption of the system, and K is the coefficient relation between the refrigerating load called from the database and the main machine refrigerating capacity of the air conditioning system; since the total power consumption of the system is related to the energy efficiency of the air conditioning system host, the cooling pump, the refrigerating pump and the cooling tower, the database is called and the relationship between the indoor cooling efficiency and the flow of single equipment in unit time is established, wherein eta is 1 For the energy efficiency relationship of the main machine of the air conditioning system, eta 2 For cooling pump energy efficiency relations, η 3 For the energy efficiency relationship of the refrigerating pump, eta 4 For the energy efficiency relationship of the cooling tower, η General assembly The energy efficiency relation of the whole air conditioning system is shown as F, the total power consumption of the system is converted into a functional relation between the refrigerating capacity of the main machine of the air conditioning system, and the energy efficiency relation and the total power consumption of the system are shown as F;
at the same flow rate of each equipment, eta is reached General assembly Maximum, so as to obtain the minimum value of X, according to eta General assembly And controlling each device in the central air-conditioning system to realize corresponding flow.
3. The energy-efficient central air conditioning system adjusting method according to claim 2, wherein the total energy consumption algorithm model comprises:
η 1 ＝f 1 (t for supplying to 、t Go back to 、G Freezing )；
η 2 ＝f 2 (G Cooling down )；
η 3 ＝f 3 (G Freezing )；
η 4 ＝f 4 (T For supplying to 、T Go back to 、G Cooling )；
Wherein t is For supplying to Temperature of supply of chilled water, t Chinese character hui Is the return water temperature of the chilled water, T For supplying to Temperature of water supplied to the cooling water, T Go back to For cooling water return temperature, G Freezing For chilled water flow, G Cooling down Calling the database to obtain f for cooling water flow 1 、f 2 、f 3 、f 4 ，f 1 Is eta 1 And t For supplying to 、t Go back to 、G Freezing Functional relationship between; f. of 2 Is eta 2 And G Cooling down Functional relationship between; f. of 3 Is eta 3 And G Freezing Functional relationship between f 4 Is eta 4 And T For supplying to 、T Go back to 、G Cooling down Functional relationship between; and the control host controls the temperature and the flow rate corresponding to each device.
4. The method as claimed in claim 1, wherein the algorithm model further comprises a time lag model, the time lag model is:
ΔT＝h·f h1 (Δt)；
wherein, the delta T is the indoor temperature rise difference, the delta T is the host temperature difference, the h is the set temperature time, and the database is called to obtain f h1 Said f h1 Is Δ T as a function of Δ T with respect to h.
5. The method of claim 1, wherein the data collection further comprises collecting outdoor weather station data and local weather station data, the database comprises a weather data function model, the algorithm model comprises an outdoor data interference model, the outdoor data interference model:
A＝f temperature 1 (a)；
Wherein A is the data of the outdoor weather station at the moment, a is the data of the local weather station at the moment, and f is obtained by calling the database Temperature 1 ，f Temperature 1 The data A of the outdoor weather station at the moment and the data a of the local weather station at the moment are in a functional relationship;
setting predicted data B of one hour in the future of the local weather station and predicted data C of two hours in the future of the local weather station, and predicting data B of one hour in the future of the outdoor weather station and data C of two hours in the future based on A ═ f (a); recording actual outdoor weather station data B1 and local weather station data B1 after one hour, verifying whether B is consistent with B1, establishing B1-F (B1), and predicting data C1 of the outdoor weather station after one hour in the future according to B1-F (B1);
recording the actual outdoor weather station data C2 and the local weather station data C2 after two hours, and verifying if C, C1 and C2 are consistent.
6. The conditioning method for an energy-efficient central air conditioning system according to claim 1, further comprising data simulation, wherein the data simulation is matched with simulation data obtained according to the total energy consumption algorithm model, the simulation data is matched with actual operation data of the central air conditioning system, and the matching rate is higher than 90% of the available use.
7. The method as claimed in claim 2, wherein the cooling load is calculated by the control unit according to a temperature difference between the indoor temperature and a specific target indoor temperature or a temperature difference between the indoor temperature and a proper human body temperature matching the outdoor temperature.
8. A central air-conditioning system, applying the method as claimed in any one of claims 1 to 7, wherein the control host comprises a data acquisition module, a network communication module, an adaptive algorithm module and a storage module, the control host is in communication connection with the central air-conditioning system, the storage module comprises a database and a storage server for storing the operation data of the central air-conditioning system, and the adaptive algorithm module is used for calling a function model of the database and establishing a total energy consumption algorithm model.
CN202210738275.XA 2022-06-27 2022-06-27 Adjusting method and system for high-energy-efficiency central air-conditioning system Pending CN115081220A (en)

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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116007250A (en) * 2022-11-19 2023-04-25 深圳市天元维视实业有限公司 Energy-saving control method and system for refrigerating system
CN116592469A (en) * 2023-05-30 2023-08-15 苏州曼凯系统集成科技有限公司 Heating management and control system, method and storage medium

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116007250A (en) * 2022-11-19 2023-04-25 深圳市天元维视实业有限公司 Energy-saving control method and system for refrigerating system
CN116007250B (en) * 2022-11-19 2023-12-05 深圳市天元维视实业有限公司 Energy-saving control method and system for refrigerating system
CN116592469A (en) * 2023-05-30 2023-08-15 苏州曼凯系统集成科技有限公司 Heating management and control system, method and storage medium
CN116592469B (en) * 2023-05-30 2023-12-22 苏州曼凯系统集成科技有限公司 Heating management and control system, method and storage medium

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