CN115581056B - Energy-saving prediction control method and system suitable for water cooling system of data center - Google Patents

Energy-saving prediction control method and system suitable for water cooling system of data center Download PDF

Info

Publication number
CN115581056B
CN115581056B CN202211394671.1A CN202211394671A CN115581056B CN 115581056 B CN115581056 B CN 115581056B CN 202211394671 A CN202211394671 A CN 202211394671A CN 115581056 B CN115581056 B CN 115581056B
Authority
CN
China
Prior art keywords
water
data
power consumption
data center
temperature
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Active
Application number
CN202211394671.1A
Other languages
Chinese (zh)
Other versions
CN115581056A (en
Inventor
吕亮
黎念
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Ningbo Liangkong Information Technology Co ltd
Original Assignee
Ningbo Liangkong Information Technology Co ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Ningbo Liangkong Information Technology Co ltd filed Critical Ningbo Liangkong Information Technology Co ltd
Priority to CN202211394671.1A priority Critical patent/CN115581056B/en
Publication of CN115581056A publication Critical patent/CN115581056A/en
Application granted granted Critical
Publication of CN115581056B publication Critical patent/CN115581056B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • H—ELECTRICITY
    • H05—ELECTRIC TECHNIQUES NOT OTHERWISE PROVIDED FOR
    • H05K—PRINTED CIRCUITS; CASINGS OR CONSTRUCTIONAL DETAILS OF ELECTRIC APPARATUS; MANUFACTURE OF ASSEMBLAGES OF ELECTRICAL COMPONENTS
    • H05K7/00—Constructional details common to different types of electric apparatus
    • H05K7/20—Modifications to facilitate cooling, ventilating, or heating
    • H05K7/20709—Modifications to facilitate cooling, ventilating, or heating for server racks or cabinets; for data centers, e.g. 19-inch computer racks
    • H05K7/20836—Thermal management, e.g. server temperature control
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01D—MEASURING NOT SPECIALLY ADAPTED FOR A SPECIFIC VARIABLE; ARRANGEMENTS FOR MEASURING TWO OR MORE VARIABLES NOT COVERED IN A SINGLE OTHER SUBCLASS; TARIFF METERING APPARATUS; MEASURING OR TESTING NOT OTHERWISE PROVIDED FOR
    • G01D21/00—Measuring or testing not otherwise provided for
    • G01D21/02—Measuring two or more variables by means not covered by a single other subclass
    • H—ELECTRICITY
    • H05—ELECTRIC TECHNIQUES NOT OTHERWISE PROVIDED FOR
    • H05K—PRINTED CIRCUITS; CASINGS OR CONSTRUCTIONAL DETAILS OF ELECTRIC APPARATUS; MANUFACTURE OF ASSEMBLAGES OF ELECTRICAL COMPONENTS
    • H05K7/00—Constructional details common to different types of electric apparatus
    • H05K7/20—Modifications to facilitate cooling, ventilating, or heating
    • H05K7/20709—Modifications to facilitate cooling, ventilating, or heating for server racks or cabinets; for data centers, e.g. 19-inch computer racks
    • H05K7/20718—Forced ventilation of a gaseous coolant
    • H—ELECTRICITY
    • H05—ELECTRIC TECHNIQUES NOT OTHERWISE PROVIDED FOR
    • H05K—PRINTED CIRCUITS; CASINGS OR CONSTRUCTIONAL DETAILS OF ELECTRIC APPARATUS; MANUFACTURE OF ASSEMBLAGES OF ELECTRICAL COMPONENTS
    • H05K7/00—Constructional details common to different types of electric apparatus
    • H05K7/20—Modifications to facilitate cooling, ventilating, or heating
    • H05K7/20709—Modifications to facilitate cooling, ventilating, or heating for server racks or cabinets; for data centers, e.g. 19-inch computer racks
    • H05K7/20763—Liquid cooling without phase change
    • 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
    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
    • Y02D10/00—Energy efficient computing, e.g. low power processors, power management or thermal management

Landscapes

  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Computer Hardware Design (AREA)
  • General Engineering & Computer Science (AREA)
  • Thermal Sciences (AREA)
  • Microelectronics & Electronic Packaging (AREA)
  • General Physics & Mathematics (AREA)
  • Air Conditioning Control Device (AREA)

Abstract

The invention relates to an energy-saving prediction control method and system suitable for a water cooling system of a data center, wherein the method comprises the following steps of S1: acquiring first data, second data, third data and fourth data; step S2: calculating to obtain the air supply quantity and the return water temperature of the chilled water; and step S3: calculating to obtain a first power consumption, a second power consumption and a third power consumption; and step S4: calculating to obtain an optimal parameter value; step S5: and constructing a finite time domain value function based on a linear model of the control system, and regulating and controlling each water cooling unit, each water pump and each fan through the obtained optimal control input sequence. According to the method, the cold quantity requirement of the data center is obtained according to the power consumption of each server, so that the parameters of each water cooling unit, each water pump and each fan are preset, and the problem of local temperature rise of the server is solved; and the water cooling system correspondingly regulates and controls each water cooling unit, each water pump and each fan according to the optimal control input sequence, so that the timeliness and the predictability control of the water cooling system are realized.

Description

Energy-saving prediction control method and system suitable for water cooling system of data center
Technical Field
The invention relates to the technical field of data center water cooling systems, in particular to an energy-saving prediction control method and system suitable for a data center water cooling system.
Background
With the rapid development of the digital world, the proliferation of the number of data center sites will also present the challenge of higher energy consumption. By 2030, the total energy consumption of the data center can reach 1.5% -2% of the power consumption of the whole society, and the data center facilities run all the day, consume a large amount of energy and generate a large amount of heat. Temperature control in a data center is critical to avoid equipment overheating, to regulate equipment cooling, and to measure overall efficiency; according to statistical data, the cooling of the data center accounts for about 40% of the total power consumption of the machine room.
The Chinese patent application CN113551373A discloses a data center air conditioner energy-saving control method based on federal reinforcement learning, which controls an air conditioner refrigeration system based on the acquired temperature of a data center machine room, and controls the air conditioner refrigeration system to cool the data center machine room when the acquired temperature exceeds a safe temperature, and has the defects that the temperature rise of individual servers of the data center machine room is too high and exceeds the safe temperature, and the temperature of the whole machine room does not reach a safe boundary, the air conditioner refrigeration system can not refrigerate the machine room, so that the individual servers are abnormal or damaged due to the too high temperature rise; in the cooling process, the air-conditioning refrigeration system takes the chilled water supply pressure difference obtained in real time as one of control conditions to enable the water-cooling unit to output chilled water with certain pressure to cool the data center machine room, and because the water pressure difference of each part of a water pipe for conveying the chilled water changes in real time, the air-conditioning refrigeration system cannot timely cool the data center machine room, namely, the air-conditioning refrigeration system cannot be controlled in advance; in addition, when the data center computer lab temperature that acquires surpassed safe temperature, air conditioning refrigeration system can lower the temperature to the data center computer lab, because the computer lab temperature that acquires has surpassed safe temperature, consequently lowers the temperature to the data center computer lab this moment, can produce the problem of cooling hysteresis nature undoubtedly, leads to unable realization to air conditioning refrigeration system's promptness and nature control in advance.
Disclosure of Invention
In order to solve the technical problems, the invention provides an energy-saving prediction control method suitable for a data center water cooling system, which can prevent local temperature rise of a server and can realize timeliness and pre-control of the data center water cooling system, wherein the data center water cooling system comprises a data center and a water cooling unit module connected with the data center, the water cooling unit module comprises a plurality of water cooling units, a plurality of water pumps and a plurality of fans which are configured in advance, the data center comprises a plurality of servers, and the energy-saving prediction control method comprises the following steps:
step S1: acquiring first data of each water chiller unit at the current moment, acquiring second data of each water pump and third data of each fan at the current moment, and acquiring fourth data of the data center at the current moment; the first data comprise the supply water temperature of the chilled water of the water chiller, the second data comprise the frequency of the water pump, the third data comprise the rotating speed of the fan, and the fourth data comprise the return air temperature of the data center;
step S2: calculating according to the first data, the third data and the fourth data to obtain the air supply quantity of the fan at the current moment and the return water temperature of the chilled water at the current moment;
and step S3: calculating to obtain corresponding first power consumption according to the return water temperature of the chilled water and the first data, and calculating to obtain corresponding second power consumption and third power consumption according to the second data and the third data respectively;
and step S4: calculating according to the first power consumption, the second power consumption and the third power consumption to obtain an economic performance index, inputting the economic performance index into a pre-configured optimal parameter model, and calculating to obtain an optimal parameter value;
step S5: and constructing a finite time domain value function based on a linear model of a control system, calculating to obtain an optimal control input sequence from the current moment to the future moment according to the optimal parameter value and the finite time domain value function, and performing corresponding regulation and control on each water-cooling unit, each water pump and each fan by the data center water-cooling system according to the optimal control input sequence.
Preferably, the first data further includes a chilled water heat capacity of the water chiller; the third data further comprises the air supply temperature of the fan; the fourth data further comprises air heat capacity of the data center and power consumption of each server; the step S2 includes:
step S21: inputting the power consumption into a preset refrigeration capacity demand model, and calculating to obtain the refrigeration capacity demand of the data center;
step S22: calculating to obtain the air supply quantity corresponding to the fan at the current moment according to the cold quantity requirement, the air heat capacity, the return air temperature and the air supply temperature; calculating to obtain the return water temperature of the chilled water corresponding to the water cooling unit at the current moment according to the cold quantity requirement, the chilled water supply temperature and the chilled water heat capacity;
step S23: the water cooling unit module respectively regulates and controls each fan, each water cooling unit and each water pump through the air supply quantity and the return water temperature of the chilled water so as to control the return air temperature within a safety threshold value.
Preferably, the first data further includes an external ambient temperature of the data center, the chilled water heat capacity, and a flow rate of the chilled water; in the step S3, the specific process of calculating the corresponding first power consumption according to the chilled water return temperature and the first data includes:
step S31: calculating to obtain the refrigerating capacity corresponding to the water chiller unit according to the external environment temperature, the chilled water supply temperature and a preset first performance parameter empirical curve corresponding to the water chiller unit;
step S32: calculating to obtain a part load rate corresponding to the water cooling unit according to the refrigerating capacity, the refrigerating water heat capacity and the flow;
step S33: and calculating to obtain the first power consumption according to the partial load rate, the chilled water supply water temperature and the external environment temperature.
Preferably, the first data further comprises a nominal consumption of the water chiller; the step S33 includes:
and calculating to obtain a corresponding first output value according to the partial load factor and a preset second performance parameter empirical curve corresponding to the water chiller unit, calculating to obtain a corresponding second output value according to the external environment temperature, the chilled water supply temperature and a preset third performance parameter empirical curve corresponding to the water chiller unit, and calculating to obtain the power consumption of the water chiller unit at the current moment according to the first output value, the second output value and the nominal consumption to serve as the first power consumption.
Preferably, the step S3 includes:
and calculating the power consumption of the water pump at the current moment according to the frequency of the water pump and a pre-configured experience curve of a fourth performance parameter corresponding to the water pump, and taking the power consumption as the second power consumption.
Preferably, step S3 comprises:
and calculating the power consumption of the fan at the current moment according to the rotating speed of the fan and a preset fifth performance parameter empirical curve corresponding to the fan to obtain the third power consumption.
Preferably, the optimal parameter values include a partial load rate of each water chiller unit, a frequency of each water pump, and a rotation speed of each fan, which are obtained from the optimal parameter model and satisfy the economic performance index, and the step S5 includes:
step S51: constructing the finite time domain value function based on the linear model of the control system, solving the finite time domain optimal control problem to obtain the optimal control input sequence from the current moment to the future moment, wherein the optimal control input sequence comprises the partial load rate of each water-cooling unit, the frequency of each water pump and the rotating speed of each fan, which meet the economic performance index;
step S52: and the data center water cooling system transmits the optimal control input sequence to each water cooling unit, each water pump and each fan, and correspondingly regulates and controls the partial load rate of each water cooling unit, the frequency of each water pump and the rotating speed of each fan.
The utility model provides an energy-conserving predictive control system suitable for data center water cooling system, data center water cooling system include data center and with the water cooling unit module that data center is connected, data center includes a plurality of servers and a plurality of fan, water cooling unit module includes a plurality of water cooling units, a plurality of water pump and control module:
the energy-saving predictive control system comprises an acquisition module, a power consumption module, a temperature monitoring module and a prediction module, wherein the acquisition module is respectively connected with the power consumption module and the temperature monitoring module;
the acquisition module is used for acquiring first data of each water chiller unit, second data of each water pump, third data of each fan and fourth data of the data center at the current moment, transmitting the acquired first data, the acquired second data and the acquired third data to the power consumption module, and transmitting the acquired first data, the acquired third data and the acquired fourth data to the temperature monitoring module;
the temperature monitoring module is used for calculating the air supply quantity of the fan at the current moment and the return water temperature of the chilled water at the current moment according to the first data, the third data and the fourth data, and transmitting the air supply quantity of the fan and the return water temperature of the chilled water to the control module and the power consumption module;
the power consumption module is used for calculating according to the chilled water return temperature and the first data to obtain first power consumption, calculating according to the second data and the third data to obtain second power consumption and third power consumption respectively, calculating according to the first power consumption, the second power consumption and the third power consumption to obtain economic performance indexes, inputting the economic performance indexes into an optimal parameter model to obtain optimal parameter values, and transmitting the optimal parameter values to the prediction module;
the prediction module is used for calculating to obtain an optimal control input sequence and transmitting the optimal control input sequence to the control module;
the control module is used for regulating and controlling the water cooling units, the water pumps and the fans according to the air supply quantity and the return water temperature of the chilled water, and regulating and controlling the water cooling units, the water pumps and the fans according to the optimal control input sequence.
Preferably, the output end of each water chiller is communicated with a first water collecting tank, the input end of each water chiller is communicated with a second water collecting tank, a bypass is arranged between the first water collecting tank and the second water collecting tank, the bypass is used for conveying chilled water to the first water collecting tank through the bypass when the first water collecting tank supplies insufficient water to the data center so as to ensure that the data center is continuously supplied with chilled water, or when the second water collecting tank supplies insufficient water to each water chiller, the first water collecting tank conveys the chilled water to the second water collecting tank through the bypass so as to ensure that each water chiller has sufficient chilled water supply.
The invention has the beneficial effects that: the method comprises the steps that the cold quantity requirement of the data center at the current moment is obtained according to the power consumption of each server at the current moment, the water cooling unit module respectively controls the partial load rate of each water cooling unit, the frequency of each water pump and the rotating speed of each fan according to the cold quantity requirement, and the servers are controlled according to the power consumption of each server, so that abnormality or damage caused by local temperature rise of each server can be prevented; a constrained finite time domain value function is constructed based on a linear model of a control system, an optimal control input sequence from the current moment to the future moment is obtained by solving an optimal control problem of the finite time domain, the optimal control input sequence is transmitted to each water cooling unit, each water pump and each fan by the water cooling system of the data center, the partial load rate of each water cooling unit, the frequency of each water pump and the rotating speed of each fan are correspondingly regulated, the requirement of lowest energy consumption is met, and the timeliness and the advance control of the water cooling system are realized.
Drawings
FIG. 1 is a schematic diagram of a data center of the present invention;
FIG. 2 is a schematic view of a water chiller and water pump according to the present invention;
FIG. 3 is a control flow chart of the energy-saving predictive control method of the present invention;
FIG. 4 is a detailed flowchart of step S2 of the energy-saving predictive control method of the present invention;
FIG. 5 is a detailed flowchart of step S3 of the energy-saving predictive control method of the present invention;
FIG. 6 is a detailed flowchart of step S5 of the energy-saving predictive control method of the present invention;
FIG. 7 is a block diagram of an energy-saving predictive control system according to the present invention.
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.
It should be noted that the embodiments and features of the embodiments may be combined with each other without conflict. The present invention will be further described with reference to the drawings and the following detailed description, but the present invention is not limited thereto, and the current time is represented by the time k.
In order to solve the above technical problems, as shown in fig. 3, the present invention provides an energy saving predictive control method suitable for a data center water cooling system, which is capable of preventing local temperature rise of servers and realizing timeliness and predictive control of the data center water cooling system, where the data center water cooling system includes a data center and a water cooling unit module connected to the data center, the water cooling unit module includes a plurality of water cooling units, a plurality of water pumps, and a plurality of fans, which are configured in advance, as shown in fig. 2, in this embodiment, the number of the water cooling units is H, the data center includes a plurality of servers, as shown in fig. 1, the number of the servers in this embodiment is M, and the energy saving predictive control method includes the following steps:
step S1: collecting first data of each water cooling unit at the moment k, collecting second data of each water pump and third data of each fan at the moment k, and obtaining fourth data of a data center at the moment k; the first data comprise the supply water temperature of chilled water of the water chiller, the second data comprise the frequency of a water pump, the third data comprise the rotating speed of a fan, and the fourth data comprise the return air temperature of a data center;
step S2: calculating according to the first data, the third data and the fourth data to obtain the air supply quantity of the fan at the moment k and the return water temperature of the chilled water at the moment k;
and step S3: calculating according to the return water temperature of the chilled water and the first data to obtain corresponding first power consumption, and calculating according to the second data and the third data to obtain corresponding second power consumption and third power consumption respectively;
and step S4: calculating according to the first power consumption, the second power consumption and the third power consumption to obtain an economic performance index, inputting the economic performance index into a pre-configured optimal parameter model, and calculating to obtain an optimal parameter value;
step S5: and constructing a finite time domain value function based on a linear model of the control system, calculating to obtain an optimal control input sequence from the k moment to the future moment according to the optimal parameter value and the finite time domain value function, and correspondingly regulating and controlling each water cooling unit, each water pump and each fan by the data center water cooling system according to the optimal control input sequence.
In a preferred embodiment of the present invention, as shown in fig. 4, the first data further includes a heat capacity of chilled water of the water chiller; the third data also comprises the air supply temperature of the fan; the fourth data also comprises air heat capacity of the data center and power consumption of each server; the step S2 comprises the following steps:
step S21: inputting the power consumption into a preset refrigeration demand model, and calculating to obtain the refrigeration demand of the data center;
step S22: calculating according to the cold quantity requirement, the air heat capacity, the return air temperature and the air supply temperature to obtain the air supply quantity corresponding to the fan at the moment k; calculating to obtain the return water temperature of the chilled water corresponding to the water cooling unit at the moment k according to the cold quantity requirement, the water supply temperature of the chilled water and the heat capacity of the chilled water;
step S23: the water cooling unit module respectively regulates and controls each fan, each water cooling unit and each water pump through the air supply volume and the return water temperature of chilled water so as to control the return air temperature within a safety threshold value.
Specifically, the power consumption of each server is input into a preset refrigeration requirement model, and the refrigeration requirement of the data center is obtained by calculating the power consumption consumed by each server in a discrete time period:
Figure DEST_PATH_IMAGE001
；
wherein the content of the first and second substances,
Figure DEST_PATH_IMAGE002
the requirement of cold quantity at the moment k is met;
m is the number of servers;
Figure DEST_PATH_IMAGE003
power consumption of the server at time k;
Figure DEST_PATH_IMAGE004
are discrete times.
Substituting the cold quantity requirement, the air heat capacity, the return air temperature of the data center at the moment k and the air supply temperature of the data center at the moment k into the following formula, and calculating to obtain the air supply quantity of the fan at the moment k:
Figure DEST_PATH_IMAGE005
；
Figure DEST_PATH_IMAGE006
；
Figure DEST_PATH_IMAGE007
；
wherein the content of the first and second substances,
Figure DEST_PATH_IMAGE008
the air output of the fan at the moment k is taken as the air output of the fan;
Figure DEST_PATH_IMAGE009
the requirement of cold quantity at the moment k is met;
Figure DEST_PATH_IMAGE010
is the air heat capacity;
Figure DEST_PATH_IMAGE011
the return air temperature of the data center at the moment k;
Figure DEST_PATH_IMAGE012
the air supply temperature of the data center at the moment k;
Figure DEST_PATH_IMAGE013
is a discrete time;
Figure DEST_PATH_IMAGE014
the lower limit value of the air supply temperature of the data center;
Figure DEST_PATH_IMAGE015
the upper limit value of the air supply temperature of the data center;
Figure DEST_PATH_IMAGE016
is a safe threshold for the return air temperature of the data center.
Substituting the cold quantity demand, the supply water temperature of the chilled water at the k moment, the return water flow of the chilled water at the k moment and the heat capacity of the chilled water into the following formula, and calculating to obtain the return water temperature of the chilled water supplied by the water cooling unit at the k moment:
Figure DEST_PATH_IMAGE017
；
wherein the content of the first and second substances,
Figure DEST_PATH_IMAGE018
the return water temperature of the chilled water at the moment k;
Figure DEST_PATH_IMAGE019
supplying water temperature for the chilled water at the time k;
Figure 350125DEST_PATH_IMAGE009
the requirement of the data center for the cooling capacity at the moment k is met;
Figure DEST_PATH_IMAGE020
the heat capacity of the frozen water is adopted;
Figure DEST_PATH_IMAGE021
the return water flow of the chilled water at the moment k;
Figure 690101DEST_PATH_IMAGE013
are discrete times.
In order to further optimize the scheme, the first data further comprises the external environment temperature of the data center, the heat capacity of the chilled water and the flow rate of the chilled water; in step S3, the specific process of calculating the corresponding first power consumption according to the chilled water return temperature and the first data includes:
step S31: calculating to obtain the refrigerating capacity corresponding to the water cooling unit according to the external environment temperature, the chilled water supply temperature and a preset first performance parameter empirical curve corresponding to the water cooling unit;
step S32: calculating according to the refrigerating capacity, the heat capacity of the chilled water and the flow to obtain a part of load rate corresponding to the water cooling unit;
step S33: and calculating to obtain first power consumption according to the partial load rate, the chilled water supply water temperature and the external environment temperature.
Specifically, the external environment temperature, the chilled water supply temperature and a first performance parameter empirical curve corresponding to a pre-configured water chiller unit are substituted into the following formula to calculate the refrigerating capacity of the water chiller unit:
Figure DEST_PATH_IMAGE022
；
wherein the content of the first and second substances,
Figure DEST_PATH_IMAGE023
the refrigerating capacity of the water cooling unit;
Figure DEST_PATH_IMAGE024
the rated refrigerating capacity of the water cooling unit;
Figure DEST_PATH_IMAGE025
a first performance parameter empirical curve corresponding to the water cooling unit;
Figure DEST_PATH_IMAGE026
is the external ambient temperature;
Figure DEST_PATH_IMAGE027
the temperature of the supplied water is the chilled water.
Meanwhile, substituting the refrigerating capacity, the heat capacity of the chilled water, the water supply temperature of the chilled water, the return water temperature of the chilled water and the flow of the chilled water into the following formula to calculate the partial load rate corresponding to the water cooling unit at the moment k:
Figure DEST_PATH_IMAGE028
；
wherein the content of the first and second substances,
Figure DEST_PATH_IMAGE029
the partial load rate of the water cooling unit at the moment k;
Figure 236883DEST_PATH_IMAGE020
the heat capacity of the frozen water is adopted;
Figure DEST_PATH_IMAGE030
the flow rate of the chilled water;
Figure 258190DEST_PATH_IMAGE018
the return water temperature of the chilled water at the moment k;
Figure 869300DEST_PATH_IMAGE019
supplying water temperature for the chilled water at the k moment;
Figure DEST_PATH_IMAGE031
the refrigerating capacity of the water chiller.
In order to further optimize the scheme, the first data further comprises nominal consumption of the water chiller; step S33 includes:
and calculating to obtain a corresponding first output value according to the partial load rate and a second performance parameter empirical curve corresponding to the pre-configured water cooling unit, calculating to obtain a corresponding second output value according to the external environment temperature, the chilled water supply temperature and a third performance parameter empirical curve corresponding to the pre-configured water cooling unit, and calculating to obtain the power consumption of the water cooling unit at the time k according to the first output value, the second output value and the nominal consumption to serve as the first power consumption.
Substituting the first output value, the second output value and the nominal consumption into the following formula to calculate the power consumption of the water chiller at the time k:
Figure DEST_PATH_IMAGE032
；
wherein, the first and the second end of the pipe are connected with each other,
Figure DEST_PATH_IMAGE033
the power consumption of the water chiller at the time k;
Figure DEST_PATH_IMAGE034
the nominal consumption of the water cooling unit;
Figure DEST_PATH_IMAGE035
is a first output value;
Figure DEST_PATH_IMAGE036
is a second output value;
Figure DEST_PATH_IMAGE037
a second performance parameter empirical curve corresponding to the water cooling unit;
Figure DEST_PATH_IMAGE038
a third performance parameter empirical curve corresponding to the water cooling unit;
Figure DEST_PATH_IMAGE039
is the external ambient temperature at time k;
Figure 255545DEST_PATH_IMAGE019
supplying water temperature for the chilled water at the time k;
Figure DEST_PATH_IMAGE040
and the partial load rate of the water chiller at the moment k is shown.
The water chilling unit comprises a water chilling unit, a first performance parameter empirical curve corresponding to the water chilling unit, a second performance parameter empirical curve corresponding to the water chilling unit and a third performance parameter empirical curve corresponding to the water chilling unit, wherein the first performance parameter empirical curve, the second performance parameter empirical curve and the third performance parameter empirical curve are provided by a manufacturer of the water chilling unit; the first performance parameter empirical curve corresponding to the water chiller unit, the second performance parameter empirical curve corresponding to the water chiller unit and the third performance parameter empirical curve corresponding to the water chiller unit (including the fourth performance parameter empirical curve corresponding to the water pump and the fifth performance parameter empirical curve corresponding to the fan in the application) are all curves obtained by fitting data table information provided by a manufacturer, namely:
Figure DEST_PATH_IMAGE041
；
Figure DEST_PATH_IMAGE042
；
Figure DEST_PATH_IMAGE043
；
Figure DEST_PATH_IMAGE044
；
Figure DEST_PATH_IMAGE045
；
wherein
Figure DEST_PATH_IMAGE046
(y=0，1，2，3，4，5)、
Figure DEST_PATH_IMAGE047
（v=0，1，2，3，4，5）、
Figure DEST_PATH_IMAGE048
（g=0，1，2，3）、
Figure DEST_PATH_IMAGE049
(e =1,2,3) and
Figure DEST_PATH_IMAGE050
(s =1,2,3) is a coefficient of the above corresponding curve.
In a preferred embodiment of the present invention, step S3 includes:
and calculating the power consumption of the water pump at the k moment according to the frequency of the water pump and a pre-configured empirical curve of the fourth performance parameter corresponding to the water pump to obtain the second power consumption.
Specifically, a pre-configured fourth performance parameter empirical curve corresponding to the water pump is provided by a manufacturer of the water pump, and the frequency of the water pump and the pre-configured fourth performance parameter empirical curve corresponding to the water pump are used as inputs and substituted into the following formula to calculate the power consumption of the water pump at the time k:
Figure DEST_PATH_IMAGE051
；
wherein the content of the first and second substances,
Figure DEST_PATH_IMAGE052
power consumption of water pump at time k
Figure DEST_PATH_IMAGE053
A fourth performance parameter empirical curve corresponding to the water pump;
Figure DEST_PATH_IMAGE054
is the frequency of the water pump.
In a preferred embodiment of the present invention, step S3 includes:
and calculating the power consumption of the fan at the k moment according to the rotating speed of the fan and a preset fifth performance parameter empirical curve corresponding to the fan to obtain the third power consumption.
Specifically, a preset fifth performance parameter empirical curve corresponding to the fan is provided by a fan manufacturer, and because the cooling capacity of the data center is supplied by the fan and a heat exchanger through which chilled water flows, the rotation speed of the fan and the preset fifth performance parameter empirical curve corresponding to the fan are used as inputs and substituted into the following formula, so that the power consumption of the fan at the time k is calculated:
Figure DEST_PATH_IMAGE055
；
wherein the content of the first and second substances,
Figure DEST_PATH_IMAGE056
the power consumption of the fan at the moment k;
Figure DEST_PATH_IMAGE057
a fifth performance parameter empirical curve corresponding to the fan;
Figure DEST_PATH_IMAGE058
the rotational speed of the fan.
In a preferred embodiment of the present invention, as shown in fig. 6, the optimal parameter values include a partial load rate of each water chiller unit, a frequency of each water pump, and a rotation speed of each fan, which are obtained from the optimal parameter model and satisfy economic performance indexes, and the step S5 includes:
step S51: constructing a finite time domain value function based on a linear model of a control system, solving a finite time domain optimal control problem to obtain an optimal control input sequence from a k moment to a future moment, wherein the optimal control input sequence comprises the partial load rate of each water cooling unit, the frequency of each water pump and the rotating speed of each fan, which meet economic performance indexes;
step S52: and the data center water cooling system transmits the optimal control input sequence to each water cooling unit, each water pump and each fan, and correspondingly regulates and controls the partial load rate of each water cooling unit, the frequency of each water pump and the rotating speed of each fan.
Specifically, substituting the power consumption of the water chiller, the power consumption of the fan and the power consumption of the water pump into the following economic performance index model to calculate to obtain the optimal economic performance index:
Figure DEST_PATH_IMAGE059
；
wherein the content of the first and second substances,
Figure DEST_PATH_IMAGE060
the economic performance index is optimal;
Figure DEST_PATH_IMAGE061
the power consumption of the water cooling unit;
Figure DEST_PATH_IMAGE062
the power consumption of the water pump;
Figure DEST_PATH_IMAGE063
is the power consumption of the fan.
Substituting the optimal economic performance index as input into the optimal cold quantity demand model, and calculating to obtain the partial load rate of each water cooling unit, the frequency of each water pump and the rotating speed of each fan, which meet the economic performance index:
Figure DEST_PATH_IMAGE064
；
wherein the content of the first and second substances,
Figure 292552DEST_PATH_IMAGE060
the economic performance index is optimal;
Figure DEST_PATH_IMAGE065
is the external ambient temperature;
Figure 117550DEST_PATH_IMAGE054
is the frequency of the water pump;
Figure 583167DEST_PATH_IMAGE058
the rotating speed of the fan;
Figure DEST_PATH_IMAGE066
the partial load rate of the water chiller unit;
Figure DEST_PATH_IMAGE067
the partial load rate of the water cooling unit, the frequency of the water pump and the rotating speed of the fan which meet economic performance indexes are respectively.
And constructing a finite time domain cost function based on a linear model of the control system, calculating to obtain an optimal control input sequence from the k moment to the k + N-1 moment (namely the future moment) according to the optimal parameter value and the finite time domain cost function, obtaining an operation strategy of the water cooling system at the future moment, and correspondingly regulating and controlling each water cooling unit, each water pump and each fan, namely controlling the partial load rate of each water cooling unit, the frequency of each water pump and the rotating speed of each fan.
Specifically, a linear model of the control system is constructed according to state input, control input and output of the controlled object at the time k; constructing a constrained finite time domain cost function based on a linear model of a control system, solving an optimal control problem of the finite time domain to obtain an optimal control input sequence from the moment k to the moment k + N-1, namely:
Figure DEST_PATH_IMAGE068
；
wherein the content of the first and second substances,
Figure DEST_PATH_IMAGE069
the partial load rate of the water cooling unit, the frequency of the water pump and the rotating speed of the fan which meet economic performance indexes at the moment k;
Figure DEST_PATH_IMAGE070
and the partial load rate of the water cooling unit, the frequency of the water pump and the rotating speed of the fan which meet economic performance indexes at k + N-1 moment.
The energy-saving prediction control system can realize the minimization of power consumption and can also control the timeliness and the predictability of the water cooling system of the data center.
With reference to fig. 1,2 and 7, an energy-saving predictive control system suitable for a data center water cooling system includes a data center and a water cooling unit module connected with the data center, the data center includes a plurality of servers and a plurality of fans, the water cooling unit module includes a plurality of water cooling units, a plurality of water pumps and a control module:
the energy-saving prediction control system comprises an acquisition module, a power consumption module, a temperature monitoring module and a prediction module, wherein the acquisition module is respectively connected with the power consumption module and the temperature monitoring module;
the acquisition module is used for acquiring first data of each water chiller unit, second data of each water pump, third data of each fan and fourth data of the data center at the current moment, transmitting the acquired first data, the acquired second data and the acquired third data to the power consumption module, and transmitting the acquired first data, the acquired third data and the acquired fourth data to the temperature monitoring module;
the temperature monitoring module is used for calculating the air supply quantity of the fan at the current moment and the return water temperature of the chilled water at the current moment according to the first data, the third data and the fourth data, and transmitting the air supply quantity of the fan and the return water temperature of the chilled water to the control module and the power consumption module;
the power consumption module is used for calculating according to the return water temperature of the chilled water and the first data to obtain first power consumption, calculating according to the second data and the third data to obtain second power consumption and third power consumption respectively, calculating according to the first power consumption, the second power consumption and the third power consumption to obtain an economic performance index, inputting the economic performance index into an optimal parameter model to obtain an optimal parameter value, and transmitting the optimal parameter value to the prediction module;
the prediction module is used for calculating to obtain an optimal control input sequence and transmitting the optimal control input sequence to the control module;
the control module is used for regulating and controlling each water cooling unit, each water pump and each fan according to the air supply quantity and the return water temperature of the chilled water, and regulating and controlling each water cooling unit, each water pump and each fan according to the optimal control input sequence.
In a preferred embodiment of the present invention, the output end of each water chiller is communicated with a first water collection tank, the input end of each water chiller is communicated with a second water collection tank, a bypass is provided between the first water collection tank and the second water collection tank, the bypass is used for conveying chilled water to the first water collection tank through the bypass when the first water collection tank supplies insufficient water to the data center, so as to ensure that the data center is continuously supplied with chilled water, or when the second water collection tank supplies insufficient water to each water chiller, the first water collection tank conveys chilled water to the second water collection tank through the bypass, so as to ensure that each water chiller has sufficient chilled water supply.
Specifically, the water cooling unit generates chilled water, the chilled water is stored in a first water collecting tank and is conveyed to a data center through a water pump for heat exchange, the chilled water enters the data center, then is heated, carries heat to a second water collecting tank, and is conveyed to the water cooling unit through the water pump for cooling; high-temperature chilled water forms lower-temperature chilled water after being subjected to heat exchange by the water cooling unit, enters the first water collecting tank and is conveyed to the data center again through the water pump to complete circulation, wherein the fan can convey heat generated by the servers to an area through which the chilled water flows in a hot air mode, and cold air after heat exchange is blown to each server through the fan, so that the return air temperature of the data center is not too high; when the quantity of the high-temperature chilled water in the second water collecting tank is insufficient, the cooling water in the first water collecting tank can be conveyed into the second water collecting tank through the bypass, so that the situation that the water supply of the water cooling unit is insufficient is prevented; when the first water collecting tank is short of water for the data center, the first water collecting tank can convey the chilled water to the second water collecting tank through the bypass, so that the first water collecting tank can provide enough chilled water for the data center.
The cooling capacity requirement of the data center at the moment k is obtained according to the power consumption of each server at the moment k, the water cooling unit module respectively controls the partial load rate of each water cooling unit, the frequency of each water pump and the rotating speed of each fan according to the cooling capacity requirement, and the servers are controlled according to the power consumption of each server, so that the abnormality or damage of each server caused by local temperature rise can be prevented; a constrained finite time domain value function is constructed based on a linear model of a control system, an optimal control input sequence from the current moment to the future moment is obtained by solving an optimal control problem of the finite time domain, the optimal control input sequence is transmitted to each water cooling unit, each water pump and each fan by the water cooling system of the data center, the partial load rate of each water cooling unit, the frequency of each water pump and the rotating speed of each fan are correspondingly regulated, the requirement of lowest energy consumption is met, and the timeliness and the advance control of the water cooling system are realized.
While the invention has been described with reference to a preferred embodiment, it will be understood by those skilled in the art that various changes in form and detail may be made therein without departing from the spirit and scope of the invention.

Claims (9)

1. The energy-saving prediction control method is suitable for a data center water cooling system, the data center water cooling system comprises a data center and a water cooling unit module connected with the data center, the water cooling unit module comprises a plurality of water cooling units, a plurality of water pumps and a plurality of fans which are configured in advance, the data center comprises a plurality of servers, and the energy-saving prediction control method is characterized by comprising the following steps:
step S1: acquiring first data of each water chiller unit at the current moment, acquiring second data of each water pump and third data of each fan at the current moment, and acquiring fourth data of the data center at the current moment; the first data comprise the supply water temperature of chilled water of the water-cooling unit and the heat capacity of the chilled water of the water-cooling unit, the second data comprise the frequency of the water pump, the third data comprise the rotating speed and the air supply temperature of the fan, and the fourth data comprise the return air temperature and the air heat capacity of the data center and the power consumption of each server;
step S2: inputting the power consumption into a preset refrigeration demand model, and calculating to obtain the refrigeration demand of the data center; calculating to obtain the air supply quantity corresponding to the fan at the current moment according to the cold quantity requirement, the air heat capacity, the return air temperature and the air supply temperature; calculating to obtain the return water temperature of the chilled water corresponding to the water cooling unit at the current moment according to the cold quantity requirement, the chilled water supply temperature and the chilled water heat capacity;
and step S3: calculating to obtain corresponding first power consumption according to the return water temperature of the chilled water and the first data, and calculating to obtain corresponding second power consumption and third power consumption according to the second data and the third data respectively;
and step S4: calculating according to the first power consumption, the second power consumption and the third power consumption to obtain an economic performance index, inputting the economic performance index into a pre-configured optimal parameter model, and calculating to obtain an optimal parameter value;
step S5: and constructing a finite time domain value function based on a linear model of a control system, calculating to obtain an optimal control input sequence from the current moment to the future moment according to the optimal parameter value and the finite time domain value function, and performing corresponding regulation and control on each water-cooling unit, each water pump and each fan by the data center water-cooling system according to the optimal control input sequence.
2. The energy-saving predictive control method suitable for the water cooling system of the data center according to claim 1, further comprising, after the step S2 is executed:
the water cooling unit module respectively regulates and controls each fan, each water cooling unit and each water pump through the air supply quantity and the return water temperature of the chilled water, so that the return air temperature is controlled within a safety threshold value.
3. The energy-saving predictive control method for a water cooling system of a data center according to claim 2, wherein the first data further includes an external ambient temperature of the data center, the capacity of the chilled water heat, and the flow rate of the chilled water; in the step S3, the specific process of calculating the corresponding first power consumption according to the chilled water return temperature and the first data includes:
step S31: calculating to obtain the refrigerating capacity corresponding to the water chiller unit according to the external environment temperature, the chilled water supply temperature and a preset first performance parameter empirical curve corresponding to the water chiller unit;
step S32: calculating to obtain a part load rate corresponding to the water cooling unit according to the refrigerating capacity, the refrigerating water heat capacity and the flow;
step S33: and calculating to obtain the first power consumption according to the partial load rate, the chilled water supply water temperature and the external environment temperature.
4. The energy-saving predictive control method for the water cooling system of the data center according to claim 3, wherein the first data further comprises a nominal consumption of the water chiller; the step S33 includes:
and calculating to obtain a corresponding first output value according to the partial load factor and a second performance parameter empirical curve corresponding to the water chiller unit which is configured in advance, calculating to obtain a corresponding second output value according to the external environment temperature, the chilled water supply temperature and a third performance parameter empirical curve corresponding to the water chiller unit which is configured in advance, and calculating to obtain the power consumption of the water chiller unit at the current moment according to the first output value, the second output value and the nominal consumption to serve as the first power consumption.
5. The energy-saving predictive control method suitable for the water cooling system of the data center according to claim 1, wherein the step S3 comprises:
and calculating the power consumption of the water pump at the current moment according to the frequency of the water pump and a pre-configured experience curve of a fourth performance parameter corresponding to the water pump, and taking the power consumption as the second power consumption.
6. The energy-saving predictive control method suitable for the water cooling system of the data center according to claim 1, wherein the step S3 comprises:
and calculating the power consumption of the fan at the current moment according to the rotating speed of the fan and a preset fifth performance parameter empirical curve corresponding to the fan, and taking the power consumption as the third power consumption.
7. The energy-saving predictive control method for the water cooling system of the data center according to claim 1, wherein the optimal parameter values include a partial load rate of each water chiller unit, a frequency of each water pump, and a rotation speed of each fan, which are obtained from the optimal parameter model and meet the economic performance index, and the step S5 includes:
step S51: constructing the finite time domain value function based on the control system linear model, solving a finite time domain optimal control problem to obtain the optimal control input sequence from the current moment to the future moment, wherein the optimal control input sequence comprises the partial load rate of each water-cooling unit, the frequency of each water pump and the rotating speed of each fan, which meet the economic performance index;
step S52: and the data center water cooling system transmits the optimal control input sequence to each water cooling unit, each water pump and each fan, and correspondingly regulates and controls the partial load rate of each water cooling unit, the frequency of each water pump and the rotating speed of each fan.
8. The utility model provides an energy-conserving predictive control system suitable for data center water cooling system, data center water cooling system include data center and with the water cooling unit module that data center connects, data center includes a plurality of servers and a plurality of fan, water cooling unit module includes a plurality of water cooling units, a plurality of water pump and control module, its characterized in that:
the energy-saving predictive control system comprises an acquisition module, a power consumption module, a temperature monitoring module and a prediction module, wherein the acquisition module is respectively connected with the power consumption module and the temperature monitoring module;
the acquisition module is used for acquiring first data of each water-cooling unit at the current moment, second data of each water pump, third data of each fan and fourth data of the data center, wherein the first data comprise chilled water supply temperature of the water-cooling unit and chilled water heat capacity of the water-cooling unit, the second data comprise frequency of the water pump, the third data comprise rotating speed and air supply temperature of the fan, the fourth data comprise return air temperature and air heat capacity of the data center and power consumption of each server, and then the acquisition module transmits the acquired first data, the acquired second data and the acquired third data to the power consumption module and transmits the acquired first data, the acquired third data and the acquired fourth data to the temperature monitoring module;
the temperature monitoring module is used for inputting the power consumption into a preset refrigeration requirement model and calculating to obtain the refrigeration requirement of the data center; calculating to obtain the air supply quantity corresponding to the fan at the current moment according to the cold quantity requirement, the air heat capacity, the return air temperature and the air supply temperature; calculating to obtain the return water temperature of the chilled water corresponding to the water cooling unit at the current moment according to the cold quantity requirement, the water supply temperature of the chilled water and the heat capacity of the chilled water, and conveying the air supply quantity and the return water temperature of the chilled water to the control module and the power consumption module;
the power consumption module is used for calculating according to the return water temperature of the chilled water and the first data to obtain first power consumption, calculating according to the second data and the third data to respectively obtain second power consumption and third power consumption, calculating according to the first power consumption, the second power consumption and the third power consumption to obtain an economic performance index, inputting the economic performance index into an optimal parameter model to obtain an optimal parameter value, and transmitting the optimal parameter value to the prediction module;
the prediction module is used for calculating to obtain an optimal control input sequence and transmitting the optimal control input sequence to the control module;
the control module is used for regulating and controlling the water cooling units, the water pumps and the fans according to the air supply quantity and the return water temperature of the chilled water, and regulating and controlling the water cooling units, the water pumps and the fans according to the optimal control input sequence.
9. The energy-saving predictive control system for the water cooling system of the data center according to claim 8, wherein an output end of each water cooling unit is connected to a first water collecting tank, an input end of each water cooling unit is connected to a second water collecting tank, a bypass is provided between the first water collecting tank and the second water collecting tank, the bypass is used for conveying chilled water to the first water collecting tank through the bypass when the first water collecting tank supplies insufficient water to the data center, so as to ensure continuous supply of chilled water to the data center, or conveying the chilled water to the second water collecting tank through the bypass when the second water collecting tank supplies insufficient water to each water cooling unit, so as to ensure sufficient supply of chilled water to each water cooling unit.
CN202211394671.1A 2022-11-09 2022-11-09 Energy-saving prediction control method and system suitable for water cooling system of data center Active CN115581056B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202211394671.1A CN115581056B (en) 2022-11-09 2022-11-09 Energy-saving prediction control method and system suitable for water cooling system of data center

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202211394671.1A CN115581056B (en) 2022-11-09 2022-11-09 Energy-saving prediction control method and system suitable for water cooling system of data center

Publications (2)

Publication Number Publication Date
CN115581056A CN115581056A (en) 2023-01-06
CN115581056B true CN115581056B (en) 2023-03-21

Family

ID=84588775

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202211394671.1A Active CN115581056B (en) 2022-11-09 2022-11-09 Energy-saving prediction control method and system suitable for water cooling system of data center

Country Status (1)

Country Link
CN (1) CN115581056B (en)

Families Citing this family (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116294089B (en) * 2023-05-23 2023-08-18 浙江之科云创数字科技有限公司 Air conditioning system control method and device, storage medium and electronic equipment

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110223005A (en) * 2019-06-21 2019-09-10 清华大学 Air conditioner load power supply reliability assessment method and assessment device
CN112686571A (en) * 2021-01-12 2021-04-20 山东电力工程咨询院有限公司 Comprehensive intelligent energy optimization scheduling method and system based on dynamic adaptive modeling
WO2022012542A1 (en) * 2020-07-15 2022-01-20 上海有孚网络股份有限公司 Data-analysis-based energy-saving control method for precision air conditioner in cloud computing data center

Family Cites Families (11)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US8447993B2 (en) * 2008-01-23 2013-05-21 Palo Alto Research Center Incorporated Integrated energy savings and business operations in data centers
AU2010362490B2 (en) * 2010-10-13 2015-09-03 Weldtech Technology (Shanghai) Co., Ltd. Energy-saving optimized control system and method for refrigeration plant room
JP6295867B2 (en) * 2014-07-17 2018-03-20 富士通株式会社 Air conditioning control system and air conditioning control method
EP3525563A1 (en) * 2018-02-07 2019-08-14 ABB Schweiz AG Method and system for controlling power consumption of a data center based on load allocation and temperature measurements
CN108990383B (en) * 2018-08-15 2020-08-04 北京建筑大学 Predictive control method for air conditioning system of data center
CN113065293A (en) * 2021-04-30 2021-07-02 中国工商银行股份有限公司 Data center environment monitoring method and system, electronic equipment and storage medium
CN113692189B (en) * 2021-08-18 2022-08-16 珠海格力电器股份有限公司 Machine room air conditioner, control method and device thereof, and storage medium
CN115309603A (en) * 2021-11-30 2022-11-08 宁波亮控信息科技有限公司 Data center energy consumption prediction optimization method, system, medium and computing device
CN114063545B (en) * 2022-01-14 2022-06-07 宁波亮控信息科技有限公司 Data center energy consumption control system and method fusing edge calculation and controller
CN114722574A (en) * 2022-03-16 2022-07-08 华南理工大学 Chilled water parameter setting method of data center central air conditioner based on AI algorithm
CN114968556A (en) * 2022-04-26 2022-08-30 武汉卓尔信息科技有限公司 Data center energy consumption management method and system

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110223005A (en) * 2019-06-21 2019-09-10 清华大学 Air conditioner load power supply reliability assessment method and assessment device
WO2022012542A1 (en) * 2020-07-15 2022-01-20 上海有孚网络股份有限公司 Data-analysis-based energy-saving control method for precision air conditioner in cloud computing data center
CN112686571A (en) * 2021-01-12 2021-04-20 山东电力工程咨询院有限公司 Comprehensive intelligent energy optimization scheduling method and system based on dynamic adaptive modeling

Also Published As

Publication number Publication date
CN115581056A (en) 2023-01-06

Similar Documents

Publication Publication Date Title
CN115325682B (en) Optimal control method and device for monitoring performance of efficient intelligent refrigeration machine room
CN107655175A (en) A kind of central air-conditioning group control energy-saving intelligence control system
CN103958985B (en) Refrigerant-cycle systems
KR20190009833A (en) Method for improving working efficiency of a cooling system by improving a building with a master controller
CN206449767U (en) A kind of central air-conditioning energy-saving system with chilled water low-temperature protection device
JP2007315695A (en) Cold and hot water control method for cold and heat source machine, and air conditioning system using it
CN111836523B (en) Three-level adjusting method and system for air conditioner of communication machine building
CN113819514B (en) Air conditioning system and control method thereof
CN104110774A (en) Air conditioner running control method and device
CN115581056B (en) Energy-saving prediction control method and system suitable for water cooling system of data center
CN102889650A (en) Integral combination type computer room air conditioning unit and control method thereof
CN111836524B (en) IT load change-based method for regulating and controlling variable air volume of precision air conditioner between data center columns
CN111867330A (en) Method and system for adjusting communication machine building machine room based on IT load change
CN212720195U (en) Cooling water system control device based on system overall energy efficiency ratio COP is best
CN111059738A (en) Heat recovery side control system of heat recovery centrifugal unit
CN202993476U (en) Energy-saving fine control system for building
CN111263562B (en) Diversified integrated cooling system of data center and control method
CN110486896B (en) Cascade air conditioning system optimization control method based on water chilling unit energy consumption model
JP2002162087A (en) Variable flow control system for exhaust heat recovery and heat source
CN211345737U (en) Heat recovery side control system of heat recovery centrifugal unit
JP2010025466A (en) Heat source system
JP2006250445A (en) Operation control method in two pump-type heat source equipment
CN203964267U (en) Central air-conditioning Auto-matching load energy conserving system
JP5200497B2 (en) Chilled water supply method, chilled water supply device, and control method of chilled water supply device
CN214757525U (en) Data center air conditioning system capable of utilizing natural cold source all year round

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant