CN112292013A - Micro-module data center heat source prejudgment and cold source regulation and control method based on time sequence - Google Patents
Micro-module data center heat source prejudgment and cold source regulation and control method based on time sequence Download PDFInfo
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- CN112292013A CN112292013A CN202011585847.2A CN202011585847A CN112292013A CN 112292013 A CN112292013 A CN 112292013A CN 202011585847 A CN202011585847 A CN 202011585847A CN 112292013 A CN112292013 A CN 112292013A
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- H05—ELECTRIC TECHNIQUES NOT OTHERWISE PROVIDED FOR
- H05K—PRINTED CIRCUITS; CASINGS OR CONSTRUCTIONAL DETAILS OF ELECTRIC APPARATUS; MANUFACTURE OF ASSEMBLAGES OF ELECTRICAL COMPONENTS
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- 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
Abstract
The invention relates to a micro-module data center heat source prejudging and cold source regulating method based on a time sequence, which belongs to the technical field of intelligent regulation and comprises the following steps: installing intelligent electric quantity collectors at the front ends of PDU of all cabinets of a micro-module data center, establishing an original time sequence through power consumption detection of IT loads in cabinets in different areas, and establishing a seasonal decomposition model to obtain the distribution condition of the heat power consumption of the IT loads; dividing a micro-module data center into N areas, and calculating IT load thermal power consumption of each area; and calculating the refrigerating capacity of the air conditioner to obtain the opening degree of a water valve and the rotating speed of a fan required by the current air conditioner. The invention implements the pre-judging advanced control of air conditioners in the micro-module by predicting the IT load power change in the micro-module data center and based on the heat balance principle. Therefore, the final aims of accurate temperature control strategy and good energy-saving control effect of the micro-module data center air conditioner are achieved.
Description
Technical Field
The invention relates to a micro-module data center heat source prejudgment and cold source regulation and control method based on a time sequence, and belongs to the technical field of intelligent regulation.
Background
The micro-module data center is gradually a hotspot for data center construction at present, and is characterized in that a row-to-row air conditioner and a cabinet are placed in a closed cold-hot channel, so that the refrigeration utilization rate of the air conditioner can be effectively improved. How to improve the automatic operation efficiency of the air conditioner among the columns in the micro-module data center, reduce the equipment failure rate, save energy and manpower becomes the key point for improving the overall operation level of the air conditioning system of the micro-module data center. Therefore, the importance of constructing an effective micro-module data center air-conditioning energy-saving control device is obvious.
At present, the energy-saving method and research of the micro-module data center air conditioner in China are carried out successively, and the traditional air conditioner control mode based on temperature and humidity set value regulation is common at present. The main method for controlling the single air conditioner is to detect the temperature deviation between the outlet air or return air temperature of the inter-row air conditioner and the set temperature, and adjust the water valve and the fan of the inter-row air conditioner according to the range of the temperature deviation. And for the control after the air conditioners inside the micro module are networked, the number of air conditioner units and the adjustment of water valve fans in each air conditioner are controlled based on the average value of the air outlet or air return temperature of the air conditioners in the micro module channel.
However, the energy-saving control method of the micro-module data center air conditioner is still based on the idea of passive control and passive adaptive adjustment. If the average temperature in the detected cold and hot channels is compared with a set value in real time, a water valve and a fan of the air conditioner between the rows are adjusted by adopting a PID control mode. However, the air conditioning water system has large heat capacity and hysteresis. Nowadays, the large and micro module data center servers are mixed in high and low density. And because the channel is closed, the reflecting speed of the influence of the instantaneous load change of the IT load power on the temperature in the channel is high, the passive control mode easily causes that the air conditioner refrigeration cannot follow the change of the server load in time, and cold and heat sources cannot be accurately matched. The consequence is that the micro-module content is easy to generate the phenomenon of uneven cold and heat, and further the energy-saving effect is not ideal enough. Therefore, the traditional passive control average refrigeration mode is difficult to meet the temperature control requirement of the existing micro-module machine room.
Disclosure of Invention
In order to solve the technical problems, the invention provides a micro-module data center heat source prejudging and cold source regulating method based on a time sequence, which has the following specific technical scheme:
a micro-module data center heat source prejudgment and cold source regulation and control method based on time series comprises the following steps:
step 1: data acquisition: firstly, dividing a micro-module data center into N areas, wherein N is a positive integer greater than or equal to 2, installing an intelligent electric quantity collector at the front end of a PDU (protocol data Unit) of each cabinet of the micro-module data center, establishing an original time sequence through power consumption detection of IT (information technology) loads in the cabinets in different areas, establishing a seasonal decomposition model, decomposing all constituent factors of the original time sequence, predicting the power consumption distribution condition of the IT loads in the micro-module in the next time period by combining a Hott and Wett prediction method, and calculating the power consumption change of IT equipment in the micro-module in the next time period through the power consumption distribution change to obtain the distribution condition of the heat power consumption of the IT loads;
step 2: calculating a heat source: the IT load heat power consumption of each area is Q1, Q2, … … and Qn in sequence, the total load of the micro module data center is Q, and Q = Q1+ Q2+ … … + Qn;
and step 3: calculating a cold source: according to the distribution situation of the IT load heat power consumption of different areas, the inter-column air conditioning cold quantity matching calculation is carried out, the cold sources of each area are sequentially represented as C1, C2, … … and Cn, the total cold source of the micro module data center is C, C = C1+ C2+ … … + Cn and Q = C, when the IT load operates in a high-load working state, the output of the air conditioning cold quantity in the area is improved, and when the equipment operates in a low-load working state, the output of the air conditioning refrigerating quantity is reduced;
and 4, step 4: refrigeration regulation: and according to the calculated air conditioner refrigerating capacity, calculating to obtain the water valve opening and the fan rotating speed required by the current air conditioner, transmitting the water valve opening and fan rotating speed parameters to the air conditioners in all the regions of the micromodule through the communication interface, and carrying out real-time adjustment on the water valve opening and the fan rotating speed by the air conditioner according to the obtained parameters.
Further, the specific process of establishing the original time sequence in step 1 is to decompose and analyze 4 constituent factors in the original time sequence before predicting the load condition of the IT equipment, and the mathematical expression of the process is as follows:
，（1）
in the formula (1), the reaction mixture is,
which represents the original time series of the time series,
shows a long-term trend,
Shows the trend of seasonal variation,
Shows a cyclic variation tendency,
Indicating an irregular variation trend;
the multiplication model in the original time series refers to that 4 kinds of constituent factors in the time series are multiplied respectively, and the mutual dependence relationship exists between the constituent factors, and the expression is as follows:
；
for distribution of IT load working conditions of a micromodule machine room, importance degrees of front and rear numerical values are equal, so that a weight equal moving average method is selected to calculate a moving average of the current moment, 7 groups of sample data are total, the data volume of each group of sample data is 1440 points, a seasonal period length in a seasonal decomposition model is built, and a calculation formula is as follows, wherein the time for starting IT load equipment is 7 × 24 hours, 1 minute is taken as a load power data sampling point, the seasonal period length is 1440:
in the formula (2), the reaction mixture is,
representing a moving average at the current timeThe number of the first and second groups is,
the representation corresponds to the original data that is,
representing data represented by moving the current original data forward and backward by i positions, obtaining a moving average value corresponding to an original data sequence through calculation, and simultaneously utilizing a sequence ratio formula: sequence ratio = raw data/moving average × 100%, rejecting long-term trends present in the time sequence
A factor;
after the IT load power consumption time sequence factor decomposition work is completed, component factors of the sequence are required to be predicted, a Hote temperature-specific number smoothing method is selected to perform data prediction on the time sequence factors, and the Hote temperature-specific number smoothing is combined with the second step
Seasonal factors of the period
Phase advance prediction, assuming that the length of the seasonal period is M, and the seasonal model is a multiplicative model, the prediction formula for the sequence trend is:
in the formula (3), Ft+kExpressing the prediction of the sequence trend, L, T, S expressing the sequence stationarity, trend, seasonality, St+k-MThe temperature-specific number smoothing process allows the level, trend and periodic model to change along with time, when more new data comes, the three components are estimated and updated again, and the corresponding three updating equations are as follows:
in the formulae (4), (5) and (6)
The parameters are respectively expressed as initial parameters of stationarity, tendency and seasonality in the model, subscripts t-1 and t-M both represent period values, the obtained time sequence does not contain seasonal factors in the original sequence and is not a final required result, and therefore the predicted time sequence group is multiplied by the seasonal factors to finally obtain a predicted value of the IT load power consumption change in the next time period within 24 hours.
Further, in the step 2, the micro module data center is divided into 3 areas, and the calculation formula of the total heat Q is as follows:
in the formula (7)
Respectively obtaining heat values of a front region, a middle region and a rear region of the micro-module data center;
is the heat transfer coefficient of the IT load device;
is the IT load power consumption value in the predicted sequence; t is an integration interval of analysis and comparison;
further, in the step 3, a calculation formula of the total cooling capacity C supplied by the air conditioner is as follows:
in the formula (8)
The air conditioner cold output values of the front area, the middle area and the rear area of the micro-module data center are respectively set; 4.2 is the specific heat coefficient of water;
is the first
The flow value of the inlet water of the chilled water of the air conditioner;
is the first
The inlet water temperature value of the chilled water of the air conditioner;
is the first
The chilled water outlet temperature value of the air conditioner; t is the integration interval for analytical comparison.
Furthermore, in the step 4, according to the heat balance principle, the predicted heat quantity of the front area, the middle area and the rear area of the micro-module data center is equal to the supply of the cooling quantity of the air conditioner at the next moment, so that the chilled water valve and the fan are adjusted in advance.
Furthermore, a chilled water valve is preferentially adjusted in the cold quantity matching process, the adjusting range of the chilled water valve is 0-100%, when the water valve is adjusted to 100%, the calculated cold quantity of the regional air conditioner cannot meet the regional heat quantity, the rotating speed of the fan is adjusted, and the adjusting range of the rotating speed of the fan is from the minimum air quantity of refrigeration to the maximum air quantity of refrigeration until the cold-heat balance of the region is dynamically adjusted.
The invention has the beneficial effects that:
the invention implements the pre-judging advanced control of air conditioners in the micro-module by predicting the IT load power change in the micro-module data center and based on the heat balance principle. Therefore, the final aims of accurate temperature control strategy and good energy-saving control effect of the micro-module data center air conditioner are achieved.
The invention mainly comprises two aspects, namely calculation and prejudgment of IT load in the micro-module data center on one hand, and regulation and control of a cold source on the other hand.
In the calculation and prejudgment process of the IT load in the micro-module data center, the micro-module data center is divided into a plurality of independent intervals, heat source data are independently collected in each interval, heat sources generated in the next time period are independently calculated and analyzed, cold sources are independently adjusted and controlled in each interval according to the heat sources in the next time period, the heat sources and the cold sources in each interval are matched, the condition that the micro-module data center is locally overheated is avoided, excessive refrigeration is also avoided, energy waste is caused, and the environment temperature stability of the micro-module data center is realized.
Detailed Description
The present invention will now be described in further detail with reference to specific embodiments.
A specific application of the method of the present invention is given below.
Assuming that 32 cabinets with 5Kw are arranged in the micro module in total and 6 chilled water inter-train air conditioners with 30Kw are arranged, the micro module can be divided into front, middle and rear 3 regions by region. Each area is mainly supplied with cold sources by two air conditioners. According to the distribution of IT load power consumption, the heat source distribution divided into three areas of Q1, Q2 and Q3 respectively corresponds to the front, middle and rear three load areas in the micro module, and the total load is Q.
Decomposing all the constituent factors of the original time sequence of the IT load power consumption, and predicting the power consumption distribution condition of the IT load in the 24-hour micro-module by combining a Hote and Wintert prediction method. Suppose that the loads of Q1, Q2 and Q3 are respectively 25w,40Kw and 50Kw which are interrupted in a certain period of 24 hours, and the loads are relatively stable in the period. According to the formula
The total IT load heat for this time period is calculated as:
。
IT load heat conversion coefficient herein
Take 0.8. According to the heat energy balance principle, the air conditioner supplies cold energy in the time period
Suppose that the air conditioner has been adjusted to a steady state at this time
Then can calculate
，
，
In light of the foregoing description of the preferred embodiment of the present invention, many modifications and variations will be apparent to those skilled in the art without departing from the spirit and scope of the invention. The technical scope of the present invention is not limited to the content of the specification, and must be determined according to the scope of the claims.
Claims (6)
1. A micro-module data center heat source prejudgment and cold source regulation and control method based on time series is characterized in that: the method comprises the following steps:
step 1: data acquisition: firstly, dividing a micro-module data center into N areas, wherein N is a positive integer greater than or equal to 2, installing an intelligent electric quantity collector at the front end of a PDU (protocol data Unit) of each cabinet of the micro-module data center, establishing an original time sequence through power consumption detection of IT (information technology) loads in the cabinets in different areas, establishing a seasonal decomposition model, decomposing all constituent factors of the original time sequence, predicting the power consumption distribution condition of the IT loads in the micro-module in the next time period by combining a Hott and Wett prediction method, and calculating the power consumption change of IT equipment in the micro-module in the next time period through the power consumption distribution change to obtain the distribution condition of the heat power consumption of the IT loads;
step 2: calculating a heat source: each regional IT load thermal power consumption is represented as Q1, Q2, … …, Qn in turn, with the total load of the micro-module data center being Q, Q = Q1+ Q2+ … … + Qn;
and step 3: calculating a cold source: according to the distribution situation of the IT load heat power consumption of different areas, the inter-column air conditioning cold quantity matching calculation is carried out, the cold sources of each area are sequentially represented as C1, C2, … … and Cn, the total cold source of the micro module data center is C, C = C1+ C2+ … … + Cn and Q = C, when the IT load operates in a high-load working state, the output of the air conditioning cold quantity in the area is improved, and when the equipment operates in a low-load working state, the output of the air conditioning refrigerating quantity is reduced;
and 4, step 4: refrigeration regulation: and according to the calculated air conditioner refrigerating capacity, calculating to obtain the water valve opening and the fan rotating speed required by the current air conditioner, transmitting the water valve opening and fan rotating speed parameters to the air conditioners in all the regions of the micromodule through the communication interface, and carrying out real-time adjustment on the water valve opening and the fan rotating speed by the air conditioner according to the obtained parameters.
2. The micro-module data center heat source anticipation and cold source regulation and control method based on the time series as claimed in claim 1, wherein: the specific process of establishing the original time sequence in the step 1 is that 4 kinds of constituent factors in the original time sequence need to be decomposed and analyzed before predicting the load working condition of the IT equipment, and the mathematical expression is as follows:
，（1）
in the formula (1), the reaction mixture is,
which represents the original time series of the time series,
shows a long-term trend,
Shows the trend of seasonal variation,
Shows a cyclic variation tendency,
Indicating an irregular variation trend;
the multiplication model in the original time series refers to that 4 kinds of constituent factors in the time series are multiplied respectively, and the mutual dependence relationship exists between the constituent factors, and the expression is as follows:
；
for distribution of IT load working conditions of a micromodule machine room, importance degrees of front and rear numerical values are equal, so that a weight equal moving average method is selected to calculate a moving average of the current moment, 7 groups of sample data are total, the data volume of each group of sample data is 1440 points, a seasonal period length in a seasonal decomposition model is built, and a calculation formula is as follows, wherein the time for starting IT load equipment is 7 × 24 hours, 1 minute is taken as a load power data sampling point, the seasonal period length is 1440:
in the formula (2), the reaction mixture is,
representing the moving average at the current time,
the representation corresponds to the original data that is,
representing data represented by moving the current original data forward and backward by i positions, obtaining a moving average value corresponding to an original data sequence through calculation, and simultaneously utilizing a sequence ratio formula: sequence ratio = raw data/moving average × 100%, rejecting long-term trends present in the time sequence
A factor;
after the IT load power consumption time sequence factor decomposition work is completed, component factors of the sequence are required to be predicted, a Hote temperature-specific number smoothing method is selected to perform data prediction on the time sequence factors, and the Hote temperature-specific number smoothing is combined with the second step
Seasonal factors of the period
Phase advance prediction, assuming that the length of the seasonal period is M, and the seasonal model is a multiplicative model, the prediction formula for the sequence trend is:
in the formula (3), Ft+kExpressing the prediction of the sequence trend, L, T, S expressing the sequence stationarity, trend, seasonality, St+k-MThe temperature-specific number smoothing process allows the level, trend and periodic model to change along with time, when more new data comes, the three components are estimated and updated again, and the corresponding three updating equations are as follows:
in the formulae (4), (5) and (6)
The parameters are respectively expressed as initial parameters of stationarity, tendency and seasonality in the model, subscripts t-1 and t-M both represent period values, the obtained time sequence does not contain seasonal factors in the original sequence and is not a final required result, and therefore the predicted time sequence group is multiplied by the seasonal factors to finally obtain a predicted value of the IT load power consumption change in the next time period within 24 hours.
3. The micro-module data center heat source anticipation and cold source regulation and control method based on the time series as claimed in claim 1, wherein: in the step 2, the micro-module data center is divided into 3 areas, and the calculation formula of the total heat Q is as follows:
in the formula (7)
Respectively obtaining heat values of a front region, a middle region and a rear region of the micro-module data center;
is the heat transfer coefficient of the IT load device;
is the IT load power consumption value in the prediction sequence, and T represents the area of analysis comparison.
4. The micro-module data center heat source anticipation and cold source regulation and control method based on the time series as claimed in claim 3, wherein: in the step 3, the calculation formula of the total supply cold quantity C of the air conditioner is as follows:
in the formula (8)
The air conditioner cold output values of the front area, the middle area and the rear area of the micro-module data center are respectively set; 4.2 is the specific heat coefficient of water;
is the first
The flow value of the inlet water of the chilled water of the air conditioner;
is the first
The inlet water temperature value of the chilled water of the air conditioner;
is the first
And the outlet water temperature value of the chilled water of the air conditioner, and T represents an analysis and comparison area.
5. The micro-module data center heat source anticipation and cold source regulation and control method based on the time series as claimed in claim 1, wherein: in the step 4, according to the heat balance principle, the predicted heat quantity of the front area, the middle area and the rear area of the micro-module data center is equal to the supply of the cooling quantity of the air conditioner at the next moment, so that the chilled water valve and the fan are adjusted in advance.
6. The micro-module data center heat source anticipation and cold source regulation and control method based on the time series as claimed in claim 5, wherein: and preferentially adjusting a chilled water valve in a cold quantity matching process, wherein the adjusting range of the chilled water valve is 0-100%, when the water valve is adjusted to 100%, and the calculated regional air conditioning cold quantity cannot meet the regional heat quantity, adjusting the rotating speed of a fan, wherein the adjusting range of the rotating speed of the fan is from the minimum air quantity of refrigeration to the maximum air quantity of refrigeration until the cold-heat balance of the region is dynamically adjusted.
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Cited By (4)
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| CN115076965A (en) * | 2021-03-10 | 2022-09-20 | 中国电信股份有限公司 | Energy-saving control method, control device and control system for communication machine room |
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