CN114240249A - Comprehensive evaluation system and method for green data center based on entropy weight method - Google Patents

Comprehensive evaluation system and method for green data center based on entropy weight method Download PDF

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CN114240249A
CN114240249A CN202111677342.3A CN202111677342A CN114240249A CN 114240249 A CN114240249 A CN 114240249A CN 202111677342 A CN202111677342 A CN 202111677342A CN 114240249 A CN114240249 A CN 114240249A
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路嘉鑫
韩立军
王娇
黄杰
王海燕
张官亮
陈元文
程传奇
冯洛潼
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Abstract

The invention discloses a comprehensive evaluation system and a comprehensive evaluation method for a green data center based on an entropy weight method, wherein the comprehensive evaluation system comprises an index acquisition processing module and a comprehensive energy efficiency evaluation module; the state information of a data center mainframe room, infrastructure of a support area and system information equipment is collected one by one through an index processing module in comparison with a comprehensive evaluation system of a green data center based on an entropy weight method, and specific numerical values are calculated according to index requirements; the comprehensive evaluation module firstly calculates each index weight by using an entropy weight method, and completes comprehensive evaluation of the green data center on the basis; the comprehensive evaluation method of the green data center based on the entropy weight method ensures objective and practical evaluation results, avoids the defects of subjective assignment, provides basis for comprehensive energy efficiency evaluation of the data center, and improves energy utilization efficiency and environmental protection level of the data center.

Description

Comprehensive evaluation system and method for green data center based on entropy weight method
Technical Field
The invention relates to the technical field of information and communication, in particular to a comprehensive evaluation system and method for a green data center based on an entropy weight method.
Background
The full coming of the world of everything interconnection and the popularization and application of the cloud computing technology promote the global explosion of the data center industry. Driven by the strategy of new capital construction in China, the number and scale of data centers in China are rapidly increased. By 2019, the total number of national data centers reaches 7.4 ten thousand, the number of cabinets exceeds 244.4 ten thousand, and the consumed electric energy accounts for 2.35% of the total electricity consumption of the whole country. However, the uninterrupted operation of 7 × 24 at high power density, in addition to the domestic power structure mainly based on thermal power, leads the data center to bear unprecedented pressure in the aspects of energy conservation, emission reduction and the like.
A scientific and reasonable evaluation system plays an important supporting role in the construction of a green data center. However, according to the existing literature, no widely accepted and widely used comprehensive evaluation method for green data centers has been found, which is mainly reflected in that: (1) the evaluation index is simplified. The existing standard mostly takes the electric energy use efficiency (PUE) as the only basis for grading, and the thought principle of 'green data center is high energy efficiency + renewable' is difficult to embody; (2) the particle size coarsening was evaluated. The macroscopic indexes such as 'electricity consumption of IT equipment' and 'electricity consumption of renewable energy sources' are emphasized, and the macroscopic indexes are lack of consideration at the microscopic level.
Disclosure of Invention
Aiming at the defects in the prior art, the invention aims to provide a comprehensive evaluation system and method for a green data center based on an entropy weight method, so that the energy utilization efficiency and the environmental protection level of the data center are improved.
In order to achieve the purpose, the invention adopts the following technical scheme:
a comprehensive evaluation system of a green data center based on an entropy weight method comprises an index acquisition processing module and a comprehensive energy efficiency evaluation module;
the index acquisition processing module is used for establishing an evaluation index system, acquiring information data and state information and calculating evaluation indexes, the indexes needing to be calculated in the evaluation index system comprise electric energy utilization efficiency, renewable energy permeability, natural cold source utilization rate, peak-valley power utilization ratio, refrigeration system energy efficiency ratio, data center cooling efficiency, UPS load rate, IT equipment operation efficiency and server utilization rate, and various information data and state information indexes of the data center are obtained through calculation according to the evaluation index system;
the comprehensive energy efficiency evaluation module is used for realizing comprehensive evaluation of the green data center, acquiring equipment data by sampling the green data center, calculating each index value through the index acquisition and processing module, constructing a decision matrix by taking each index as a column vector and each sample as a row vector, uniformly converting matrix elements into dimensionless values through decision matrix standardization, then calculating weights, calculating comprehensive evaluation values of the data center, and evaluating the comprehensive energy efficiency of the data center through the comprehensive evaluation values.
Further, the calculation process of each index in the index acquisition and processing module is as follows:
the electric energy use efficiency calculation formula is as follows:
Figure BDA0003452463570000021
wherein, PTotalThe total power of the data center is kW; pITThe unit is the IT equipment power kW;
the renewable energy permeability calculation formula is as follows:
Figure BDA0003452463570000022
wherein E isrewSupplying power for renewable energy sources in kWh; eTotalThe unit kWh is the annual power consumption of the data center;
the natural cold source utilization rate calculation formula is as follows:
Figure BDA0003452463570000023
wherein eta isNCSThe utilization rate of a natural cold source is zero, and the dimension is zero; t isNCSThe refrigeration system is in unit hour of shutdown time due to the use of a natural cold source;
the peak-to-valley power consumption ratio calculation formula is as follows:
Figure BDA0003452463570000031
wherein E isPeakThe unit kWh is the electricity consumption of the data center in the peak electricity price period; eAvgThe unit kWh is the electricity consumption of the data center in the usual electricity price time period; eValThe unit kWh is the electricity consumption of the data center in the off-hour electricity price period;
the energy efficiency ratio calculation formula of the refrigerating system is as follows:
Figure BDA0003452463570000032
wherein, PcoolingThe unit kW is the power of a refrigeration system; pchillThe unit kW is the refrigerating capacity of a refrigerating system; pcooling＝PCRAH+Pchiller，PchillerThe unit is kW of cold source equipment power; pCRAHThe unit is the air conditioner power of a machine room, namely kW;
the data center cooling efficiency calculation formula is as follows:
Figure BDA0003452463570000033
wherein E iscoolingThe cooling efficiency of the data center is dimensionless; pITIs the power of IT equipment in kW, PIT＝Pserver+Pswitch+Pstorage，PstorageIs the power of the information storage equipment, in kW; pswitchIs the power of the exchanger, in kW; pserverIs the server power, in kW;
the UPS load factor calculation formula is as follows:
Figure BDA0003452463570000034
wherein, PUPS-rateThe rated output power of UPS is in kVA; pUPSThe actual output power of the UPS is kW; etaUPSThe UPS load rate is dimensionless;
the IT equipment operation efficiency calculation formula is as follows:
Figure BDA0003452463570000035
wherein, Pserver_dyn_iThe dynamic power of the ith server is kW; pserver_dynThe dynamic power of the IT equipment is kW;
Figure BDA0003452463570000036
Pserver_dyn_i＝(Pserver_full_i-Pserver_idle_i)×βi
wherein, betaiThe CPU utilization rate of the ith server is set; pserver_idle_iThe idle power of the ith server is kW; pserver_full_iThe power is the full load power of the ith server in kW;
the server utilization calculation formula is as follows:
Figure BDA0003452463570000041
wherein, betaiThe CPU utilization rate of the ith server is set; u shapeiIs the ith server specification, unit U.
Further, the specific evaluation process of the comprehensive energy efficiency evaluation module comprises the following steps:
(1) constructing a decision matrix
Determining a sampling period according to evaluation requirements, sampling a green data center to obtain equipment data, calculating each index value through an index acquisition processing module, taking each index as a column vector and each sample as a row vector, and constructing a decision matrix as follows:
Figure BDA0003452463570000042
in the formula, A is a decision matrix; wherein n is the index number, and m is the sample number; a isijA value representing the jth evaluation index of the ith data center;
(2) decision matrix normalization
Through decision matrix standardization, matrix elements are uniformly converted into dimensionless numerical values, so that the transverse comparison of cross-type and cross-magnitude indexes is realized, and the method specifically comprises two types of benefit indexes and cost indexes:
the benefit index, i.e. the ideal index value, is as close to 1 as possible, and is calculated as follows:
Figure BDA0003452463570000043
the cost index, i.e. the ideal index value, is as close to 0 as possible, calculated as follows:
Figure BDA0003452463570000044
in the formula, maxj(aij) And minj(aij) Respectively representing the maximum value and the minimum value of the j index; normalized decision matrix, each element rijWill be at [0,1]]Values are taken, and the numerical value is closer to 1, so that the effect is better; the final decision matrix R is obtained as follows:
Figure BDA0003452463570000051
(3) calculating weights
First, the characteristic specific gravity p is calculated according to the following formulaij：
Figure BDA0003452463570000052
Second, the entropy e is calculated according to the following formulaj：
Figure BDA0003452463570000053
Thirdly, calculating a difference coefficient g according to the following formulaj：
gj＝1-ej
Fourthly, calculating the entropy weight omega according to the following formulaj：
Figure BDA0003452463570000054
In the formula, ωj∈[0,1]And is and
Figure BDA0003452463570000055
(4) comprehensive evaluation
The comprehensive evaluation value was calculated according to the following formula:
Figure BDA0003452463570000056
in the formula of UiThe comprehensive energy efficiency evaluation value is the ith data center comprehensive energy efficiency evaluation value, and the green data center is comprehensively evaluated according to the comprehensive energy efficiency evaluation value.
A comprehensive evaluation method of a green data center based on an entropy weight method comprises the following steps:
(1) determining a sampling period according to evaluation requirements, sampling the green data center to obtain equipment data, and calculating numerical values of indexes, wherein the indexes comprise electric energy utilization efficiency, renewable energy permeability, natural cold source utilization rate, peak-valley power utilization ratio, refrigeration system energy efficiency ratio, data center cooling efficiency, UPS load rate, IT equipment operation efficiency and server utilization rate;
(2) taking each index as a column vector and each sample as a row vector to construct a decision matrix, uniformly converting matrix elements into dimensionless numerical values through decision matrix standardization, then calculating weights, calculating comprehensive evaluation numerical values of the data center, and evaluating the comprehensive energy efficiency of the data center through the comprehensive evaluation numerical values;
the decision matrix is constructed as follows:
Figure BDA0003452463570000061
in the formula, A is a decision matrix; wherein n is the index number, and m is the sample number; a isijA value representing the jth evaluation index of the ith data center;
decision matrix standardization is to uniformly convert matrix elements into dimensionless numerical values so as to realize transverse comparison of cross-type and cross-magnitude indexes, and specifically comprises two types of benefit indexes and cost indexes:
the benefit index, i.e. the ideal index value, is as close to 1 as possible, and is calculated as follows:
Figure BDA0003452463570000062
the cost index, i.e. the ideal index value, is as close to 0 as possible, calculated as follows:
Figure BDA0003452463570000063
in the formula, maxj(aij) And minj(aij) Respectively representing the maximum value and the minimum value of the j index; normalized decision matrix, each element rijWill be at [0,1]]Values are taken, and the numerical value is closer to 1, so that the effect is better; the final decision matrix R is obtained as follows:
Figure BDA0003452463570000064
the specific process of calculating the weight is as follows:
first, the characteristic specific gravity p is calculated according to the following formulaij：
Figure BDA0003452463570000065
Second, the entropy e is calculated according to the following formulaj：
Figure BDA0003452463570000071
Thirdly, calculating a difference coefficient g according to the following formulaj：
gj＝1-ej
Fourthly, calculating the entropy weight omega according to the following formulaj：
Figure BDA0003452463570000072
In the formula, ωj∈[0,1]And is and
Figure BDA0003452463570000073
the comprehensive evaluation value was calculated according to the following formula:
Figure BDA0003452463570000074
in the formula of UiThe comprehensive energy efficiency evaluation value is the ith data center comprehensive energy efficiency evaluation value, and the green data center is comprehensively evaluated according to the comprehensive energy efficiency evaluation value.
Further, the calculation process of each index is as follows:
the electric energy use efficiency calculation formula is as follows:
Figure BDA0003452463570000075
wherein, PTotalThe total power of the data center is kW; pITThe unit is the IT equipment power kW;
the renewable energy permeability calculation formula is as follows:
Figure BDA0003452463570000076
wherein E isrewSupplying power for renewable energy sources in kWh; eTotalThe unit kWh is the annual power consumption of the data center;
the natural cold source utilization rate calculation formula is as follows:
Figure BDA0003452463570000077
wherein eta isNCSThe utilization rate of a natural cold source is zero, and the dimension is zero; t isNCSThe refrigeration system is in unit hour of shutdown time due to the use of a natural cold source;
the peak-to-valley power consumption ratio calculation formula is as follows:
Figure BDA0003452463570000081
wherein E isPeakThe unit kWh is the electricity consumption of the data center in the peak electricity price period; eAvgThe unit kWh is the electricity consumption of the data center in the usual electricity price time period; eValThe unit kWh is the electricity consumption of the data center in the off-hour electricity price period;
the energy efficiency ratio calculation formula of the refrigerating system is as follows:
Figure BDA0003452463570000082
wherein, PcoolingThe unit kW is the power of a refrigeration system; pchillThe unit kW is the refrigerating capacity of a refrigerating system; pcooling＝PCRAH+Pchiller，PchillerThe unit is kW of cold source equipment power; pCRAHThe unit is the air conditioner power of a machine room, namely kW;
the data center cooling efficiency calculation formula is as follows:
Figure BDA0003452463570000083
wherein E iscoolingThe cooling efficiency of the data center is dimensionless; pITIs the power of IT equipment in kW, PIT＝Pserver+Pswitch+Pstorage，PstorageIs the power of the information storage equipment, in kW; pswitchIs the power of the exchanger, in kW; pserverIs the server power, in kW;
the UPS load factor calculation formula is as follows:
Figure BDA0003452463570000084
wherein, PUPS-rateThe rated output power of UPS is in kVA; pUPSThe actual output power of the UPS is kW; etaUPSThe UPS load rate is dimensionless;
the IT equipment operation efficiency calculation formula is as follows:
Figure BDA0003452463570000085
wherein, Pserver_dyn_iThe dynamic power of the ith server is kW; pserver_dynThe dynamic power of the IT equipment is kW;
Figure BDA0003452463570000086
Pserver_dyn_i＝(Pserver_full_i-Pserver_idle_i)×βi
wherein, betaiThe CPU utilization rate of the ith server is set; pserver_idle_iThe idle power of the ith server is kW; pserver_full_iThe power is the full load power of the ith server in kW;
the server utilization calculation formula is as follows:
Figure BDA0003452463570000091
wherein, betaiThe CPU utilization rate of the ith server is set; u shapeiIs the ith server specification, unit U.
The invention has the following beneficial effects:
the invention constructs a set of system-level and equipment-level covered comprehensive evaluation standard system and method for a green data center, the energy utilization efficiency and the environmental protection level of the data center are comprehensively improved by two modules of index processing and comprehensive evaluation, the state information of a data center mainframe room, infrastructure of a support area and system information equipment is acquired one by comparing the index processing module with an entropy weight method-based comprehensive evaluation system of the green data center, and specific values are calculated according to index requirements; the comprehensive evaluation module firstly calculates each index weight by using an entropy weight method, and completes the comprehensive evaluation of the green data center on the basis. The comprehensive evaluation method of the green data center based on the entropy weight method ensures objective and practical evaluation results, avoids the defects of subjective assignment, provides basis for comprehensive energy efficiency evaluation of the data center, and improves energy utilization efficiency and environmental protection level of the data center.
The invention covers a green data center system layer and an equipment layer by arranging a comprehensive evaluation index system with clear layers and comprehensive systems. On the system level, 4 indexes of 'natural cold source utilization rate', 'peak-to-valley electricity utilization ratio', 'data center cooling efficiency' and 'server utilization rate' are provided and defined for the first time, and 2 indexes of 'electric energy utilization efficiency' and 'renewable energy permeability' are used in a matching mode; in the equipment level, 3 indexes of 'energy efficiency ratio of a refrigeration system', 'IT equipment operation efficiency' and 'UPS load rate' are used. In practical application, the operation and maintenance platform deployed in the data center is used for acquiring specific data and state information, and a subsystem self-contained monitoring system can be used for acquiring data.
Drawings
FIG. 1 is a diagram of a comprehensive evaluation system of a green data center
FIG. 2 is a flow chart of a comprehensive evaluation method of a green data center
Detailed Description
The present invention will be explained in further detail with reference to examples.
The comprehensive evaluation system of the green data center based on the entropy weight method comprises an index acquisition processing module and a comprehensive energy efficiency evaluation module;
the index acquisition processing module is used for establishing an evaluation index system, acquiring information data and state information and calculating evaluation indexes, wherein the indexes needing to be calculated in the evaluation index system comprise electric energy utilization efficiency, renewable energy permeability, natural cold source utilization rate, peak-to-valley electricity utilization ratio, refrigeration system energy efficiency ratio, data center cooling efficiency, UPS load rate, IT equipment operation efficiency and server utilization rate, and various information data and state information indexes of the data center are calculated according to the evaluation index system;
the comprehensive energy efficiency evaluation module takes each index as a column vector and each sample as a row vector according to each index value obtained by calculation, constructs a decision matrix, uniformly converts matrix elements into dimensionless values through decision matrix standardization, calculates weights, obtains a calculated comprehensive evaluation value, and evaluates the comprehensive energy efficiency of the data center through the comprehensive evaluation value.
The calculation process of each index in the index acquisition processing module is as follows:
(1) efficiency of electric energy usage
The electric energy use efficiency (PUE) is one of the most widely applied and universal data center energy efficiency evaluation indexes, the value of the PUE is the ratio of the power consumption of the data center to the power consumption of IT equipment, the PUE is a macroscopic evaluation and comprehensive display of the energy efficiency of the data center, and the closer the value of the PUE is to 1, the lower the power consumption of non-IT equipment is, the higher the energy efficiency level of the data center is. The calculation formula is as follows:
Figure BDA0003452463570000101
wherein, PTotalThe total power of the data center is kW; pITIT is the power of the IT equipment in kW.
(2) Permeability of renewable energy
With the popularization of the concept of 'green data center', the energy supply by preferentially and comprehensively utilizing renewable energy sources becomes an important development trend of the data center. In order to visually and quantitatively describe the degree of meeting the energy consumption requirement of the data center by the Renewable energy, the invention uses the Renewable energy permeability (RF) to indicate the proportion composition of primary energy and secondary energy in the energy consumption structure of the data center, and the value range [0,1] is larger, and the higher the value is, the higher the energy consumption ratio of the Renewable energy in the data center is. The calculation formula is as follows:
Figure BDA0003452463570000102
wherein E isrewSupplying power for renewable energy sources in kWh; eTotalThe unit kWh is the annual power consumption of the data center.
(3) Utilization rate of natural cold source
In order to maintain the optimal temperature of the core area of the data center, the refrigeration system needs to be continuously operated all the year around to carry the heat dissipated during the operation process of the IT equipment to the outside, and the huge energy consumption of the refrigeration system becomes a main reason for causing the high power consumption of the data center. The natural cold source is directly utilized to supply cold to the data center, the running time and corresponding energy consumption of a refrigeration system can be greatly compressed, and the method is one of important ways for optimizing energy use of the data center and improving energy efficiency.
The invention innovatively provides a 'natural cold source utilization rate' index, and quantitatively shows the annual occupation ratio of the shutdown time of the refrigeration system due to the use of the natural cold source. The calculation formula is as follows:
Figure BDA0003452463570000111
wherein eta isNCSThe utilization rate of a natural cold source is zero, and the dimension is zero; t isNCSThe refrigerating system is shut down in unit of hour due to the use of natural cold source.
(4) Peak-to-valley power consumption ratio
The data center computing tasks are divided into delay type tasks and interactive type tasks, the interactive type tasks need to be processed immediately, the requirement of the delay type tasks on the processing instantaneity is not high, and the delay type tasks can be regarded as time-shifting loads. Under the background of time-of-use electricity price, the task processing time is adjusted in the end period, so that the operation processing amount in the peak time electricity price time period can be reduced, the energy utilization is optimized, and the energy expenditure is reduced.
The invention innovatively provides a peak-valley power consumption ratio index, and quantitatively expresses the ratio of the peak-hour power consumption of the data center to the annual power consumption. The calculation formula is as follows:
Figure BDA0003452463570000112
wherein E isPeakThe unit kWh is the electricity consumption of the data center in the peak electricity price period; eAvgThe unit kWh is the electricity consumption of the data center in the usual electricity price time period; eValFor the off-hour electricity price period consumption of the data centerElectricity, in kWh.
(5) Energy efficiency ratio of refrigeration system
The refrigerating system consists of cold source equipment and a machine room air conditioner, and is a general name of equipment configured for maintaining the temperature required by the operation of electronic information equipment. The energy efficiency ratio of the refrigeration system is the ratio of the refrigerating capacity to the power of the refrigeration system, and can measure the refrigerating capacity obtained by the refrigeration system consuming unit power. The larger the number, the more efficient the refrigeration system. Can be calculated as follows:
Pcooling＝PCRAH+Pchiller
wherein, PcoolingThe unit kW is the power of a refrigeration system; pchillerThe unit is kW of cold source equipment power; pCRAHIs the power of the air conditioner in the machine room in kW unit. On the basis of obtaining the power of the refrigerating system, the energy efficiency ratio of the refrigerating system can be calculated according to the following formula:
Figure BDA0003452463570000121
wherein, PchillThe unit kW is the refrigerating capacity of the refrigerating system.
(6) Data center cooling efficiency
The invention innovatively provides a data center cooling efficiency index, which represents the ratio of refrigeration capacity of refrigeration equipment to IT equipment power and represents the refrigeration capacity required by IT equipment for dissipating unit heat. The closer the value is to 1, the closer the refrigerating capacity and the cooling capacity required by the data center are, the higher the cooling efficiency is. The calculation formula is as follows:
Figure BDA0003452463570000122
wherein E iscoolingThe cooling efficiency of the data center is dimensionless; pITFor IT equipment power, in kW, IT can be calculated as follows:
PIT＝Pserver+Pswitch+Pstorage
wherein, PstorageFor information storageEquipment power, unit kW; pswitchIs the power of the exchanger, in kW; pserverIs the server power, in kW.
(7) UPS load factor
The 'UPS load rate' index is used for quantitatively describing the influence of actual load rate change on the UPS operation efficiency. The value can be calculated by dividing the actual output power of the UPS by the rated input power. The calculation formula is as follows:
Figure BDA0003452463570000123
wherein, PUPS-rateThe rated output power of UPS is in kVA; pUPSThe actual output power of the UPS is kW; etaUPSThe UPS load rate is dimensionless.
(8) Operating efficiency of IT equipment
The IT equipment operation efficiency is the ratio of the IT equipment dynamic power to the total power of the data center. Wherein the IT device dynamic power represents the portion of the device power consumption that varies in real time as CPU usage fluctuates. The calculation formula is as follows:
Figure BDA0003452463570000131
wherein, Pserver_dyn_iThe dynamic power of the ith server is kW; pserver_dynIs the dynamic power of the IT equipment, and has unit kW. The calculation formula is as follows:
Pserver_dyn_i＝(Pserver_full_i-Pserver_idle_i)×βi
wherein, betaiThe CPU utilization rate of the ith server is set; pserver_idle_iThe idle power of the ith server is kW; pserver_full_iThe unit is kW for the full load power of the ith server.
Based on the above results, the calculation formula of the operation efficiency of the IT equipment is obtained as follows:
Figure BDA0003452463570000132
(9) rate of server usage
The invention innovatively provides a server utilization rate index, and quantitatively describes the average utilization rate of the server with unit specification. The calculation formula is as follows:
Figure BDA0003452463570000133
wherein, UiIs the ith server specification, unit U.
In addition, in order to improve the objectivity of the evaluation method provided by the invention, an entropy weight method-based comprehensive evaluation method for the green data center is provided by combining the objective fact that the invention index system is composed of quantitative indexes.
The comprehensive evaluation value is obtained through the comprehensive energy efficiency evaluation module, the comprehensive energy efficiency is integrated through the comprehensive evaluation value data center, and the specific process is as follows:
firstly, the numerical calculation of each index is completed, and on the basis, a comprehensive evaluation module of a green data center is used for evaluation. The process comprises four parts of 'construction decision matrix', 'standardization decision matrix', 'calculation weight' and 'comprehensive evaluation', and is specifically explained as follows:
(1) constructing a decision matrix
And according to the evaluation requirement, determining the sampling period, acquiring equipment data and calculating an index value. On the basis, each index is taken as a column vector, each sample is taken as a row vector, and a decision matrix is constructed as follows:
Figure BDA0003452463570000141
wherein A is a decision matrix. Wherein n is the index number, and m is the sample number; a isijAnd the numerical value of the j evaluation index of the ith data center is shown.
(2) Decision matrix normalization
Through decision matrix standardization, matrix elements are uniformly converted into dimensionless numerical values, and therefore cross-type and cross-magnitude index transverse comparison is achieved. The method specifically comprises two types of 'benefit type indexes' and 'cost type indexes':
the benefit index. I.e. the ideal index value is as close to 1 as possible. Can be calculated as follows:
Figure BDA0003452463570000142
cost type index. I.e. the ideal index value is as close to 0 as possible. Can be calculated as follows:
Figure BDA0003452463570000143
in the formula, maxj(aij) And minj(aij) Respectively representing the maximum value and the minimum value of the j index. Normalized decision matrix, each element rijWill be at [0,1]]The value is taken, and the numerical value is closer to 1, which indicates that the effect is better. The final decision matrix R is obtained as follows:
Figure BDA0003452463570000144
(3) calculating weights
First, the characteristic specific gravity p is calculated according to the following formulaij：
Figure BDA0003452463570000145
Second, the entropy e is calculated according to the following formulaj：
Figure BDA0003452463570000146
Thirdly, calculating a difference coefficient g according to the following formulaj：
gj＝1-ej
Fourthly, calculating the entropy weight omega according to the following formulaj：
Figure BDA0003452463570000151
In the formula, ωj∈[0,1]And is and
Figure BDA0003452463570000152
(4) comprehensive evaluation
The comprehensive evaluation value was calculated according to the following formula:
Figure BDA0003452463570000153
in the formula of UiThe comprehensive energy efficiency evaluation value is the comprehensive energy efficiency evaluation value of the ith data center, and the value is the basis of the comprehensive evaluation of the green data center.

Claims (5)

1. The comprehensive evaluation system of the green data center based on the entropy weight method is characterized in that: the system comprises an index acquisition processing module and a comprehensive energy efficiency evaluation module;
the index acquisition processing module is used for establishing an evaluation index system, acquiring information data and state information and calculating evaluation indexes, the indexes needing to be calculated in the evaluation index system comprise electric energy utilization efficiency, renewable energy permeability, natural cold source utilization rate, peak-valley power utilization ratio, refrigeration system energy efficiency ratio, data center cooling efficiency, UPS load rate, IT equipment operation efficiency and server utilization rate, and various information data and state information indexes of the data center are obtained through calculation according to the evaluation index system;
the comprehensive energy efficiency evaluation module is used for realizing comprehensive evaluation of the green data center, acquiring equipment data by sampling the green data center, calculating each index value through the index acquisition and processing module, constructing a decision matrix by taking each index as a column vector and each sample as a row vector, uniformly converting matrix elements into dimensionless values through decision matrix standardization, then calculating weights, calculating comprehensive evaluation values of the data center, and evaluating the comprehensive energy efficiency of the data center through the comprehensive evaluation values.
2. The entropy weight method-based green data center comprehensive evaluation system of claim 1, wherein: the index acquisition and processing module comprises the following index calculation processes:
the electric energy use efficiency calculation formula is as follows:
Figure FDA0003452463560000011
wherein, PTotalThe total power of the data center is kW; pITThe unit is the IT equipment power kW;
the renewable energy permeability calculation formula is as follows:
Figure FDA0003452463560000012
wherein E isrewSupplying power for renewable energy sources in kWh; eTotalThe unit kWh is the annual power consumption of the data center;
the natural cold source utilization rate calculation formula is as follows:
Figure FDA0003452463560000013
wherein eta isNCSThe utilization rate of a natural cold source is zero, and the dimension is zero; t isNCSThe refrigeration system is in unit hour of shutdown time due to the use of a natural cold source;
the peak-to-valley power consumption ratio calculation formula is as follows:
Figure FDA0003452463560000021
wherein E isPeakThe unit kWh is the electricity consumption of the data center in the peak electricity price period; eAvgThe unit kWh is the electricity consumption of the data center in the usual electricity price time period; eValThe unit kWh is the electricity consumption of the data center in the off-hour electricity price period;
the energy efficiency ratio calculation formula of the refrigerating system is as follows:
Figure FDA0003452463560000022
wherein, PcoolingThe unit kW is the power of a refrigeration system; pchillThe unit kW is the refrigerating capacity of a refrigerating system; pcooling＝PCRAH+Pchiller，PchillerThe unit is kW of cold source equipment power; pCRAHThe unit is the air conditioner power of a machine room, namely kW;
the data center cooling efficiency calculation formula is as follows:
Figure FDA0003452463560000023
wherein E iscoolingThe cooling efficiency of the data center is dimensionless; pITIs the power of IT equipment in kW, PIT＝Pserver+Pswitch+Pstorage，PstorageIs the power of the information storage equipment, in kW; pswitchIs the power of the exchanger, in kW; pserverIs the server power, in kW;
the UPS load factor calculation formula is as follows:
Figure FDA0003452463560000024
wherein, PUPS-rateThe rated output power of UPS is in kVA; pUPSThe actual output power of the UPS is kW; etaUPSThe UPS load rate is dimensionless;
the IT equipment operation efficiency calculation formula is as follows:
Figure FDA0003452463560000025
wherein, Pserver_dyn_iThe dynamic power of the ith server is kW; pserver_dynThe dynamic power of the IT equipment is kW;
Figure FDA0003452463560000031
Pserver_dyn_i＝(Pserver_full_i-Pserver_idle_i)×βi
wherein, betaiThe CPU utilization rate of the ith server is set; pserver_idle_iThe idle power of the ith server is kW; pserver_full_iThe power is the full load power of the ith server in kW;
the server utilization calculation formula is as follows:
Figure FDA0003452463560000032
wherein, betaiThe CPU utilization rate of the ith server is set; u shapeiIs the ith server specification, unit U.
3. The entropy weight method-based green data center comprehensive evaluation system of claim 1, wherein the specific evaluation process of the comprehensive energy efficiency evaluation module is as follows:
(1) constructing a decision matrix
Determining a sampling period according to evaluation requirements, sampling a green data center to obtain equipment data, calculating each index value through an index acquisition processing module, taking each index as a column vector and each sample as a row vector, and constructing a decision matrix as follows:
Figure FDA0003452463560000033
in the formula, A is a decision matrix; wherein n is the index number, and m is the sample number; a isijA value representing the jth evaluation index of the ith data center;
(2) decision matrix normalization
Through decision matrix standardization, matrix elements are uniformly converted into dimensionless numerical values, so that the transverse comparison of cross-type and cross-magnitude indexes is realized, and the method specifically comprises two types of benefit indexes and cost indexes:
the benefit index, i.e. the ideal index value, is as close to 1 as possible, and is calculated as follows:
Figure FDA0003452463560000041
the cost index, i.e. the ideal index value, is as close to 0 as possible, calculated as follows:
Figure FDA0003452463560000042
in the formula, maxj(aij) And minj(aij) Respectively representing the maximum value and the minimum value of the j index; normalized decision matrix, each element rijWill be at [0,1]]Values are taken, and the numerical value is closer to 1, so that the effect is better; the final decision matrix R is obtained as follows:
Figure FDA0003452463560000043
(3) calculating weights
First, the characteristic specific gravity p is calculated according to the following formulaij：
Figure FDA0003452463560000044
Second, the entropy e is calculated according to the following formulaj：
Figure FDA0003452463560000045
Thirdly, calculating a difference coefficient g according to the following formulaj：
gj＝1-ej
Fourthly, calculating the entropy weight omega according to the following formulaj：
Figure FDA0003452463560000046
In the formula, ωj∈[0,1]And is and
Figure FDA0003452463560000047
(4) comprehensive evaluation
The comprehensive evaluation value was calculated according to the following formula:
Figure FDA0003452463560000051
in the formula of UiThe comprehensive energy efficiency evaluation value is the ith data center comprehensive energy efficiency evaluation value, and the green data center is comprehensively evaluated according to the comprehensive energy efficiency evaluation value.
4. A comprehensive evaluation method of a green data center based on an entropy weight method is characterized by comprising the following steps:
(1) determining a sampling period according to evaluation requirements, sampling the green data center to obtain equipment data, and calculating numerical values of indexes, wherein the indexes comprise electric energy utilization efficiency, renewable energy permeability, natural cold source utilization rate, peak-valley power utilization ratio, refrigeration system energy efficiency ratio, data center cooling efficiency, UPS load rate, IT equipment operation efficiency and server utilization rate;
(2) taking each index as a column vector and each sample as a row vector to construct a decision matrix, uniformly converting matrix elements into dimensionless numerical values through decision matrix standardization, then calculating weights, calculating comprehensive evaluation numerical values of the data center, and evaluating the comprehensive energy efficiency of the data center through the comprehensive evaluation numerical values;
the decision matrix is constructed as follows:
Figure FDA0003452463560000052
in the formula, A is a decision matrix; wherein n is the index number, and m is the sample number; a isijA value representing the jth evaluation index of the ith data center;
decision matrix standardization is to uniformly convert matrix elements into dimensionless numerical values so as to realize transverse comparison of cross-type and cross-magnitude indexes, and specifically comprises two types of benefit indexes and cost indexes:
the benefit index, i.e. the ideal index value, is as close to 1 as possible, and is calculated as follows:
Figure FDA0003452463560000053
the cost index, i.e. the ideal index value, is as close to 0 as possible, calculated as follows:
Figure FDA0003452463560000054
in the formula, maxj(aij) And minj(aij) Respectively representing the maximum value and the minimum value of the j index; normalized decision matrix, each element rijWill be at [0,1]]Values are taken, and the numerical value is closer to 1, so that the effect is better; the final decision matrix R is obtained as follows:
Figure FDA0003452463560000061
the specific process of calculating the weight is as follows:
first, the characteristic specific gravity p is calculated according to the following formulaij：
Figure FDA0003452463560000062
Second, the entropy e is calculated according to the following formulaj：
Figure FDA0003452463560000063
Thirdly, calculating a difference coefficient g according to the following formulaj：
gj＝1-ej
Fourthly, calculating the entropy weight omega according to the following formulaj：
Figure FDA0003452463560000064
In the formula, ωj∈[0,1]And is and
Figure FDA0003452463560000065
the comprehensive evaluation value was calculated according to the following formula:
Figure FDA0003452463560000066
in the formula of UiThe comprehensive energy efficiency evaluation value is the ith data center comprehensive energy efficiency evaluation value, and the green data center is comprehensively evaluated according to the comprehensive energy efficiency evaluation value.
5. The comprehensive evaluation method of the green data center based on the entropy weight method as claimed in claim 4, characterized in that the calculation process of each index is as follows:
the electric energy use efficiency calculation formula is as follows:
Figure FDA0003452463560000071
wherein, PTotalThe total power of the data center is kW; pITThe unit is the IT equipment power kW;
the renewable energy permeability calculation formula is as follows:
Figure FDA0003452463560000072
wherein E isrewSupplying power for renewable energy sources in kWh; eTotalThe unit kWh is the annual power consumption of the data center;
the natural cold source utilization rate calculation formula is as follows:
Figure FDA0003452463560000073
wherein eta isNCSThe utilization rate of a natural cold source is zero, and the dimension is zero; t isNCSThe refrigeration system is in unit hour of shutdown time due to the use of a natural cold source;
the peak-to-valley power consumption ratio calculation formula is as follows:
Figure FDA0003452463560000074
wherein E isPeakThe unit kWh is the electricity consumption of the data center in the peak electricity price period; eAvgThe unit kWh is the electricity consumption of the data center in the usual electricity price time period; eValThe unit kWh is the electricity consumption of the data center in the off-hour electricity price period;
the energy efficiency ratio calculation formula of the refrigerating system is as follows:
Figure FDA0003452463560000075
wherein, PcoolingThe unit kW is the power of a refrigeration system; pchillThe unit kW is the refrigerating capacity of a refrigerating system; pcooling＝PCRAH+Pchiller，PchillerThe unit is kW of cold source equipment power; pCRAHThe unit is the air conditioner power of a machine room, namely kW;
the data center cooling efficiency calculation formula is as follows:
Figure FDA0003452463560000076
wherein E iscoolingThe cooling efficiency of the data center is dimensionless; pITIs the power of IT equipment in kW, PIT＝Pserver+Pswitch+Pstorage，PstorageIs the power of the information storage equipment, in kW; pswitchIs the power of the exchanger, in kW; pserverIs the server power, in kW;
the UPS load factor calculation formula is as follows:
Figure FDA0003452463560000081
wherein, PUPS-rateThe rated output power of UPS is in kVA; pUPSThe actual output power of the UPS is kW; etaUPSThe UPS load rate is dimensionless;
the IT equipment operation efficiency calculation formula is as follows:
Figure FDA0003452463560000082
wherein, Pserver_dyn_iThe dynamic power of the ith server is kW; pserver_dynThe dynamic power of the IT equipment is kW;
Figure FDA0003452463560000083
Pserver_dyn_i＝(Pserver_full_i-Pserver_idle_i)×βi
wherein, betaiThe CPU utilization rate of the ith server is set; pserver_idle_iThe idle power of the ith server is kW; pserver_full_iThe power is the full load power of the ith server in kW;
the server utilization calculation formula is as follows:
Figure FDA0003452463560000084
wherein, betaiThe CPU utilization rate of the ith server is set; u shapeiIs the ith server specification, unit U.
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