CN108427840A - Data center's air conditioner system energy saving amount computational methods based on the prediction of benchmark efficiency - Google Patents
Data center's air conditioner system energy saving amount computational methods based on the prediction of benchmark efficiency Download PDFInfo
- Publication number
- CN108427840A CN108427840A CN201810194018.8A CN201810194018A CN108427840A CN 108427840 A CN108427840 A CN 108427840A CN 201810194018 A CN201810194018 A CN 201810194018A CN 108427840 A CN108427840 A CN 108427840A
- Authority
- CN
- China
- Prior art keywords
- energy saving
- sampling interval
- kth
- air
- conditioning
- 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.)
- Granted
Links
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
Abstract
The invention discloses the data center's air conditioner system energy saving amount computational methods predicted based on benchmark efficiency, include the following steps：Step 1, sampling kth time sampled I T equipment energy consumptions and kth time sampling air conditioning energy consumption；Step 2 calculates kth time sampling interval information technoloy equipment energy consumption and kth time sampling interval air conditioning energy consumption；Step 3 calculates kth sampling interval air conditioner system energy saving amount；Step 4 calculates kth sampling interval air conditioner system energy saving rate；Step 5 calculates the energy saving optimizing phase total amount of energy saving of air-conditioning system；Step 6 calculates energy saving optimizing phase air-conditioning system average energy saving.The present invention objectively responds the amount of energy saving after taking Optimized Measures, fractional energy savings, calculates simply, can calculate sampling interval amount of energy saving, fractional energy savings in real time.
Description
Technical field
The present invention relates to amounts of energy saving to assert technical field, more particularly to the air-conditioning system of data center based on the prediction of benchmark efficiency
System amount of energy saving computational methods.
Background technology
(Power Usage Effectiveness, electric energy service efficiency, the present invention is referred to as using PUE for data center's industry
Efficiency) evaluate the infrastructure energy efficiency of data center.PUE is equal to total facility energy requirements divided by information technoloy equipment energy consumption.
Wherein EITRepresent information technoloy equipment energy consumption, ENonITNon- information technoloy equipment energy consumption is represented, PUE represents number
According to center efficiency.
Data center quickly grows, and energy consumption is huge, speedup is fast.Data center of China is averaged efficiency 2.0~2.5.Data
Power consumption is mainly made of the loss of information technoloy equipment energy consumption, air conditioning energy consumption and power supply-distribution system and other parts, wherein air-conditioning
System energy consumption accounts for the major part of non-information technoloy equipment energy consumption, up to 70% or more.
Data center is energy saving imperative, it has also become and industry is known together, however, data center's efficiency optimization service is rare, it is main
One of reason is wanted to be a lack of the computational methods of objective effective amount of energy saving.《Energy Efficient Retrofit of Public Building technical specification》JGJ176-
2009 list mensuration, bill analytic approach, calibrationization analogy method.Mensuration, bill analytic approach are to be transformed preceding 1 year system or set
On the basis of standby energy consumption, project of the operation less than 1 year can not be directly applied to；Usual data center projects at the initial stage of putting into operation,
Rate of load condensate changes greatly, as a result unreliable directly compared with the base period before reducing energy consumption.
Invention content
In view of the drawbacks described above of the prior art, technical problem to be solved by the invention is to provide pre- based on benchmark efficiency
Data center's air conditioner system energy saving amount computational methods of survey, objectively respond the amount of energy saving after taking Optimized Measures, fractional energy savings, calculate
Simply, sampling interval amount of energy saving, fractional energy savings can be calculated in real time.
To achieve the above object, the present invention provides the data center's air conditioner system energy saving gauge predicted based on benchmark efficiency
Calculation method, includes the following steps：
Step 1, acquisition kth time sampled I T equipment energy consumptions and kth time sampling air conditioning energy consumption；
Step 2 calculates kth time sampling interval information technoloy equipment energy consumption and kth time sampling interval air conditioning energy consumption；
Step 3 calculates kth sampling interval air conditioner system energy saving amount；
Step 4 calculates kth sampling interval air conditioner system energy saving rate；
Step 5 calculates the energy saving optimizing phase total amount of energy saving of air-conditioning system；
Step 6 calculates energy saving optimizing phase air-conditioning system average energy saving.
Further, the calculating kth time sampling interval information technoloy equipment energy consumption of the step 2 and kth time sampling interval air-conditioning system
System energy consumption be specially：
ΔEIT, k=EIT, k-EIT, k-1
ΔEIT, kFor kth sampling interval information technoloy equipment energy consumption, unit：kW·h；
EIT, kFor kth time sampled I T equipment energy consumption measured values, unit：kW·h；
EIT, k-1For -1 sampled I T equipment energy consumption measured value of kth, unit：kW·h；
ΔEAC, k=EAC, k-EAC, k-1
ΔEAC, kFor kth time sampling interval air conditioning energy consumption, unit：kW·h；
EAC, kFor kth time sampling air conditioning energy consumption measured value, unit：kW·h；
EAC, k-1For -1 sampling air conditioning energy consumption measured value of kth, unit：kW·h.
Further, the step 3 calculating kth sampling interval air conditioner system energy saving amount is specially：
For kth sampling interval air conditioner system energy saving amount, unit：kW·h；
For kth sampling interval air-conditioning fractional prediction reference energy consumption, unit：KWh, calculation formula are
For kth sampling interval air-conditioning fractional prediction benchmark efficiency predicted value.
Further, the step 4 calculating kth sampling interval air conditioner system energy saving rate is specially：
ζAC, kFor kth sampling interval air conditioner system energy saving rate；
ΔEAC, kFor kth time sampling interval air conditioning energy consumption, unit：kW·h；
For kth sampling interval air conditioner system energy saving amount, unit：kW·h.
Further, the step 5 calculating energy saving optimizing phase total amount of energy saving of air-conditioning system is specially：
For the total amount of energy saving of energy saving optimizing phase air-conditioning system, unit：kW·h；
For kth sampling interval air conditioner system energy saving amount, unit：kW·h.
Further, the step 6 calculating energy saving optimizing phase air-conditioning system average energy saving is specially：
For energy saving optimizing phase air-conditioning system average energy saving；
EAC, OptimStartFor energy saving optimizing beginning beginning air conditioning energy consumption, unit：kW·h；
EAC, OptimEndFor the final only air conditioning energy consumption of energy saving optimizing, unit：kW·h.
Further, the kth sampling interval air-conditioning fractional prediction benchmark efficiency predicted valueAcquisition side
Method is：
First, air-conditioning part efficiency prediction model is obtained by supervised learning method using history samples data, it is public
Formula is as follows：
Model is supervised learning model, utilizes history samples data, trains and obtains via supervised learning；
Sample is characterized sample input；
For kth-τ sampling interval air-conditioning fractional prediction efficiencies；
F () indicates the mapping relations of mode input output；
The feature samples fetched according to center air-conditioner system design conditions parameter as air-conditioning fractional prediction benchmark efficiency, i.e.,：
The beneficial effects of the invention are as follows：
The present invention objectively responds the amount of energy saving after taking Optimized Measures, fractional energy savings, calculates simple, can calculate in real time between sampling
Every amount of energy saving, fractional energy savings.
The technique effect of the design of the present invention, concrete structure and generation is described further below with reference to attached drawing, with
It is fully understood from the purpose of the present invention, feature and effect.
Description of the drawings
Fig. 1 is the flow chart of the present invention.
Specific implementation mode
As shown in Figure 1, data center's air conditioner system energy saving amount computational methods based on the prediction of benchmark efficiency, including following step
Suddenly：
Step 1, acquisition kth time sampled I T equipment energy consumptions and kth time sampling air conditioning energy consumption；
Step 2 calculates kth time sampling interval information technoloy equipment energy consumption and kth time sampling interval air conditioning energy consumption；
Step 3 calculates kth sampling interval air conditioner system energy saving amount；
Step 4 calculates kth sampling interval air conditioner system energy saving rate；
Step 5 calculates the energy saving optimizing phase total amount of energy saving of air-conditioning system；
Step 6 calculates energy saving optimizing phase air-conditioning system average energy saving.
In the present embodiment, the calculating kth time sampling interval information technoloy equipment energy consumption and kth time sampling interval air-conditioning of the step 2
System energy consumption is specially：
ΔEIT, k=EIT, k-EIT, k-1
ΔEIT, kFor kth sampling interval information technoloy equipment energy consumption, unit：kW·h；
EIT, kFor kth time sampled I T equipment energy consumption measured values, unit：kW·h；
EIT, k-1For -1 sampled I T equipment energy consumption measured value of kth, unit：kW·h；
ΔEAC, k=EAC, k-EAC, k-1
ΔEAC, kFor kth time sampling interval air conditioning energy consumption, unit：kW·h；
EAC, kFor kth time sampling air conditioning energy consumption measured value, unit：kW·h；
EAC, k-1For -1 sampling air conditioning energy consumption measured value of kth, unit：kW·h.
In the present embodiment, the step 3 calculates kth sampling interval air conditioner system energy saving amount and is specially：
For kth sampling interval air conditioner system energy saving amount, unit：kW·h；
For kth sampling interval air-conditioning fractional prediction reference energy consumption, unit：KWh, calculation formula are
For kth sampling interval air-conditioning fractional prediction benchmark efficiency predicted value.
In the present embodiment, the step 4 calculates kth sampling interval air conditioner system energy saving rate and is specially：
ζAC, kFor kth sampling interval air conditioner system energy saving rate；
ΔEAC, kFor kth time sampling interval air conditioning energy consumption, unit：kW·h；
For kth sampling interval air conditioner system energy saving amount, unit：kW·h.
In the present embodiment, the step 5 calculates the energy saving optimizing phase total amount of energy saving of air-conditioning system and is specially：
For the total amount of energy saving of energy saving optimizing phase air-conditioning system, unit：kW·h；
For kth sampling interval air conditioner system energy saving amount, unit：kW·h.
In the present embodiment, the step 6 calculates energy saving optimizing phase air-conditioning system average energy saving and is specially：
For energy saving optimizing phase air-conditioning system average energy saving；
EAC, OptimStartFor energy saving optimizing beginning beginning air conditioning energy consumption, unit：kW·h；
EAC, OptimEndFor the final only air conditioning energy consumption of energy saving optimizing, unit：kW·h.
In the present embodiment, the kth sampling interval air-conditioning fractional prediction benchmark efficiency predicted valueAcquisition
Method is：
First, air-conditioning part efficiency prediction model is obtained by supervised learning method using history samples data, it is public
Formula is as follows：
Model is supervised learning model, utilizes history samples data, trains and obtains via supervised learning；
Sample is characterized sample input；
For kth-τ sampling interval air-conditioning fractional prediction efficiencies；
F () indicates the mapping relations of mode input output；
The feature samples fetched according to center air-conditioner system design conditions parameter as air-conditioning fractional prediction benchmark efficiency, i.e.,：
The present invention objectively responds the amount of energy saving after taking Optimized Measures, fractional energy savings, calculates simple, can calculate in real time between sampling
Every amount of energy saving, fractional energy savings.
The preferred embodiment of the present invention has been described in detail above.It should be appreciated that those skilled in the art without
It needs creative work according to the present invention can conceive and makes many modifications and variations.Therefore, all technologies in the art
Personnel are available by logical analysis, reasoning, or a limited experiment on the basis of existing technology under this invention's idea
Technical solution, all should be in the protection domain being defined in the patent claims.
Claims (7)
1. data center's air conditioner system energy saving amount computational methods based on the prediction of benchmark efficiency, which is characterized in that including following step
Suddenly：
Step 1, acquisition kth time sampled I T equipment energy consumptions and kth time sampling air conditioning energy consumption；
Step 2 calculates kth time sampling interval information technoloy equipment energy consumption and kth time sampling interval air conditioning energy consumption；
Step 3 calculates kth sampling interval air conditioner system energy saving amount；
Step 4 calculates kth sampling interval air conditioner system energy saving rate；
Step 5 calculates the energy saving optimizing phase total amount of energy saving of air-conditioning system；
Step 6 calculates energy saving optimizing phase air-conditioning system average energy saving.
2. data center's air conditioner system energy saving amount computational methods as described in claim 1 based on the prediction of benchmark efficiency, special
Sign is that the calculating kth time sampling interval information technoloy equipment energy consumption and kth time sampling interval air conditioning energy consumption of the step 2 are specific
For：
ΔEIT, k=EIT, k-EIT, k-1
ΔEIT, kFor kth sampling interval information technoloy equipment energy consumption, unit：kW·h；
EIT, kFor kth time sampled I T equipment energy consumption measured values, unit：kW·h；
EIT, k-1For -1 sampled I T equipment energy consumption measured value of kth, unit：kW·h；
ΔEAC, k=EAC, k-EAC, k-1
ΔEAC, kFor kth time sampling interval air conditioning energy consumption, unit：kW·h；
EAC, kFor kth time sampling air conditioning energy consumption measured value, unit：kW·h；
EAC, k-1For -1 sampling air conditioning energy consumption measured value of kth, unit：kW·h.
3. data center's air conditioner system energy saving amount computational methods as described in claim 1 based on the prediction of benchmark efficiency, special
Sign is that the step 3 calculates kth sampling interval air conditioner system energy saving amount and is specially：
For kth sampling interval air conditioner system energy saving amount, unit：kW·h；
For kth sampling interval air-conditioning fractional prediction reference energy consumption, unit：KWh, calculation formula are
For kth sampling interval air-conditioning fractional prediction benchmark efficiency predicted value.
4. data center's air conditioner system energy saving amount computational methods as described in claim 1 based on the prediction of benchmark efficiency, special
Sign is that the step 4 calculates kth sampling interval air conditioner system energy saving rate and is specially：
ζAC, kFor kth sampling interval air conditioner system energy saving rate；
ΔEAC, kFor kth time sampling interval air conditioning energy consumption, unit：kW·h；
For kth sampling interval air conditioner system energy saving amount, unit：kW·h.
5. data center's air conditioner system energy saving amount computational methods as described in claim 1 based on the prediction of benchmark efficiency, special
Sign is that the step 5 calculates the energy saving optimizing phase total amount of energy saving of air-conditioning system and is specially：
For the total amount of energy saving of energy saving optimizing phase air-conditioning system, unit：kW·h；
For kth sampling interval air conditioner system energy saving amount, unit：kW·h.
6. data center's air conditioner system energy saving amount computational methods as described in claim 1 based on the prediction of benchmark efficiency, special
Sign is that the step 6 calculates energy saving optimizing phase air-conditioning system average energy saving and is specially：
For energy saving optimizing phase air-conditioning system average energy saving；
EAC, OptimStartFor energy saving optimizing beginning beginning air conditioning energy consumption, unit：kW·h；
EAC, OptimEndFor the final only air conditioning energy consumption of energy saving optimizing, unit：kW·h.
7. data center's air conditioner system energy saving amount computational methods as claimed in claim 3 based on the prediction of benchmark efficiency, special
Sign is, the kth sampling interval air-conditioning fractional prediction benchmark efficiency predicted valuePreparation method be：
First, air-conditioning part efficiency prediction model is obtained, formula is such as by supervised learning method using history samples data
Under：
Model is supervised learning model, utilizes history samples data, trains and obtains via supervised learning；
Sample is characterized sample input；
For kth-τ sampling interval air-conditioning fractional prediction efficiencies；
F () indicates the mapping relations of mode input output；
The feature samples fetched according to center air-conditioner system design conditions parameter as air-conditioning fractional prediction benchmark efficiency, i.e.,：
。
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810194018.8A CN108427840B (en) | 2018-03-09 | 2018-03-09 | Energy saving amount calculation method of data center air conditioning system based on reference energy efficiency prediction |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810194018.8A CN108427840B (en) | 2018-03-09 | 2018-03-09 | Energy saving amount calculation method of data center air conditioning system based on reference energy efficiency prediction |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN108427840A true CN108427840A (en) | 2018-08-21 |
| CN108427840B CN108427840B (en) | 2022-04-12 |
Family
ID=63157929
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201810194018.8A Active CN108427840B (en) | 2018-03-09 | 2018-03-09 | Energy saving amount calculation method of data center air conditioning system based on reference energy efficiency prediction |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN108427840B (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109586285A (en) * | 2018-12-06 | 2019-04-05 | 成都载信智能科技有限公司 | Energy consumption mark post value calculating method, energy consumption management method and energy consumption management system |
| CN112747416A (en) * | 2019-10-31 | 2021-05-04 | 北京国双科技有限公司 | Energy consumption prediction method and device for air conditioning system |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102520679A (en) * | 2011-11-07 | 2012-06-27 | 朱建斌 | Energy saving data metering and calculating method |
| CN102711136A (en) * | 2012-01-12 | 2012-10-03 | 李彬 | Energy saving amount calculation method and system for communication base station |
| US20130190930A1 (en) * | 2011-01-27 | 2013-07-25 | International Business Machines Corporation | Energy Saving Control for Data Center |
| CN104794269A (en) * | 2015-04-09 | 2015-07-22 | 重庆大学 | Energy-saving transformation energy-saving amount checking and ratifying method based on standard effect judgment |
| CN105959975A (en) * | 2016-06-22 | 2016-09-21 | 湖南大学 | Automatic assessment method for energy saving quantity of large-scale base station energy saving project |
| CN106874581A (en) * | 2016-12-30 | 2017-06-20 | 浙江大学 | A kind of energy consumption of air conditioning system in buildings Forecasting Methodology based on BP neural network model |
-
2018
- 2018-03-09 CN CN201810194018.8A patent/CN108427840B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20130190930A1 (en) * | 2011-01-27 | 2013-07-25 | International Business Machines Corporation | Energy Saving Control for Data Center |
| CN102520679A (en) * | 2011-11-07 | 2012-06-27 | 朱建斌 | Energy saving data metering and calculating method |
| CN102711136A (en) * | 2012-01-12 | 2012-10-03 | 李彬 | Energy saving amount calculation method and system for communication base station |
| CN104794269A (en) * | 2015-04-09 | 2015-07-22 | 重庆大学 | Energy-saving transformation energy-saving amount checking and ratifying method based on standard effect judgment |
| CN105959975A (en) * | 2016-06-22 | 2016-09-21 | 湖南大学 | Automatic assessment method for energy saving quantity of large-scale base station energy saving project |
| CN106874581A (en) * | 2016-12-30 | 2017-06-20 | 浙江大学 | A kind of energy consumption of air conditioning system in buildings Forecasting Methodology based on BP neural network model |
Non-Patent Citations (3)
| Title |
|---|
| SHENGLIANG LI等: "Research of Airflow Distribution for Center Data Equipment Room Air Conditioning System", 《2012 INTERNATIONAL CONFERENCE ON COMPUTING, MEASUREMENT, CONTROL AND SENSOR NETWORK》 * |
| 昂超: "基于相似日法的空调系统节能改造节能量计算方法", 《暖通空调》 * |
| 顾小杰等: "大型数据中心机房新风空调节能研究", 《暖通空调》 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109586285A (en) * | 2018-12-06 | 2019-04-05 | 成都载信智能科技有限公司 | Energy consumption mark post value calculating method, energy consumption management method and energy consumption management system |
| CN112747416A (en) * | 2019-10-31 | 2021-05-04 | 北京国双科技有限公司 | Energy consumption prediction method and device for air conditioning system |
Also Published As
| Publication number | Publication date |
|---|---|
| CN108427840B (en) | 2022-04-12 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109800066B (en) | Energy-saving scheduling method and system for data center | |
| CN107688958B (en) | User energy anomaly analysis method based on multi-table centralized reading data | |
| Bynum et al. | Development and testing of an automated building commissioning analysis tool (ABCAT) | |
| CN105512455B (en) | According to the method and apparatus of temperature estimation power consumption | |
| KR101762543B1 (en) | System and method to estimate energy consumption by end use in buildings | |
| CN107606745B (en) | Metro Air conditioner season by when ring control energy consumption prediction technique | |
| KR20150013670A (en) | Methods and systems for improved time cost and accuracy of energy usage baselining | |
| CN108709287B (en) | Air conditioning system cold load prediction method and water chilling unit group control strategy | |
| KR20130074043A (en) | A method of forecasting of power demand using error correction algorithm and system for it | |
| CN104794269A (en) | Energy-saving transformation energy-saving amount checking and ratifying method based on standard effect judgment | |
| CN107103387B (en) | Load prediction method based on total power and occupancy coefficient of household average equipment | |
| CN105959975B (en) | Energy-saving automatic evaluation method for large-scale base station energy-saving project | |
| CN108427840A (en) | Data center's air conditioner system energy saving amount computational methods based on the prediction of benchmark efficiency | |
| CN105465946A (en) | Method and system for energy consumption analysis of central air conditioner refrigeration station | |
| CN102708259B (en) | Method for modeling generator set excitation system based on frequency-domain method | |
| CN113139672A (en) | Resident life electricity consumption prediction method | |
| Zubair et al. | Explicit data-driven prediction model of annual energy consumed by elevators in residential buildings | |
| TWI492181B (en) | Saving cost type power energy management apparatus and method | |
| Zhou et al. | Building energy use prediction using time series analysis | |
| Wang et al. | Field evaluation of advanced controls for the retrofit of packaged air conditioners and heat pumps | |
| CN109741213A (en) | Zero energy consumption architectural energy consumption control method of one kind and system | |
| CN103472297A (en) | Electricity subentry measurement method | |
| CN104112077A (en) | Method for calculating per capita air conditioner electricity consumption of office building | |
| CN104091227A (en) | Method for analyzing load density of power consumers in city | |
| Lamba et al. | Energy audit on academic building |
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 |