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
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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.,:
。
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