Data center energy efficiency optimization control method based on neural network model

Abstract

The invention discloses a data center energy efficiency optimization control method based on a neural network model. The data center energy efficiency optimization control method comprises the following steps of 1) carrying out data center key point temperature sensing and acquisition work, 2) storing the acquired temperature data of the data center, and displaying the data in the database in a Web browser, 3) based on a large amount of data collected under different working conditions, learning the relationship among different machine room airflow modes, power states and machine room key point temperature distribution by using a neural network model, and establishing a nonlinear prediction model of the data machine room key point temperature, and 4) on the basis of the nonlinear temperature prediction model, considering the influence of various factors such as machine room equipment power consumption, refrigeration performance, safety and the like, and carrying out optimal regulation and control on the energy consumption of the machine room refrigeration system. Compared with the prior art, data transmission is safe and reliable, the method can be suitable for data center operation working conditions in different airflow modes, energy efficiency optimization can be carried out in real time, and the method has the advantages of being high in speed, high in precision, easy to operate and the like.

Description

technical field [0001] The invention relates to an energy efficiency optimization method, in particular to a data center energy efficiency optimization control method based on a neural network model. Background technique [0002] With the explosive growth of digital information and the recent rapid development of cloud computing, the data center has become the core infrastructure of future IT technology, and the use of big data services and cloud is becoming more and more common in daily life. The infrastructure of the service, the scale of the server room is also getting bigger and bigger, and it is playing an increasingly important role. The server is a high-precision device, and any problem with any of the devices will cause relatively large losses, such as interruption of banking services, loss of cloud storage data, etc. A failure in the data center computer room will result in the loss of data stored by its users, and a failure in the communication computer room will ...

AI Technical Summary

Problems solved by technology

For the data center temperature prediction model, previous researches all assumed a steady-state flow field and proposed a fast temperature estimation model for the data center. The main disadvantage of this type of model is that the assumptions are too ideal. When the state of the design, its model accuracy will be reduced or the model parameters will need to be readjusted

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