Prediction method and device for energy utilization efficiency, storage medium and terminal equipment

Abstract

The embodiment of the invention discloses a prediction method and device for energy use efficiency, a storage medium and terminal equipment, which are applied to the technical field of information processing of artificial intelligence. The prediction device for the energy utilization efficiency can obtain environment variable parameters of the modular data center at N moments in a preset time period, and utilizes a feature extraction module in the efficiency prediction model to extract feature information of the environment variable parameters at N moments, associates the feature information at the N moments to obtain comprehensive feature information, and a prediction module in the efficiency prediction model outputs the future energy use efficiency of the modular data center according tothe comprehensive feature information. The efficiency prediction model is a machine learning model, the machine learning model can be combined with environmental variable parameters collected for multiple times in a period of time to obtain future energy use efficiency, the future energy use efficiency can be accurately predicted, and operation and maintenance personnel can be well guided to create a modular data center with a high degree of greenization.

Description

technical field [0001] The present invention relates to the technical field of information processing of artificial intelligence, in particular to a prediction method, device, storage medium and terminal equipment of energy use efficiency. Background technique [0002] With the high-density integration of information technology (internet Technology, IT) equipment in modular data centers such as electronic information system computer rooms, solving the phenomenon of increasing heat dissipation of equipment and computer rooms has attracted strong attention from all walks of life. According to research, IT / telecom-related carbon emissions have become one of the largest sources of greenhouse gas emissions, and the momentum of emissions in this field is still rising rapidly with the growth of global demand for computing, data storage and communication technologies. The construction of green computer rooms is required. [0003] Power Usage Effectiveness (PUE) is an indicator for ...

AI Technical Summary

Problems solved by technology

[0004] In the prior art, a method for predicting the PUE of a modular data center is mainly based on the ridge regression algorithm and the cross-cloud data center energy efficiency PUE prediction method based on the fusion of filtering domain adaptation models. However, in this process, when estimating the coefficients, it may be Some information will be lost, which will reduce the prediction accuracy; another PUE prediction method is a rule-based expert system, which uses actual operating data to predict and evaluate PUE. This method is relatively customizable and not scalable.

It can be seen that there is no common and highly accurate PUE prediction method in the prior art that can predict the PUE of a modular data center

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