Model prediction controlling method achieving data center energy conservation temperature control combined with machine learning

Abstract

The invention discloses a model prediction controlling methodachieving data center energy conservation temperature control combined with machine learning. The model prediction controlling method achieving data center energy conservation temperature control combined with a machine learning combines artificial neural network and a model prediction control algorithm to adjust a heating ventilation air conditioning system in a data center; and applies the artificial neural network to analyze data including the outside temperature, time, and energy consumption and the like to estimate the inside optimum temperature; and then inputs the estimating temperature to the model prediction control algorithm to operate, control and adjust. The selected artificial neural network model is an NARX neural network arithmetic. The model prediction controlling method achieving data center energy conservation temperature control combined with machine learning is used in the data center. Model algorithm of self-learning model prediction control based on the energy conservation and the temperature can solve prior problems that the temperature requirement is not met and the consumption of the heating ventilation air conditioning system is not minimized.

Description

Technical field [0001] The invention relates to a model predictive control method for energy-saving temperature control of a data center, and in particular to a model predictive control method that combines machine learning to realize energy-saving temperature control of a data center. Background technique [0002] The data center needs to be equipped with heating, ventilation and air conditioning (HVAC; Heating, Ventilation and Air Conditioning; HVAC system), but HVAC is very energy-intensive and can account for 15% of the total basic consumption of the data center. Therefore, it is very important to design a control system that saves energy and meets temperature requirements. However, it is challenging to implement because it involves various factors that affect the built environment. It is often difficult to meet all requirements and may vary from situation to situation. [0003] Most of the existing temperature control systems are based on model algorithms, in which environmen...

AI Technical Summary

Problems solved by technology

[0004] The purpose of the present invention is to provide a model predictive control method for energy-saving temperature control in data centers, based on

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