Control method and device of refrigerating unit, electronic equipment and storage medium

Abstract

The invention provides a control method and device of a refrigerating unit, electronic equipment and a computer readable storage medium. The method comprises the following steps of carrying out training of a linear regression model based on historical operation parameters and historical power of each piece of refrigerating equipment in a refrigerating unit, and obtaining a trained linear regression model; according to the historical operation parameters and historical power of the refrigeration equipment, training a hybrid model fusing the linear regression model and a neural network model ofthe refrigeration equipment to obtain a power prediction hybrid model; constructing a target function according to the power prediction hybrid model; and controlling the corresponding refrigeration equipment according to the refrigeration control parameter in the operation parameters corresponding to each refrigeration equipment when the function value is minimum. According to the technical scheme, the objective function is constructed based on the power prediction hybrid model corresponding to each refrigeration equipment, and the refrigeration control parameters in the operation parameters are used for controlling the refrigeration equipment when the function value of the objective function is minimum, so that the energy consumption of the refrigeration unit is accurately optimized.

Description

technical field [0001] The present application relates to the technical field of energy consumption optimization, and in particular to a control method and device for a refrigeration unit, electronic equipment, and a computer-readable storage medium. Background technique [0002] With the development of cloud services, big data, artificial intelligence computing and other technologies, enterprises and governments have invested in building a large number of data centers. The energy consumption of data centers is very high, and the power consumption of data centers in China accounts for 3% of the electricity consumption of the whole society. It is imperative to optimize the energy saving of data centers. Generally, PUE (Power Usage Effectiveness) can be used as an indicator to evaluate the energy efficiency of data centers. PUE is the quotient of dividing the total energy consumption of data centers by the energy consumption of IT equipment. The higher the PUE, the higher the...

AI Technical Summary

Problems solved by technology

However, due to the limited sample data used to train the deep learning model in practical applications, the generalization ability of the trained deep learning model is very poor, and the deep learning model performs poorly on the curve distribution and sensitivity of the input and output.

In this case, the optimal solution cannot be obtained in the subsequent optimization solution

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