A data center water cooling unit energy consumption optimization method and system based on DQN

By using a DQN-based energy consumption optimization method for water-cooled units, and leveraging IoT data and neural network models, the cooling temperature of the water-cooling system is optimized. This solves the generalization and stability problems of traditional methods, and effectively reduces data center energy consumption and optimizes PUE.

CN117236181BActive Publication Date: 2026-09-11NANJING UNIV
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Patent Information

Application Number
CN202311239197.X
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2023-09-25
Publication Date
2026-09-11
Estimated Expiration
2043-09-25

AI Technical Summary

Technical Problem

Traditional energy consumption optimization methods for data center water cooling systems face challenges in terms of generalization ability, stability, and timeliness, making it difficult to effectively reduce PUE.

Method used

A water-cooled unit energy consumption optimization method based on deep reinforcement learning (DQN) is adopted. The device measurement data is obtained through the Internet of Things, a neural network model is constructed, and the model is modeled as a Markov decision process to optimize the cooling water outlet temperature (CWOT) to reduce energy consumption.

Benefits of technology

It significantly reduces the energy consumption of water-cooled units, optimizes the PUE of data centers, and features fast algorithm convergence, resulting in better optimization performance and greater flexibility.

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Abstract

The application discloses a data center water cooling unit energy consumption optimization method and system based on DQN, and provides an efficient solution for PUE optimization of a water cooling data center. The application mainly comprises the following steps: collecting massive data by means of Internet of Things equipment, modeling water cooling unit energy consumption based on a neural network, considering normal working conditions and safety boundary conditions of the water cooling unit, converting the water cooling unit energy consumption optimization process into a Markov decision process, and designing a water cooling unit energy consumption optimization algorithm based on the water cooling unit energy consumption model, so that the energy consumption is reduced under the premise of ensuring the safe operation of the data center. The application can significantly reduce the energy consumption of the water cooling unit under the premise of ensuring the normal operation of the water cooling unit and the normal operation of the data center, has the advantages of fast convergence speed and remarkable optimization effect, and can exhibit greater potential in future more complex optimization scenarios.
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