Efficient refrigerating machine room energy-saving automatic control system based on reinforcement learning

By combining the adaptive particle swarm optimization algorithm with the improved DSAC algorithm, and integrating multi-dimensional parameter acquisition and energy consumption mathematical modeling, the problems of insufficient energy efficiency optimization and response lag in refrigeration systems are solved. This enables intelligent optimization and dynamic adaptive adjustment of refrigeration systems under changing environments, thereby improving energy efficiency and response speed.

CN121763908AInactive Publication Date: 2026-03-31ARMSTRONG (XIAN) INTELLIGENT FLUID TECH CO LTD
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Patent Information

Application Number
CN202610004725.0
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2026-01-05
Publication Date
2026-03-31
Estimated Expiration
Not applicable · inactive patent

AI Technical Summary

Technical Problem

Existing refrigeration system control technologies lack the ability to dynamically adjust and intelligently optimize real-time data, resulting in insufficient energy management, difficulty in coping with load fluctuations and environmental changes, low system energy efficiency, and slow response, making it impossible to achieve personalized and intelligent control.

Method used

By employing an adaptive particle swarm optimization algorithm and an improved DSAC algorithm, combined with multi-dimensional parameter acquisition, energy consumption mathematical modeling, dynamic optimization decision-making, and adaptive strategy updates, the cooling system can achieve intelligent control and dynamic optimization under varying load conditions by automatically adjusting the control strategy through reinforcement learning.

Benefits of technology

It significantly improves the energy efficiency management of chillers, chilled water pumps, cooling water pumps and cooling towers, enhances the system's energy efficiency performance and response speed, and realizes the system's adaptability and energy efficiency management accuracy in changing environments.

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Abstract

The invention discloses an efficient refrigerating machine room energy-saving automatic control system based on reinforcement learning, and the system comprises the following modules: an operation data collection module which is used for collecting key equipment operation parameters and generating unified time sequence data; the energy consumption modeling module constructs energy consumption mathematical models of the water chilling unit, a water pump and a cooling tower and stores the energy consumption mathematical models into a database; the cooling capacity prediction module is used for predicting the future cooling capacity demand based on the historical data and the environmental parameters; the feed-forward optimization module is used for optimizing power distribution by using an adaptive particle swarm algorithm and generating control parameters; the state correction module automatically adjusts the running state according to the running efficiency deviation condition; the cooling strategy optimization module introduces an improved DSAC algorithm to optimize a cooling control strategy; and the instruction deployment module deploys the strategy to an actual system and outputs a regulation and control instruction. The energy efficiency management level of the refrigerating machine room is improved, and integration of energy conservation and intelligent control is achieved.
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