Subway station air conditioning system energy-saving control method based on deep reinforcement learning

Abstract

The invention provides a subway station air conditioning system energy-saving control method based on deep reinforcement learning. According to the invention, the method includes collecting data parameters of a subway station air conditioning system; performing moving average filtering processing, normalization and anti-normalization processing on the acquired data, and converting the data into numerical values in a range of 0-1 by using a linear function conversion method; constructing a neural network model of the subway station air conditioning system by using a neural network and the data obtained in the step; determining a state variable, an action variable, a reward signal and a structure of the DDPG agent; and using the multi-step prediction DDPG algorithm for solving the final control strategy. According to the invention, the control method provided by the invention has good temperature tracking performance; compared with a traditional DDPG algorithm, the number of times of agent training is reduced by 86, the system can stably operate under the condition that the system load changes, the station temperature requirement is met, and meanwhile, compared with an operation system in a current practical project, the energy is saved by 17.908%.

Description

technical field [0001] The invention relates to the field of air-conditioning energy saving in subway stations, in particular to an energy-saving control method for air-conditioning systems in subway stations based on deep reinforcement learning. Background technique [0002] As a necessary link to realize the functionality of urban rail transit, subway stations are of great significance to people's daily life. In recent years, with the rapid construction and operation of many subway stations, their corresponding energy consumption has also increased rapidly, and the energy consumption problem has become increasingly prominent. Among them, HVAC (Heating, ventilation and air conditioning, HVAC) system is the main source of energy consumption, accounting for more than 40% of the total energy consumption of the station, second only to the train traction system, the equipment of the air conditioning system of the subway station is generally in accordance with Long-term peak hour...

AI Technical Summary

Problems solved by technology

[0003] Existing studies have shown that intelligent control methods have self-adaptation, self-learning and self-coordination capabilities, which can improve the performance and energy-saving effect of air conditioning systems. Among them, the agent in reinforcement learning (Reinforcement learning, RL) is directly connected with the environment. Interacting to maximize the reward signal, which can realize the global optimal control of complex systems, is one of the effective ways to give full play to the energy-saving potential of the air-conditioning system. Applying reinforcement learning methods to control the air-conditioning system of subway stations can effectively improve the energy-saving effect of the system. However, there are still Two problems need to be solved. One is that the online training agent based on the model-free reinforcement learning method takes a long time to converge, and it is difficult to meet the real-time requirements of the control system.

Secondly, the state space and action space of the air-conditioning system of the subway station are both multi-dimensional and continuous. However, most of the relevant research results can only deal with the problem of limited parameter space, and only generate control laws for a single discrete control variable, which limits their impact on Applicability of Complex System Control

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