Intelligent heating temperature control system based on deep reinforcement learning

Tao LI; Qinglai WEI

doi:10.11959/j.issn.2096-6652.202037

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Intelligent heating temperature control system based on deep reinforcement learning

Special Issue: Deep Reinforcement Learning | 更新时间：2024-06-05

- Intelligent heating temperature control system based on deep reinforcement learning
- Chinese Journal of Intelligent Science and Technology Vol. 2, Issue 4, Pages: 348-353(2020)
- 作者机构：
  
  1. 中国科学院自动化研究所复杂系统管理与控制国家重点实验室，北京 100190
  2. 中国科学院大学，北京 100049
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.2096-6652.202037
  CLC： TP301.6
- Published Online：2020-12，
  
  Published：15 December 2020
- 稿件说明：
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Tao LI, Qinglai WEI. Intelligent heating temperature control system based on deep reinforcement learning[J]. Chinese journal of intelligent science and technology, 2020, 2(4): 348-353.
DOI：

Tao LI, Qinglai WEI. Intelligent heating temperature control system based on deep reinforcement learning[J]. Chinese journal of intelligent science and technology, 2020, 2(4): 348-353. DOI： 10.11959/j.issn.2096-6652.202037.

摘要

研究如何通过暖气设备自适应地调节室温，提升室内环境的舒适度，具有非常重要的意义。因此，提出了基于双深度Q网络方法的智能暖气温度控制系统，根据人的表情信息控制暖气设备的阀门开度，实时调整室温。首先，介绍针对原始输入状态的预处理算法。然后，设计通过双深度Q网络方法学习控制暖气设备阀门开度的最佳策略。最后，通过仿真结果验证提出的方法的有效性。

Abstract

It is of great significance to study how to adjust the room temperature adaptively through heating equipment to improve the comfort of the indoor environment.Therefore

a double deep Q network method was developed to control the valve opening of heating equipment to adjust the indoor temperature in real time via human expressions.Firstly

the preprocessing algorithm for the original input state was introduced.Secondly

a double deep Q network method was designed to learn the optimal control policy of the valve opening of heating equipment.Finally

simulation results were given to illustrate the effectiveness of the method proposed.

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references

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