基于深度强化学习的智能暖气温度控制系统

doi:10.11959/j.issn.2096-6652.202037

Abstract

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.

Key words: deep reinforcement learning, heating equipment, temperature control, fatigue detection, image processing

CLC Number:

TP301.6

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.

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References 14

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序号	房间温度/℃	EAR^r
1	18	0.095
2	20	0.212
3	22	0.243
4	24	0.329
5	26	0.381
6	28	0.324
7	30	0.271
8	32	0.118
9	34	0.105

Intelligent heating temperature control system based on deep reinforcement learning

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