Intelligent heating temperature control system based on deep reinforcement learning
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...
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| Format: | Article |
| Language: | zho |
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POSTS&TELECOM PRESS Co., LTD
2020-12-01
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| Series: | 智能科学与技术学报 |
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| Online Access: | http://www.cjist.com.cn/thesisDetails#10.11959/j.issn.2096-6652.202037 |
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| _version_ | 1846171160223416320 |
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| author | Tao LI Qinglai WEI |
| author_facet | Tao LI Qinglai WEI |
| author_sort | Tao LI |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-f712900d2a6e4e719a04c765c76082d9 |
| institution | Kabale University |
| issn | 2096-6652 |
| language | zho |
| publishDate | 2020-12-01 |
| publisher | POSTS&TELECOM PRESS Co., LTD |
| record_format | Article |
| series | 智能科学与技术学报 |
| spelling | doaj-art-f712900d2a6e4e719a04c765c76082d92024-11-11T06:52:11ZzhoPOSTS&TELECOM PRESS Co., LTD智能科学与技术学报2096-66522020-12-01234835359638256Intelligent heating temperature control system based on deep reinforcement learningTao LIQinglai WEIIt 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.http://www.cjist.com.cn/thesisDetails#10.11959/j.issn.2096-6652.202037deep reinforcement learning;heating equipment;temperature control;fatigue detection;image processing |
| spellingShingle | Tao LI Qinglai WEI Intelligent heating temperature control system based on deep reinforcement learning 智能科学与技术学报 deep reinforcement learning;heating equipment;temperature control;fatigue detection;image processing |
| title | Intelligent heating temperature control system based on deep reinforcement learning |
| title_full | Intelligent heating temperature control system based on deep reinforcement learning |
| title_fullStr | Intelligent heating temperature control system based on deep reinforcement learning |
| title_full_unstemmed | Intelligent heating temperature control system based on deep reinforcement learning |
| title_short | Intelligent heating temperature control system based on deep reinforcement learning |
| title_sort | intelligent heating temperature control system based on deep reinforcement learning |
| topic | deep reinforcement learning;heating equipment;temperature control;fatigue detection;image processing |
| url | http://www.cjist.com.cn/thesisDetails#10.11959/j.issn.2096-6652.202037 |
| work_keys_str_mv | AT taoli intelligentheatingtemperaturecontrolsystembasedondeepreinforcementlearning AT qinglaiwei intelligentheatingtemperaturecontrolsystembasedondeepreinforcementlearning |