Intelligent Multi-zone Residential HVAC Control Strategy Based on Deep Reinforcement Learning

Li, Fangxing; Du, Yan

doi:10.1007/978-3-031-45357-1_4

Power Electronics and Power Systems

2023

DOI: 10.1007/978-3-031-45357-1_4

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Intelligent Multi-zone Residential HVAC Control Strategy Based on Deep Reinforcement Learning

Fangxing Li,

Yan Du

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2024

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Cited by 2 publications

References 30 publications

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Towards various occupants with different thermal comfort requirements: A deep reinforcement learning approach combined with a dynamic PMV model for HVAC control in buildings

Shi,

Zheng,

Zhao

et al. 2024

Energy Conversion and Management

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Towards various occupants with different thermal comfort requirements: A deep reinforcement learning approach combined with a dynamic PMV model for HVAC control in buildings

Shi,

Zheng,

Zhao

et al. 2024

Energy Conversion and Management

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Development of Grain Dryer Control Technology from the Perspective of Low Carbon and Intelligentization

Chang,

Li,

Jin

et al. 2024

Applied Sciences

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The grain-drying process plays a critical role in grain storage and quality assurance. In recent years, with the advancement of low-carbon and intelligent technologies, the control technology of grain dryers has significantly improved. This paper systematically reviews the development status of grain dryer control technology from the perspective of low-carbon and intelligentization, analyzing the technological differences in control systems between domestic and international approaches. Current research challenges include the insufficient integration of control technologies with the drying process, limited control variables, the inadequate application of intelligent control strategies, and unstable sensor accuracy. To enhance the performance of grain-drying systems, this paper suggests optimizing control mechanisms, adopting efficient and environmentally friendly energy sources, improving sensor performance, introducing advanced intelligent control algorithms, and strengthening system monitoring capabilities. Looking ahead, with the further integration of AI, IoT, and green energy, grain-drying control systems are expected to evolve towards greater intelligence, remote operation, and low carbonization, providing technical support for enhancing drying efficiency and environmental performance.

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Intelligent Multi-zone Residential HVAC Control Strategy Based on Deep Reinforcement Learning

Cited by 2 publications

References 30 publications

Towards various occupants with different thermal comfort requirements: A deep reinforcement learning approach combined with a dynamic PMV model for HVAC control in buildings

Towards various occupants with different thermal comfort requirements: A deep reinforcement learning approach combined with a dynamic PMV model for HVAC control in buildings

Development of Grain Dryer Control Technology from the Perspective of Low Carbon and Intelligentization

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