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Energies 2018, 11(10), 2575; https://doi.org/10.3390/en11102575

Thermal and Energy Management Based on Bimodal Airflow-Temperature Sensing and Reinforcement Learning

1
China Astronaut Research and Training Center, Beijing 100094, China
2
Department of Precision Instrument, Tsinghua University, Beijing 100084, China
*
Author to whom correspondence should be addressed.
Received: 27 July 2018 / Revised: 13 September 2018 / Accepted: 17 September 2018 / Published: 27 September 2018
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Abstract

Multi-physical field sensing and machine learning have drawn great attention in various fields such as sensor networks, robotics, energy devices, smart buildings, intelligent system and so on. In this paper, we present a novel efficient method for thermal and energy management based on bimodal airflow-temperature sensing and reinforcement learning, which expedites an exploration process by self-learning and adjusts action policy only through actuators interacting with the environment, being free of the controlled object model and priori experiences. In general, training of reinforcement learning requires a large amount of data iterations, which takes a long time and is not suitable for real-time control. Here, we propose an approach to speed up the learning process by indicating the action adjustment direction. We adopt tailor-designed bimodal sensors to simultaneously detect airflow and temperature field, which provides comprehensive information for reinforcement learning. The proposed thermal and energy management incorporates bimodal parametric sensing with an improved actor-critic algorithm to realize self-learning control. Experiments of thermal and energy management in a multi-module integrated system validate the effectiveness of the proposed methodology, which demonstrate high efficiency, fast response, and good robustness in various control scenarios. The proposed methodology can be widely applied to thermal and energy management of diverse integrated systems. View Full-Text
Keywords: multi-physical field sensing; integrated system; thermal and energy management; reinforcement learning multi-physical field sensing; integrated system; thermal and energy management; reinforcement learning
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Zhang, Z.; Ma, C.; Zhu, R. Thermal and Energy Management Based on Bimodal Airflow-Temperature Sensing and Reinforcement Learning. Energies 2018, 11, 2575.

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