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Energy saving pre-cooling pattern search of an inverter air conditioner using a deep reinforcement learning algorithm.

Number: No 339

Author(s) : YOON M. S., YOON W. S.

Summary

In this study, we experiment with the energy saving pre-cooling operation of an inverter air conditioner using a deep reinforcement learning algorithm under a fixed going out time duration. An air conditioner with a rated cooling capacity of 6500 W is used. An indoor building load of 4500 W sensible heat and an outdoor temperature of 35°C are continuously applied throughout the study. During the going out time, three remote control temperature options (21, 26, 31°C) are given for simplicity, and a remote controlling pattern (set temperature sequences) that minimizes power consumption upon arrival home while satisfying the arrival target temperature condition is searched using deep reinforcement learning. For a given going out time duration, the power consumptions of the temperature operation pattern found by the computer deep reinforcement learning algorithm and a human are compared. Although the artificial intelligence searches for the power consumption operation pattern over a long period (7 ~ 9 days), depending on the learning environment, it identifies a more accurate and more energy saving and an unexpected operation pattern than by human analysis. 

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Pages: 12 p.

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  • Original title: Energy saving pre-cooling pattern search of an inverter air conditioner using a deep reinforcement learning algorithm.
  • Record ID : 30030108
  • Languages: English
  • Subject: Technology
  • Source: 13th IEA Heat Pump Conference 2021: Heat Pumps – Mission for the Green World. Conference proceedings [full papers]
  • Publication date: 2021/08/31

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