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The application of reinforcement learning (RL) to optimize residential power systems presents a promising research avenue, particularly by leveraging the flexibility of RL in learning and the vast data resources made available by the advancement of IoT technologies. This approach holds significant potential in addressing challenges related to reducing power consumption and carbon emissions. However, several challenges hinder the practical application of RL models in this domain. A primary obstacle is the difficulty in accurately defining the reward function (objective function), a critical factor that profoundly impacts both the training process and the alignment with the actual needs of the power system users, due to the diverse range of evaluation parameters. This study is undertaken to explore the practicality of RL in controlling residential power systems and to assess the impact of various reward functions on both the agent’s learning process and the system’s performance. The research aims to identify the effective reward functions that yield a favorable balance among energy consumption, system stability, and user comfort under specific scenarios.

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On the Effects of Training Objectives of Multi-agent Reinforcement Learning for Energy Consumption in Residential Buildings

  • 会议论文

  • 发表日期：

    2025年

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  • Quang Van Tran,
  • Van Bich Nguyen,
  • Ngoc Hoang Luong

摘要

The application of reinforcement learning (RL) to optimize residential power systems presents a promising research avenue, particularly by leveraging the flexibility of RL in learning and the vast data resources made available by the advancement of IoT technologies. This approach holds significant potential in addressing challenges related to reducing power consumption and carbon emissions. However, several challenges hinder the practical application of RL models in this domain. A primary obstacle is the difficulty in accurately defining the reward function (objective function), a critical factor that profoundly impacts both the training process and the alignment with the actual needs of the power system users, due to the diverse range of evaluation parameters. This study is undertaken to explore the practicality of RL in controlling residential power systems and to assess the impact of various reward functions on both the agent’s learning process and the system’s performance. The research aims to identify the effective reward functions that yield a favorable balance among energy consumption, system stability, and user comfort under specific scenarios.