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Many recent research studies have focused on the use of renewable energies, which are clean, inexhaustible, and help to safeguard the environment by lowering carbon emissions. The output power of the solar panel is low and maximum power point approaches are used to increase it. This study describes a deep reinforcement learning-based technique (DRL) and demonstrates aimed at optimizing the operation of PV panels by tracking their maximum power point (MPP). The core focus of this research lies in investigating how the training process of the DRL agent named deep Q_Learning (DQN), measured in terms of training episodes, impacts the output power of PV systems. The output power is effectively dependent on the number of training episodes, as demonstrated by the simulation results.

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A Deep Reinforcement Learning Approach for Tracking the Maximum Power Point

  • 会议论文

  • 发表日期：

    2025年

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  • S. Belarbi,
  • N. Drir,
  • L. Barazane

摘要

Many recent research studies have focused on the use of renewable energies, which are clean, inexhaustible, and help to safeguard the environment by lowering carbon emissions. The output power of the solar panel is low and maximum power point approaches are used to increase it. This study describes a deep reinforcement learning-based technique (DRL) and demonstrates aimed at optimizing the operation of PV panels by tracking their maximum power point (MPP). The core focus of this research lies in investigating how the training process of the DRL agent named deep Q_Learning (DQN), measured in terms of training episodes, impacts the output power of PV systems. The output power is effectively dependent on the number of training episodes, as demonstrated by the simulation results.