Reinforcement learning-based dynamic cleaning scheduling framework for solar energy system

Summary

This study introduces a reinforcement learning (RL) framework using Proximal Policy Optimization (PPO) and Soft Actor-Critic (SAC) to optimize the cleaning schedules of photovoltaic panels in arid regions. Applied to a case study in Abu Dhabi, the PPO-based framework demonstrated up to 13% cost savings compared to simulation optimization methods by dynamically adjusting cleaning intervals based on environmental conditions. The research highlights the potential of RL in enhancing the efficiency and reducing the operational costs of solar power generation.