用强化学习动态调度光伏板清洁，降低沙漠地区运维成本。
Reinforcement learning-based dynamic cleaning scheduling framework for solar energy system
- 基于PPO和SAC算法动态调整清洁周期，适应不确定天气。
- 在阿布扎比实验中实现最高13%成本节约，优于传统方法。
- 适合关注光伏运维优化与智能决策的能源系统研究者。
提升太阳能光伏（PV）系统的自主绿色技术是提高可再生能源生产可持续性和效率的关键。本研究提出一种基于强化学习（RL）的框架，自主优化干旱地区光伏板的清洁调度，因灰尘等颗粒物导致的污染会显著降低发电量。通过采用先进的RL算法——近端策略优化（PPO）和软动作-评论家（SAC），该框架根据不确定环境条件动态调整清洁间隔。研究以阿布扎比为案例，结果表明PPO优于SAC及传统仿真优化（Sim-Opt）方法，在应对天气不确定性时实现最高13%的成本节省。结果凸显了灵活自主调度相比固定周期方法的优势，尤其在适应随机环境动态方面。这符合自主绿色能源生产的愿景，可降低运营成本并提升光伏发电效率。本工作强调了强化学习驱动的自主决策在可再生能源系统维护优化中的潜力。未来研究需增强模型泛化能力，并考虑更多因素与约束，以推广至不同地区。
原文摘要 · Abstract (English)
Advancing autonomous green technologies in solar photovoltaic (PV) systems is key to improving sustainability and efficiency in renewable energy production. This study presents a reinforcement learning (RL)-based framework to autonomously optimize the cleaning schedules of PV panels in arid regions, where soiling from dust and other airborne particles significantly reduces energy output. By employing advanced RL algorithms, Proximal Policy Optimization (PPO) and Soft Actor-Critic (SAC), the framework dynamically adjusts cleaning intervals based on uncertain environmental conditions. The proposed approach was applied to a case study in Abu Dhabi, UAE, demonstrating that PPO outperformed SAC and traditional simulation optimization (Sim-Opt) methods, achieving up to 13% cost savings by dynamically responding to weather uncertainties. The results highlight the superiority of flexible, autonomous scheduling over fixed-interval methods, particularly in adapting to stochastic environmental dynamics. This aligns with the goals of autonomous green energy production by reducing operational costs and improving the efficiency of solar power generation systems. This work underscores the potential of RL-driven autonomous decision-making to optimize maintenance operations in renewable energy systems. In future research, it is important to enhance the generalization ability of the proposed RL model, while also considering additional factors and constraints to apply it to different regions.
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