mlsanityData to 14 Oct 2026

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

arXiv 2603.07518 ↗First posted 8 Mar 2026cs.LGcited by 0

On the map: Deep reinforcement learning methods → RL for energy, networks and operations

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods. Builds on 3 of its 34 references that are in the map. First posted on arXiv 8 Mar 2026.

Builds on

3 of 34 references.

References · 3

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,885
  3. 2015Trust Region Policy Optimizationcited by 2,580

Built on by

Not cited yet.

Similar, not cited

Close in wording, no citation either way

  1. 2024Reinforcement Learning for Efficient Design and Control Co-optimisation of Energy Systems
  2. 2026Reinforcement Learning for Sequential Solar PV Policy Design under Uncertainty: An Agent-Based Approach
  3. 2025A Teacher-Student MPC-PPO Coupled Reinforcement Learning Framework for Winter Temperature Control of Solar Greenhouses in Northern China
  4. 2024Reinforcement Learning for Sustainable Energy: A Survey
  5. 2026Emission-Aware Reinforcement Learning for Sustainable Electric Vehicle Charging and Carbon Dioxide Reduction Under Varying Renewable Penetration
  6. 2024Learning a local trading strategy: deep reinforcement learning for grid-scale renewable energy integration
  7. 2025Green Energy Management for Sustainable Data Centers Using Deep Reinforcement Learning
  8. 2025Optimizing Power Grid Topologies with Reinforcement Learning: A Survey of Methods and Challenges