面向高效奶牛场电力负荷调度的预报感知深度强化学习
Forecast Aware Deep Reinforcement Learning for Efficient Electricity Load Scheduling in Dairy Farms
AI总结：
本研究提出一种面向奶牛场的预报感知深度强化学习框架，通过优化电池存储和水加热，实现高效电力负荷调度，降低运营成本并提升能源利用效率。
摘要：
奶牛养殖是一个能耗密集型行业，严重依赖电网电力。随着可再生能源的逐步整合，可持续能源管理已成为减少电网依赖和支持联合国可持续发展目标7（可负担且清洁能源）的关键。然而，可再生能源的间歇性特性在实时平衡供需方面带来了挑战。因此，智能负荷调度对于在保持可靠性的同时降低运营成本至关重要。强化学习在提高能源效率和降低成本方面显示出潜力。然而，大多数基于强化学习的调度方法假设对未来价格或发电量有完全的知识，这在动态环境中是不现实的。此外，标准PPO变体依赖于固定的裁剪或KL散度阈值，往往在电价变化时导致训练不稳定。为了解决这些挑战，本研究提出了一种适用于奶牛场高效负荷调度的深度强化学习框架，重点于电池存储和水加热，并在现实运营约束下进行优化。所提出的预报感知PPO利用基于小时和月份的残差校准来整合短期需求和可再生能源发电的预测，而PID KL PPO变体则采用比例积分微分控制器来调节KL散度，以实现稳定的策略更新。在真实世界奶牛场数据上训练后，该方法在电力成本上分别比PPO低1%，比DQN低4.8%，比SAC低1.5%。在电池调度方面，PPO将电网进口减少了13.1%，展示了其在现代奶牛养殖可持续能源管理中的可扩展性和有效性。
英文摘要：
Dairy farming is an energy intensive sector that relies heavily on grid electricity. With increasing renewable energy integration, sustainable energy management has become essential for reducing grid dependence and supporting the United Nations Sustainable Development Goal 7 on affordable and clean energy. However, the intermittent nature of renewables poses challenges in balancing supply and demand in real time. Intelligent load scheduling is therefore crucial to minimize operational costs while maintaining reliability. Reinforcement Learning has shown promise in improving energy efficiency and reducing costs. However, most RL-based scheduling methods assume complete knowledge of future prices or generation, which is unrealistic in dynamic environments. Moreover, standard PPO variants rely on fixed clipping or KL divergence thresholds, often leading to unstable training under variable tariffs. To address these challenges, this study proposes a Deep Reinforcement Learning framework for efficient load scheduling in dairy farms, focusing on battery storage and water heating under realistic operational constraints. The proposed Forecast Aware PPO incorporates short term forecasts of demand and renewable generation using hour of day and month based residual calibration, while the PID KL PPO variant employs a proportional integral derivative controller to regulate KL divergence for stable policy updates adaptively. Trained on real world dairy farm data, the method achieves up to 1% lower electricity cost than PPO, 4.8% than DQN, and 1.5% than SAC. For battery scheduling, PPO reduces grid imports by 13.1%, demonstrating scalability and effectiveness for sustainable energy management in modern dairy farming.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
