Dairy farming consumes a significant amount of energy, making it an energy-intensive sector within agriculture. Integrating renewable energy generation into dairy farming could help address this challenge. Effective battery management is important for integrating renewable energy generation. Managing battery charging and discharging poses significant challenges because of fluctuations in electrical consumption, the intermittent nature of renewable energy generation, and fluctuations in energy prices. Artificial Intelligence (AI) has the potential to significantly improve the use of renewable energy in dairy farming, however, there is limited research conducted in this particular domain. This research considers Ireland as a case study as it works towards attaining its 2030 energy strategy centered on the utilization of renewable sources. This study proposes a Q-learning-based algorithm for scheduling battery charging and discharging in a dairy farm setting. This research also explores the effect of the proposed algorithm by adding wind generation data and considering additional case studies. The proposed algorithm reduces the cost of imported electricity from the grid by 13.41\%, peak demand by 2\%, and 24.49\% when utilizing wind generation. These results underline how reinforcement learning is highly effective in managing batteries in the dairy farming sector.

翻译：奶牛养殖消耗大量能源，是农业中能源密集型行业。将可再生能源发电整合到奶牛养殖中有助于应对这一挑战。有效的电池管理对整合可再生能源发电至关重要。由于电力消耗波动、可再生能源发电的间歇性以及能源价格波动，管理电池充放电面临重大挑战。人工智能（AI）具有显著改善奶牛养殖中可再生能源利用的潜力，然而在该特定领域的研究仍十分有限。本研究以爱尔兰为案例，因其正致力于实现以可再生能源利用为核心的2030年能源战略。本文提出了一种基于Q学习的算法，用于安排奶牛场环境中电池的充放电调度。此外，本研究还通过添加风力发电数据并考虑更多案例，探讨了所提算法的影响。该算法将电网购电成本降低了13.41%，峰值需求降低了2%，而在利用风力发电时降低了24.49%。这些结果凸显了强化学习在奶牛场电池管理中的高效性。