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.

通过采用基于 Q 学习的算法，将可再生能源集成到奶牛养殖中，能够有效降低奶牛养殖对电网进口电力的成本，减少峰值需求，并且在利用风力发电时可获得更好的效果。

基于强化学习的奶牛场蓄电池管理的 Q 学习方法

A Reinforcement Learning Approach to Dairy Farm Battery Management using  Q Learning

Dairy farming is a particularly energy-intensive part of the agriculture
sector. Effective battery management is essential for renewable integration
within the agriculture sector. However, controlling battery
charging/discharging is a difficult task due to electricity demand variability,
stochasticity of renewable generation, and energy price fluctuations. Despite
the potential benefits of applying Artificial Intelligence (AI) to renewable
energy in the context of dairy farming, there has been limited research in this
area. This research is a priority for Ireland as it strives to meet its
governmental goals in energy and sustainability. This research paper utilizes
Q-learning to learn an effective policy for charging and discharging a battery
within a dairy farm setting. The results demonstrate that the developed policy
significantly reduces electricity costs compared to the established baseline
algorithm. These findings highlight the effectiveness of reinforcement learning
for battery management within the dairy farming sector.

本研究利用 Q 学习在奶牛养殖场景中学习了一种有效的电池充放电策略，结果显示与基准算法相比，该策略显著降低了电费，突出了奶牛养殖业中强化学习在电池管理方面的有效性。

奶牛养殖中的电池管理强化学习

Reinforcement Learning for Battery Management in Dairy Farming

Early prediction of remaining useful life (RUL) is crucial for effective
battery management across various industries, ranging from household appliances
to large-scale applications. Accurate RUL prediction improves the reliability
and maintainability of battery technology. However, existing methods have
limitations, including assumptions of data from the same sensors or
distribution, foreknowledge of the end of life (EOL), and neglect to determine
the first prediction cycle (FPC) to identify the start of the unhealthy stage.
This paper proposes a novel method for RUL prediction of Lithium-ion batteries.
The proposed framework comprises two stages: determining the FPC using a neural
network-based model to divide the degradation data into distinct health states
and predicting the degradation pattern after the FPC to estimate the remaining
useful life as a percentage. Experimental results demonstrate that the proposed
method outperforms conventional approaches in terms of RUL prediction.
Furthermore, the proposed method shows promise for real-world scenarios,
providing improved accuracy and applicability for battery management.

通过使用神经网络模型判断降解数据的健康状态并预测第一次预测周期之后的降级模式，本文提出了一种用于锂离子电池剩余寿命（RUL）预测的新方法，并通过实验证明该方法在 RUL 预测方面优于传统方法，显示出在实际场景中提供更准确可靠的电池管理能力。