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

Forecasting of renewable energy generation provides key insights which may
help with decision-making towards global decarbonisation. Renewable energy
generation can often be represented through cross-sectional hierarchies,
whereby a single farm may have multiple individual generators. Hierarchical
forecasting through reconciliation has demonstrated a significant increase in
the quality of forecasts both theoretically and empirically. However, it is not
evident whether forecasts generated by individual temporal and cross-sectional
aggregation can be superior to integrated cross-temporal forecasts and to
individual forecasts on more granular data. In this study, we investigate the
accuracies of different cross-sectional and cross-temporal reconciliation
methods using both linear regression and gradient boosting machine learning for
forecasting wind farm power generation. We found that cross-temporal
reconciliation is superior to individual cross-sectional reconciliation at
multiple temporal aggregations. Cross-temporally reconciled machine learning
base forecasts also demonstrated a high accuracy at coarser temporal
granularities, which may encourage adoption for short-term wind forecasts. We
also show that linear regression can outperform machine learning models across
most levels in cross-sectional wind time series.

研究通过 hierarchies、linear regression 和 machine learning 方法比较不同时间和横截面调和方法的准确性，发现 cross-temporal reconciliation 能在多个时间聚合上优于 individual cross-sectional reconciliation，同时机器学习模型在较粗的时间粒度上也表现出高准确性，鼓励其在短期风力预测中的采用。此外，研究还发现在横截面风力时间序列上，线性回归模型可以胜过机器学习模型。