  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.
