Reinforcement Learning for Battery Management in Dairy Farming
Nawazish Ali Abdul Wahid Rachael shaw Karl Mason
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.