Reinforcement Learning for Battery Management in Dairy Farming
This work addresses energy cost reduction for dairy farmers, but it is incremental as it applies an existing reinforcement learning method to a new domain.
The paper tackled the problem of battery management in dairy farming by applying Q-learning to control charging and discharging, resulting in a significant reduction in electricity costs compared to a baseline algorithm.
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.