A reinforcement learning approach to dairy farm battery management using Q learning

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. However, fluctuations in renewable generation pose a challenge to this integration. Effective battery management techniques are needed to store and utilize the energy generated from renewable sources. The objective of this research is to leverage Reinforcement Learning to develop an effective approach for battery management systems in dairy farming. Our work contributes by implementing a Q-learning algorithm for dairy farm battery management, incorporating wind and solar energy, exploring the state space of the algorithm, and considering Ireland as a case study as it works towards attaining its 2030 energy strategy centered on the utilization of renewable sources. The findings show that the proposed algorithm reduces the cost of imported electricity from the grid by 13.41%, 24.49% when utilizing wind generation, and peak demand by 2%. These findings highlight the effectiveness of Reinforcement Learning for battery management in the dairy farming sector.