Reinforcement Learning for Battery Management in Dairy Farming

TL;DR

This paper applies Q-learning, a reinforcement learning technique, to optimize battery management in dairy farms, reducing energy costs amidst demand variability and renewable energy stochasticity.

Contribution

It introduces a novel application of reinforcement learning for battery control in dairy farming, addressing energy cost reduction and renewable integration challenges.

Findings

Significant reduction in electricity costs using the developed policy

Effective handling of demand variability and renewable stochasticity

Demonstrates reinforcement learning's potential in agricultural energy management

Abstract

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.