# A Reinforcement Learning Approach to Dairy Farm Battery Management using Q Learning

arXiv: https://arxiv.org/abs/2403.09499 · HTML: https://mlsanity.com/paper/2403.09499/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 47 references that are in the map.
- First posted on arXiv 14 Mar 2024.

## Builds on (most cited first, 5 of 5)

- [Asynchronous Methods for Deep Reinforcement Learning](https://mlsanity.com/paper/1602.01783/) (2016)
- [Deep Reinforcement Learning for Smart Home Energy Management](https://mlsanity.com/paper/1909.10165/) (2019)
- [Reinforcement Learning Applied to an Electric Water Heater: From Theory to Practice](https://mlsanity.com/paper/1512.00408/) (2015)
- [Energy management of small-scale PV-battery systems: A systematic review considering practical implementation, computational requirements, quality of input data and battery degradation](https://mlsanity.com/paper/1905.09498/) (2019)
- [Modelling Electricity Consumption in Irish Dairy Farms Using Agent-Based Modelling](https://mlsanity.com/paper/2308.09488/) (2023)

## Built on by (most cited first, 3 of 3)

- [A Deep Reinforcement Learning Approach to Battery Management in Dairy Farming via Proximal Policy Optimization](https://mlsanity.com/paper/2407.01653/) (2024)
- [Optimizing Energy Management of Smart Grid using Reinforcement Learning aided by Surrogate models built using Physics-informed Neural Networks](https://mlsanity.com/paper/2510.17380/) (2025)
- [Forecast Aware Deep Reinforcement Learning for Efficient Electricity Load Scheduling in Dairy Farms](https://mlsanity.com/paper/2601.08052/) (2026)

## Abstract (arXiv)

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.