A Deep Reinforcement Learning Approach to Battery Management in Dairy Farming via Proximal Policy Optimization

TL;DR

This paper applies deep reinforcement learning, specifically Proximal Policy Optimization, to optimize battery management in dairy farms, reducing grid electricity reliance and promoting sustainable energy use.

Contribution

It introduces the use of PPO for dairy farm battery management, demonstrating its superiority over Q-learning in real-world energy efficiency improvements.

Findings

PPO reduces grid electricity import by 1.62% compared to Q-learning.

The approach enhances energy efficiency and sustainability in dairy farming.

Real-world data validates the effectiveness of DRL in this context.

Abstract

Dairy farms consume a significant amount of electricity for their operations, and this research focuses on enhancing energy efficiency and minimizing the impact on the environment in the sector by maximizing the utilization of renewable energy sources. This research investigates the application of Proximal Policy Optimization (PPO), a deep reinforcement learning algorithm (DRL), to enhance dairy farming battery management. We evaluate the algorithm's effectiveness based on its ability to reduce reliance on the electricity grid, highlighting the potential of DRL to enhance energy management in dairy farming. Using real-world data our results demonstrate how the PPO approach outperforms Q-learning by 1.62% for reducing electricity import from the grid. This significant improvement highlights the potential of the Deep Reinforcement Learning algorithm for improving energy efficiency and sustainability in dairy farms.