Multi-Agent Deep Reinforcement Learning for Multi Objective Battery Management in Dairy Farms
arXiv:2607.06489v1 Announce Type: new Abstract: The dairy industry in Ireland has a large potential for the integration of renewable energy and the reduction of carbon emissions. However, researchers of distributed generation control are mainly focused on residential and commercial applications. To contribute to the effective integration of renewable energy in the dairy sector, this paper presents a multi-objective optimisation control system based on differential evolution and multi agent Deep Reinforcement Learning. The proposed control is organised in two layers: the upper layer uses dynami
The increasing focus on renewable energy integration and carbon emission reduction, combined with advancements in AI and multi-agent systems, drives this specific application to agriculture.
This development highlights the practical application of advanced AI to critical industrial sectors like agriculture, offering solutions for energy efficiency and sustainability that can scale.
The approach to grid management and energy optimization in industries like dairy farming can become more intelligent and autonomous, leveraging AI for multi-objective control.
- · Renewable energy sector
- · Dairy farms
- · AI/ML solution providers
- · Smart grid technology developers
- · Traditional energy management systems
- · High carbon emission energy sources
Increased energy efficiency and reduced carbon footprint in agricultural operations through AI-driven battery management.
Broader adoption of similar multi-agent deep reinforcement learning systems across various industrial and commercial sectors beyond agriculture for energy optimization.
The acceleration of integrated, intelligent energy grids that dynamically balance supply and demand using localized AI, potentially leading to more decentralized energy autonomy.
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Read at arXiv cs.AI