Integrating Renewable Energy in Agriculture: A Deep Reinforcement Learning-based Approach

TL;DR

This paper presents a deep reinforcement learning framework using Deep Q-Networks to optimize photovoltaic system decisions in agriculture, aiming to enhance sustainability, efficiency, and profitability.

Contribution

It introduces a novel DQN-based decision-making model tailored for PV integration in agriculture, considering economic and environmental factors.

Findings

DQN effectively learns optimal PV installation strategies.

The approach improves energy efficiency and reduces environmental impact.

It supports better investment decisions in agricultural energy systems.

Abstract

This article investigates the use of Deep Q-Networks (DQNs) to optimize decision-making for photovoltaic (PV) systems installations in the agriculture sector. The study develops a DQN framework to assist agricultural investors in making informed decisions considering factors such as installation budget, government incentives, energy requirements, system cost, and long-term benefits. By implementing a reward mechanism, the DQN learns to make data-driven decisions on PV integration. The analysis provides a comprehensive understanding of how DQNs can support investors in making decisions about PV installations in agriculture. This research has significant implications for promoting sustainable and efficient farming practices while also paving the way for future advancements in this field. By leveraging DQNs, agricultural investors can make optimized decisions that improve energy efficiency, reduce environmental impact, and enhance profitability. This study contributes to the advancement of PV integration in agriculture and encourages further innovation in this promising area.