2023/08/16 by Abdul Wahid, Wahid, A., Iias Faiud +3
Engineering · Environmental Science · #Artificial Intelligence (cs.AI) #Energy and Environment Impacts #FOS: Computer and information sciences #Machine Learning (cs.LG) #Photovoltaic Systems and Sustainability #Smart Grid Energy Management
paper · pdf · doi:10.48550/arxiv.2308.08611
openalex publication_date 2023/08/16 · openalex created_date 2023/08/22 · openalex updated_date 2026/07/28
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.